I last took a look at the SwiftNav Piksi Multi low-cost dual-frequency receiver back in November last year when they introduced the 1.2 version of FW. They are now up to a 1.4 version of firmware so I thought it was time to take another look. The most significant improvement in this release is the addition of GLONASS ambiguity resolution to the internal RTK solutions but they also have made some improvements in the quality of the raw observations.
I started with a quick spin around the neighborhood on my usual test route. The initial results looked quite good, so for the next test I expanded my route to include a drive to and around Boulder, Colorado, a small nearby city of just over 100,000. The route included some new challenges including underpasses, urban canyons, higher velocities, and even a pass underneath a parking structure. This is the first time I have expanded the driving test outside my local neighborhood.
My test configuration was similar to previous tests. I used a ComNav AT330 antenna on my house roof for the base station, and a SwiftNav GPS-500 antenna on top of my car for the rover. I split the antenna signals and in both cases, fed one side to a Piksi receiver and the other side to a to a u-blox M8T single frequency receiver. I ran an internal real-time RTK solution on the Piksi rover and an RTKNAVI RTK real-time solution on the M8T rover. The M8T receivers ran a four constellation single frequency solution (GPS/GLONASS/Galileo/SBAS) to act as a baseline while the Piksi receivers ran a two constellation (GPS/GLONASS) dual frequency solution. Both rovers were running at a 5 Hz sample rate and both bases were running at a 1 Hz sample rate. The distance between rover and base varied from 0 to just over 13 km. The photos below show different parts of the route.
Here are the real-time solutions for the two receiver pairs, internal Swift on the left, and RTKNAVI M8T on the right.
Both solutions had similar fix rates (79.9% for Swift, 82.6% for M8T) and in both cases the float sections occurred for the most part either in the older neighborhood with larger trees (top middle) of after underpasses (bottom left). The higher velocity (100 km/hour) on the highway (center) did not cause any trouble for either solution.
Based on a comparison of the two solutions, accuracy was relatively good for the fix sections of both solutions. Below on the left, is the difference between both solutions for points where both solutions had a fix. In the center and right are plots of both solutions (Swift internal=green,M8T RTKNAVI=blue) for the two locations with the longest duration discrepancies of any magnitude. Both look like false fixes by the Swift internal solution, based on the discontinuities. Overall, though the errors between the two were reasonably small and of short duration.
Post-processing the Swift data with RTKLIB produced the solution on the left below with an 85.5% fix rate and a good match to the M8T solution. The difference between both solutions for the fixed point is shown on the right. This solution was run with continuous ambiguity resolution.
For more challenging environments like this I often add some tracking gain to the ambiguities by enabling “fix-and-hold” for the ambiguity resolution mode but setting the variance of the feedback (input parameter pos2-varholdamb) to a fairly large number (0.1 or 1.0) to effectively de-weight the feedback and keep the tracking gain low. For comparison, the default variance for fix-and-hold mode feedback is 0.001 which results in quite a high tracking gain. I find that with the low tracking gain, I generally do not have an issue with fix-and-hold locking on to false fixes.
Running RTKLIB solutions for Swift and M8T with this change (fix-and-hold AR enabled, pos2-varholdamb=1.0) improved the fix ratio for the Swift RTKLIB solution from 85.5% to 91.1% and the M8T RTKLIB solution from 82.6% to 92.6% with no apparent degradation in accuracy.
Using a combined solution instead of a forward solution (only a choice for post-processing) improved the fix ratios even further, again with no apparent degradation in accuracy. The Swift RKLIB solution increased to a 96.2% fix rate and the M8T RTKLIB solution increased to 94.1%.
Overall, the Swift RTKLIB solutions were noticeably better and more consistent than in my previous test. Considering the difficulty of the environment, I consider all of these solutions to be very good.
In my next post, I will look specifically at how the two receivers handled going through a narrow urban canyon, underneath three underpasses and underneath a parking structure.