matlab-compute-gnss-position

name: matlab-compute-gnss-position description: > Computes multi-constellation Global Positioning System (GPS) or Global Navigation Satellite System (GNSS) positions from RINEX v3 data using rinexread, gnssmeasurements, receiverposition, and gnssoptions. Filters by constellation, elevation mask, C/N0, and observation code. Reports DOP, scatter RMS, and satellite count. Use when processing GNSS data, computing positions from RINEX files, analyzing accuracy, comparing constellations, or evaluating satellite geometry. Do NOT use for carrier-phase RTK/PPP, IMU fusion, orbit propagation, NMEA streaming, or RINEX v4. metadata: author: MathWorks version: "1.0" license: MathWorks BSD-3-Clause

Multi-Constellation GNSS Positioning

Pseudorange-based single-point positioning (SPP) from RINEX v3 data. Supports GPS, GLONASS, Galileo, BeiDou, QZSS, NavIC/IRNSS, and SBAS.

Requires: Navigation Toolbox R2026a or later.

When to Use

• Processing RINEX observation/navigation files into position solutions
• Computing multi-constellation receiver positions
• Analyzing positioning accuracy, DOP, and satellite geometry
• Comparing single-constellation vs multi-constellation performance
• Evaluating the effect of processing options (elevation mask, C/N0, corrections)
• Teaching or demonstrating GNSS positioning concepts

When NOT to Use

• Carrier-phase positioning (RTK, PPP, ambiguity resolution)
• Sensor fusion with IMU — use matlab-system-identification or INS filters
• Satellite orbit propagation or scenario simulation — use Aerospace Toolbox
• Real-time NMEA stream processing — use nmeaParser directly
• RINEX v4 files — nested struct format requires different handling

Must-Follow Rules

• Always use rinexinfo first to confirm RINEX v3 and inspect available constellations/observation codes before reading
• Use the correct observation code per constellation — wrong code produces empty measurements (see default codes table)
• Pre-filter satellites with missing navigation data before calling gnssmeasurements — it errors on any satellite without matching nav data
• Always apply atmospheric corrections via gnssoptions — uncorrected SPP has >20 m vertical error
• Use tiledlayout/nexttile for multi-panel figures (never subplot)
• Label all axes with units and include titles on every plot
• Report DOP quality — HDOP > 6 is degraded, HDOP > 20 is unusable
• Skyplot requires a full-size figure — never place skyplot inside a tile of a compact tiledlayout. Use a dedicated figure for the skyplot, or if it must share a layout, size the figure so the skyplot tile is at least 560×560 pixels. Skyplots in small tiles produce unreadable, overlapping satellite markers.

Workflow

1. Inspect RINEX files — call rinexinfo on each file to confirm v3, identify constellations, and list available observation codes. Use SatelliteSystem and Descriptors to iterate over ObservationTypes:

   info = rinexinfo('rover.obs');
   for i = 1:numel(info.ObservationTypes)
       fprintf('%s: %s\n', info.ObservationTypes(i).SatelliteSystem, ...
           strjoin(info.ObservationTypes(i).Descriptors, ', '));
   end
   

2. Resolve observation codes — confirm default codes exist in the RINEX data; if not, suggest alternatives from the header
3. Gather processing options — constellation selection, elevation mask (degrees, default 10), C/N0 threshold (dB-Hz, default 0), observation code overrides
4. Read data — call rinexread for observation and navigation files
5. Extract measurements — call gnssmeasurements per constellation with the confirmed observation code; pre-filter observation data to remove satellites without matching nav data
6. Filter by C/N0 — remove rows from observation timetable where signal strength column falls below threshold before calling gnssmeasurements
7. Validate constellations — solve each constellation independently first; if any produces wildly wrong positions (>1 km error, extreme altitudes), exclude it from the combination
8. Combine constellations — vertically concatenate validated measurement timetables: combinedMeas = [gpsMeas; galMeas; ...]
9. Apply elevation mask — compute initial position from one epoch, use lookangles to get satellite elevations, remove satellites below mask
10. Solve position — call receiverposition with gnssoptions for atmospheric corrections
11. Report metrics — per-epoch (HDOP, VDOP, satellite count) and aggregate (scatter RMS, mean DOP)
12. Visualize — skyplot, position time series, DOP and satellite count panels
13. (Optional) Ground truth — if user provides LLA, compute ENU error via lla2enu

Default Observation Codes (Band 1 Preferred)

Prefer frequency band 1 codes across all constellations. Consistent band-1 codes produce more robust multi-constellation solutions by avoiding inter-frequency bias.

