Using RTKLIB to process ComNav receiver data

Previously I collected some data with a pair of ComNav K708 receivers and compared the internal real-time ComNav position solution with a real-time RTKLIB solution from a pair of u-blox M8T receivers.  The results showed, at least for the moving rover case, that the two were quite similar.  In this post I will post-process the ComNav raw data with RTKLIB and compare the results both to the internal ComNav solution and the M8T post-processed RTKLIB solution .  The ComNav receiver configured to sell for sub-$1000 is setup to receive only GPS and GLONASS L1 and L2 frequencies so this is how I ran this test.  The M8T will also receive Galileo and SBAS satellites so I included these in the M8T solutions.

RTKLIB is not able to directly process raw binary ComNav data, but I had configured the receivers to output the raw data in RTCM3, then converted this to Rinex using RTKCONV, so this was not a problem.

Before we can get a solution however, we need to deal with the issue that the ComNav GPS L2 observations are a mix of L2 and L2C as seen in the RINEX observation data below.


RTKLIB handles multiple code types for the same frequency like this by choosing the code with higher priority as set in the code priority table in rtkcmn.c.  Unfortunately this choice is made for the full data set, not each individual observation, so the lower priority observations are thrown out even for epochs when the higher priority code observations are not present.  There is an option in the ComNav configuration to force all observations to L2 and this would have been one solution to the problem.  However, in theory, the L2C code should be more robust than the L2 code, so using L2C when we have the option as the ComNav default setup does is probably the right choice.

I was not able to find any simple fix for the RTKLIB code or configuration that would allow it to process both observation types so I simply edited the list of observation types in the RINEX file headers and changed “C2X L2X S2X” in the list to “C2W L2W S2W”.  With this change, all the observations are now described as L2 and none will be thrown out.  It would be a problem if we were mixing L2 observations from the rover with L2C observations from the base, but since both will be consistently L2 or L2C, the differences should cancel, and this should not cause any significant errors in the solution.

I looked at three data sets, all from a moving rover with a local base station.  The first data set is configured as described in my earlier post, with both the M8T and the ComNav rover receivers sharing the same antenna.  In the second two data sets, the rovers are connected to separate antennas. I ran with continuous ambiguity resolution set and GLONASS ambiguity resolution enabled for all solutions.  The only change I made from the default ComNav config settings was to change the “rtkquality” setting from “quick” to “normal” since I had trouble with false fixes when it was set to”quick”.

First of all, let’s compare the ComNav internal solution to the ComNav RTKLIB solution.  Here’s the internal solution on the left, and the RTKLIB solution on the right.  In this case RTKLIB had a little higher fix rate at 76.8% vs the internal solution at 68.8%.


What I found more surprising was that the internal solution did not stay fixed for the circles I drove in the parking lot from  23:37 to  23:40.  Normally I rarely have trouble maintaining 100% fix for this part of the route since there are very few trees here and the sky views are almost 100%.

Here’s a comparison of the difference between the two solutions.


I expect the difference to be near zero for all fixed sections and for the most part this is true.  There is an exception though between  23:32 and 23:33.   I don’t have an absolute reference to compare to so I can’t be absolutely certain which is correct and which is in error.  However I can compare to the M8T solution and in this case the ComNav RTKLIB solution nearly exactly matches the M8T solution so I am fairly certain that it is the internal ComNav solution that is wrong.  This error is fairly large and is most likely caused by a false fix.  Also, in general, the error in the U-D axis is fairly large in both cases relative to what I am used to, but is worse in the COMNAV internal solution.  Below is the difference between the RTKLIB u-blox and RTKLIB ComNav solutions on the left, and the difference between the RTKLIB u-blox and internal ComNav solutions on the right, both for the U-D axis only.


Here is the M8T RTKLIB solution for comparison.  Fix rate was 81.8%, just slightly higher than the ComNav RTKLIB solution.


One advantage of post-processing the data over a real-time solution is that solutions can be run in combined mode where the result is a combination of running the solution through the kalman filter forwards and backwards.  Here’s the combined mode results, ComNav on the left, and M8T on the right.


