Real-time solutions with RTKLIB and NTRIP using a cell phone as data link

As I mentioned in an earlier post, I’ve recently acquired access to some low cost dual frequency receivers, specifically a Tersus Precis BX306 and a pair of Swift Piksi Multis.  I have been playing with them over the past few weeks and plan to share my experiences with them over a series of posts.

Both receivers provide internal RTK solutions as well as raw measurements that can be processed with RTKLIB.  I’m interested in how the RTKLIB solutions compare to the internal solutions as well as how both of these compare to solutions derived from single frequency data collected simultaneously with the dual frequency data.

The first issue I ran into with this experiment, however, is that both receivers will only provide an RTK solution for real-time data, neither have the capability to post-process previously collected data.  This meant that I needed a way to provide a real-time stream of dual frequency base station data to the receivers.  I wanted to be able to  do this while driving a car around the local area so I needed more range than a low cost set of radios would give.

Fortunately, I have fairly good cell phone coverage in this area so I was able to rely on my cell phone for the data link.  In this post I will explain how I did that, both for an external CORS reference station and for my own base station.  In both cases I used  NTRIP server/caster/clients to do this.  NTRIP is a protocol for streaming of DGPS or RTK correction data via the internet using TCP/IP.  The NTRIP server sends out the data to an NTRIP caster and the NTRIP client receives it. For more details, there is a good description here.

Using this setup I was able to run real-time solutions with RTKLIB as well as with the intenal RTK engines in the Swift and Tersus receivers.  Here’s a diagram from the RTKLIB manual showing the setup I used for running a real-time RTKLIB solution using RTKNAVI.  When I ran a Swift or Tersus solution, the configuration was similar, but the NTRIP caster streamed the base station data to STRSVR instead of RTKNAVI, and STRSVR then streamed it to the receiver where it was combined with the raw receiver observations to create an internal RTK solution.  Also missing in this diagram is the cell phone which should be in between the internet and the rover PC.

ntrip.rtklib

The amount of free base station reference data that is available online on a real-time basis is a fair bit more limited that what is available after the fact for post-processing.  Fortunately I was able to find a CORS reference station about 17 km away that is available real-time through the UNAVCO NTRIP caster.  The service is free if the data is used for educational purposes and appropriately attributed.   Most of their stations are on the west coast of the U.S. but they do have some scattered across the rest of the country as you can see in this map from their site.  There are other networks available in other parts of the world that can be found by searching online.

unavco_map

To access the UNAVCO data I had to request access through email but the process was very simple and within a couple hours of my request I was all setup with an account and password.

Once I had my account set up, I used RTKLIB on my laptop computer to collect the data from the internet and stream it to the rover receiver over a serial port.  If I were doing this experiment within range of a wireless router then I could leave the computer connected to the wireless.  In this case though, I wanted to roam outside the range of my home wireless.  To do this, I enabled a hot spot on my cell phone and logged into that with my computer.

I was able to access the raw observation data stream from the UNAVCO NTRIP caster directly using the NTRIP client option in RTKLIB.  If I had wanted to generate a real-time RTKLIB solution, I would have configured the input streams of RTKNAVI but in this case I want to stream the raw data directly to the receiver so it can use the observation data for it’s internal solution.  I did this using the STRSVR app in RTKLIB.  I specifed the “NTRIP Client” option as input type and then entered the information from my UNAVCO account into the “Ntrip Client Options” as shown below.

ntrip_client

In this case I wanted the data from station P041 in RTCM3 format so I had to specify the Mountpoint as “P041_RTCM3”.  For other networks, the mountpoint details may be a little different.  Most NTRIP casters use Port 2101, and that was the case for this one.  For the STRSVR output type, I specified “Serial” and then configured the serial port options for whichever rover receiver I was using.  Before doing the configuration, I had connected the receiver to the laptop using a USB cable.

I then had to configure the receiver to tell it to get its base station data from the COM port and specify that it is in RTCM3 format.  The details for doing this on the two receivers are a little different but fairly straightforward in both cases.  You may also need to specify the exact base station location manually or the receiver may be able to get it from the data stream depending on the receiver and NTRIP stream details.

