New Swift firmware with a moving rover

In the last couple of posts I compared the u-blox M8T receiver and the Swift Piksi Multi receiver for a stationary rover and an external base station using the latest 1.2 firmware from Swift.  I did this for both RTK and PPP solutions.  In this post I will look at a moving rover case with a pair of Piksi receivers and a pair of M8T receivers.

The new Swift firmware supports raw observations and float solutions for the GLONASS satellites, but does not yet support ambiguity resolution for GLONASS.  In the previous experiments the lack of GLONASS ambiguity resolution did not affect the comparison since an external (non-matching reciever) base was used. This meant that the RTKLIB solutions were not able to use GLONASS ambiguity resolution either.

In this experiment I did use matching receivers for the base data, so the RTKLIB solutions do include GLONASS ambiguity resolution and so should provide an advantage to the Swift internal solutions.  As I did last time, I will compare the real-time Swift RTK solution with post-processed RTKLIB solutions for the Piksi pair and the M8T pair.  In this case the M8T receivers are from CSG Shop and are running the 3.01 firmware so they include Galileo observations in addition to GPS, GLONASS, and SBAS.

In my previous moving rover experiments I shared a base antenna for the two base receivers but used separate antennas for the two rover receivers.  In this case I decided to also share the rover antenna between the two rover receivers to enable some more direct comparisons.  The base antenna is the same Swift GPS-500 antenna mounted on my roof that I used for the rover in the previous experiment.  The rovers are using a Tallysman TW7872 antenna mounted on top of a car.  In both cases I use a capacitively coupled splitter to isolate the two receivers and allow only one DC supply voltage to reach the antenna.  As usual, the data was collected while driving around my local neighborhood.

Whenever connecting two receivers to one antenna, or even locating to antennas close to each other there is always the concern that they may interfere with each other.  So, as a baseline, I first did a run with separate antennas for the rovers.  I used my inexpensive u-blox antenna for the M8T since it has a very long cable allowing me to separate both the receivers and the antennas by over a meter.  Here are the position solutions for the baseline test.  The Swift internal solution is on the left, the RTKLIB post-processed solution in the middle, and the M8T post-processed solution on the right.  As always, green is fixed points and yellow is float.


The post-processed solutions were both run forward-only and with continuous ambiguity resolution including GLONASS.  The percent fix for the three solutions were 69% for the Swift internal solution, 33% for the Swift RTKLIB solution, and 75% for the M8T RTKLIB solution.  I will use percent fix as a very coarse measure of goodness for now, later I will look at the solutions in more detail.  In this case I consider the Swift internal solution and the M8T RTKLIB solution roughly equal.  Both achieved 100% fix for a number of circles in a parking lot with open skies (22:30 to 22:35), and both struggled with partial fixes on the rest of the route which had more obstructed sky views driving along residential streets.  The Swift RTKLIB solution was noticeably worse and had 0% fix in the parking lot, and lower percent fix everywhere else.

I then re-ran a similar route a short time later, but this time with the M8T rover sharing the dual frequency antenna with the Swift rover, produced the following results in the same order as above.  The percent fixes were 53% for the Swift internal solution, 79% for the Swift RTKLIB solution, and 62% for the M8T RTKLIB solution.


Again I would consider the Swift internal and the M8T RTKLIB solutions roughly equal.  A different parking lot with open skies (1:43 to 1:48), gave no trouble to either solution, but both had only partial fix for the rest of the route.  In this case the Swift RTKLIB solution was noticeably better than the other two solutions which is interesting since last time it was noticeably worse.  This has been my experience in general with the Swift RTKLIB solutions, that they tend to be quite inconsistent.  With the previous firmware I traced some of the reason for this to unreported cycle slips.  I suspect the same is probably going on here but haven’t looked close enough to fully verify that.  As far as any interference occurring between the antennas, if it exists, it is too small to detect with this simple comparison.

Here are the raw observations for the two rovers for the second run, M8T on the left, Swift on the right.  Green lines are dual frequency, yellow are single frequency and red ticks are cycle slips.  There are 19 measurements available for the M8T solution and 22 for the Swift solution.  All are used for ambiguity resolution in the RTKLIB solutions but only the 11 GPS measurements are used for ambiguity resolution in the internal Swift solution.  The relatively cycle-slip-free region from 1:43 to 1:48 is from the parking lot where the sky views are open.


Since the exact same signal was fed into both receivers the observations can be compared more directly than in previous experiments.  Here is a zoom into the time in the parking lot and shortly after.   If you look carefully, you will notice that there are definitely more cycle slips reported in the M8T observations than in the Swift data.


We can verify if those cycle slips are real or not by plotting the double differences of the bias states from the RTKLIB statistics output file.  I selected two satellites that had more cycle slips in the M8T data, G03 and R04, and plotted their double differences relative to the most cycle-free satellite in their constellation.  In the plots below, the red lines are the double differences from the M8T bias states and the blue lines are the double differences from the Swift bias states.  The red circles are cycle slips reported in the observations and the discrete jumps are actual cycle slips.  The green x’s are half cycle invalid flags and not relevant to this particular example.



Both plots show good correlation between the reported cycle slips and the true cycle slips as well as confirming that their are more slips in the M8T observations than in the Swift observations.  So, at least in this example, the quality of the Swift observations is noticeably better than the M8T observations, even though both came from the same antenna.  Of course this might be expected since the Swift receiver, although low cost by dual frequency standards,  is still significantly more expensive than the M8T receiver.

The solutions however, don’t reflect this difference in observation quality.  The Swift internal solution appears to be no better, and possibly even a little worse than the RTKLIB M8T solution.  This is probably because the M8T solutions include GLONASS ambiguity resolution and the Swift solutions do not.  Once Swift adds this feature to their firmware I would expect the Swift internal solutions to be better than the M8T solutions.

The variability for the results from the Swift RTKLIB solutions are harder to explain and I don’t have any good answers for this yet.  I still do see cases of unreported cycle slips causing problems even with the new firmware and suspect this is a part of it.  I hope to investigate this further.

