A first look at RTKLIB with dual frequency receivers

As I mentioned in my last couple of posts, I have recently been exploring the use of RTKLIB with a couple of different low-cost dual frequency receivers.  Low-cost is a relative term here.  At $600 to $1700 for the receivers, plus the cost of the antenna, these configurations are significantly more expensive than the u-blox based single frequency versions I usually work with.  Still, they are quite a bit less expensive than models from the more traditional manufacturers.

The first receiver I have is a Piksi Multi from Swift Navigation.

piksi-multi-032817

It is available from their website for $595 for the receiver board, or $1995 for a complete evaluation kit including two receivers, antennas, and radios.  This receiver relies on the new L2C codes for the second (L2) frequency and so does not support the traditional P2 codes.  L2C is an unencrypted code that is only available on the newer GPS satellites.  Roughly half the GPS satellites are currently broadcasting these codes but this number will increase as newer satellites are launched.  This does mean that the Multi can only make dual frequency measurements on the satellites that have L2C capability.  Also, although the receiver hardware is capable of supporting GLONASS G1/G2, BeiDou B1/B2, Galileo E1/E5b, QZSS L1/L2 and SBAS signals, these constellations are not supported in the current firmware.  This means that the current capability of this receiver is somewhat limited, but it should improve as they release new firmware and more satellites are launched.

The second receiver I have been evaluating is a more expensive option, the Precis-BX306 from Tersus which is available from their website for $1699.

BX306 (2)

 

This receiver does support the P2 codes on the L2 frequency and therefore is able to receive dual frequency signals from all the GPS satellites.  It also supports Glonass G1/G2 and Beidou B1/B2 in the current hardware and firmware.  Tersus also has similar receivers that are less expensive but also less capable than this one.  The Precis-BX305 fully supports GPS L1/L2 but only has support for GLONASS G1 and Beidou B1/B3.  The Precis-BX316R supports all the same constellations as the BX306 but only provides raw measurements, it has no internal RTK engine.  Both of these models sell for $999.

In the spirit of full disclosure, I should mention that Tersus gave me the BX306 receiver to evaluate and one of my consulting clients gave me permission to use their Piksi Multi receiver for this evaluation.  I appreciate both of them for their generosity.

Before digging into the details of the receivers, it’s worth first discussing what the advantages of a dual frequency receiver are over a single frequency receiver and also to what extent RTKLIB is capable of exploiting these advantages.  All of this is fairly new to me so the following analysis is based on my somewhat limited understanding.  If I get anything wrong, I am hoping one of my more experienced readers will jump in and correct me.

The most obvious advantage of the dual frequency receiver is that it provides more measurements than the single frequency receiver for the same satellite constellation.  However, if this were the only advantage, then the Piksi Multi, with GPS support only, would still be less capable than the u-blox M8T when the additional GLONASS, SBAS, and Galileo measurements are all taken into account.  Dual frequency receivers do also tend to have more high-end circuitry and tend to be paired with more expensive antennas.

The biggest advantage, though, comes from having multiple measurement of the same path through the atmosphere made with different frequencies.  Using linear combinations of these pairs of measurements in different ways we can glean information that is just not available with the single frequency measurements.  Two linear combinations that are particularly useful are the ionosphere-free combination and the wide lane combination.

The ionosphere-free combination takes advantage of the fact that the ionospheric delay is inversely proportional to the square of the frequency.  By taking the difference of the squares of the two phase measurements,  more than 99% of the ionospheric delay error can be eliminated.  The ionosphere-free combination provides the ability to deal with much longer baselines between the two receivers and also makes possible accurate PPP measurements.

The wide lane combination is simply the difference of the phase measurements made at the two frequencies and the advantage of this combination is that the effective wavelength of this measurement is a function of the difference in frequencies between the two measurements.  In the L1/L2 case, the difference in frequencies is 348 Mhz and the wavelength is 86 cms.  Resolving integer cycle ambiguities over an 86 cm cycle is significantly easier than resolving them over the much shorter L1 wavelength of 19 cm, the only option available with the single frequency receivers.  Once the wide lane ambiguities have been resolved, they can be used to assist in resolving the shorter cycle L1 and L2 ambiguities.  This can lead to much faster times to first fix with the dual frequency receivers.