In this case both solutions were near 100% fix.  ComNav does sell post-processing software that would probably also let you do the same thing but I do not have a copy of it and don’t know how much it cost.

The results for the second two data sets with separate antennas showed similar differences to this one so I’ll include the plots here but won’t go into any detailed analysis.  In both sets of plots, the left is the ComNav internal solution, the middle is the ComNav RTKLIB solution, and the right is the M8T RTKLIB solution.



The biggest difference in these two data sets is that the M8T results were slightly worse relative to the ComNav results than in the above example but this is most likely because the antennas were separate and I used a low-cost single feed L1 antenna instead of the higher performance dual feed L1 antenna I prefer to use in these comparisons.  There is a good description here of why the dual-feed antenna should give better results.

It is always dangerous to conclude too much from a single experiment but this data does support what I have found in my other single frequency to dual frequency comparisons.  For short baselines with a pair of matched receivers and moving rovers, an L1+L2 GPS/GLONASS solution tends to be similar in performance to a L1-only GPS/GLONASS/SBAS/Galileo solution.   I expect this would be less true for longer baselines and stationary receivers.


Initial look at the ComNav K708 receiver

ComNav was kind enough to recently lend me two of their K708 receivers for evaluation.   I also have a Tersus BX306 receiver that was given to me earlier by Tersus for evaluation.  Both of these are relatively low-cost dual frequency receivers that offer full GPS L2 support., unlike the SwiftNav receiver I evaluated in my previous posts which is GPS L2C only.  I have described the Tersus BX306 before in a previous post but last time I was not able to evaluate it with a local base since I did not have a second dual frequency receiver that supported L2.  Tersus has also just recently released their new V1_19 firmware so I included that in this evaluation.   As usual I’ve also included  a pair of u-blox M8T receivers to use as a baseline.

Here’s a photo that shows the three receivers each with their associated serial port and power cabling.  The u-blox M8T is on the left, Tersus BX306 in the center, and ComNav K708 on the right.  The ComNav receiver is actually only the smaller daughter board in the center of the larger board, everything else is part of the very sturdy but rather clunky dev kit.


The Tersus BX306 is priced at $1699 but lower priced versions are available. For example, the BX305 supports GPS L1/L2 but Glonass G1 only, and the BX316R is GPS L1/L2 and Glonass G1/G2 but provides only raw observations for post-processing.  Both of these options are priced at $999.

The ComNav K708 is similar to the better known K501G but newer and more capable.  ComNav doesn’t list their prices on their website but they have told me that both the K501G and the K708 configured to be equivalent to the K501G (GPS L1/L2 and GLO G1/G2) are available for less than $1000.

Both the Tersus and the ComNav receivers come with GUI console apps which are good for initially getting familiar with the receivers.  However each had their unique quirks and I found myself fairly quickly abandoning them for the more familiar quirks of the RTKLIB apps.  Managing three simultaneous real-time solutions involving five separate receivers while also logging raw observations for all five was actually quite challenging and I made a couple of unsuccessful runs before I got everything working at the same time.

I found that the key to turning this into a manageable and automated process was replacing each of the different manufacturer’s GUIs with an RTKLIB stream server (STRSVR) and a plotter (RTKPLOT) each with it’s own dedicated .ini file.  Eliminating the GUIs also gave me a better understanding of exactly what the receivers were doing and what the GUIs were doing.

STRSVR provides a standardized, always visible red/yellow/green indicator for each stream along with a continuously updated bps number that indicates not only that the connection is alive, but that data is flowing.  This allowed me to tell at a glance that all streams were flowing and that all the log files were being updated.  Using the “-t” option in the command line to specify a title for each window also helped keep things straight.

Both receivers are configured by sending Novatel-like ASCII commands over the serial port and these can be added to the STRSVR Serial “Cmd” window and saved to a “.cmd” file, similar to configuring the u-blox receiver.  Notice in this example, I also sent a reset to the receiver every three minutes which was a convenient way to automate the testing of acquisition times.