Another thing I wanted to look at is how good are the float solutions.  In Swift’s description of the new firmware they state that:

“Swift also suggests that users utilize the estimated accuracy fields
in navigation outputs for an indication of solution quality rather than using the
transition to RTK “fixed” mode as an indicator of solution quality, as the new
and improved float solution performance can often fulfill precision navigation

So, let’s take a look at the float portion of the internal Swift solution.  I loaded the .csv output file from the Swift internal solution into matlab, then plotted histograms for the horizontal and vertical accuracies for the fix and float solution points.  Here’s what it looks like:


Notice that the float accuracies are significantly larger than the fix accuracies but they are probably realistic unlike the RTKLIB float accuracies which I demonstrated to be at least a factor of two overly optimistic in a recent post.

Here is an example of the solution points from the above data set in which the RTKLIB solution re-converges more quickly than the Swift internal solution.  This allows us to evaluate the accuracy of the Swift float values.  The top plot is one component of the horizontal position, and the bottom plot is the vertical position. Yellow is the Swift float solution, Olive green is the RTKLIB float solution and blue is the RTKLIB fix solution.  From the beginning of the data to 22:47:14 both solutions are float.  The RTKLIB solution converges to a fix at 22:47:14 and the Swift internal solution converges to a fix at 22:47:37.  Since both solutions eventually converge to the same value I will assume the RTKLIB fix solution is correct.

swift_newfw_13 The errors in the float solutions appear to be similar between RTKLIB and Swift and consistent with the Swift estimates of accuracy.  So the Swift float solution does not appear to be any more accurate than the RTKLIB float solution but the Swift estimates of the accuracy do seem to be more realistic.  Three quarters of a meter vertical accuracy is better than a single point solution but I suspect it is too large to be useful for many applications.  Still, it is a noticeable improvement from the very noisy float values I saw in the internal Swift solution in my previous comparison.

So what does all this mean?  I would summarize by saying that the results for a pair of Swift receivers with the new firmware are noticeably better than the results in my previous comparison and so are definitely a step in the right direction.  At least in my particular example, though, it still only puts them roughly on par with the results from a pair of M8T receivers.  They certainly seem to have the potential to be better than the M8T’s and hopefully with further improvements from Swift and maybe improvements in RTKLIB as well, we will see this soon.

I did not include a single M8T or Swift receiver paired with an external CORS or similar base station example in this moving rover comparison but would expect the Swift receiver to outperform the M8T receiver in that case because  of a better overlap in satellite pairs.  Also keep in mind that the Swift receiver does have some definite advantages in static and long baseline experiments as I showed in my previous two posts, especially the ability to get accurate locations for the base station using PPP solutions.

As always I want to emphasize that these are only one users results in one particular configuration and other users experiences in other environments could be quite different.  I have uploaded the raw data and RTKLIB config files to the sample data section on the website if anybody would like to explore further.

I am just getting my newly borrowed NavCom receivers up and running and have upgraded my Tersus receiver with their new firmware so I hope to have results for these other low-cost dual frequency receivers soon.




PPP solutions with the Swiftnav Piksi Multi

I have had a couple recent questions about the Swiftnav receiver and PPP solutions so before leaving the stationary rover for a moving rover, I thought I would take a quick look at this subject.

Unlike RTK or PPK which are differential solutions using two receivers, PPP (precise point positioning) is an absolute solution done with just a single receiver.  Since we don’t have the advantage of eliminating the errors through differencing, it is a more challenging problem and is usually (but not always) done with dual frequency receivers for stationary targets.  RTKLIB does support PPP solutions and we will look at them too, but in general I prefer using one of the free online services since it is easier to do this and the answers are more accurate.  PPP solutions require precise clock and ephemeris data which must be downloaded from the internet.  Since you need to be connected to the internet anyways to download these, I see very little advantage to trying to run your own solution with RTKLIB unless you are using it as a learning tool.

There are several different online services available and they all have their own advantages and disadvantages.  I will use the CSRS service, provided by the government of Canada, in this experiment for a few reasons.

First of all, unlike some of the other services, CSRS uses the GLONASS satellites in the PPP solution.  This is particularly relevant for the Swift receiver since it is L2C and only half of the GPS satellites include dual frequency measurements.  In this case, including the GLONASS satellites roughly triples the number of measurements.  CSRS will also process L2C data directly.  Some of the other services won’t work with L2C data unless you modify the header of the observation file.   If you do run into this problem trying to use another service, manually editing the Rinex file header and changing the observation type from C2 to P2 in the file header will usually work.

Another reason for using CSRS for this experiment is that it will solve for single frequency data sets as well as dual frequency.  The single frequency solutions are based on code observations only and significantly less accurate than the dual frequency solutions but they are available.   In this case I will take advantage of this to compare the PPP solution for both single frequency M8T data and dual frequency Swift data.

CSRS also has a very convenient data submission tool that can be downloaded to your computer.  With this tool installed and configured, you simply need to drag any observation file onto the tool icon and it will email you a solution a few minutes later.  It’s hard to get much simpler than that!  You do need to setup an account before accessing the service or downloading the tool but that is a relatively quick and easy process and only has to be done once.

One last feature that CSRS provides that many of the other services don’t is the option to process kinematic data sets as well as static but I have not tried this out yet.

For this experiment, I used eight hours of data collected from the Swiftnav GPS-500 antenna on my roof which was connected to both a Swiftnav receiver and a u-blox M8T receiver through a splitter.  The antenna is mounted on a one meter pole at the bottom edge of a low-angled roof so has a reasonably good, but certainly not ideal, sky view.

The accuracy of the PPP solution will depend on the accuracy of the ephemeris used.  This will vary based on how long it has been since the measurement data was collected.  Here is a list from their website of the three possibilities along with their wait times and accuracies for the CSRS solutions.

  • FINAL (+/- 2 cm): combined weekly and available 13 -15 days after the end of the week
  • RAPID (+/- 5 cm): available the next day
  • ULTRA RAPID (+/- 15 cm): available every 90 minutes

In my case, I had collected this data a few days earlier so CSRS was able to use the rapid precise ephemeris data for the solution.

I converted both raw binary files to Rinex format using the RTKCONV app in RTKLIB.  CSRS only accepts the older 2.11 format so I did need to specify this in the RTKCONV options.  Usually I use the newer 3.03 format since it is easier to read and to parse with Matlab.

Next I dragged the two files onto the CSRS tool icon and a few minutes later both solutions appeared in my email folder.  I had previously configured the tool with a few bits of information including my email address.  The results included a pdf summary file and a csv file with the epoch by epoch convergence of the solution.