Of course, these additional opportunities are only valuable if the solution algorithm takes advantage of them.  Unfortunately RTKLIB appears to be quite disappointing in this regard.  For the most part, the default configuration of RTKLIB for RTK handles the two frequency measurements independently and takes very little advantage of the linear combinations.  This makes them no more valuable than if they were two measurements from different satellites.  There is an option to enable ionosphere free combinations (pos1-ionoopt =dual-freq) in the config file which uses the ionosphere-free combinations to estimate the phase biases instead of the individual measurements.  The user manual indicates, though, that the ionosphere-free model is not applied for the RTK solution modes and I have found that setting this option when running an RTK solution breaks the ambiguity resolution.  There is also an option in the code for a wide lane ambiguity resolution but this option is not mentioned in the user manual and if set it attempts to call an external function that is not included with the RTKLIB source code.  There may be a little more support for dual frequency in the PPP solution modes.  However, the current RTKLIB version does not make any attempt at ambiguity resolution in the PPP modes.  The 2.4.2 release of RTKLIB does include what the manual describes as a beta version of ambiguity resolution but that has been removed in the 2.4.3 release.  Without ambiguity resolution, my experience with PPP solutions has been that I can get much better solutions using some of the free online PPP services that do use ambiguity resolution than I can get with RTKLIB.  I am hoping someone can prove me wrong and provide a config file that generates an RTK or PPP solution with RTKLIB that takes full advantage of the linear combinations of the dual frequency measurements but from everything I can see, there is not much code to support this capability.

Fortunately, both receivers do include the capability to calculate their own RTK solutions without RTKLIB.    So the goal in the following experiments will be to both compare the two receivers against an M8T single frequency receiver and also to compare their internal solutions to the RTKLIB solutions.  Unfortunately, neither receiver is set up to handle the post-processing of previously collected measurements and so all of the internal RTK solutions need to be done in real-time.  In my last post I described how I configured the receivers to receive real-time base station data over a cell phone link.

So, let’s start by taking a look at some actual measurement data.

Here is a set of measurement observations collected simultaneously from two receivers on a moving car.  The observations on the left are from a u-blox M8T receiver and on the right are from the Tersus receiver.  Satellites with lock to L1 only are indicated in yellow, those locked to L1 and L2 are in green.

tersus_obs

At the start of the data set, the M8T is locked to 8 GPS, 7 Glonass, 4 Galileo satellites, and 3 SBAS, for a total of 21 measurements.  The Tersus receiver is locked to 8 GPS L1, 7 GPS L2, 6 Glonass L1 and 5 Glonass L2 for a total of 26 measurements.  The greater number of satellites should give the Tersus an advantage over the M8T even before considering the extra advantages of the L1/L2 combinations or the more expensive electronics and antenna.

Here is a similar set of data.  The M8T receiver is on the left again, this time the Swift is on the right.  Again yellow is a single frequency measurement, green is for measurements at two frequencies.

 

swift_obs This time there were a few less satellites in the sky.  At the start of the data set the M8T is locked to 7 GPS, 7 Glonass, 2 Galileo, and 3 SBAS for a total of 19 measurements.  The Swift receiver has 6 GPS L1, and 4 GPS L2 for a total of only 10 measurements.  Particularly for RTKLIB which does not take advantage of the extra information in the L1/L2 combinations, it will be difficult to make up for the small number of measurements.

As mentioned before, this should improve as Swift releases firmware to support GLONASS, BeiDou, Galileo, QZSS, and SBAS, and as more GPS satellites are launched with L2C capability.

In my next post I will compare solutions generated with these different measurements, both from RTKLIB and from the internal RTK engines.

 

 

 

 

 

 

New b27 release of demo5 code

I’ve just released a b27  version of the demo5 code that incorporates all the changes in the b27 release of the official code.  I’ve also included some fixes for the Galileo satellites.  The biggest fix I added was for an array overrun that was causing solutions to intermittently report zero status when Galileo satellites were enabled.  Another fix was to correctly decode the range accuracy estimates in the Galileo ephemeris data.  The code can be downloaded from the code download section of the rtkexplorer.com website.   The source code is available in my Github repository.  Please let me know if you find any issues with this code, especially if it was something that was working in the previous code.

