Switch branches/tags
Nothing to show
Find file Copy path
Fetching contributors…
Cannot retrieve contributors at this time
1470 lines (1222 sloc) 58.2 KB

GPS accuracy

I work with a lot of GPS data at my day job, and I always wondered how accurate it was in practice. Devices will report things like "within 4-8 meters," but in many ways that's an ideal case; what about noise or reflected signals? So I started doing a bunch of experiments to figure it out.

First experiment: overnight home location recording

I installed a GPS logger on my phone and used it to record my phone's position every second as it charged overnight. Due to the lack of major tectonic activity in Albuquerque, the phone remained stationary during this recording.

I started with the GPX trace from the phone and converted it to TSV of (lat, lng, timestamp, source):

$ sudo cpan install Date::Parse
$ nfu --run 'use Date::Parse' *.gpx \
      -m 'row %0 =~ /^.trkpt.*lat="([^"]+).*lon="([^"]+)".*<time>(.*)<\/time>.*<src>(.*)<\/src>/, $ARGV =~ s/\..*$//r' \
      -k1m 'row %0, %1, str2time(%2), %3, %4' -o2 --pipe uniq \
  | gzip > trace.gz

I used Google Maps' satellite view to find the coordinates of the phone inside (to within a meter or so):

phone location

Initial results

Here are the traced data points sourced from the GPS. The plus-sign on the map is where the phone was physically located (35.194193, -106.481589):

$ nfu trace.gz -k '%3 eq "gps" && %4 eq "20150616"' \
      -nm 'row %0 % 2 ? 35.194193 + rand() * 0.00005 - 0.000025 : 35.194193,
               %0 % 2 ? -106.481589 : -106.481589 + rand() * 0.00005 - 0.000025,
               %2' \
      -m 'row(%1, %0), row(%3, %2)' -p %d

phone gps

The most distant point was 35.5m away:

$ nfu trace.gz -k '%3 eq "gps" && %4 eq "20150616"' \
               -m 'edist(%0, %1, 35.194193, -106.481589)' -OT1
0.0355021736061369      # km

Some weird things about this data:

  1. It's really biased; almost all of the error is directed southeast.
  2. Android seems like it's inferring motion, probably from random GPS jitter. (NOTE: I found out later that Android doesn't do this at all; these are proper GPS hardware signals with no motion inference. This means that GPS is very low-variance; the error is all bias.)

Here's a map of the location deltas (by ascending time):

$ nfu trace.gz -k '%3 eq "gps" && %4 eq "20150616"' -f10S01D1p %d

gps deltas

Here's the absolute error rate (x axis = meters, y axis is frequency):

$ nfu trace.gz -k '%3 eq "gps" && %4 eq "20150616"' \
      -m 'edist(%0, %1, 35.194193, -106.481589) * 1000' \
      -q0.1ocf10p %l

error rate

So a typical error amount is 20 meters, quite a lot more than the phone's estimated 4-8 meter accuracy. It's clear there's some kind of interference going on, so I set up another log from the other end of the house to see whether the errors were consistent.

Trying again on the other side of the house

Just in case.

west location

$ nfu trace2.gz -k '%3 eq "gps"' \
      -nm 'row %0 % 2 ? 35.194130 + rand() * 0.00005 - 0.000025 : 35.194130,
               %0 % 2 ? -106.481832 :  -106.481832 + rand() * 0.00005 - 0.000025,
               %2' \
      -m 'row(%1, %0), row(%3, %2)' -p %d

west errors

Looks surprisingly similar. Here's the delta map:

$ nfu trace2.gz -k '%3 eq "gps"' -f10S01D1p %d

west deltas

Ok, so the error is not being caused by the house itself (and probably also not by sporadic ionospheric interference, since the logs were from two separate nights); the most likely explanation is some type of environmental interference.

Satellite visibility experiment

I live in the foothills, so we've probably got all kinds of interference from terrain. This comes in two forms: sometimes the satellite signals will just be blocked, and sometimes they'll be reflected. Reflected interference is harder to analyze, so I decided to start by just mapping out the horizon angles and correlating the moments of error with satellites' horizon intersections.

Measuring satellite visibility

GPS satellites have 12-hour orbits, which matters because it means that any signal interference pattern will be time-dependent. I live in the foothills, so the horizon isn't flat; in order to know which satellites were visible at any moment in time, we need to digitize the horizon line.

Camera-mount transit

I built a horizontal transit to hold the phone and took a 360-degree video, marking every 15-degree increment. The front of the transit has a ruler spaced 32 inches from the phone, so some simple trig will establish baseline angles. I used the tower as the zero-degree point because I could later find its exact coordinates on a satellite map (in practice it's 112 degrees from north, an adjustment factor I apply just before saving the horizon profile).

camera transit

tower coordinates

Vertically calibrated with incredible precision...

camera vertical

Here are some examples of the measurement photos, going clockwise from the tower:

measurement 0 measurement 1 measurement 2 measurement 3

My hand is in some of them because I was indicating the horizontal angles in the original video. The first thing to do is load one up in Octave and experiment with some edge detection strategies until we get one that highlights the terrain properly. I started by converting to grayscale and using default edge detection:

$ sudo apt-get install octave-image
$ octave
> i = imread("gpsa-measurement-0.png")
> imshow(edge(rgb2gray(i)))


Nowhere near good enough for automatic line extraction, so I did some experimentation and ended up with this:

> [dx, dy] = gradient(rgb2hsv(i)(:,:,3))        % 3 = value channel
> imshow(abs(dy) * 5)                           % 5 just helps visibility

better edges

Once the camera was facing away from the sun, though, the value channel became insufficient:

blurry edge

I fixed it by using the hue instead:

> [dx, dy] = gradient(rgb2hsv(i)(:,:,1))        % 1 = hue
> imshow(abs(dy) * 5)

better edge

Lens distortion

As a quick aside, before we rely too much on any line tracing from the camera we need to invert any distortion caused by the lens; typically this will either be pincushion or barrel distortion. Either of these will deform a grid, so I found one online and took a picture, then copied cells to other locations to check for consistency:

lens distortion test

This is really good; there's a little bit of pincushion distortion towards the middle and some barrel distortion at the corners, but for the most part sizes and lines are preserved (there's probably some software correction happening). Other sources of error will probably dominate here.

