Triangulation

Triangulation

Triangulation estimates where a transmitter is from the signal levels measured by several receivers.

How the fix is produced

1. Every enabled receiver measures the target frequency.
2. The log-distance path-loss model converts RSSI to a distance: d = 10 ^ ((P0 - RSSI) / (10 * n))
3. Each receiver therefore defines a distance circle around its position.
4. The solver finds the point that minimises the weighted Huber loss across all circles.
5. A numerical Hessian produces the confidence ellipse and the reported uncertainty.

Huber loss is used instead of plain least squares because RSSI varies with multipath. A single faded receiver can move a least-squares fix by kilometres; Huber limits that influence.

Requirements

• At least two enabled receivers with coordinates
• Three or more for a meaningful fix; two are underconstrained and the UI reports that explicitly
• A measured signal at the target frequency. Weak readings are labelled and excluded or used as a low-confidence fallback

Add receivers from the dashboard or CLI:

spectrum-alert geo add-receiver Home --lat 35.130 --lon 33.310 --gain 30 --ppm 20
spectrum-alert geo add-receiver Hill --lat 35.080 --lon 33.380 \
  --mode rtltcp --host 10.0.0.5 --port 1234
spectrum-alert geo receivers

Using the workspace

1. Open Workspace.
2. Select the target frequency or click the waterfall.
3. Press Triangulate.
4. Read the map and the receiver readings table.

The map shows:

Layer	Meaning
Blue pins	Receivers with coordinates
Green/amber/red marker	Estimated emitter, coloured by fit quality
Filled circle	Reported uncertainty radius
Ellipse	Confidence region
History	Previous fixes, if enabled

The readings table reports:

Column	Meaning
dBFS	Measured level at the target
Floor	Local noise floor
Prom	Prominence above the floor
Rel	Measurement reliability
Model m	Distance implied by the RSSI model
Resid dB	Measured minus predicted level

Fit quality

Quality is graded good, fair or poor from:

• RMS RSSI residual
• RMS distance residual
• Receiver geometry and spread
• Confidence-ellipse size
• Outlier behaviour across receivers

The quality reasons are returned with the fix and available in the map popup instead of being reduced to a colour.

CLI

# Scan one frequency from every enabled receiver
spectrum-alert geo triangulate 446100000

# Calibrate receivers against known transmitters before locating
spectrum-alert geo calibrate
spectrum-alert geo calibrate --apply

# Fit the path-loss exponent to the local environment
spectrum-alert geo fit-ple

The web API exposes the same operation:

curl -X POST localhost:8000/api/triangulate \
  -H 'Content-Type: application/json' \
  -d '{"frequencyHz": 145312500, "windowHz": 12000, "integrationSec": 0.5}'

You can also send readings measured elsewhere:

curl -X POST localhost:8000/api/triangulate/readings \
  -H 'Content-Type: application/json' \
  -d '{
    "frequencyHz": 145312500,
    "readings": [
      {"id":"north","name":"North","lat":35.185,"lon":33.382,"rssiDbm":-92.1},
      {"id":"central","name":"Central","lat":35.130,"lon":33.340,"rssiDbm":-89.8},
      {"id":"south","name":"South","lat":35.090,"lon":33.380,"rssiDbm":-95.3}
    ]
  }'

Improving accuracy

• Calibrate every receiver against a known transmitter.
• Use three or more receivers with good angular spread; collinear receivers produce a long, uncertain ellipse.
• Avoid placing all receivers at similar distances from the target.
• Fit the path-loss exponent for the local environment.
• Treat a fix with a large residual or a high Resid dB on one receiver as suspect.

Signal levels

Readings are dBFS. Calibration offsets convert relative levels to a consistent scale across receivers; without calibration, receiver gain differences are indistinguishable from distance differences.

Related

• Web Dashboard
• Data and Training
• CLI Reference
• FAQ