One issue I am aware of is the time tag feature which enables simulated real-time runs in RTKNAVI appears to be broken in the new code.  There was an existing incompatibility  between time-tag files that were created in the win32 version of RTKLIB and run in the win64 version or vice-versa.   It looks like an attempt to fix this in the latest release code actually made things worse and now the time-tag files don’t work at all, at least in the win32 version.

By the way, for anyone using the free starter edition of the Embarcadero compiler, be aware that it does not support building win64 executables.  The code will build, but the option to specify win64 is not available, so the compiler will just build the win32 code.  I hadn’t realized this until I tried to build the win64 version recently.

Also, I’ve recently got a hold of two low cost dual frequency receivers, one from Swift Navigation and one from Tersus, so I am collecting and analyzing data with them now.  I should have some results and comparisons to share in my next post soon.

Update to RTKLIB config file recommendations

I’ve just updated my “RTKLIB: Customizing the input configuration file” post from a few months ago with information on all of the new config parameters I have added to the demo5 code up through B26B.  I’ve also added more notes to some of the existing features based on my more recent experiences.

RTKLIB on a drone with u-blox M8T receivers

Drones are a popular application for RTKLIB and quite a few readers have shared their drone-collected data sets with me, usually with questions on how they can get better solutions. In many cases, the quality of this data has been fairly poor and it has been difficult to get satisfactory results. I was curious to understand why this environment tends to be so challenging since in theory a drone should have more open skies than just about any other application.

To do an experiment, I bought an inexpensive consumer drone from Amazon. I chose the X8C from Syma since it is beginner model and a little larger than some options. I figured the larger size should make it better able to carry some extra weight.

After a few practice flights to get the hang of flying it, I used some duct tape and double-sided foam adhesive to attach a u-blox antenna and 90 mm diameter ground plane to the top of the drone and a u-blox M8T receiver with my custom CHIP data logger underneath where the camera usually goes. I used the landing gear as a spool to wind the unnecessary five meters of antenna cable which was the heaviest part of the whole setup. From a weight perspective, the Emlid Reach units would have been a better choice, but I wanted to collect data from the Galileo constellation of satellites as well as GPS and GLONASS so I used my CSG receiver with the newer 3.0 firmware. I used a second CSG receiver mounted on top of my car as the base station.  Here’s a stock photo of the drone on the left and after my modifications on the right.

drone1drone2a

Although the drone was able to lift the extra weight fairly easily, it seemed to affect the stability of the flight control system and after a few minutes the prop motors would start to fight each other. At that point the drone would start to descend even at full throttle and the drone would land hard enough to usually bounce on its side or back. Still I was able to make a number of short flights which were adequate for testing purposes.

Here’s the observation data for the first set of flights, base station on the left and drone on the right. Red ticks are cycle-slips and gray ticks are half-cycle ambiguities. Ideally, the drone data would look as clean as the base but as you can see it is significantly worse and it turned out to be unusable for any sort of reliable position solution.  The regions without cycle-slips in the drone observations are the times in between flights in which the drone is sitting on the ground.

drone3

Clearly, while the drone is flying, something is interfering with the GPS receiver or antenna, most likely either EMI or mechanical vibration. I could have used a fancy test stand and RF sniffer to try and locate the source of interference but since this blog is focused on low-cost solutions I just used some duct tape, a steel bar, and the RTKLIB code instead.

I used two types of duct tape, both the polyester/fabric type that everyone calls duct tape, and also the metal foil type that is actually used to repair or install ducts. I first used the non-metal duct tape to securely attach the landing gear to the heavy steel bar. The steel bar was convenient because it was easy to attach but anything heavy enough to prevent the drone lifting off under full throttle would work fine.

I then started an instance of RTKNAVI on my laptop and connected it to the receiver on the drone.  The goal was to simulate a complete drone flight while the drone was sitting on the ground and at the same time watch the RTKNAVI observations to detect any degradation of the measurements.  I used a wireless connection but a USB cable would have worked too.