Line tracing

After more experimentation than I care to admit, I ended up with a fairly straightforward line tracing function that operates on the signed gradient dy. The idea here is to track the maximum value in each column of the image, expecting vertical deviations with Gaussian-distributed probabilities. The core of the algorithm looks like this:

[rs cs] = size(m);
for i = 1:cs
  [y, yi]   = max(m(:, i) .* normpdf(1:rs, yi, sigma));
  result(i) = yi;

The full source for the line-tracing utility is in the attached trace-profile. Most of the images came out perfectly (we only need stuff to the right of the ruler):

# if invoked with a number, trace-profile debugs just that image
$ octave --persist trace-profile 1

good alignment

Only one of them didn't; the pattern of rocks created a high-contrast path that diverted the measurement.

bogus alignment

Fixing it was just a matter of convolving with a narrow Gaussian; this removed enough detail that the tracer took the upper path.

@(i) conv2(rgb2hsv(i)(:,:,3), normpdf(1:10, 5, 2))(:,5:end-5);

fixed alignment


Each image is a plane (as opposed to the spherical horizon profile we're ultimately modeling), so any affine transformation will introduce perspective-projection distortion since the planes have different angles. The way to fix this is to transform the images into cylindrical coordinates; that way all shifts are in terms of angles and are uniform.

The 16" ruler measures 670 pixels and is 32" away from the camera, so the view plane is 25.8" tall by 45.85" wide at 32". Solving for pixels gives 2680.

$ nfu profile.gz \
      -m 'my $z0 = 2680;
          my $r  = dist(%1 - 960, $z0) / $z0;
          row %0, %1 / $r, %2' \
      --partition %0 \
        [ --octave "xs = [xs(1:1750,1), \
                          (1:1750)', \
                          interp1(xs(:,2), xs(:,3), 1:1750, \"spline\")']" ] \
      -o01 \
  | gzip > cylindrical.gz

Now let's grab everything to the right of the ruler (about X = 1000) in each image. This will result in a bunch of curves, each the same size and valid at every point.

$ nfu cylindrical.gz -k '%1 >= 1000' \
                     --partition %0 'nfu -f12 | gzip > right-{}.gz'

We have three degrees of freedom due to mechanical imprecision in the transit:

dof diagram

Since the curves overlap, the goal is to solve for these variables by minimizing error. The first step is to find the overlap amount by generating micro-adjustments from 380, a reasonable baseline. I'm doing this by taking cumulative sums of dy² and measuring the average X distance between those curves.

$ nfu cylindrical.gz -m 'row %0, "right-" . %0              . ".gz",
                                 "right-" . (%0 + 15) % 360 . ".gz"' \
      -ocf1.k -e%2 \
      -m "row %0, %1, %2, 380 + int
            qx[nfu %1 -S1D1m 'row \%0, \%1 ** 2' \
                      -i0 @[ %2 -m 'row \%0 + 750 - 380, \%1' \
                                -S1D1m 'row \%0, \%1 ** 2' -s1 ] \
                      -s1 \
                      --octave \"xs = [interp1(xs(:,2), xs(:,1), (0:1000))', \
                                       interp1(xs(:,3), xs(:,1), (0:1000))']\" \
                      -nK 'grep /NA/, \@_' -m '\%2 - \%1' -a0T+1]" \
  | gzip > dx.gz

# check the alignment:
$ nfu dx.gz \
      -nm "map row(\$_),
               rl qq[sh:nfu %2 -m 'row \%0 + 750 * %0 - 750, \%1' \
                     -I0 @[ %3 -m 'row \%0 + 750 * %0 -  %4, \%1' ]]" \
      -m 'map row(%0, -$_), @_[1..$#_]' -p %d

valigned curves

Looks good. Now let's use those overlaps to solve for dy and dθ. This is a little tricky because if we're not careful we'll infer some crazy angles for stuff. To prevent bias in dθ, we first solve for dys such that ∑dy = 0.

$ nfu dx.gz \
      -m "map row(%0, \$_),
              rl qq[sh:nfu %1 -m 'row \%0 + 750 * %0 / 15 - 750, \%1' \
                    -i0 @[ %2 -m 'row \%0 + 750 * %0 / 15 -  %3, \%1' ]]" \
      --partition %0 [ \
        -f123o --octave "dy = mean(xs(:,3) - xs(:,2)); \
                         dx = xs(end, 1)   - xs(1,1); \
                         s1 = xs(end, 3) - xs(end, 2); \
                         s0 = xs(1,   3) - xs(1,   2); \
                         xs = [xs(1,1), \
                               mean(xs(:,1)), \
                               mean(xs(:,3)), \
                               dy, \
                               (s1 - s0) / dx]" \
        -m 'row {}, @_' ] \
      -m 'row @_[0..4], (rect_polar %5, 1)[1]' \
      -i0 @[ dx.gz -f032 ] -o \
  | gzip > solved-initial.gz

$ nfu solved-initial.gz \
      -m "%4 -= `nfu solved-initial.gz -f4a0T+1`;
          %5 -= `nfu solved-initial.gz -f5a0T+1`;
          row @_" \
      -s46a5,0 \
      -nm 'map row(@_, $_), rl %8' \
      -m  'my ($r, $h) = rect_polar %9 - (%3 - %2) - 1000, $_[10] - %4;
           my ($x, $y) = polar_rect $r, $h + %6;
           row %1, $x + 750 * %0 - %7, $y + %4 - %5' \
  | gzip > horizon.gz

# check alignment again:
$ nfu horizon.gz -m 'row %1, -%2' -p %d

aligned curves

This really isn't bad considering how egregious the setup was. We can finalize the profile by inserting the run of zero data between 45 and 165 degrees, vertically averaging, and converting the pixel angles to real degrees. The cell camera is level at 7/8" on the ruler, which appears at pixel 655 from the top of the image.

# how many pixels wide is the horizon image?
$ nfu dx.gz -f3m '750 - %0' -a0T+1m '%0 / 15 * 360'

The "0" measurement image was aligned to pixel 5638 against the 345 image. 960 pixels across the image marks the zero point, so we now adjust 40 pixels to the left to correct for the 1000 floor we used earlier. (We couldn't have used 960 for image processing because the ruler isn't perfectly vertical and we need some margin.) While we're at it, we account for the 112-degree angle from due north to the tower I used for zero.

$ nfu horizon.gz \
      -m 'row((%1 + (%0 >= 165 ? 8766 - 5638 : 0) + 40
              + 112 / 360 * 8766) % 8766,
              max 0, (rect_polar 655 - %2, 2680 / 2)[1])' \
      -q1oA 'row $_, mean @{%1}' \
      --octave "xs = interp1(xs(:,1), xs(:,2), (1:8765)')" \
      -nm 'row %0 / 8766 * 360, %1' \
  | gzip > horizon-final.gz

final horizon

Awesome. Now it's time to get satellite positions for each moment in time and figure out when each one was visible.