Here is the solution from the csv file for the Swift receiver.  The results in the file are in LLH format where latitude and longitude are both in degrees.  I converted both from degrees to meters using the appropriate meters per degree constants for my particular location, then subtracted the final point to generate the plot below.    Note that this is a different coordinate system than I used in the plots in my last post in which I converted the LLH coordinates to earth-centric XYZ coordinates.  In XYZ coordinates, the Z coordinate is only equivalent to height if you made the measurement at the North or South pole.  In this case I have used the angle to meter conversion to preserve the separation between vertical and horizontal components to better show their relative accuracies.


In this case the horizontal components converged to something very close to the final answer in about two and a half hours whereas the vertical component doesn’t seem to have fully converged even in eight hours.

The 95% confidence levels for the results reported in the summary file were about one cm for the combined horizontal components and 2.5 cm for the vertical component.  This was consistent with my best guess for the actual errors based on both RTK and PPP measurements made with multiple receivers and online services. I estimate the actual error being about one cm in the combined horizontal components and about 1.5 cm in the vertical component.

I did not include any antenna calibration corrections in my solutions since I am not aware of a calibration file available for the GPS-500 antenna.  This means my solutions will be for the location of the phase center of the antenna, not the geometric center.    In this particular experiment, since I am only using the results to compare with other results from the same antenna, the errors will cancel and can be ignored.   Normally though, this offset will an add additional error to the position measurements.  Ideally for accurate absolute measurements, a calibrated antenna would be used, in which case the calibration file can be specified in the solution and RTKLIB will apply the correction to remove this error.

Unfortunately the CSRS PPP single frequency results for the M8T data were much less accurate with about half a meter of error in the horizontal components and three quarters of a meter error in the vertical axis.


I then ran a PPP solution with RTKLIB for both data sets using a configuration similar to what is recommended in this tutorial.   The Swift data produced a result with very similar accuracies to the CSRS result in the horizontal components but nearly five cm of error in the vertical component.  Note that the convergence times are longer in the RTKLIB solution.  It is likely that both solutions would have reduced vertical errors if I had run with a longer data set.  The typical recommendation for PPP solutions is at least two hours of measurement data but longer data sets will generally improve accuracy.  Here is a plot of the RTKLIB PPP solution for the Swift data.


In this case I was not able to get an RTKLIB PPP solution for the M8T data because of too large residual errors.  In other cases I have got PPP solutions with single frequency data but the accuracy of the solutions has always been much lower than the dual frequency data.  I do not have a lot of experience with the PPP settings in RTKLIB so it is possible I am not getting the most out of RTKLIB.  I hope to dig into this side of things more in the future.

PPP is great for locating static receivers but if you need to track moving rovers, you will still want to use RTK or PPK solutions for that.  The ability to get accurate locations for your base station using a PPP solution though is a significant advantage of using dual frequency receivers rather than single frequency receivers.  This is particularly true  if your base station is not close enough to a CORS type reference station to get an RTK/PPK solution for your base station location.

There is a significant advantage in having two identical receivers for RTK/PPK solutions since it will give the maximum number of overlapping measurements to difference and will allow ambiguity resolution with the GLONASS satellites.   In this case the simplest configuration would be to use Swift receivers for both base and rover.  A less expensive alternative worth considering would be to connect a Swift receiver and M8T receiver to the base station antenna through a splitter and then use an M8T receiver for the rover.   You could then use the Swift receiver to find your base location and the two M8T receivers to find the rover location relative to the base position.

For me at least, this ability to locate the base with PPP would be the most compelling reason to justify the extra cost of the Swift receivers over the M8T receivers.

Hopefully, this time in my next post, I will actually get to looking at the moving rover case with the Swift receivers.  After that I hope to do a four way comparison between the M8T, and low cost dual frequency receivers from Swift, Tersus, and ComNav.  I met Andy from ComNav at the recent drone expo in Las Vegas and he was kind enough to lend me two of their receivers and antennas for a couple months to use for evaluation.  I also understand that Tersus has recently updated their firmware so I’m quite excited about all the different options becoming available in the low cost dual frequency receiver market.


A first look at the new SwiftNav firmware

Swift Navigation just released a major upgrade to the firmware for the Piksi Multi low cost dual frequency receiver.  This should significantly improve performance since it adds support for the GLONASS L1 and L2 satellites.  This is an L1/L2C only receiver so it only supports L2 on about half of the GPS satellites, those recent enough to support the new codes.    On GLONASS however, there is no L2/L2C distinction and the Swift receiver supports the second frequency on all of the GLONASS satellites.  There are fewer GLONASS satellites than GPS satellites but overall this change roughly doubles the measurements available to the solution.  Looking at a recent six hour data set I took, the number of GPS satellites varied from 7 to 9 and the total L1 plus L2 GPS measurements varied from 11 to 14.  During the same time there were only 5 to 7 GLONASS satellites but 10 to 14 total GLONASS measurements including L1 and L2.

Unfortunately, the update does not include ambiguity resolution for the GLONASS satellites within the Swift internal RTK solution.  The raw GLONASS measurements are available though and RTKLIB supports GLONASS ambiguity resolution provided both receivers are identical.  This means you can use GLONASS ambiguity resolution if you have two Swift receivers and are doing post-processing with RTKLIB.

I did a comparison between the Ublox M8T and the Swift Piksi Multi a few months ago and found that with only L1 GPS and partial L2 GPS, the Piksi simply did not provide enough measurements to generate a robust solution.   With the new measurements available, along with other improvements they have added with the new firmware, I thought it was time to give it another test.

As I did previously, I will compare a Swift internal RTK solution to an RTKLIB post-processed PPK solution using the Swift data as well as an RTKLIB post-processed solution from a u-blox M8T L1 receiver.   Swift does not provide any tools for doing post-processed solutions so the Swift internal solution will have to be real-time.

My first test was with a static rover to make evaluation of the results easier.  Even though the rover was stationary, I ran all solutions in kinematic mode to get a better sense for the errors.   For the rover I used a SwiftNav GPS-500 antenna mounted on my roof and connected through a signal splitter to both a Piksi Multi and a u-blox M8T receiver.  For the base I used the P041 CORS/UNAVCO station which is the closest station for which I have real time access to through the UNAVCO network.  It is about 17 kilometers away so a long enough baseline to be fairly challenging.  I did run from early evening to late night so the ionospheric errors were not as large as they would have been at mid-day.  There also happened to be about two inches of snow that fell during the measurement period, possibly affecting the result through snow accumulation on base or rover antennas?  Other than the snow, this is very similar to the test I ran previously comparing  a Tersus dual frequency receiver to an M8T receiver.

