Squaring receiver for Galileo blind search

I’ve been following the drama of the two recently-launched Galileo FOC satellites. To my knowledge, nothing is yet known about the status of the transmitters, nor have the PRNs been revealed, nor does an acquisition search over all 50 codes in the Galileo ICD yield any signals when the satellites are in view (though PRNs 11, 12, and 19 show up as expected).

So if the satellites are transmitting at all, perhaps it’s with a nonstandard code. One way to check is to take a trip back in time and revisit one of the very oldest GNSS receiver techniques, that of squaring a BPSK signal to recover a tone, then driving a PLL with this tone. (The Macrometer V-1000 receiver used this scheme.)

Now Galileo E1 when viewed in a 4 MHz bandwidth is not BPSK—it is, I believe, a three-level signal, {-1,0,1}, being the sum of equal-power E1-B and E1-C with negligible contribution in quadraphase from PRS. But squaring still works on a signal of this type, though not quite as well as with BPSK.

Here is a waterfall plot with several satellites visible, and indeed Galileo PRN 11 is one of them. Its identity is confirmed by acquiring and tracking in the conventional way, then verifying that the Doppler is identical (modulo the factor of 2 from squaring).

squaring

As of September 20, though, there is still nothing from the new satellites. A predicted Doppler curve can be derived from orbital elements, but there is nothing in the spectrogram at the anticipated Doppler. So we wait.

Code for this “receiver” (all of a dozen lines) is at the GitHub repository (squaring.py). Its output can be piped to the indispensable baudline spectrum analysis tool for interactive viewing.

GNSS stamp collecting

Here’s a fun montage of the various GNSS signals-in-space. I think this accounts for all extant open-access signals that are likely to remain so (ruling out Galileo E6 for example) and that are intended for systems having global coverage (so no QZSS or IRNSS). A few comments on the signals:

  • Each waveform is 400 ms in length and is sampled at 1 ms intervals after code and carrier wipeoff
  • Secondary codes have not been removed
  • Carrier-to-noise ratios vary. The weakest signals are Galileo E5a-I and E5a-Q; by eye there is not much to see, but the acquisition metric is clearly well above threshold, and the histograms are clearly bimodal. My L1/L2 antenna receives some L5 but with about 15 dB of attenuation (!). I really need an antenna that covers down to L5. The GPS L5 signals must have been blisteringly strong to come through as well as they did.
  • The pilot signals with no secondary codes, GPS L2CL and GLONASS L2 P, are shown correctly offset from the (undrawn) centerline. I’m not sure much is known about the data modulation on GLONASS P. Apparently L1 has some data at 50 Hz (no secondary/meander code?) and L2 is unmodulated.
  • So far there are two GLONASS satellites transmitting L3OC CDMA signals. The signals shown are from PRN 30, but PRN 33 is also active and of comparable quality.

The tools for acquiring and tracking these signals are in my GitHub repository:

https://github.com/pmonta/GNSS-DSP-tools

They are command-line-ish and not very polished yet.

track-collection

Height-based multipath mitigation

Here’s a crazy idea which I might as well put on the blog.

Multipath is an important error source for GNSS reference stations. Monuments for antennas are nearly always placed close to the Earth’s surface, so the ground will act as a reflector with a grazing geometry that generates short-delay multipath. Usually other objects contribute as well (nearby buildings or fences for example).

Many solutions exist for multipath mitigation, both at the antenna and correlator levels. Another possible system technique, though, would seem to be to move the antenna upwards, far enough away from local objects that any reflected signals have delays larger than the support of the autocorrelation function for any signal of interest.

Conceptually, an antenna could simply be placed at the top of a tall tower a few hundred meters in height. The tower would ideally be transparent to RF (perhaps of lightweight dielectric construction). Of course there are many practical problems with this, but the environment around the antenna would be nearly ideal.

Another possibility is to place the GNSS antenna on a UAV, which would keep station above the reference monument and several auxiliary sensors (whose location and stability are not critical). The UAV would simultaneously maintain links with visible GNSS satellites (aided by an on-board inertial system) and with sensors on the ground using any of a variety of accurate (~0.1 mm say) short-range ranging techniques. In this way the pristine airborne GNSS signal environment is transferred to the reference monument despite the relative movement.

The UAV could execute certain maneuvers to continuously calibrate its antenna, similar to robot absolute antenna calibrations on the ground. The craft could spin slowly around the vertical axis, or tilt slightly, or both. The attitude from the inertial system would become part of the observation stream to close the calibration loop. By contrast, there is great reluctance to move ground-based GNSS reference antennas to carry out any sort of ongoing calibration program on them. With flyers, continuous monitoring comes for free.

Small rotations and tilts on an airborne platform are impossible to completely avoid, and high-wind situations may force some loss of observing time. But for most of the time, the environment should permit an accurate tie from UAV track to ground network. Depending on the choice of flying craft, several may be needed, spelling each other for charging or refueling. Automatic fleet management will probably have many good solutions over the next few years.

It’s hard to know whether there’s a benefit without more detailed study, but the prevailing trends in GNSS system accuracy seem to be increasing the relative importance of multipath. If we assume progressively better satellite orbit and clock estimates and ionosphere and troposphere sensing, then multipath may well loom as the last remaining large, difficult, uncertain systematic error. (Perhaps a UAV could help with estimating the wet troposphere delay as part of normal operation, to the extent that measurements on the very bottom segment of the troposphere are predictive of the full path.)

Finally, I wonder whether UAVs could help with urban canyons or tree-canopy issues. A surveyor might deal with an awkward situation by tossing a UAV into the air and replacing a bad-GNSS-signal problem with a perhaps easier-to-solve UAV-to-ground-sensor problem using vastly stronger optical or RF links.