A quick aside: orbital geometry and coordinate systems

When satellites are launched, their orbits are specified with respect to the celestial sphere rather than to Earth's surface (the obvious exception being geosynchronous orbits). As a result, there are a few different coordinate systems people use to refer to things in space:

  • ECI (Earth-centered inertial): The X axis is aligned to the celestial sphere, the Z axis to Earth's rotational axis. As a result, the X-Y plane contains the equatorial ring. Orbital models like SGP4 return coordinates in ECI.
  • ECEF (Earth-centered, earth-fixed): This is just like ECI, but the X and Y axes are defined with respect to points on the earth's surface. Converting from ECI to ECEF involves calculating the earth's celestial rotation, called sidereal time, which differs from solar time because during one solar day the earth progresses by almost one degree in its orbit around the sun.
  • Azimuth/elevation/distance (look angles): Ground-referenced coordinates from a given surface location on Earth. This is what we need to figure out whether the horizon we built in the last post obscures a satellite at any given moment.

If you look at some of the ISS time-lapse videos, particularly the ones at night, Earth appears to be rotating independently of the celestial sphere because while the ISS orbit would look like a flat ring in ECI, it would look quite curved in ECEF.

Generating the satellite positions

I downloaded the TLE orbit descriptions for the GPS satellites from NORAD, then used satellite.js to generate azimuth/elevation/distance for each second the phone was recording.

$ sudo apt-get install nodejs
$ sudo npm install satellite.js
$ curl \
  | sed -r '/^GPS/ {s/^/"/;  s/\s*$/":/}
            /^1/   {s/^/["/; s/.$/",/}
            /^2/   {s/^/"/;  s/.$/"],/}
            1      {i\
            $      {s/,$//; a\
            }' \
  > gps-tle.json

Now let's write up a quick driver script:

// satellite-locations.js
var fs        = require('fs');
var satellite = require('satellite.js').satellite;
var tau       = Math.PI * 2;
var tles      = JSON.parse(fs.readFileSync('gps-tle.json', 'utf8'));
var sats      = {};

var observer  = {
  latitude:    35.194193 / 360 * tau,  // radians
  longitude: -106.481589 / 360 * tau,  // radians
  height:       2.0                    // km above sea level

for (var k in tles) sats[k] = satellite.twoline2satrec(tles[k][0], tles[k][1]);
for (var t = +process.argv[2]; t <= +process.argv[3]; ++t) {
  var d = new Date(t * 1000);
  for (var k in sats) {
    var pv = satellite.propagate(sats[k],
              d.getUTCFullYear(), d.getUTCMonth() + 1, d.getUTCDate(),
              d.getUTCHours(),    d.getUTCMinutes(),   d.getUTCSeconds());
    var gmst = satellite.gstimeFromDate(
                d.getUTCFullYear(), d.getUTCMonth() + 1, d.getUTCDate(),
                d.getUTCHours(),    d.getUTCMinutes(),   d.getUTCSeconds());

    var ecef = satellite.eciToEcf(pv.position, gmst);
    var look = satellite.ecfToLookAngles(observer, ecef);
    var tsv  = [k, t,
                look.azimuth * 360 / tau, look.elevation * 360 / tau,
                pv.position.x, pv.position.y, pv.position.z,
                ecef.x,        ecef.y,        ecef.z].join("\t");

    // Blocking writes are important; otherwise we'll run out of memory if the
    // downstream process is slower than we are (and in this case, it is).
    fs.writeSync(1, tsv + "\n");

The output format here is TSV like this, where LOOK, ECI, and ECEF each comprise three columns:

sat_name  timestamp  LOOK  ECI  ECEF

LOOK = azimuth_degrees  elevation_degrees  distance_km
ECI  = x  y  z
ECEF = x  y  z

We don't really need ECI or ECEF for this analysis, but I wanted to export them for debugging, just in case.

Let's generate satellite locations for times when the phone was recording:

$ nfu trace.gz -f2o --duplicate ^T1 ^T+1 -o
1433801944              # minimum
1435108548              # maximum
$ nodejs satellite-locations.js 1433801944 1435108548 \
  | gzip > satellite-angles.gz

Let's take a quick look at some satellite positions in ECI:

$ nfu satellite-angles.gz -E100f567 --splot %d

orbit diagram

Here's what they look like in ECEF:

$ nfu satellite-angles.gz -E100f8. --splot %d

orbit diagram

Horizon and visibility

Right now we have a bunch of azimuth+elevation coordinates, but some of these fall below the horizon. In that case the GPS signal would be unavailable. We can filter out those points using the horizon profile:

$ nfu --run '/(\S+)\t(\S+)/ and $::h{$1} = $2
             for rl
               q{sh:nfu horizon-final.gz -q0.1oA "row %0[0], mean @{%1}"}' \
      satellite-angles.gz -q2,0.1k '%3 >= $::h{%2}' \
  | gzip > visible-angles.gz

Checking the ECEF to make sure it looks reasonable (we should see a partial hemisphere):

$ nfu visible-angles.gz --sample 0.01 -f8. > visible-hemisphere
$ gnuplot <<'EOF'
set terminal gif animate delay 5 optimize size 856,720
set output "visible-hemisphere.gif"
do for [i = 0:360] {
  set view 75,i
  splot "visible-hemisphere" with dots title "" lc "red"

Correlating satellite visibility with error

The first thing to do here is to join one of the GPS traces to the visible satellite list by timestamp:

$ nfu gpsdata/20150616trace.xz \
      -k '%3 eq "gps"' -i2 @[ visible-angles.gz -f10. ] \
  | gzip > 20150616-satellite.gz

Now we can construct a preliminary time-series plot of satellite visibility windows and absolute error, which I've annotated with some vertical lines. Time is on the X axis. The bottom half is error over time, and the top half contains one horizontal line per satellite -- shaded in moments when that satellite is visible.

$ nfu 20150616-satellite.gz \
      -z4m 'row(%0, 1000 * edist %1, %2, 35.194193, -106.481589),
            row(%0, 30 + %4)' -p %d

satellite error correlation

This doesn't line up particularly well, so let's see whether it could be a byproduct of measurement errors. The gnuplot image above represents the trace across 1254 horizontal pixels.