C/N0 column pattern: Replace the C prefix in the observation code with S (e.g., C1C → S1C, C1P → S1P).

Band-1 codes may have higher NaN rates than higher-band alternatives but produce more robust positions in multi-constellation solutions. Always check code availability with rinexinfo and prefer band 1.

Processing Defaults

Elevation mask and C/N0 filtering are not independent in urban environments. C/N0 filtering typically removes the same low-elevation, multipath-affected satellites. Applying both rarely improves results beyond C/N0 filtering alone. Elevation mask is most effective when the receiver does not report signal strength.

Key Functions

Patterns

GPS-Only Positioning

dataDir = fullfile(matlabroot, 'toolbox', 'nav', 'positioning', ...
    'core', 'positioningdata');

obsData = rinexread(fullfile(dataDir, ...
    'GODS00USA_R_20211750000_01H_30S_MO.rnx'));
gpsNav = rinexread(fullfile(dataDir, ...
    'GODS00USA_R_20211750000_01D_GN.rnx'));

gpsMeas = gnssmeasurements(obsData.GPS, gpsNav.GPS);
opts = gnssoptions( ...
    Ionosphere=gnssIonosphere("klobuchar"), ...
    Troposphere=gnssTroposphere("saastamoinen"));

[recPos, recVel, hdop, vdop, info] = receiverposition(gpsMeas, opts);

Multi-Constellation (GPS + Galileo)

galNav = rinexread(fullfile(dataDir, ...
    'GODS00USA_R_20211750000_01D_EN.rnx'));

gpsMeas = gnssmeasurements(obsData.GPS, gpsNav.GPS);
galMeas = gnssmeasurements(obsData.Galileo, galNav.Galileo, "C1X");

combinedMeas = [gpsMeas; galMeas];
[recPos, recVel, hdop, vdop] = receiverposition(combinedMeas, opts);

Pre-Filter Satellites Missing Navigation Data

BeiDou and other constellations may have observed satellites without matching navigation messages. gnssmeasurements errors if any satellite lacks nav data.

bdsNav = rinexread(fullfile(dataDir, ...
    'GODS00USA_R_20211750000_01D_CN.rnx'));

% Find satellite IDs present in both obs and nav
obsIDs = unique(obsData.BeiDou.SatelliteID);
navIDs = unique(bdsNav.BeiDou.SatelliteID);
validIDs = intersect(obsIDs, navIDs);

% Filter observation data to valid satellites only
bdsObs = obsData.BeiDou(ismember(obsData.BeiDou.SatelliteID, validIDs), :);
bdsMeas = gnssmeasurements(bdsObs, bdsNav.BeiDou, "C1X");  % Use C1P or C1X or any C1* (band 1 preferred)

C/N0 Signal Strength Filtering

Filter weak signals before extracting measurements. The C/N0 column name follows the pattern: replace the C prefix of the observation code with S.

% Filter GPS observations with C/N0 < 30 dB-Hz
cn0Threshold = 30;
gpsObs = obsData.GPS;
gpsObs = gpsObs(gpsObs.S1C >= cn0Threshold, :);  % S1C corresponds to C1C

gpsMeas = gnssmeasurements(gpsObs, gpsNav.GPS);

Elevation Mask Filtering

Elevation filtering requires a position estimate. Use an initial fix from one epoch, then apply lookangles to filter low-elevation satellites.