Let me start by giving my usual warning that these comparisons are only meant to be a snapshot of one users experience and not a comprehensive evaluation of any sort.  Measurement environments vary tremendously and there is no guarantee that other people’s results will match mine.

I ran the test for approximately eight hours.  Here are the rover observations for both receivers, Swift on the left, and M8T on the right.  Green lines indicate dual frequency measurements and yellow are single frequency.  The Swift receiver has GPS and GLONASS observations, the M8T has GPS, GLONASS, Galileo, and SBAS.  Both receivers averaged roughly 25 measurements with the additional L2 measurements on the Swift roughly balancing the additional satellites on the M8T.


To more easily compare the internal Swift solutions to the RTKLIB solutions I wrote a simple matlab script that translates the csv logs written by the Swift receiver to the format of an RTKLIB  .pos file, allowing me to plot both together using RTKPLOT.

When I first plotted the solutions I saw DC offsets between the Swift solution and the RTKLIB solutions.  I had seen this before in my previous test.  Last time I had ignored the offsets assuming they were coming from datum differences but this time wanted to pinpoint exactly where they were coming from.  Usually it is easier to do these comparisons with relative measurements between the two receivers (ENU coordinates) rather than absolute solutions since the absolute solutions often involve translating between different datums.  In this case though , the relative solutions were giving differences in the z axis exceeding 45 meters!  I eventually traced the source of this error to different origins for the ENU coordinates between Swift and RTKLIB.  Swift uses the rover as the origin and RTKLIB uses the base as origin, causing a rotation of the axis of the two ENU coordinate systems.  Combined distance in three dimensions is the same either way but the distribution between axes changes significantly.

I then switched to comparing absolute solutions using LLH coordinates.  I specified the same base location to both solutions using the numbers I got from the base station location file.  The offsets were still there but smaller now, just a few centimeters.  Eventually I realized that Swift was not using the base location I specified on the settings page but was substituting a slightly different base location generated from location messages in the incoming base RTCM stream, presumably from a different datum.  I switched the RTKLIB solutions to use this base location and this eliminated the DC offsets.  RTKCONV places the base location provided in the RTCM stream into the “APPROX POSITION XYZ” field in the Rinex file header, so selecting “rinexhead” option for the base station location in the config file accomplishes this.

Here are the position solutions for the first hour of the test after sorting out the offsets.  On the left is a comparison of the internal Swift solution to the RTKLIB Swift solution.  The yellow/green line is the internal Swift solution and the olive/blue line is the RTKLIB Swift solution.  On the right is the RTKLIB M8T solution (yellow/green) and the RTKLIB Swift solution (olive/blue).  The large errors are caused by the initial acquires of the RTKLIB solution (the Swift solution had already acquired when I started the data collection) and from me disconnecting the antenna twice to force a re-acquire of all solutions.


The Swift solution is re-acquiring after losing fix much more quickly than the RTKLIB solutions.  In these two examples, the Swift solution re-acquired in less than one minute while the RTKLIB solutions required 5 to 10 minutes.  Interestingly, the RTKLIB re-acquire times were roughly the same for the M8T or the Swift data.  Presumably the Swift solution is taking advantage of linear combinations of the L1 and L2 measurements in ways that RTKLIB is not.  Previously I was seeing the Swift solution sometimes take very long to re-acquire (well over ten minutes).  This issue seems to be fixed and the Swift solution now seems to re-acquire quickly and reliably.  So, by this metric, the Swift receiver is a clear winner, provided you are using the internal solution.  For the RTKLIB solutions, the two receivers are very similar.  This might be expected since they are both working with roughly the same number of raw measurements.  I should mention that GLONASS ambiguity resolution was disabled in the RTKLIB solutions since the base and rover were not using identical hardware.

Next let’s compare the relative accuracy of the three solutions.  I will ignore any errors that are common to all three solutions and focus on differences between the three.  To do this I will use the mean of all three solutions in each axis as the “correct rover location”.  I will also only use the fixed points for now, and ignore the float.  First let me show the rest of the eight hours of data in which I did not disconnect the antenna at all.   Again the Swift internal (green/yellow) vs Swift RTKLIB (olive/blue) solutions on the left, and M8T RTKLIB (green/yellow) vs Swift RTKLIB (olive/blue) solutions on the right.


Clearly several disruptions are common to all three solutions.  Again, the Swift solution re-converges much more quickly than the RTKLIB solutions.  Given that the disruptions are common to both M8T and Swift receivers, the most likely suspect is the base station.  Sure enough, the base observations show simultaneous cycle slips on multiple satellites at most of the disruption points.  I’m not sure what caused these anomalies but am more interested in the solution responses to them than their actual cause, as they are common to all solutions.


The mean rover position was very close for all three solutions, as were the standard deviations for each axis. It is difficult for me to interpret errors measured in degrees of latitude or longitude so I converted the LLH positions to ECEF for comparison.  Here are the results, all in meters.  The maximum deviation from the combined mean was 7 mm and the largest standard deviation was 1.5 cm.  These errors are all small enough and similar enough that I would say the differences between them are probably not very meaningful.

Swift internal: dx=0.003  dy=0.007  dz=0.003
Swift RTKLIB:  dx=-0.002 dy=-0.003 dz=-0.001
M8T RTKLIB:   dx=-0.001 dy=-0.004 dz=-0.002

Swift Internal: stdx=0.006 stdy=0.013 stdz=0.010
Swift RTKLIB:  stdx=0.006 stdy=0.012 stdz=0.014
M8T RTKLIB:   stdx=0.005 stdy=0.012 stdz=0.015

Here are the plots of the three solutions all laid on top of each other, all relative to the combined means.  All three are fairly well correlated, but in particular I find it surprising how correlated the Swift RTKLIB solution is to the M8T RTKLIB solution.  They overlay on top of each other so well that it is difficult to even see the Swift solution but that is only because the M8T is on top, both had roughly the same percent of fixed points.


So in terms of accuracy, there is very little difference in any of the three solutions.  However, it is clear from these plots that in relatively long baseline situations like this, the Swift internal solution will fix and re-acquire more quickly and hence would take less time to make a measurement.   This difference would be smaller for shorter baselines where all solutions would fix fairly quickly and be nearly 100% fixed.