$ nfu 20150616-satellite.gz -f0 --duplicate ^T1 ^T+1 -oSD1
30576               # total duration of the trace (seconds)

$ nfu 20150616-satellite.gz -f406zK0f12S01D1m 'row %0, abs(%1)' \
                            -s01T+1m 'row %1 / %0'
0.00414757596657057 # average d(|elevation|)/dt for visible arc of sat 0

# How many degrees of elevation error are implied by each horizontal pixel of
# difference? (1254 is unitless because GNU units doesn't know what pixels are.)
$ units -t '0.00414 deg/s * 30576s / 1254' deg

Given how the horizon was measured we might be off by as much as three degrees vertically, which means 30 pixels here. Most of the alignment errors are much less than that, so I think we might be ok. Some of the errors might also be caused by reflective interference while the satellite remains visible; let's look at that next.

Correlating satellite positions with error

Although there are some hills near the house, none obscure a substantial fraction of the sky by area. This means that the GPS receiver probably has enough satellites to get a fairly good signal the whole time; errors wouldn't be caused by dilution of precision as much as they would by multipath artifacts. This also makes sense given that (1) the phone thinks the accuracy is higher than it is, and (2) phones tend to have inexpensive, low-power receivers that would be less likely to do effective multipath filtering.

If we model it as a ray tracing problem, multipath effects should correspond to satellite sky positions with consistently large ground errors. Here's a graph of ground error (color and distance: blue is more, yellow is less) for each satellite position:

# Visualize the horizon profile so we can more easily tell which way is north
# (the mountains are to the east)
$ nfu horizon-final.gz \
      -m 'my ($x, $y) = polar_rect 1, %0;
          my ($r, $z) = polar_rect 1, 90 - %1;
          $. % 10 ? row $x * $r, $y * $r, $z
                  : map row($x * $r, $y * $r, $_ / 40),$z * 40)' \
  > horizon-trace

$ nfu 20150616-satellite.gz \
      -f5612m 'row %0, %1, rect_polar %2 - 35.194193, %3 - -106.481589' \
      -m 'my ($z, $r) = polar_rect 0.3 + 2e3*%2, %1;
          my ($x, $y) = polar_rect $r, %0;
          row $x, $y, $z, 0.00035 - %2' \
      --append @[ horizon-trace -m 'row @_, 0' ] \
  > 20150616-satellite-error

$ gnuplot <<'EOF'
unset colorbox
unset border
unset tics
set terminal gif animate delay 5 optimize size 856,480
set output "20150616-error-animation.gif"
do for [i = 0:360] {
  set view 55,i
  splot "20150616-satellite-error" with dots lc palette title ""


TODO: Be a little more resourceful here; given the nature/direction of the multipath error, I think we can still do the terrain surface mapping stuff.

Nothing very informative here either. Although some satellite positions correspond to lower ground error, because the satellites are locked relative to one another we can't determine which one is responsible for the multipath problems from this data alone. All we really know is that the ground errors do in fact correspond to satellite locations, so they're inherently predictable (though possibly very chaotic).

Experiment to nowhere: multipath tracing

NOTE: This was a fun but misguided detour. Doing this properly would involve a much more complete 3D model of the surrounding terrain, but I haven't gotten a quadcopter to build it yet. A lot of the video processing stuff would apply in either case, though, particularly on an unstable device like a quadcopter.

This is where things start to get challenging. In order to predict how multipath interaction is going to work, we need to first construct a 3D model of the landscape. To do this, I used a rooftop crane I had made earlier:

roof crane

The idea here is to attach the camera 9ft away from the hinge point and sweep it across a circular path. The hinges are precise enough that the resulting images should be mostly coplanar, and since we know the radius we can recover the horizontal displacement by comparing angles. Here are some stills from the resulting video:

beginning of arc middle of arc end of arc

One problem with these pictures, or really with the video, is that my cell phone has a cheap camera that captures frames using a rolling shutter. This means that any vibration will result in distortion of the image, since each row of pixels is offset in time. Luckily we can fix the issue by disassembling the video into its constituent frames and reverse time-shifting each row of pixels by its offset.

Rolling shutter correction

Mathematically speaking, if the shutter rolls over a time window of Δt then the ith row of pixels from the top is displaced by Δt * i / h. The rolling shutter in my camera might not be perfectly uniform with respect to the video framerate, so I recorded a board spinning on a drill to check. I was willing to assume a constant rotational speed for the board because it's lightweight and mostly balanced (which means the primary force acting on it should be air resistance). Here's a frame from the video:

drill frame

The rolling shutter produces the curved distortion here. Here's the next frame:

drill frame 2

I need to solve for Δt as a fraction of the framerate. A simple strategy is to take all measurements in terms of rotation of the board. I can't measure it using the screw head because air resistance might cause some slip; instead I'll take a segment of video where the camera is reasonably still and Fourier-transform a rectangle around the drill light. The result will be a mode at twice the rotational frequency of the drill.

$ mkdir drill-frames
$ avconv -ss 9 -i drill-video.mp4 -t 10 -f image2 drill-frames/%03d.jpg
$ nfu n:1 --octave "for i = 1:150
                      img      = imread(sprintf(\"drill-frames/%03d.jpg\", i));
                      means(i) = mean(mean(img(594:680, 906:1012, 2))');
                    xs = abs(fft(means'))" \
          -p %i

drill fft

The mode is 62 over 150 frames, so the board's speed is 74.4 degrees per frame. Now we just need to measure the rotation between two shutter positions:

drill angle measurement

So in 1010 vertical pixels the board has rotated about 40 degrees, which gives a total of 44.772 degrees of rotation within each frame. This means the shutter takes 0.602 frames to hit the bottom of the image, and there's a 0.398-frame delay before the next scan starts. Now we know the time offset of each row of pixels. Unlike above, I need to extract the parallax frames using a lossless image encoding.

$ mkdir parallax-frames
$ avconv -ss 129 -i parallax-video.mp4 -t 17 -f image2 parallax-frames/%04d.png

There are 510 images, each of which requires 1920 x 1080 x 3 bytes = 6.22MB, so I'd have to close some Chrome tabs to load them all into memory at once. Rather than do that, a more feasible strategy is to use nfu to rearrange the rows among Octave subprocesses, each of which handles either one row across all images, or one image. The intermediate sorts make sure --partition subprocesses aren't restarted due to discontinuous data.