% Remove rows with non-finite satellite positions (can occur in multi-GNSS data)
validRows = all(isfinite(combinedMeas.SatellitePosition), 2);
combinedMeas = combinedMeas(validRows, :);

% Get initial position from first epoch (unfiltered)
[initPos] = receiverposition(combinedMeas);
recLLA = initPos(1,:);  % [lat lon alt]

% Get unique epochs
epochs = unique(combinedMeas.Time);
filteredMeas = timetable();

for i = 1:numel(epochs)
    epochMeas = combinedMeas(combinedMeas.Time == epochs(i), :);
    satPos = epochMeas.SatellitePosition;
    [~, el, vis] = lookangles(recLLA, satPos, elevationMask);
    filteredMeas = [filteredMeas; epochMeas(vis, :)]; %#ok<AGROW>
end

Atmospheric Corrections

opts = gnssoptions( ...
    Ionosphere=gnssIonosphere("klobuchar"), ...
    Troposphere=gnssTroposphere("saastamoinen"));

[recPos, recVel, hdop, vdop, info] = receiverposition(combinedMeas, opts);
% info.ClockBias, info.ClockDrift, info.TDOP also available

Quality Metrics

% Per-epoch metrics from receiverposition outputs
nSats = arrayfun(@(t) sum(combinedMeas.Time == t), unique(combinedMeas.Time));

% Aggregate scatter RMS (no ground truth needed)
meanPos = mean(recPos, 1, 'omitnan');
enu = lla2enu(recPos, meanPos, 'ellipsoid');
hRMS = rms(vecnorm(enu(:,1:2), 2, 2));  % Horizontal scatter
vRMS = rms(enu(:,3));                     % Vertical scatter

fprintf('Horizontal scatter RMS: %.2f m\n', hRMS);
fprintf('Vertical scatter RMS:   %.2f m\n', vRMS);
fprintf('Mean HDOP: %.2f\n', mean(hdop, 'omitnan'));
fprintf('Mean VDOP: %.2f\n', mean(vdop, 'omitnan'));

Choosing a C/N0 threshold. Sweep a small set of thresholds (e.g., 25, 30, 35, 40 dB-Hz) and use the appropriate quality signal to select the best value:

C/N0 filtering typically improves vertical accuracy more than horizontal. Persistent horizontal errors after filtering are structural multipath that SPP cannot resolve. Note: scatter RMS is only meaningful for static receivers — for a moving receiver it reflects vehicle trajectory, not positioning error.

Ground Truth Comparison (Optional)

% User provides known ground truth LLA
truthLLA = [39.020734 -76.826657 -8.95];  % Example: GODS station

enuError = lla2enu(recPos, truthLLA, 'ellipsoid');
error2D = vecnorm(enuError(:,1:2), 2, 2);
error3D = vecnorm(enuError, 2, 2);

fprintf('2D RMS error: %.2f m\n', rms(error2D));
fprintf('3D RMS error: %.2f m\n', rms(error3D));
fprintf('Mean East error:  %.2f m\n', mean(enuError(:,1)));
fprintf('Mean North error: %.2f m\n', mean(enuError(:,2)));
fprintf('Mean Up error:    %.2f m\n', mean(enuError(:,3)));

Visualization

% Satellite skyplot — always in its own dedicated figure (never inside tiledlayout).
figure;
epochs = unique(combinedMeas.Time);
epoch1 = combinedMeas(combinedMeas.Time == epochs(1), :);
satPos1 = epoch1.SatellitePosition;
[az, el] = lookangles(recPos(1,:), satPos1);
skyplot(az, el, string(epoch1.SatelliteID))
title('Satellite Skyplot — First Epoch')

% Multi-panel metrics (separate figure)
figure;
t = tiledlayout(2, 2);
title(t, 'GNSS Positioning Metrics')

nexttile
plot(epochs, hdop, 'b-', epochs, vdop, 'r-', 'LineWidth', 1.5)
legend('HDOP', 'VDOP')
ylabel('DOP')
title('Dilution of Precision')
grid on

nexttile
plot(epochs, nSats, 'k-', 'LineWidth', 1.5)
ylabel('Count')
title('Satellites Used per Epoch')
grid on

nexttile
plot(epochs, recPos(:,1), 'b-', 'LineWidth', 1.5)
ylabel('Latitude (deg)')
title('Position — Latitude')
grid on

nexttile
plot(epochs, recPos(:,3), 'b-', 'LineWidth', 1.5)
ylabel('Altitude (m)')
title('Position — Altitude')
grid on

Iterative Refinement

Store results across runs to compare processing configurations.