RTKLIB does have some additional flexibility over the Swift internal solution, especially in a post-processing situation which could be used to improve the RTKLIB solution, including static vs kinematic, continuous AR vs kinematic AR, and combined (forward/backward) mode instead of just forward.  In this case, probably combined mode would be the most effective choice since it causes the solution to approach the anomalies in the raw data from both directions and minimize their disruption.  Here is the same solution as above for the M8T RTKLIB solution (green/yellow) and the Swift RTKLIB solution (olive/blue) , but processed in combined mode.  It improves both solutions significantly and brings the Swift solution to nearly 100% fix.


Overall, the new Swift firmware looks like a significant improvement over the previous version, particularly for real-time applications where time to first fix is most important and post-processing options are not available.

In my next post, I will extend the comparison to a moving rover case.














RTKLIB solution accuracy

OK, so let’s say you’ve just gone out and collected some decent raw measurement data, everything looks like it is working properly and you get a nice-looking solution from RTKLIB, maybe something like this.


The next thing you might want to know is how accurate is that solution?  Fortunately, RTKLIB does include accuracy estimates in the solution file.  These are expressed as standard deviations, one for each axis.  Here’s an example for a solution calculated in ENU (east/north/up) coordinates.  The sde, sdn, and sdu columns are the standard deviation estimates for each solution point and are given in meters.  If the solution were calculated in XYZ coordinates, the columns would be labelled sdx, sdy, and sdz.


You can display these on the solution plot with RTKPLOT by setting “Error Bar/Circle” in the options menu to “Dots” or “Bar/Circle”.  You can see the gray lines in the plot below that represent the solution value plus and minus one standard deviation.


Visually, I prefer to see three standard deviations plotted, rather than just one.  Three standard deviations represents 99.7% of the distribution, so the correct value should nearly always appear between the two gray lines.  One standard deviation, on the other hand,  represents only 68% of the distribution which is hard for me to attach any physical meaning to.  This can be easily changed in RTKLPLOT by replacing SQRT(s[j]) with 3*SQRT(s[j]) all three places it appears in the plotdraw.cpp file and recompiling RTKPLOT which gives this:


Of course these are just estimates of the solution uncertainty made by the kalman filter in RTKLIB and we can not assume they are accurate without some analysis.  They are a function of the input parameters that set the input measurement uncertainties and process noises for the kalman filter.  These are all in the “stats” section of the input configuration file.  Even if these are all set to correctly match your actual configuration, there will be additional errors in these estimates caused by non-linearities in the system, non-gaussian distributions, and time-correlated measurements among other things.

So, let’s do a little analysis to get a feel for how useful these estimates are.  We’ll start with the above data set which includes eight hours of measurements from a stationary u-blox M8T receiver and a CORS reference station 8 km away.  I enabled fix-and-hold for ambiguity resolution with relatively low tracking gain (pos2-varholdamb=0.1).  I ran both static and kinematic solutions on the data, then turned off ambiguity resolution and re-ran the solutions to get examples of both fixed and float solutions for static and kinematic modes.

After running the solutions, I pulled the solution files into matlab for plotting and analysis.  Since the rover was stationary and I knew it’s precise location I was able to easily calculate the true errors.  I then plotted for each solution point, the absolute value of the errors and three times the standard deviation estimate for each point, similar to the above RTKPLOT plot.  The raw data is in E/N/U coordinates but I combined the East and North values into a single horizontal error and error estimate.  After finding that in some cases the error estimates were too low, I also plotted a line using double the error estimate, to see if that might be a usable value .  In the title of each plot I’ve included the percent of samples that were inside the error lines, the first number is for the undoubled estimate, the second is for the doubled estimate.  I also made one more modification to the error estimates by limiting them to a minimum value.   In the case of the static solutions, the RTKLIB error estimate will continue to decrease almost all the way to zero but this is not realistic since there are errors that are not visible to the solution.  I arbitrarily limited each horizontal axis standard deviation to no less than 5 mm, and the vertical axis to 1 cm.   I don’t have good justification for these particular numbers but do believe there needs to be some limits set.

For a perfect Gaussian distribution, plus or minus three standard deviations should include 99.7% of the data.  Given that these are not perfect distributions and will also contain outliers, I would consider the estimates useful if more than 95% of the data fell within them.

Here are the plots for the above data set run with static solutions, float on the left, and only the fixed values of the solution with ambiguity resolution enabled on the right.  The blue line is the actual error, the red line is the RTKLIB estimate, and the yellow line is double that.  The top plots are for the horizontal axes and the bottom plots for the vertical axis.  In the static cases, all the error lines dropped below the minimum estimate values I set above fairly quickly, so only the initial convergence of the float solution was really of any interest.   Still the error estimates in that region look somewhat reasonable, although the undoubled estimates are clearly optimistic. The undoubled estimates (red) are not visible on the plots where both estimates have dropped below the minimum and they get overwritten by the doubled estimate.


Next are the same plots for the kinematic solutions.  Just to confuse you, I have accidentally put the float solution on the right this time.  Again, the solution with ambiguity resolution enabled (on the left) only includes points that were fixed.  These are more interesting than the previous plots since they are not dominated by the arbitrary minimum error estimate limits I added.  Looking at the float solution first, the undoubled estimates again look too optimistic with only 92.2% and 75.2% of the data within that limit.  At 100% and 99.6% the doubled values (yellow lines) appear to be better estimates.  For the fixed values on the left however the undoubled estimate percentages and plot lines appear to be fairly reasonable.


In one last example, I used a different data set, this one is one of the sample data sets on my website which was taken with two M8T receivers, one on each end of a kayak out on the ocean.  In this case we know that the distance between the two receivers will remain constant so we can use that information to do a similar analysis as above.  Because the kayak is moving in all three axes with the waves we can not separate the horizontal and vertical components but can do an actual error to estimated error comparison for the three axes combined.

The float solution is back on the left again, just the fixed values from the ambiguity resolution enabled solution on the right.  Based on the calculated percentages and the shapes of the curves, it seems fairly reasonable to again choose the doubled estimates for the float solution and the undoubled estimates for the fixed values.  Even with the doubling, the percent of solution values within three standard deviations is still only 88.5% which is rather low but most of the outliers were in the initial convergence where we might expect more issues.