$ mkdir parallax-fixed
$ nfu sh:'ls parallax-frames' -om 'row %0 =~ /^0*(\d+)/' \
      --partition %0 [ \
        --octave "i  = double(imread(sprintf(\"parallax-frames/%04d.png\", {})));
                  id = [(1:1080)', ones(1080, 1) * {}];
                  xs = [ones(1080, 1) * 1, id, i(:,:,1);
                        ones(1080, 1) * 2, id, i(:,:,2);
                        ones(1080, 1) * 3, id, i(:,:,3)];" ] \
      -o01 --partition "%0,%1" [ \
        -o2 \
        --octave "dt = 0.602 * (xs(1,2) - 1) / 1080.0;
                  xs = [xs(:,1:3), interp1(xs(:,3) + dt,
                                           \"spline\")]" ] \
      -o2 --partition %2 [ \
        --octave "for c = 1:3
                    rs = find(xs(:,1) == c);
                    i(xs(rs,2), :, c) = uint8(xs(rs, 4:end));
                  imwrite(i, sprintf(\"parallax-fixed/%04d.png\", xs(1,3)));
                  xs = []" \
        -R /dev/null ]

3D reconstruction

The basic idea at this point is that we have a moving vantage point from which coplanar images are provided. We know the exact coordinates of each frame with minimal effort because they all lie along the same arc and are rotated proportionally to the arc distance. There are some challenges, however:

  1. The rolling shutter correction wasn't perfect, probably in part because the source frames were motion-compressed by the video encoder. This means there's still some distortion.
  2. The frames are not truly coplanar, though they're probably close.
  3. Any given frame may have some degree of directional error within the arc path due to vibrations in the rotating arm or shifting of the base.
  4. The hill we're digitizing has trees in front of it.

The most immediate challenge is to recover the rotational angle of each image, which we can do using phase correlation in cropped log-polar space. I use the value channel because video codecs tend to preserve its high-frequency signal (in hindsight I should have also done the rolling-shutter interpolation in HSV terms for the same reason).

Log-polar transformation

We transform an image into log-polar space by resampling it radially, which is easy in Octave if we use interp2. Here's an example of what this does to the first video frame:

$ octave
> function r = lp(i)
    [h, w, c] = size(i);
    thetas    = linspace(0, 2*pi, h)(1:end-1);
    rhos      = log(1:h);
    rhos     *= h/2.0 / max(rhos);
    [tt, rr]  = meshgrid(thetas, rhos);
    [xs, ys]  = pol2cart(tt, rr);
    for c = 1:3
      r(:,:,c) = interp2(i(:,:,c), xs + w/2.0, ys + h/2.0);
> imshow(lp(imread("parallax-fixed/0002.png")))

logpolar example

Log-polar space is useful because like polar coordinates, an image rotation becomes a log-polar translation. This makes it possible to use FFT-based phase correlation to recover the rotational delta, which we do in batch here:

$ mkdir parallax-logpolar
$ nfu n:1 \
      --octave "function r = lp(i)
                  [h, w, c] = size(i);
                  thetas    = linspace(0, 2*pi, h)(1:end-1);
                  rhos      = log(1:h);
                  rhos     *= h/2.0 / max(rhos);
                  [tt, rr]  = meshgrid(thetas, rhos);
                  [xs, ys]  = pol2cart(tt, rr);
                  for c = 1:3
                    r(:,:,c) = interp2(i(:,:,c), xs + w/2.0, ys + h/2.0);
                base_i = lp(imread('parallax-fixed/0002.png'));
                base_f = fft(rgb2hsv(base_i)(1:10,:,3));
                for i = 3:510
                  alt_i    = lp(imread(sprintf('parallax-fixed/%04d.png', i)));
                  alt_f    = conj(fft(rgb2hsv(alt_i)(1:10,:,3)));
                  p        = base_f .* alt_f;
                  r        = ifft(p ./ abs(p));
                  [m, mi]  = max(r(:));
                  [mt, mr] = ind2sub(size(r), mi);
                  ys(i,:)  = [mt, mr];
                  imwrite(alt_i, sprintf('parallax-logpolar/%04d.png', i));
                xs = ys;" \
  | gzip > parallax-lp-shifts.gz

TODO: finish this

Experiment, possibly to somewhere: using OSM data

At this point I'm confident that a significant amount of GPS error comes from landscape features, even in places without a lot of buildings nearby. Since the ultimate goal is to end up with a way to correct for this error, we need to either predict the error from known location features or use an empirical model to measure it. OpenStreetMaps gives us a way to do the latter (and possibly the former too).

OSM provides both a world map and user-submitted GPX traces. The GPX traces are timestamped, which is crucial here: we can look for systematic divergence based on satellite positions. We need to have more than one GPX trace for a given OSM feature because OSM uses GPS to correct their maps. We'll observe errors only when we have multiple GPS datapoints representing the same physical location.

Preparing the OSM data

I downloaded the OSM planet XML file, which contains a world map encoded using nodes and ways. A node is a physical location and a way is a series of these locations; some ways are roads, and that's what we need here. The XML looks like this:

 <node id="1" lat="48.5669850" lon="13.4465242" timestamp="2015-05-24T21:25:26Z" version="13" changeset="31430396" user="Peda" uid="13010">
 <node id="10" lat="51.3806887" lon="9.3601583" timestamp="2014-08-01T14:23:42Z" version="8" changeset="24482318" user="dhuyp" uid="1779584"/>
 <node id="11" lat="4.0838053" lon="73.5122471" timestamp="2010-04-02T19:47:35Z" version="5" changeset="4307385" user="mabapla" uid="24748">
 <node id="12" lat="51.3400541" lon="9.4818121" timestamp="2014-02-20T15:19:56Z" version="5" changeset="20677113" user="emilde" uid="250214"/>
 <way id="41" timestamp="2014-04-02T17:09:47Z" version="7" changeset="21461939" user="blackadder" uid="735">
  <nd ref="200541"/>
  <nd ref="2715159905"/>
  <nd ref="200575"/>
  <nd ref="180180789"/>
  <nd ref="200576"/>
  <tag k="name" v="Rowan Road"/>
  <tag k="is_in" v="Sutton Coldfield"/>
  <tag k="highway" v="residential"/>
  <tag k="incline" v="-6.9%"/>
  <tag k="abutters" v="residential"/>
  <tag k="maxspeed" v="30 mph"/>
  <tag k="postal_code" v="B72"/>

We're interested in the geometry of ways so we can look for GPX traces that appear to be following them. This means we need to remove the way/node indirection and index by geohash tile.