Unfortunately RTKNAVI won’t plot the observation data real-time, but by selecting the tiny “RTK Monitor” box in the bottom left corner of the main RTKNAVI screen, then choosing “Obs Data” from the menu, I was able to get a continuously updating listing of the observations.  Cycle-slips show up as non-zero values in the first column with the I heading. I chose a location outdoors with open enough skies that any degradation in the observation data would be obvious.

drone4

I first observed the cycle-slip column with the drone powered down to verify I wasn’t getting any cycle-slips on all but the lowest elevation satellites. I then continued to observe the cycle-slip column while sequencing through the steps required to fly the drone. I first powered on the drone, then powered on the transmitter, then issued the calibration/connection sequence, then turned on the throttle to low. So far, so good, no sign of cycle-slips. I then started moving the joysticks to issue steering commands which caused the motors to change speeds. All of a sudden I started getting cycle-slips, the more aggressive the steering commands, the more cycle-slips I saw. Aggressive changes in throttle also caused cycle-slips but full throttle with no adjustments or steering commands was fine.

Next I moved just the antenna, then just the receiver away from the drone while issuing steering commands. Moving the antenna away had no effect but moving the receiver away eliminated the cycle-slips.

At this point my guess was that the interference was coming from the relatively high power switching in the motor control circuits and that the antenna ground plane was shielding the antenna from this interference but nothing was shielding the receiver. To test this theory, I attached a layer of the metal duct tape to the bottom of the drone to act as a shield between the drone controller board and the receiver.  I then re-attached the receiver to the bottom of the drone and re-ran the experiment. This time there were almost no cycle-slips even with the most aggressive steering.

I then removed the steel bar and ran a second set of short flights with the layer of metal tape still in place. I was a little concerned that the new shield would interfere with commands sent from the transmitter to the drone so I first tested everything while still on the ground and then kept the drone fairly close during the flight. Fortunately I didn’t see any sign of commands not getting through.

The drone data looked much cleaner in this flight!  Unfortunately, this time the base data was no good with many simultaneous cycle-slips throughout the observation data. At this point I realized that I had placed the base station receiver directly on the top of the car when collecting the data which was very hot at the time. Usually I keep the receiver in the car to avoid this and only place the antenna on the roof. I have seen this kind of severe temperature effects cause simultaneous cycle-slips before but never to this extent. Again the data was completely unusable.

So, back out there again for a third round of flights. This time, everything looked much better. I still saw a few cycle-slips, especially when first applying the throttle at take-off, so my shielding was not perfect but a dramatic improvement over the first flight. The plots below show the results. The top two plots are position solutions for the z-axis. The top plot is with continuous ambiguity resolution and the middle plot is with fix-and-hold enabled. The bottom plot is the drone observation data.

drone5

I made three adjustments to the input configuration file from what I would normally use for my car based measurements.  First of all, since the drones have very open skies, I adjusted the minimum elevation angles from 15 degrees to 10 degrees.   Then, after plotting and observing the accelerations from an initial solution, I increased the vertical acceleration dynamics estimate (stats-prnaccelv) from 0.25 to 1.0.  Finally, because I was seeing slightly higher position variances in the initial solution than I usually do, I adjusted the position variance AR threshold (pos2-arthres1) from 0.004 to 0.1  Both of these last two changes would make sense if the level of vibration were higher in the drone than I am used to seeing, which is quite likely.

Each time the drone landed/crashed due to the unstable flight control system it would bounce to the side or upside-down and that is what is causing the cycle-slips and switch from fix to float at the end of each flight. As you can see though in every case I quickly get another fix after I put the drone upright again. The fixes are solid enough to hold through the entire flight even in continuous mode for all but one of the flights. With fix-and-hold enabled all flights maintained 100% fix rate. The data is as good as or better than similar experiments where I have mounted the rover on top of a car.

This is not surprising since the skies are more open in this experiment. Having over twenty satellites available for ambiguity resolution also helped. I used all the satellites (GPS/GLONASS/Galileo/SBAS) for ambiguity resolution and took advantage of the new feature in the demo5 b26 code that cycles through all the satellites and will throw a single one out if it is preventing a fix. This will automatically occur anytime the number of satellites available for ambiguity resolution is greater than the config parameter “pos2-mindropsats” which defaults to twenty.