So, based on these few examples I do see correlation between the RTKLIB error estimates and the actual errors, not as much as I would have liked, but there is some.   Maybe these adjustments I made would hold up for other cases as well, but I would not have a lot of confidence without trying more examples first.

Clearly these error estimates can not be taken at face value.  Do they have any value?  It probably depends on what you are trying to do with them.  If you are just trying to get a rough idea of the accuracy of the measurements, especially if it is in a statistical sense, and you have done some previous analysis with similar data sets, then you can most likely derive some value from them.  If the absolute accuracy of a single measurement is important to you then you will probably need to find another solution.

What about trying to adjust the input configuration parameters (measurement and process noises) to make the accuracy estimates more accurate?  You may be able to make small improvements but I suspect they will not be significant enough to avoid post-solution adjustments.  You will also find that adjusting these parameters will affect the quality of the solution and it will be difficult to optimize for both.

I am interested, though, if anyone has any ideas for simple (or at least only moderately complicated) code changes that could be made to RTKLIB to improve these estimates or any other ideas on how to improve them.




New code, new gps data

I’ve just released the b28 version of the demo5 code.  It includes all of the updates in the b28 update of the official RTKLIB 2.4.3 code.  It also now supports both Tersus and Swift low cost dual frequency receivers.  The Tersus updates were part of the official 2.4.3 release although I did make a few changes to fully support the L2 measurements as well as some changes to the makefiles to get all apps to build.  The Swift receiver support is based on code I pulled from the Swift RTKLIB Github page.  I suspect the receiver specific RTKLIB code for both receivers could use some improvements in translating from the raw binary formats to the Rinex/internal observation formats but at least this is a starting point.  The executables are available from the download section at  The source code is available on my Github page.

I’ve also uploaded some data sets comparing the Swift Piksi Multi and the u-blox M8T as well as between the Tersus BX306 receiver and the u-blox M8T.  This data is also available from the download section at



Tersus/M8T moving rover comparison

In my last couple of posts I compared a u-blox M8T single frequency receiver to a Tersus BX306 dual frequency receiver for a static rover using a fairly distant CORS receiver for base data.  Both receivers had over twenty raw phase measurements, but the Tersus receiver had much better overlap with the CORS receiver with twelve measurements available for ambiguity resolution (GPS L1 and L2) while the M8T had only six (GPS L1).  Not surprisingly, the Tersus provided a much better solution than the M8T.  I also compared the RTKLIB solution and the internal Tersus RTK solution and showed that they appeared to be roughly comparable.

In this post, I will add a second M8T receiver and compare a M8T to M8T short baseline solution to the Tersus to CORS longer baseline solution.  While this may not sound like a fair comparison, it could be a reasonable choice given that two M8T receivers are still significantly less expensive than one Tersus receiver.   Also, to make things more interesting,  I will use a moving rover this time rather than a stationary one.

For the experiment, I mounted both receivers in a car, each with it’s own antenna on the roof.  Given that we are making a comparison to a relatively expensive solution I felt it wouldn’t be unreasonable to add $20 to the M8T solution and upgraded its antenna from the standard $20 u-blox antenna I usually use to a Tallysman 1421 antenna available at Digikey for $42.   For the Tersus receiver I used a Tallysman dual frequency 3872 antenna which I believe is roughly a $200 antenna.  For the M8T base station, I used the same antenna on my house roof as in the previous experiment which gave a baseline less than 1 km for most of the M8T pair solution whereas the Tersus/CORS baseline was roughly 16-18 km.  For RTKLIB post-processing, I also ran a solution using base data from the nearest CORS station which gave a baseline of 7-9 km but I couldn’t use this data for the Tersus internal RTK solution because it is not available real-time.   Also, it should be noted that I collected all this data a few weeks ago before Tersus released their most recent firmware so it was all done using their previous version.

I chose a driving route very similar to the one I used for this M8N to M8T comparison in which I drive through a residential neighborhood with a moderate tree canopy.  This time I added a section of the route in a parking lot with no tree obstructions.  The parking lot is intended to be a low-stress environment and the neighborhood streets a moderate-stress environment.  Here’s a Google Earth image of the previous route to give a feel for the terrain.  Unfortunately this map feature no longer works in RTKLIB because Google has discontinued the API to Google Earth.



In this case the M8T  was receiving signals from the GPS, GLONASS, SBAS, and Galileo satellites and started the data set with a total of 21 phase measurements.  All of these can be used for ambiguity resolution since the two receivers are identical hardware.   The Tersus receiver measured only GPS and GLONASS but for all but a couple of satellites got both an L1 and an L2 measurement.  It started the data set with 24 phase measurements of which I would expect that only the 14 GPS phase measurements are available for ambiguity resolution because the receivers are not identical.

The previous time I ran this experiment I was able to get a nearly 100% fix solution from both the M8N and the M8T  receiver pairs but had to use some solution tracking gain (fix-and-hold) to achieve that.

In this case, with the extra Galileo satellites and the more expensive antenna, I was able to get nearly 100% fix using continuous ambiguity resolution instead of fix-and-hold. Continuous AR has the advantage of reducing the chances of locking to a false fix and is normally a preferrable solution if it is achievable.  The only float part of the solution was at the very end of the route where I parked the car underneath a large tree.

Here are three versions of the M8T receiver pair solution all run with continuous ambiguity resolution.  In all the plots, green is a fixed solution and yellow is a float solution.  The top left solution was run with 5 Hz measurements which is what I normally use for moving rovers.  I then realized that the Tersus data was only 1 Hz, so I re-ran the M8T solution after decimating the raw data down to 1 Hz (the latest Tersus firmware supports 5 Hz RTK solution).  The decimation can sometimes cause problems because the cycle slips aren’t always handled properly in the decimated data but in this case it seemed to work fine as can be seen in the plot on the top right.   The only noticeable difference is that the 1 sec data took a little longer to get to first fix.  This is less important in post-processed solutions because the solution can always be run in combined (forward/backward) mode which will usually get a fix for the beginning of the data.  This can be seen here in the bottom left solution which was run in combined mode.


The zig-zag line from 21:22 to 21:26 is the lower stress circles in the parking lot followed by the moderate stress route through the residential neighborhood.