Map data conversion

The XML is in a huge bzip2 file, which is just about the worst thing for data processing because bzip2 -dc is single-threaded and CPU-bound. I converted the file to lzo, which removes that bottleneck:

$ pv osm-planet*.bz2 | bzip2 -dc | lzop > osm-planet.lzo

Once that was done I extracted the node list into a TSV. Normally I'd use nfu for this, but a hand-written shell pipeline is faster and not much more verbose (if I were redoing this today I'd use ni, which is much faster than nfu and will soon support XML destructuring):

# nfu osm-planet.lzo -k '%0 =~ /<node/' \
#                    -m 'row %0 =~ /id="(\d+)"/,
#                            %0 =~ /lat="([-0-9.]+)/,
#                            %0 =~ /lon="([-0-9.]+)/' \
# | gzip > nodes.gz

$ pv osm-planet.lzo | lzop -dc \
  | grep '\<node' \
  | perl -ne 'print join("\t", /id="(\d+)"/,
                               /lon="([-0-9.]+)/), "\n"' \
  | gzip > nodes.gz

osm-planet.lzo contains changesets, nodes, ways, and other stuff, in that order. There are enough nodes that it takes a lot of time to get to ways, so I extracted the ways verbatim so I could iterate on a parser quickly:

$ pv osm-planet.lzo | lzop -dc \
  | egrep -v '<node' \
  | perl -ne 'print if /<way/../<\/way/' \
  | gzip > ways-xml.gz

Before I get into how the parser works, let's talk about how the node/way dereferencing process is implemented.

Dereferencing nodes in ways

We've got a node TSV in this form:

node_id  latitude  longitude

Each way will have an ID, multiple node IDs, and some tags indicating what it is (and whether we're interested in it). This analysis is about roads, so we want things tagged with highway. We also need to preserve the node ordering.

$ export NFU_SORT_COMPRESS=lzop
$ export NFU_SORT_BUFFER=4096M

$ nfu ways-xml.gz -m '$::id = $1, $::road = 0 if /<way id="(\d+)"/;
                      push @::nodes, $1 if /<nd ref="(\d+)"/;
                      $::road = 1 if /<tag k="highway"/;
                      if ($::road && /<\/way/) {
                        print join("\t", $::id, $_, $::nodes[$_]), "\n"
                          for 0..$#::nodes;
                        @::nodes = ();
                      ()' \
                  -i2 nodes.gz \
  | gzip > joined-ways.gz

Now we need to group by way ID. This is just a sort operation, and nfu introduces more overhead than we want by looping over lines (cut and sort use all kinds of optimizations to avoid this); since we're running on 24 processors, nfu's -f1. operation becomes a bottleneck to the high-throughput sort process. We can work around this by removing perl line-loops from the equation entirely and using cut instead, for a total of about 20x better performance:

# nfu joined-ways.gz -f1.o01 | gzip > assembled-ways.gz
$ zcat joined-ways.gz | pv | cut -f2-5 \
  | sort --parallel=24 -S 128G -g --compress-program=lzop \
         -g -k 1,1 -k 2,2 \
  | gzip > assembled-ways.gz

GPX data conversion

The GPX traces are provided in a .tar.xz archive, so we need to extract that. The contents are organized like this:

$ find gpx-planet-2013-04-09

These files are highly redundant and small, so I compressed each 942-level directory using xzip (in hindsight not the best option; xz -9e is really slow on the compression side and I switched to gzip, as shown here, halfway through):

$ cd gpx*/identifiable/000
$ ls | grep -v z$ | xargs -P4 -I{} sh -c 'cat {}/* | gzip > {}.gz && rm -r {}'

The data in the gpx files looks like this, where identifiable and trackable contain timestamps:

<gpx xmlns="" version="1.0" creator="OSM">
    <name>Track 0</name>
      <trkpt lat="54.4794300" lon="-6.2304850">
      <trkpt lat="54.4794300" lon="-6.2304800">
      <trkpt lat="54.4794300" lon="-6.2304750">

We're interested in contiguous travel, delineated here by <trkseg> elements. The simplest representation that captures this is a TSV like this:

segment_id  lat  lng  time

Parsing it out using some super-janky stateful logic:

$ nfu --run '$::id = $::lat = $::lng = 0' \
      identifiable/000/* \
      -m '++$::id if /<trkseg/;
          ($::lat, $::lng) = ($1, $2) if /lat="([-0-9.]+)" lon="([-0-9.]+)"/;
          return row $::id, $::lat, $::lng, $1 if /<time>([^<]+)</;
          ()' \
      --pipe q[ \
        ruby -rdate \
          -ne 'id, lat, lng, t = $_.split /\t/
               puts [id, lat, lng, DateTime.parse(t).to_time.to_i].join("\t")' ] \
  | gzip > identifiable-000.gz

# same thing for identifiable/001, trackable/000, and trackable/001


Right now we have enormous lists of spatially-unordered worldwide data. The next thing to do is to shard it into geo-tiles. Before we do that, though, we need to measure the distribution of way lengths to see how big the tiles should be. Let's start by getting the maximum distance between the first and any other point for each way:

$ nfu assembled-ways.gz \
      -A 'max map edist(${%2}[0], ${%3}[0], ${%2}[$_], ${%3}[$_]),
                  1..$#{%2}' \
      -T100000 \
      -q0.01ocf10p %l


Awesome, looks like most ways are about 50m long. Let's check the GPX traces:

$ nfu trackable-000.gz \
      -A 'max map edist(${%1}[0], ${%2}[0], ${%1}[$_], ${%2}[$_]),
                  1..$#{%1}' \
      -T10000 \
      -q0.01ocf10p %l


Looks great. We can index into 1km² tiles and have about 5% redundancy. A geohash-6 has ±610m of error, which is about 1.5km²; let's use that. (Note: this turns out to be wrong; I went with gh4s for reasons described in the next section.)

Geohash-4 tiling

Let's take each way/trace and collapse it, then generate a list of GH6s per object. We can shard in place using --partition. Because nfu's partitioning will close and reopen a given shard, we're relying on the convenient property that the concatenation of gzip files is the same as the gzip of their concatenation.

Before we commit to the process, let's just confirm that we're not getting very much tile duplication:

$ nfu assembled-ways.gz \
      -A 'scalar uniq map ghe(${%2}[$_], ${%3}[$_], 6), 0..$#{%2}' \
      -T100000 \
60334   1
25849   2
6307    3
2198    4
1083    5
694     6
469     7
350     8
265     9
255     10
177     11
139     12
131     13
100     14
87      15
70      16
57      17
65      18
58      19
61      20

We're actually getting about 3x, which is horrible. Let's back out until we can get this down to about 1.1, in this case gh4.