And that’s it.  With this configuration, either receiver was able to fairly quickly lock to a fixed RTK solution and continue to receive base data as long as I stayed in range of cell reception.  Any lag in the base station observations appeared to be less than a second.

That worked great for using an existing external reference as base station.  However, I also wanted to run another real-time experiment where I used one Swift receiver as base and the other as rover.   To do this, I needed to set up an NTRIP server to stream the data to  a caster on the internet as well as an NTRIP client to receive it.

I started by connecting the second Swift receiver to an old laptop with a USB cable and then downloading RTKLIB, the Swift console app,  and the right USB drivers.  The base station antenna is on top of my roof and the laptop is in the house so I was able to connect the laptop to the internet using my home wireless.

For the NTRIP caster, I found it convenient to use RTK2GO which is a community caster available for anyone to use at no cost.  To send the data to the caster, I used the “NTRIP Server” as the STRSVR output type and configured it as shown below.

strsvr_server

Again, the port is 2101.  You can choose any name for the mountpoint.  If that name is already in use, then rtk2go will assign a suffix to it, so it is best to choose a name that is unlikely to already be in use.  The password at the current time is BETATEST but that may change from time to time so it’s worth verifying it is still correct.

For the STRSVR input, I selected “Serial” and specified the correct COM port for the base station receiver.  In this case the raw observations are in Swift binary format which RTKLIB does not support so it sends them unaltered.  If they were in a format that RTKLIB did support, then they could be converted to RTCM3 to reduce bandwidth and make them more easily usable by someone else not using a Swift receiver as rover.  You can specify the conversion to RTCM3 using the “Conv” menu on the STRSVR output.

Start STRSVR and your base station observations are now accessible to anyone in the world through RTK2GO.com!

On the rover side, the NTRIP client is set up as I previously described using STRSVR except you want to use the same caster/mountpint/password as you just did on the base station.  In this case the user-id is left blank.  Again, set the STRSVR output to “Serial” to send it to the receiver.   Then set up the receiver to get it’s base station data from the serial port and, in this case, specify that it is in the Swift Binary Protocol (sbp).  Start the receiver and it should fairly quickly get a fix.  If you are seeing baseline data but not a solution, then most likely you have not specified the base station location to the rover.

I was now able to drive around almost anywhere and get continuous real-time RTK solutions using either my own base station or the CORS reference station as base.  In the next post I will discuss some of the data I collected and analyzed.

 

 

 

 

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 …

 

Base station data for RTKLIB

RTKLIB has a number of different algorithms it can use to calculate position. The first two in the list are “Single” and “DGPS”. Both of these methods use only the pseudorange data and not the carrier phase information. Without the carrier phase information, the precision of these methods is quite low, and probably worse than what the GPS receiver would provide without RTKLIB. In general they are not very interesting other than possibly for some initial debug during setup. The rest of the algorithms can be divided into two groups, differential and PPP. The differential algorithms determine position relative to a known nearby location while the PPP (precise point positioning) algorithms determine absolute position. In general, the data quality of the low cost hardware we are using is only good enough to use with the differential methods and not the PPP methods, so we will focus on those. RTKLIB supports four differential modes: Static, Kinematic, Moving-Base, and Fixed. The kinematic mode is designed to calculate the relative position between a fixed base and a moving rover and that is what we will use. It can also be used with a moving base if we are concerned only with finding the distance between the two and not trying to translate that to a fixed position, and we will use it this way as well.

In my particular application, I am interested only in the distance between two receivers and not in any absolute locations. In this case I will collect data from two low-cost receivers and use one as the base and the other as the rover. In many cases, though, it is useful to use an existing ground station as the base and the low cost receiver as the rover. I use this method for testing and verification, even though I don’t plan to use it in my final solution.

In the US, base data is available online for free from many GPS stations in the CORS network.  Here’s a map of CORS station locations.

cors_map.PNG

For the ground stations I have used, it is generally available online less than an hour after it is taken. It is fairy easy to pull it manually using a user-friendly form from the CORS webpage, or you can use the RTKGET utility in RTKLIB. Be aware that some stations have only GPS data, and some have both GPS and GLONASS data.