I have added the raw data and the configuration file to the  sample data set section at rtkexplorer.com

I imagine different drones will have different issues and not all will be as easy to fix as this one, but the method described here or something similar should be helpful any time drone data is not looking as clean as the base station data.

The fix I chose was very easy to implement but a better fix would probably have been to wrap just the receiver in a shield rather than placing a shield between the control board and the receiver. This would protect the receiver better and avoid affecting commands sent from the transmitter.  In fact, based on these results, I suspect shielding the GPS receiver on a drone is always a good idea.

Zero baseline experiment

I’ve been busy with some consulting projects recently so it’s been a while since my last post but I’m finally caught up and had some time to write something.  I thought I would describe an experiment I did to both try out the “fixed” mode in RTKLIB and also provide some insight into the composition of the errors in the pseudorange and carrier phase measurements in the u-blox M8T receiver.

The “fixed” mode is an alternative to “static” or “kinematic” in which the exact rover location is specified as well as the base position and remains fixed.  The residual errors are then calculated  from the actual position rather than the measured position.  I describe it in a little more detail in this post.  It is intended to be used as a tool to characterize and analyze the residual errors in the pseudorange and carrier phase measurements.

The basic idea in this experiment was to connect two M8T receivers to a single antenna and then compare residuals between the two receivers.  I first looked at the solution using one receiver as base and the other as rover (the zero baseline case) and then compared solutions between each receiver and a local CORS reference station about 8 km away.

The M8T is typically setup to use an active antenna for which it provides power on the antenna input.  I was concerned about connecting the two antenna power feeds together, so to avoid this, I added a 47 pf capacitor in series in one of the antenna feeds to act as a DC block.  In the photo below, the capacitor is inside the metal tape wrapped around a male to male SMA adapter.  I cut the adapter in half, soldered the capacitor to each end, then wrapped it in metal tape as a shield.

zeroBL

The receivers are from CSG and each one is connected to a Next Thing CHIP single board computer, which logs the data and transmits it over wireless to my laptop.  They are very similar to the Raspberry Pi data loggers I described in a previous post, but the on-board wireless makes them more convenient to use.  At $9 each, they are also quite affordable, especially since they do not need micro SD cards like the Raspberry Pi Zeros.  They also have a built-in LiPo battery connector which can be convenient for providing power., although they can also be powered over the USB connectors.  They are also linux based, so setting them up is very similar to the instructions in my Raspberry Pi post.

I first looked at the zero baseline case where I used one receiver as base and the other as rover.  In this case the two receivers are seeing exactly the same signal from the single antenna.  Any error contributions from the satellites, atmosphere, or antenna should cancel and the only contributor to the residual errors should be from the receivers.

I collected about an hour of measurement data from my back patio.  It is next to the house and nearby trees so as usual, the data quality is only mediocre and will include both some multipath and signal attenuation.  I prefer to look at less than perfect data because that is where the challenges are, not in the perfect data sets collected in wide-open skies.

Here are the residuals for a high elevation, high signal strength GPS satellite.  Standard deviations are 0.24 meters for the pseudorange and 0.0008 meters for the carrier phase.

zeroBL1

For a lower elevation GPS satellite with low and varying signal strength, the standard deviations increased to 0.46 meters for the pseudorange and 0.0017 meters for the carrier phase.  Notice how the residuals increase as the signal strength decreases as you would expect.

 

zeroBL2

The GLONASS satellites had noticeably higher residuals.  Here is an example of a satellite with high elevation and reasonable signal strength.  The standard deviations were 1.02 meters for pseudorange and 0.0039 meters for carrier phase, more than twice the GPS residuals.

zeroBL3

I’m not quite sure how relevant it is, but the ratio between the pseudorange residuals and carrier phase residuals in each case is roughly 300, the same value I have found works best for “eratio1”, the config file input parameter that specifies the ratio between the two.

RTKLIB also estimates the standard devations of the GLONASS satellites measurements at 1.5 times the standard deviations of the GPS satellites which is less than the difference I see in the example above.

However, my numbers are for only the receiver components of the measurement errors, I’m not sure exactly which components the RTKLIB config parameters are intended to include.