$ mkdir ways-shards
$ export NFU_PARALLELISM=512
$ pv assembled-ways.gz | zcat \
  | nfu -k23A 'my @ghs    = uniq map ghe(${%2}[$_], ${%3}[$_], 4), 0..$#{%2};
               my $points = join "\t", map "${%2}[$_],${%3}[$_]", 0..$#{%2};
               print "$_\t${%0}[0]\t$points\n" for @ghs; ()' \
        --partition 'substr %0, 0, 2' 'gzip >> ways-shards/{}.gz' \
  > /dev/null

We can do the same thing with traces:

$ mv identifiable-000.gz i000.gz
$ mv identifiable-001.gz i001.gz
$ mv trackable-000.gz t000.gz
$ mv trackable-001.gz t001.gz

$ mkdir traces-{i000,i001,t000,t001}-shards
$ export NFU_PARALLELISM=512
$ for f in i000 i001 t000 t001; do
    nfu $f.gz -A 'my $n1 = $#{%0};
                  return () unless ${%3}[$n1] > ${%3}[0];
                  my @ghs    = uniq map ghe(${%1}[$_], ${%2}[$_], 4), 0..$n1;
                  my $points = join "\t", map "${%1}[$_],${%2}[$_],${%3}[$_]",
                  print "$_\t${%0}[0]\t$points\n" for @ghs; ()' \
        --partition 'substr %0, 0, 2' "gzip >> traces-$f-shards/{}.gz" \
      > /dev/null &

Trace/way resolution

This is where things get fun. Let's find a densely-populated GH4, like dr5r for Manhattan:

$ nfu ways-shards/dr.gz -k '%0 eq "dr5r"' -f2.m @_ -F , --sample 0.1 -f10p %d


Um... crap. That's not well-sharded data at all. There must be something horribly wrong with my ways-sharder. Let's quickly verify that each way at least contains one point within its shard:

$ pv ways-shards/dr.gz | zgrep ^dr5r | gzip > dr5r.gz
$ nfu dr5r.gz -m 'my ($gh4, $way) = (%0, %1);
                  my @latlngs     = @_[2..$#_];
                  my @shards      = map ghe(split(/,/), 4), @latlngs;
                  my $correct     = grep $_ eq $gh4, @shards;
                  my %incorrect   = frequencies grep $_ ne $gh4, @shards;
                  row $gh4, $way, $correct, %incorrect' \
  | gzip > dr5r-errors.gz

$ nfu dr5r-errors.gz -k '%1 == 0' | wc -l

Interesting, so our sharder isn't completely wrong anyway; it seems to be doing its job well enough. Let's look at the way data now, plotting vectors between waypoints for any gh4 with "incorrect" entries:

$ nfu dr5r-errors.gz -k3f1i0 @[ dr5r.gz -f1. ] \
      -m 'my @latlngs = @_[1..$#_];
          map {row $latlngs[$_ - 1] =~ y/,/\t/r,
                   $latlngs[$_]     =~ y/,/\t/r} 1..$#latlngs' \
      -m 'row %0, %1, %2 - %0, %3 - %1' \
      -f1032p 'with vectors lc rgb "#f0203050"'

gnuplot window id 0 on rorschach2_740 gnuplot window id 0 on rorschach2_741 gnuplot window id 0 on rorschach2_742 gnuplot window id 0 on rorschach2_743

So it looks like most of these ways ultimately tie back to Manhattan, but for some reason join up with paths all over the world. I doubt this is really how the OSM ways work, so let's make a rule that no two waypoints can be more than ~5km apart (if they are, we'll split the way):

$ nfu dr5r-errors.gz -k3f1i0 @[ dr5r.gz -f1. ] \
      -m 'my @latlngs = @_[1..$#_];
          map {row $latlngs[$_ - 1] =~ y/,/\t/r,
                   $latlngs[$_]     =~ y/,/\t/r} 1..$#latlngs' \
      -m 'row %0, %1, %2 - %0, %3 - %1' \
      -k 'dist(%2, %3) < 0.05' \
      -f1032p 'with vectors lc rgb "#f0203050"'

gnuplot window id 0 on rorschach2_749

This looks a lot more reasonable.

Trace/way resolution, for real this time

Ok, for now ignoring the worldwide-way problem, let's take a look at some Manhattan GPX traces overlaid onto OSM content:

$ nfu trace*shards/dr.gz -k '%0 eq "dr5r"' -f2.m @_ -F , \
      -f10m 'row @_, "NA", "NA"' \
      --append @[ dr5r.gz -f2.m @_ -F , -f10m 'row "NA", "NA", @_' ] \
      --mplot '%u1:2%d%t"GPX" lc rgb "#80ffa040";
               %u3:4%d%t"OSM" lc rgb "#c0203050"'

gnuplot window id 0 on rorschach2_745 gnuplot window id 0 on rorschach2_746 gnuplot window id 0 on rorschach2_747 gnuplot window id 0 on rorschach2_748

And that's exactly what we're looking for: path deflection due to the urban canyon problem. Let's study this particular case in more detail, this time plotting vectors:

$ nfu trace*shards/dr.gz -k '%0 eq "dr5r"' \
      -m 'map row(@_[$_ - 1, $_]), 3..$#_' -F , -f0134 \
      -m 'row %1, %0, %3 - %1, %2 - %0, ("NA") x 4' -k 'dist(%2, %3) < 0.05' \
      --append @[ dr5r.gz \
        -m 'map row(@_[$_ - 1, $_]), 3..$#_' -F , \
        -m 'row(("NA") x 4, %1, %0, %3 - %1, %2 - %0)' \
        -k 'dist(%6, %7) < 0.05' ] \
      --mplot '%u1:2:3:4 with vectors %t"GPX" lc rgb "#80ffa040";
               %u5:6:7:8 with vectors %t"OSM" lc rgb "#c0203050"'

gnuplot window id 0 on rorschach2_750 gnuplot window id 0 on rorschach2_751 gnuplot window id 0 on rorschach2_752 gnuplot window id 0 on rorschach2_753 gnuplot window id 0 on rorschach2_754 gnuplot window id 0 on rorschach2_755 gnuplot window id 0 on rorschach2_756 gnuplot window id 0 on rorschach2_757 gnuplot window id 0 on rorschach2_758 gnuplot window id 0 on rorschach2_759 gnuplot window id 0 on rorschach2_760

Ok, this just looks too cool not to make a wallpaper out of it.