I connected both dual frequency rover receivers to my laptop, using two COM ports for each one and using a USB hub to get enough ports.  I set up both receivers to output NMEA solution messages and raw RTCM observation messages on COM1 at 5 Hz and accept RTCM base station data on COM2.  Both receivers have decent reference manuals to describe their command set but I also found this Hackers Guide to the K501G from Deep South Robotics quite useful for getting started.

For reference, here are the commands I used to configure the Tersus rover:

fix none
log com1 gpgga ontime 1 nohold
rtkcommand reset
log com1 gpgga ontime 0.2

log com1 rtcm1004 ontime 0.2
log com1 rtcm1012 ontime 0.2
log com1 rtcm1019 ontime 1
log com1 rtcm1020 ontime 1
interfacemode com2 auto auto on


and here are the commands I used for the ComNav rover:

interfacemode compass compass on
unlogall com1
fix none
refautosetup off
set cpufreq 624
rtkobsmode 0
rtkquality normal
set pvtfreq 5
set rtkfreq 5
log com1 gpgga ontime 0.2 0 nohold
log com1 gprmc ontime 2 0 nohold
log com1 rtcm1005b ontime 10
log com1 rtcm1004b ontime 0.2
log com1 rtcm1012b ontime 0.2
log com1 rtcm1019b ontime 2
log com1 rtcm1020b ontime 2

My intent was to setup the receivers in default RTK mode with a 5 Hz output for NMEA solution messages and RTCM raw observation and navigation messages.  The one exception to default was that I found the “rtkquality” setting on the ComNav receiver defaulted to “quick” which was giving me false fixes, so I changed this to “normal” and that seemed to fix the problem.

By setting things up this way, I only need to click on the correct combination of icons (each tied to it’s own .ini file) from my RTKLIB menu to bring up the correct windows and a few more clicks to start the streams in a simple and repeatable way.


I’m jumping ahead a little bit, but here is a screen capture of the rover-connected laptop streaming two NTRIP sets of base station data to the rovers while simultaneously logging and plotting the computed solutions for all three rovers along with raw observations for all five receivers,  and also computing an RTK solution for the M8T receivers with RTKNAVI.


I should mention that there was one very annoying bug that was introduced to STRSVR in one of the recent RTKLIB releases that gives an error if a data file already exists instead of an overwrite dialog but I did fix this and add it to a new demo5 b29b code release available at the download page on  The new release also includes a fix for another bug that prevented the “-i” command line option to specify a config file for RTKPLOT from working properly.

I then setup the second ComNav receiver as a base station for both dual frequency rovers and used a single COM port to stream RTCM messages from the receiver to a PC.  I used an STRSVR window on the PC to stream the messages to a NTRIP caster using the free RTK2GO NTRIP caster service as I have previously described.  I used ComNav AT330 antennas for both the base and rovers with the rover antenna shared by all three rover receivers.   I did not have enough connector hardware to share the base antenna so used a separate u-blox antenna for the M8T base receiver.

The next step was to collect some data.  I started with a relatively simple challenge, a static rover with a reasonably open sky view and a short baseline.  The ComNav and Tersus solutions both assume the rover may be moving so I set up the M8T solution as kinematic as well.

Let’s first look first at the ComNav solution compared to the M8T solution.  Both solutions were computed real-time.  RTKPLOT will plot NMEA data but it did not seem to like the mix of NMEA and RTCM data in the same file.  To deal with this, I wrote a simple matlab script to strip the NMEA messages from the log file and put them in a separate file.  Below I have plotted only the Up/Down axis for both receivers just to avoid too much data,  the M8T is on top, and the ComNav below.  Each of the larger breaks in the fix was caused by me disconnecting then reconnecting the antenna to force a re-acquire.


The M8T configuration was identical in the left and right plots, but the ComNav “rtkquality” parameter was set to “quick” in the left plot, and “normal” in the right plot.  It’s not as obvious here as it is in the other axes but the third ComNav fix in the left plot is a false fix and had over 0.2 meters of error in the N/S axis.  Changing the “rtkquality” parameter to “normal” seemed to help and I did not notice any more false fixes after making that change.

The ComNav receiver typically achieved a fix very quickly regardless of the “rtkquality” setting, usually in less than 30 sec although in one case it took a minute and a half.  This was noticeably faster than the M8T receiver, which took from 1 to 3 minutes each time in this example to achieve a first fix.