Next, let’s look at the Tersus solutions.  The internal Tersus RTK solution was run with the Tersus default settings.  The user interface for the Tersus console app is much simpler than RTKLIB so there are many fewer options to play with.  For most users this is probably an advantage because it avoids the rather overwhelming array of options that RTKLIB gives.   The RTKLIB solution was run with continuous ambiguity resolution with settings very similar to the M8T solution, just adjusted for dual frequency.  The internal solution is on the left and the RTKLIB solution on the right.


The two solutions are fairly similar, both did well in the lower stress parking lot environment but struggled with the moderate stress on the residential streets.  The internal solution did a little better with scattered fixes in the latter part of the data.

Comparing differences between the internal and RTKLIB solutions and between the Tersus and M8T solutions for only the fixed points, it looks like most of the errors between the different solutions when they have a fix are small.  The Tersus/M8T differences are indicated by the distance from the circle as I have described before. I’m not too worried about the DC offsets between them.  It is somewhat tricky to get all the offsets correct and I did not spend a lot of time on that.  It is likely to be a issue with coordinate differences or handling of antenna offsets that explains the DC shifts.


The above Tersus RTKLIB solutions were run with only GPS ambiguity resolution as I would not expect the GLONASS measurements to be useful for ambiguity resolution because of the inter-channel bias differences between the non-identical receivers.  However I was surprised to find that I did get fixes with the GLONASS ambiguity resolution set to “On” in the RTKLIB configuration file.  The solution was slightly worse than the GPS-only AR but I did verify that the GLONASS satellites were included in the ambiguity resolution.  I’m not quite sure what to make of this observation, whether or not it makes sense to include the GLONASS measurements in the ambiguity resolution, but I suspect it makes sense to leave them out for the reason mentioned above.


I then ran another RTKLIB post-processed solution using the Tersus and base station data from a closer CORS base station.  This was to see how reducing the baseline affected the answer.  Here’s the result from a base station that is only 7-9 km away.


Even though we reduced the baseline by a factor of two the solution only got slightly better and time to first fix actually increased.  This suggests that the long baseline may not be the primary reason for the poorer Tersus solution.

My suspicion is that it is a combination of two things,  at least for the RTKLIB solutions.  First of all I believe there is a mismatch between how RTKLIB interprets a cycle slip flag and how the cycle slip flag is defined in the Rinex spec.  The problem is that RTKLIB resets the phase bias estimate in the same epoch as the cycle slip is logged regardless of whether the receiver has had time to relock or not.  This can cause large errors in the bias estimates if the receiver flags a cycle slip before it has recovered from it.  In some of my earlier posts I have described having the same problem with the M8T receiver but in that case I have made some changes in the u-blox specific RTKLIB code to delay the cycle slips until the receiver has re-locked.  Something similar may need to be done for other RTKLIB receiver specific code  including the Tersus or it may be possible to modify the main RTKLIB code to better interpret these cycle slip flags.

Maybe more important, though, is the difference in the measurements between the two receivers.  As mentioned before, the M8T receiver has 21 phase measurements all of which can be used for ambiguity resolution while the Tersus has 24 of which only 14 can be used for ambiguity resolution assuming we don’ t try and use the GLONASS satellites.  Note, though, that there are only seven different satellite-receiver paths for the Tersus since each satellite is providing two measurements.  This compares to the 21 satellite-receiver paths for the M8T receiver where each satellite only provides a single measurement.  Now imagine that the receivers are under a partial tree canopy and four of the satellites are obstructed for both receivers.   The M8T will lose four measurements and still have 17 to work with but the Tersus receiver will lose 8 measurements and only have six to work with.  This is a significant disadvantage and I suspect can explain a large part of the difference in results.

If I had used a local Tersus base station, then the matched Tersus receiver pair would enable use of the GLONASS satellites for ambiguity resolution.  In the case of four obstructed satellites, the two cases would be much more similar with 17 available measurements for the M8T and 16 for the Tersus.  As more satellites were obstructed the M8T would start to gain a bigger advantage since the Tersus would lose two measurements for each obstructed satellite and the M8T would only lose one.  Of course the M8T would tend to have more obstructed satellites than the Tersus since it has more satellites to start with that can be obstructed.  That would work in favor of the Tersus reciever.  It’s hard to say which would give a better solution but my suspicion would be that if the cycle slip handling issue in RTKLIB was fixed the two solutions would be fairly similar when calculated with RTKLIB.  I don’t know enough about the internal Tersus RTK engine to predict how it would do.  Hopefully I can get my hands on a second full dual frequency receiver and run this experiment soon.

Although I ran this experiment at a random time without looking at the satellite alignment first, it may be that the satellite alignment was such that it accentuated this effect.  Note in the observations (Tersus on the top, M8T on the bottom) that the Galileo (Exx) and SBAS (Ixx) satellites have less cycle slips than any of the other satellites.


Looking at the skyplot for those observations we see that three of the four Galileo satellites are at very high elevations which will tend to be blocked less from nearby trees. This would have helped the M8T solution since the Tersus receiver did not have access to these high elevation satellites.


I will try to summarize what I think this data suggests but let me first emphasize that this is by no means intended to be any sort of rigorous analysis.  I don’t have the time, resources or knowledge to do that.  Instead, please take these as no more than the sharing of my thought process as I try to understand some of the differences between single and dual frequency RTK solutions.

Rover to CORS or other traditional dual frequency receiver:  Tersus has a significant advantage over the M8T both because of more matched measurements and opportunities to take advantage of the nature of the dual frequency measurements.  This advantage applies both to the RTKLIB solution and the Tersus solution although I suspect the Tersus solution takes better advantage of the dual-frequency measurements.  The advantage also increases as the baseline increases.

Matched pair of receivers with short baseline:  Good results with the RTKLIB solution will be limited to low stress environments for a pair of Tersus receivers because of limitations in the cycle slip flag handling.   With the M8N and M8T, RTKLIB can also handle moderate stress environments because of receiver specific changes in the RTKLIB cycle slip handling code.   Relative to a Tersus/CORS combination, the M8T matched pair solution will in general be superior for short baselines because of more matched measurements.

Matched pair of receivers with long baseline:  The data in this experiment doesn’t cover this case but as the baseline increases the dual frequency receiver pair should have a greater advantage because of the additional information that can be derived from the dual frequency measurements.