$ wallpaper() {
    [[ -e /tmp/$1-gpx ]] \
    || nfu trace*shards/${1:0:2}.gz -k "%0 eq '$1'" \
           -m 'map row(@_[$_ - 1, $_]), 3..$#_' -F , -f0134 \
           -m 'row %1, %0, %3 - %1, %2 - %0' -k 'dist(%2, %3) < 0.05' \
       > /tmp/$1-gpx

    [[ -e /tmp/$1-osm ]] \
    || nfu ways-shards/${1:0:2}.gz -k "%0 eq '$1'" \
           -m 'map row(@_[$_ - 1, $_]), 3..$#_' -F , \
           -m 'row %1, %0, %3 - %1, %2 - %0' \
           -k 'dist(%2, %3) < 0.05' \
       > /tmp/$1-osm

    gnuplot5 <<EOF
    set terminal pngcairo enhanced background rgb "#040404" size 3840,2160
    set output "$1.png"
    unset border
    plot [$3-0.25:$3+0.25] [$2-0.140625:$2+0.140625] \
        "/tmp/$1-gpx" with vectors title "" lw 1 lc rgb "#a0ff9020", \
        "/tmp/$1-osm" with vectors title "" lw 1 lc rgb "#d0e0e4e8"

$ wallpaper dr5r -74.03 40.705


Anyway. There are a lot of places above where the GPX path clearly diverges from the OSM one, particularly when the GPS is in a canyon. There's also some very weird divergence along the Manhattan Bridge. It's especially weird given that it's all in the same direction; the directional vectors themselves are parallel with the Brooklyn Bridge. I think it's reasonable to treat this as an outlier for now.

Apparent rate of travel

This figures a lot into our analysis, simply because we're interested in just a narrow range of speeds: stuff between about 20 and 60mph. Dividing Euclidean distance by time-deltas gives us ~100km/sec, which we want to convert to mph:

$ units
You have: 100km/sec
You want: mph
        * 223693.63

$ speedplot() {
    nfu trace*shards/${1:0:2}.gz -k "%0 eq '$1'" \
        -m 'map row(@_[$_ - 1, $_]), 3..$#_' -F , \
        -m 'row %1, %0, %4 - %1, %3 - %0, %5 - %2' \
        -k '%4 > 0 && dist(%2, %3) < 0.05' \
        -m 'row @_[0..3], dist(%2, %3) / %4 * 223693.63, ("NA") x 4' \
        -k '%4 < 80' \
        --append @[ ways-shards/${1:0:2}.gz -k "%0 eq '$1'" \
          -m 'map row(@_[$_ - 1, $_]), 3..$#_' -F , \
          -m 'row(("NA") x 5, %1, %0, %3 - %1, %2 - %0)' \
          -k 'dist(%7, %8) < 0.05' ] \
        --mplot '%u1:2:3:4:5 with vectors %t"GPX" lc palette;
                 %u6:7:8:9   with vectors %t"OSM" lc rgb "#f0202020"'

$ speedplot dr5r

gnuplot window id 0 on rorschach2_761 gnuplot window id 0 on rorschach2_762 gnuplot window id 0 on rorschach2_763 gnuplot window id 0 on rorschach2_764 gnuplot window id 0 on rorschach2_765 gnuplot window id 0 on rorschach2_766 gnuplot window id 0 on rorschach2_767 gnuplot window id 0 on rorschach2_768 gnuplot window id 0 on rorschach2_769 gnuplot window id 0 on rorschach2_770 gnuplot window id 0 on rorschach2_771

This is interesting. Some of the divergent paths from earlier are clearly low-speed (most likely walking), so we can't attribute this to GPS error. Higher-speed paths are much more regular; by the time we get up to 40-50mph they're good to a couple of meters.

The assumption is that all high-speed traffic follows roads of some kind, but OSM doesn't give us exact road boundaries. Instead, it provides what I assume is the center of each road. The way metadata also doesn't specify exactly how large each road is, but it does classify them into different categories using the highway attribute. Let's go ahead and extract those, sharded like the ways themselves so we can pull it into memory by tile:

$ nfu ways-shards/*.gz -f01gcf120 | gzip > ways-by-tile.gz
$ nfu ways-xml.gz --fold '!/<\/way>/' \
      -m 'my @attrs;
          push @attrs, "$1=$2" while /<tag k="([^"]+)" v="([^"]+)"/g;
          row /<way id="(\d+)"/, @attrs' \
  | gzip > way-metadata.gz
$ mkdir way-metadata-shards
  nfu --run '/(^\d+)\t(\w{4})/ and ($::shards{$1} //= "") .= ":$2"
             for rl "sh:nfu ways-by-tile.gz -f10"' \
      way-metadata.gz \
      -m 'map row($_, @_), grep length, split /:/, $::shards{%0}' \
      --partition 'substr(%0, 0, 2)' 'gzip >> way-metadata-shards/{}.gz'

TODO: Finish this at some point

Appendix: failed experiments

Original coarse transit (too imprecise, and too much work)

I live near some mountains that are probably blocking the GPS signal by quite a bit. For context, here's what it looks like from the roof facing NE, SE, SW, and NW respectively:

northeast southeast southwest northwest

The phone will tell you which GPS satellites it can see, but since they aren't geosynchronous (and therefore change as the log is running) I needed to first get a rough horizon profile to find out when each satellite would be blocked. To do this, I built a simple surveying transit:

surveying transit

I didn't have a magnetic compass, so I calibrated the zero angle to something that would be visible on a satellite map:

zero calibration tower coordinates

Then I measured the horizon angle every 15 degrees. The transit was on top of the air conditioner at 35.194155, -106.481735, and the tower is at 35.191365, -106.473310. I got the angle between them by constructing a third point due south of one and due west of the other, then converting the third-point distances to polar coordinates:

$ echo | nfu -m 'my ($y1, $x1) = (35.194155, -106.481735);
                 my ($y2, $x2) = (35.191365, -106.473310);
                 row rect_polar( edist($y2, $x1, $y2, $x2),
                                -edist($y2, $x1, $y1, $x1))'
0.826065086148791       112.058692110008

Adding in the 112-degree offset, the measurements were:

adjusted angle horizon
7 10
22 18
37 19
52 20
67 23
82 23
97 21
112 14
127 11
142 6
157 4
172 3
187 0
202 0
217 0
232 0
247 0
262 0
277 0
292 0
207 0
322 3
337 6
352 7