% Initialize or append to run history
if ~exist('runHistory', 'var')
    runHistory = table('Size', [0 6], ...
        'VariableTypes', ["string","double","double","double","double","double"], ...
        'VariableNames', ["Config","MeanHDOP","MeanVDOP","HorizRMS","VertRMS","MeanSats"]);
end

newRow = table(configLabel, mean(hdop,'omitnan'), mean(vdop,'omitnan'), ...
    hRMS, vRMS, mean(nSats), ...
    'VariableNames', runHistory.Properties.VariableNames);
runHistory = [runHistory; newRow];
disp(runHistory)

Gotchas

• skyplot in a compact tiledlayout is unreadable. Satellite markers and labels overlap when the axes are smaller than the default figure size. Always give skyplot its own dedicated figure. If you must include it in a tiledlayout, size the figure so the skyplot tile is at least 560×560 pixels.
• gnssmeasurements errors on missing nav data. If any observed satellite ID is absent from the navigation timetable, the function throws an error. Always pre-filter observation data to satellites that have matching navigation messages using intersect on satellite IDs.
• Non-finite satellite positions in multi-GNSS data. Some rows from gnssmeasurements may have NaN/Inf in SatellitePosition or Pseudorange. Filter these before calling lookangles or receiverposition: validRows = all(isfinite(meas.SatellitePosition), 2);
• Validate each constellation independently before combining. A constellation with poor pseudorange quality (e.g., heavy urban multipath) can corrupt the entire multi-constellation solution. Test each system alone first — if its standalone position is wildly wrong (>1 km error or extreme altitude), exclude it or try a different observation code.
• Constellation standalone failure is not always recoverable. Any constellation can produce wildly wrong altitudes (e.g., ±tens of km) even when other constellations give consistent positions at the same location. The root cause may be receiver-specific pseudorange biases, poor satellite geometry, or incomplete clock corrections — and is not always diagnosable from the RINEX files alone. If a constellation fails standalone validation and C/N0 filtering does not fix it, exclude it rather than continuing to tune parameters.
• Prefer band-1 observation codes for all constellations. Consistent band-1 codes (C1C for GPS/GLONASS/Galileo/QZSS, C1P or C1X for BeiDou) produce more robust multi-constellation solutions. Higher-band codes may have more data but can degrade accuracy.
• Observation code varies by receiver. Galileo may use "C1X" or "C1C" depending on the receiver. Always check with rinexinfo first.
• C/N0 filtering happens on the observation timetable, not the measurements timetable. Filter obsData.GPS rows by the S1C column before passing to gnssmeasurements.
• Elevation mask needs a position estimate. You cannot filter by elevation before computing a position. Use an initial unfiltered fix, then apply lookangles to identify low-elevation satellites.
• receiverposition returns NaN for epochs with < 4 satellites. Check the output for NaN rows and report skipped epochs to the user.
• RINEX v4 mixed nav files have nested structs (e.g., navData.GPS.LNAV.EPH), unlike v3 flat timetables. This skill targets v3 only.
• GLONASS orbital validity is ±15 minutes vs ±2 hours for GPS/Galileo/BeiDou. Use navigation data close in time to the observations.
• Time system alignment across constellations (GPST, GLONASS UTC, GST, BDT) is handled by MATLAB's datetime indexing in the timetable — no manual conversion needed.
• HDOP quality scale: < 1 = ideal, 1–2 = excellent, 2–5 = good, 5–10 = moderate, 10–20 = fair, > 20 = poor. Flag > 6 as degraded.

Sample Data

Shipped RINEX v3 files for testing are at fullfile(matlabroot, 'toolbox', 'nav', 'positioning', 'core', 'positioningdata'):

GODS station approximate ground truth: [39.020734, -76.826657, -8.95] (LLA from RINEX header).

Public RINEX data sources: IGS Data Centers, CORS Network.

Error Handling

Skill Feedback

If this skill's guidance was wrong, incomplete, or didn't work, emit a plain-text feedback marker in your response, then continue with the task.

Format: NAV-SKILL-FEEDBACK [matlab-compute-gnss-position] [category]: what failed; what you did instead Categories: wrong-command (incorrect/outdated), missing-guidance (omitted step), discovery-failure (suggested approach didn't work), platform-issue (environment-specific)

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