From a cost trade-off perspective, this suggests that the ideal way to combine these receivers might be to build the base with both an M8T single frequency receiver and a Tersus dual frequency receiver, both sharing a single antenna.  The rover would then be a second M8T receiver.  This would give the advantage of the dual frequency receiver for locating the absolute position of the base using long baseline solutions to distant reference stations or even PPP solutions while taking advantage of the matched pair of lower cost receivers for the moving rover piece of the solution.


RTKLIB with a Tersus BX306 dual frequency receiver

[Update 6/27/17:  There is a significant error in the data in this post.  I used the RINEX files from the CORS website for the base station data for the post-processing solutions described below. These contain only GPS data.  However, the real-time stream I used from UNAVCO for the same receiver contains GLONASS observations as well.  I did not realize this when I compared the Tersus real-time solution to the RTKLIB post-processed solution and concluded that the Tersus solution was better than the RTKLIB solution.  Once I re-processed the RTKLIB solution with the GLONASS measurements, the two solutions were much more similar.  See the following post for more details]

Last post I finished up by comparing some raw observation data collected from a moving rover with both Tersus and Swift receivers.  Before analyzing that data I will start with some static data collected simultaneously with the Tersus receiver and a u-blox M8T, both connected to a dual frequency antenna through a signal splitter.

I’d like to compare the RTKLIB solution for the Tersus data with the solution from the internal Tersus RTK engine as well as compare solutions for the Tersus data to solutions with the u-blox M8T data.  Since Tersus does not provide any post-processing tools I needed to run the experiment in real-time.  For the base data I used the closest available CORS station with real-time data access.  This was a GPS only station located 17 km from my home.  This makes it a fairly challenging exercise both because of the long distance between receivers and the small number of base station measurements.

The Tersus receiver does not offer a configuration setting for static or kinematic mode, it always assumes the receiver may be moving.  To put both receivers on equal footing, I also set up RTKLIB for a kinematic solution with continuous ambiguity resolution .  Therefore, although the rover was not actually moving, neither the internal Tersus RTK engine or RTKLIB knew this, and both treated it as if it were a moving rover.  To make the experiment more interesting, I intentionally obstructed the antenna view fairly severely every few minutes to simulate a rover passing under a heavy tree canopy or other obstruction.   I did this because I wanted to compare how well the RTKLIB and internal solutions recovered from losing a fix.

In addition, the location of the “rover” antenna was not ideal.  It was located on one edge of a low angle sloping roof.  This meant partial blockage from the roof as well as a few nearby trees and probable multipath from a few metal vents on the roof.  As always, I choose non-ideal conditions to add stress to the solution and make it more representative of real-world conditions.

Here are the raw observations, M8T above and Tersus below (yellow are single freq measurements, green are dual freq)


I show only the GPS observations since they are the only ones with matching observations in the base station data and hence the only ones that double differences can be calculated for.  At the beginning of the data set, the M8T has six GPS observations (all L1) and the Tersus has twelve (L1+L2).  The points where I obstructed the antenna are obvious in both data sets from the cycle slips.  The additional cycle slips seen in the Tersus data occur on the L2 observations for the most part.

First let’s look at the M8T RTKLIB solution.  With only six double difference observations and a 17 km baseline, the opportunity to resolve the ambiguities is just too limited and nearly the entire solution is float rather than fix.  In some similar data sets the solution may be better than this, but in general I find when using only the L1 GPS satellites there is very little margin and the results can vary tremendously from run to run.



Here are the solutions for the Tersus receiver.  Yellow/Green is the internal solution and Olive/Blue is the RTKLIB solution.  They are both significantly better than the M8T solution with fixes acquired reasonably quickly then broken by the antenna obstructions, followed by a re-acquire.



In this case having 12 double-differenceable observations to work with instead of 6 makes a huge difference, and for this particular comparison, there doesn’t seem any point in spending more time examining the M8T solution.

What is more interesting is the differences between the internal solution and the RTKLIB solution.   The Tersus advertises a 60 second time to first fix and most of the time, it achieved that easily even with the long baseline, significantly outperforming the RTKLIB solution which often took two minutes or more to recover. In the worst case however, (around 1:00 in the above plot), RTKLIB did significantly better than the internal solution acquiring a fix in just over two minutes while the internal solution required over eight.  I think this must be a glitch in the Tersus firmware.  For this excercise I used the new firmware just released last week but it does not appear to be perfect yet.  They acknowledge that they are still maturing the firmware and it should improve with time.  I don’t know what they are doing differently in their internal solution from the RTKLIB solution that gives them significantly faster re-acquire times, but if I had to guess, I would suspect that they are taking better advantage of the dual frequency measurements as I discussed in my previous post.

Here is a zoom in of the previous plot showing some of the higher frequency smaller amplitude differences.  I don’t believe the small DC offsets are significant, they most likely come from me not paying close attention to the various offsets in the setup.  Notice that the Tersus solution often loses fix momentarily where the RTKLIB solution stays fixed.  This may just be a more conservative approach in the Tersus solution to declaring a fix.  The momentary float values do not appear to add much error to the solution.


The above example was done with a fairly distant reference station to meet our requirement for real-time base station data. What if we don’t need real-time?  There are many more CORS stations available for post-processing than real-time so that often means being able to use a closer base station, maybe one that is more likely to have GLONASS satellites as well.  In my case, the closest CORS station is only 7 km away and does have GLONASS measurements as well as GPS.  The receivers are not identical so the GLONASS measurements can only be used for the float solution, not for ambiguity resolution, but they should still help some.  Here are the results using that base station.  Yellow/Green is the Tersus RTKLIB solution and Olive/Blue is the M8T RTKLIB solution.


Clearly more satellites and shorter baselines helps.  At this point the Tersus solution is still better than the M8T solution but the difference is not as dramatic.

So to summarize, this one example suggests that given the case of a single local receiver working with a distant reference station, there could be a significant advantage in using a Tersus dual-frequency receiver over a u-blox M8T single frequency receiver and that there is also an advantage in using the internal real-time RTK solution over the RTKLIB solution.  Part of this advantage is simply because the satellite set that the dual frequency receiver uses (L1+L2) better matches what is available from the reference stations and allows for more double difference pairs.

It would be unwise to conclude too much from this one example but hopefully it at least provides a little insight in how the two receivers and the two RTK engines differ.

So this example was a comparison between one dual frequency receiver and one single frequency receiver, both paired with a fairly distant base station.  In the next post I will compare a pair of matched local single frequency receivers to the same dual frequency receiver again paired with a fairly distant base.