For the second experiment, I calculated solutions for both receivers relative to a CORS reference station about 8 km away.  In this case, I was curious to see how close the two solutions are as they will have common satellite, atmospheric, and antenna errors but will differ in their receiver errors.  The plot below shows the residuals for a GPS satellite from each solution plotted on top of each other.  As you can see the errors are quite a bit larger than before and the correlation between the two receivers is very high.  Based on the frequency of the errors, I suspect they are dominated by multipath which will vary roughly sinusoidally as the direct path and reflected path go in and out of phase with each other.

I found it quite impressive to see how repeatable the errors are between the two solutions.  It indicates, at least at this distance, that the errors from the receiver are small compared to the other errors in the system.zeroBL4

Again, the GLONASS results were not as good as the GPS results and include a DC shift in the carrier phase that I’m not sure exactly what the cause is.

zeroBL5

I haven’t spent a lot of time trying to figure out how to best use the information in these plots but in particular I found the similarity between the two receiver solutions in the longer baseline experiment quite encouraging.  If the errors are dominated by multipath as I expect, then the baseline length isn’t that relevant and I would expect to see similar results with shorter baselines.  If that’s true, then it may be possible to derive information about the receiver’s environment from the multipath data.  People do this with more expensive dual frequency receivers to monitor things like tides and ground moisture content.  It would be interesting to see if it can be done with these low cost receivers.  Or maybe it already has been done …

 

New firmware, new satellites, new code

CSG Shop is now shipping all of their M8N and M8T u-blox receivers with the latest version 3 firmware.  This is not such good news for the M8N units since the raw measurements are scrambled and these receivers need to be downgraded to the previous firmware version before using with RTKLIB.  For the M8T receivers though, the new firmware is good news because it contains support for the Galileo satellite system.

I now have two of their M8T receivers with the new firmware and did a little testing to see how RTKLIB works with the Galileo measurements.  I did have to make a couple small changes to get things working.

First of all, the RNX2RTKP compile options for including the Galileo code was not enabled.  For some reason, all the other apps did have this option enabled.  To enable it, I had to add “ENAGAL” to the “Preprocessor Defintions”  for C/C++ in the Project menu in Visual Studio.

The second issue I ran into was in the decode_rxmrawx() function that decodes the raw u-blox RXM-RAWX messages.  There is a line of code in this function that sets the code type based on the system.

raw->obs.data[n].code[0]=
       sys==SYS_CMP?CODE_L1I:(sys==SYS_GAL?CODE_L1X:CODE_L1C);

This line sets the code to L1X for Galileo, but that code type doesn’t seem to be supported by RTKLIB and the measurements in the RINEX file for the Galileo satellites get left blank.  Changing the “L1X” in the above statement to “L1C” resolves the problem.  That leaves an unnecessary check in the code but I will leave it there at least until I understand what it was supposed to do.  After that everything else worked fine including ambiguity resolution with the Galileo satellites, so that was quite encouraging.

Next,  I put the two receivers outside in the front yard to collect a longer set of data.  Not an ideal environment because they were close to the house but fairly open skies otherwise.  In an hour of data collection I got measurements from 11 GPS satellites, 8 GLONASS satellites, 5 Galileo satellites, and 3 SBAS satellites. After collecting the data, I processed it with various constellation options to see how they compared.  For all the solutions, I set ambiguity resolution mode to “continuous”, position mode to “kinematic”, and opened up the position variance threshold for AR (arthres1) to allow the solution to lock up as early as possible.  I also enabled all constellations for ambiguity resolution in each case.  Here’s how they compared:

satcombos1a

satcombos1b

Note that the time scale on the GPS-only plot is very different than the others since it took much longer to lock up than any of the other combinations.  With the GPS satellites only, there was an initial short false fix after 14 minutes, then a good fix at 27 minutes that lasted a few minutes but it did not get a solid fix until 43 minutes after it started.  That’s a long time to wait!  Adding a second constellation significantly improved the results, with solid fixes coming after two minutes with GLONASS added, five minutes with SBAS added, and 7 minutes with Galileo added.  Adding a third consellation improved things even more, with times to first solid fix varying from 12 secs for GPS+SBAS+GLO, 3.5 min for GPS+GAL+GLO, and 6 min for GPS+SBAS+GAL.  Using all four constellations gave a time to first solid fix of 2 minutes, not the fastest time, but better than two out of three of the three constellation answers.