The scales are the same in the two sets of plots, so as you can see, the ComNav fixes are a fair bit noisier than the M8T fixes.  I don’t know why this is but it is something that I hope to investigate more.

Unfortunately I got a mix of good and not so good results from the Tersus receiver.   I did not see this behavior in my previous evaluation so I’m fairly certain this is not a problem with the hardware.  I suspect it has something to do either with my setup or with the new firmware.  I am going to hold off on sharing any of the Tersus data until I understand better what is going on.

Next, for a more challenging test, I moved the rover antenna to a spot with fairly poor sky views located between several large trees.  The sky view directly above the antenna was clear but a large percent of the overall view was blocked.   Again, I just plotted the Up/Down axis with the M8T position solution on the top and the ComNav solution on the bottom.


I disconnected and reconnected the antenna three times in this experiment.  The M8T did not get a fix in the first try before I gave up after 12 minutes, but it did after 13 and 11 minutes in the second two tries after briefly getting a false fix in the second try.  Definitely marginal conditions for the M8T.  The ComNav receiver did significantly better with two fixes in less than 3 minutes and one in 9 minutes.  The errors were relatively large in the first fix but based on the other two axes it was not a false fix.  You can also see that the ComNav third fix was noticeably noisier than any of the other fixes on either receiver, again for unknown reasons.

For the third part of the experiment I moved the receivers into my car and attached the antenna to the roof and collected data for three spins around the neighborhood.  The results are plotted below.  In each case the M8T real-time solution is on the left, and the ComNav is on the right.  In the data in the first row, I shared a single antenna for all three receivers.  For the data in the second and third row I used separate antennas.  I did not change any of the config settings for any of the receivers between these runs and the above runs except that the rtkquality setting was still set to “quick” for the ComNav receiver for the second and third rows.








I have not had a chance to look at this data closely but at first glance, from a fix percentage perspective only, I don’t see significant differences between either of the receivers.  The obvious advantages the ComNav receiver demonstrated in faster fixes in the static tests did not seem to carry over to the moving rover case.  I do plan to look at the raw data more carefully to see if I can understand better why this is.  For whatever reason, the Tersus receiver seemed to perform better with a moving rover than it did with a static rover, and was very similar in fix percentage to the other two receivers in this part of the experiment.

Next I planned to post-process the raw data through RTKLIB to better understand what is going on but as usual, nothing is as simple as you hope for, and I ran into another issue.

Both the Tersus and the ComNav receiver report a mix of 2W and 2X  measurements for the raw GPS L2 measurements.  If the satellite supports the newer L2C code it locks to that and reports a 2X code, if not, it locks to the older L2  and reports a 2W code.   You can see this in this example observation epoch from the Rinex conversion of the ComNav receiver RTCM output.  The left three columns are the L1 measurements, the middle three columns are the L2 (2W) measurements and the right three columns are the L2C (2X) measurements.  You can see that all the GLONASS satellites report L2 measurements only but that the GPS satellites are a mix of L2 and L2C measurements.


This is new for the Tersus receiver, it did not do this when I evaluated it with the older firmware.  For the ComNav receiver, this is the default behavior but it is possible to change this through a command to specify L2 only, no L2C.  As far as I can tell, the Tersus only supports the mixed L2/L2C mode.  All the data I collected for this experiment was in the mixed L2/L2C mode.

Unfortunately RTKLIB does not like this format and throws away all of the L2C measurements.  It is possible to fool RTKLIB into using all the measurements by changing the 2X’s in the “Obs Types” list in the file header to 2W’s but I haven’t looked yet at to what extent mixing the code types affects the solution or how to avoid throwing away the L2C data without editing the header.

I will leave a more detailed analysis of the data to a future post.  My initial impression from these results though, is that although there are some obvious advantages with the ComNav receivers, replacing a pair of low cost single frequency receivers with a pair of low cost dual frequency receivers does not magically make the challenges of precision GNSS go away and that it will still require close attention to the details and recognition of their limits to get good results with either set of receivers.