In the previous posts, I demonstrated reasonably clean looking solutions using the default RTKLIB configuration options for rover data from a roving M8N receiver and base data from a stationary COREX base station.

I also showed a second solution for the slightly more challenging problem where roving M8N receivers are used for both the base and rover inputs. I mentioned that run was not done with the default settings but did not go into the details. Let us now go back and try that run using the default settings and see what happens. Remember what we hope to see is a circle with radius equal to the separation between the two receivers, ideally plotted in green, indicating RTKLIB was able to resolve the integer ambiguities and provide a fixed solution.

For reference, here is the solution we previously calculated with RNX2RTKP using non-default settings and code. The plot on the left is from the ground track option in the RTKPLOT menu which plots the x axis vs the y axis and the second plot is the position option which plots all 3 directions separately vs time.

Below is what we get running with the default settings with the two basic modifications I mentioned earlier. The red indicates the kinematic solution is unable to converge and the plotted result is done in single mode, which is not differential, and therefore is indicating absolute position, and not distance between the two receivers.

Clearly, the default settings are not adequate when both receivers are lower quality and both are moving. Let’s see if we can improve this.

First, let’s make a few small changes that won’t improve the solution but will make the input assumptions better match our problem.

Edit the input configuration file for rnx2rtkp to make the following changes. These are in addition to the two changes we made earlier (pos1-posmode and ant2-postype)

- pos1-frequency: L1+L2 → L1, both receivers are L1 only, so no need to look for L2 data
- pos1-navsys: 1 → 5, we have GLONASS data, so let’s use it
- pos2-gloarmode: on->off, not ready for this yet, in current state, it prevents fixed solution
- pos2-bdsarmode: on->off, no Bediou data so disable
- out-solformat: llh → enu, save solution to output file with equal units in x and y axis
- out-outstat: off → residual, save residuals for later analysis

Rerunning RNX2RTKP gives us a solution very similar to the previous one with little improvement, even with the additional data from the GLONASS satellites. I won’t bother to plot it here.

Next, let’s turn on receiver dynamics with the “pos1-dynamics” input option. This adds nine states to the kalman filter, 3 for receiver position, 3 for velocity, and 3 for acceleration. This enables the solution to take into account the previous position of the receiver when calculating the new solution and allows us to better define likely changes in position, velocity, and acceleration. For now, we will use the default filter settings. With this change, the solution looks like this:

Not quite there yet, but much better. We can now see the expected circle but there are fairly large errors when the solution is not fixed. We do sometimes resolve the integer ambiguities but only 26.6% of the time.

Next, we’ll take a very brief look at the input parameters defining the error characteristics of the input data. For the kalman filter to effectively combine the data from multiple inputs, it requires information about the error distribution of each input. RTKLIB calculates the variances of each code and carrier phase input based on a set of input parameters and a simple model based on satellite elevation, the assumption being that lower elevation satellites are noisier. For now, we will look at only the first input parameter. This is “stats-eratio1” and it sets the ratio of standard deviations of the pseudorange errors to the standard deviations of the carrier-phase errors. I believe it is not unreasonable to expect, with the lower quality receiver and antenna we are using, that the pseudorange errors will degrade more quickly than the carrier-phase errors. If this is true, then it would make sense to increase this ratio from the default of 100. Setting this to 300 give the following solution.

This looks a lot better. We converge much more quickly to a fixed solution and when it loses lock it also re-converges more quickly.

We still need to make a few more changes to get to the quality of the solution at the top of this post, but most of those changes require making some code changes so I will leave them to another post.

The physical justification for using 300 for the error ratio is a bit weak at this point but we’ll just go with it for now and I hope to come back and do a more complete analysis of the way RTKLIB generates the variances and covariances for input to the kalman filter later.

Anyone else come up with a good way to optimize the RTKLIB input parameters for the kalman filter convariances? If so, please leave a comment, I’m interested in how other people handle this problem.

**Update 6/12/16: **Since writing this post, I have found that increasing eratio1 can have a tendency to increase the number of false fixes if “pos2-rejionno” is not also increased at the same time. I now recommend that if you increase eratio1 that you also increase rejionno from 30 to 1000. This should eliminate false rejection of outliers. For more details see this post.