It is risky to conclude too much from one data set, but these results are consistent with other data I’ve looked at (for three constellations) that show the more satellites you use the better the answer.  This seems to make sense to me since more information should be better than less information.  However, I often hear or read recommendations to use only the GPS data for better results which I don’t understand.  If anyone has data to support that recommendation I would like to see it to understand it better.

I do sometimes see that one bad satellite can prevent or delay a solution no matter how many good satellites there are and this may be part of the answer.  The more satellites you use, the higher chance there is of having a bad one and RTKLIB is not great at rejecting a bad satellite.  The “arlockcnt” and “ARFilter” features do help prevent bad satellites from getting into the AR solution but they do not reject a satellite if it goes bad after being accepted into the solution.  I have added a new feature starting with the demo5 b26a code that does try to reject bad satellites after they have been accepted into the AR solution but have not had a chance to do a lot of testing on it yet.  It was enabled for the test above and may possibly have helped, I did not look into the details.  The feature is enabled by setting the “pos2-mindropsats” to a value lower than the number of satellites in the solution, in which case it will cycle through dropping all the satellites, one by one, one each epoch, and reject a satellite that has a large negative effect on the AR ratio.  If you try this feature, be careful not to set the minimum satellite threshold too low or you will increase the chances of a false fix.  I would recommend values no lower than 10 satellites.

I have released a new version of the demo 5 code (b26b) with the fixes for Galileo, a couple of new features and fixes, and GUI updates for RTKPOST and RTKNAVI for all the new input parameters for both b26a and b26b codes.  The binaries and a list of the changes are available here.  The source code is available on my Github page.

 

 

 

A fix for the RTCM time tag issue

In my last post I described a problem with a loss of some of the raw measurement information caused by the lack of resolution in the time tags in the RTCM format.  Since the RTCM format is typically used to reduce bandwidth requirements in real-time applications, it is causing real-time solutions to fail when post-processing the same raw data without the translation to RTCM gives good results.  In this post I will describe a fix for this problem.

First of all I want to thank Felipe Nievinski, Igor Vereninov from Emlid, and Anthony Woolridge for their comments to the last post that pointed me to the solution.  They make this a collaborative effort between the U.S., Brazil, Russia, and the U.K!  It still amazes me how enabling the internet can be!

I’ll start by showing again this example of a RINEX output from an M8T receiver with the official raw measurement output (RXM_RAWX) and the debug raw measurement output (TRK_MEAS) enabled simultaneously.  I think  this provides a good insight to what is going on.  The RXM_RAWX message is the top 5 lines and the TRK_MEAS message is the bottom 5 lines for a single epoch.  The first line in each message is the time stamp and the following lines are the measurements for each satellite.  In the satellite measurements, the second column contains the pseduorange value.

trkmeas1

The time stamp specifies the receiver time of the received signals and the sixth column is the number of seconds.  For the TRK_MEAS message these values are always aligned to round numbers based on alignment to the sample rate.  For example in this case the measurement rate was 5 Hz and all the time stamps occur on multiples of 0.2.  This is because they are based on the raw receiver clock without any corrections.

The time stamps from the RXM_RAWX messages however often differ from the round numbers by small arbitrary amounts.  This is because the receiver has estimated the error in its own clock and adjusted the measurements to remove this error.  In this case the estimate of clock error is 0.001 seconds and so the time stamp is adjusted by this value (18.8000000 to 18.7990000).

To keep the time stamps consistent with the other parts of the measurement, the clock error also needs to be removed from the psuedorange and carrier phase values since they are based on the difference in time between satellite transmission and receiver reception and will include any errors in the receiver clock.  We see from the above observations that the pseudorange measurement for satellite G24 has been adjusted from 22675327.198 to 22375547.970, a difference of 299779.228 meters.   The speed of light is 299792458 meters per second so the clock error of 0.001 seconds is equivalent to 299792.458 meters,  a value very close to the amount that the pseudorange was adjusted by.

A similar adjustment needs to be made to the carrier phase measurement as well but it is not as easy to see in this example because the carrier phase measurements are relative rather than absolute and the two messages in this case use different references.  The carrier phase measurements are in cycles, not meters, so the frequency of the carrier phase needs to be included in the translation from clock error to carrier phase cycles but is otherwise the same as the pseudorange adjustment.  In equation form, the adjustments are:

P = P -toff*c
L =L – toff*freq

where P=pseudorange, L=carrier phase, c= speed of light, and freq=carrier frequency

So, basically, the receiver is trying to help us out by removing its best estimate of the clock error from the measurements.  This is unnecessary since RTKLIB is quite good at estimating this clock error on its own, but by itself this adjustment does not cause a problem.

It is when the adjusted measurement is translated to RTCM that we get in trouble.  The resolution of the time stamps in the RTCM format is 0.001 seconds.  In this particular example it would not be an issue because the error is exactly 0.001 seconds or one count of the RTCM format.  Most of the time, however, this error is not an exact multiple of 1 millisec.

For example, here is a time stamp for the data set described in the previous posts.

> 2017  1 17 20 31 48.9995584  0  9

And here is the same time stamp after being translated to RTCM and then to RINEX

> 2017  1 17 20 31 49.0000000  0  9

As you can see, the clock adjustment was less than half a millisec so was completely lost in the roundoff to the RTCM format.  However, the adjustments the receiver made to the pseudorange and carrier phase are still present in those measurements.  We now have a problem because the clock correction is in part of the measurement and not the other pieces.  RTKLIB can not correct for this lack of consistency within the measurement.

So, how do we avoid this problem?  Fortunately, RTKLIB has an option to adjust the time stamps to round values using the same equations described above to adjust time stamp, pseudorange, and carrier phase to maintain consistency within the measurement.   I imagine it was put in specifically to solve this problem. We can invoke this option by adding “-TADJ=0.001” in the “Options” box in the “Conversion Options” menu in STRSVR or using the “-opt” option in the command line with STR2STR.  Note that this option needs to be set in the conversion from raw binary format to RTCM format, not the conversion from RTCM to RINEX.  It is possible to set this option when converting from RTCM to RINEX but this won’t help because the damage has already been done in the earlier conversion.

Unfortunately, there is a bug in the implementation of this option in RTKLIB, at least for the u-blox receivers, so by itself, this is not enough.  The problem is that invalid carrier phase measurements are flagged in RTKLIB by setting the carrier phase value to zero.  The time stamp adjustment feature adjusts these zero values slightly so they are no longer recognized as invalid.  They end up getting included in the output as valid measurements and corrupt the solution.

Fortunately, the fix for this bug is very simple.  Here is the code in the decode_rxmrawx() function in ublox.c that makes the adjustment:

/* offset by time tag adjustment */
if (toff!=0.0) {
fcn=(int)U1(p+23)-7;
freq=sys==SYS_CMP?FREQ1_CMP:
(sys==SYS_GLO?FREQ1_GLO+DFRQ1_GLO*fcn:FREQ1);
raw->obs.data[n].P[0]-=toff*CLIGHT;
raw->obs.data[n].L[0]-=toff*freq;
}

If we add a check to the first line of code to skip the adjustment if the carrier phase is zero, then all is fine.

if (toff!=0.0&&cp1!=0) {

Below is the original solution after RTCM conversion on the left and with time tag adjustment and the bug fix on the right.  If you compare the solution on the right to the solution with no  RTCM correction in the previous post you will see they are nearly identical.

timetag

I am still wary of using RTCM because of its other limitations described in the last  post, particularly the loss of the half cycle invalid flag and the doppler information, but I believe this fix eliminates the most serious issue that comes from using RTCM.

I will release a new version of the demo5 code with this fix sometime in the next few days.  It will take a little while because I also want to include some other features that have been waiting in the pipeline.  If you want to try the fix right away, you just need to  modify the one line of code described above and rebuild.

Update 2/2/17:    I have taken Anthony Woolridge’s suggestion and modified the RTCM conversion code to automatically adjust the pseudorange and carrier phase measurements to compensate for any round off done to the time tag.  This means it is not necessary to set the time-tag adjust receiver option.  This change is currently checked into my Github page and I hope to post new executables in the next couple of days.