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Simulation Test Report — sonar-vision

Scope

This report describes a realistic end-to-end simulation scenario written to validate the sonar_vision pipeline:

Sonar ping simulation → noisy detections → ObjectTracker → SpatialMap

The scenario is saved as tests/test_simulation_scenarios.py and is intended as a pytest-based integration test complementing the existing unit tests in tests/test_all.py.

Scenario Design

Sonar parameters

Parameter Value Rationale
Frequency 200 kHz Typical fish-finder / small-ROV imaging sonar
Sound speed 1500 m/s SOUND_SPEED_WATER — realistic nominal value
Pulse length 1 ms Common short-pulse sonar
Max range 300 m Fishing / shallow-water robotics context
Beam width 30° Representative single-beam transducer
Absorption 30 dB/km Approximate high-frequency seawater value
Source level 200 dB (arbitrary scale) Keeps 10–200 m targets detectable
Noise level 60 dB Comparable arbitrary scale
Detection threshold signal_strength ≥ 0.05 Soft SNR-derived cutoff

Targets

Three moving targets are used for the primary scenario:

ID Start position Velocity Role
A (50 m, 10 m) (2.0, 0.0) m/s Range-increasing crosser
B (80 m, -20 m) (0.0, 1.5) m/s Vertical pass
C (100 m, 20 m) (-1.0, -1.0) m/s Diagonal inward mover

All targets start inside the 30° beam (±15° from boresight) at realistic fish-finder ranges (50–120 m).

Measurement model

Each time step:

  1. Targets move according to constant velocity.
  2. A Sonar.ping() is simulated for each target that lies within beam and max range.
  3. Range and bearing noise are added (σ_range = 0.5 m, σ_bearing = 1°).
  4. Detections are converted to Cartesian (x, y).
  5. ObjectTracker.update() receives the detection frame.
  6. SpatialMap is updated with free rays and occupied cells.

What Was Verified

1. Well-separated multi-target tracking

  • All three targets are detected on every frame.
  • ObjectTracker maintains exactly three active tracks after 25 s.
  • Final track velocities have the correct signs (e.g. positive x velocity for target A, positive y velocity for target B).

2. Close-approach target association

A second scenario uses two targets moving toward each other on offset paths with a closest approach of roughly 12 m. With the default tracker gate of 8 m this stays outside the association threshold, and the tracker still reports two distinct tracks with opposing velocity signs.

3. Beam-edge intermittent detection

A stationary target placed near the edge of the beam (≈14.5°) produces partial detection rates (around 60–80% depending on random seed) because bearing noise sometimes pushes the apparent return outside the beam. This confirms the beam gate behaves realistically.

4. Occupancy map consistency

  • The map accumulates occupied cells along the target trajectories.
  • Free rays are marked between the sonar origin and each detection.
  • The nearest obstacle to a final track position is within a few meters of the track.

Bugs and Inaccuracies Found and Fixed

While building the scenario, several mismatches between documentation/code and reality were found and corrected:

  1. Sonar.ping parameter name — README used sonar.ping(distance=100), but the implementation named the parameter target_distance. Renamed to distance so the Quick Start works as written.

  2. Missing public exports — Detection (used in README and GETTING_STARTED examples) was not exported from sonar_vision.__init__. Added Detection, Track, Obstacle, CellState, and PingResult to __all__.

  3. One-way vs two-way propagation — Sonar.ping() and Sonar.ping_return_signal() were applying only one-way transmission loss. Active sonar has a two-way path (transmit + echo), so both methods now use 2.0 * total_loss(distance). This makes long-range detections materially more realistic (e.g. SNR at 100 m drops from ~79 dB to ~38 dB).

  4. Incorrect method names in docs — API_REFERENCE.md and GETTING_STARTED.md referenced methods that do not exist (detect, filter_lowpass, filter_highpass, fft, predict_next, update, grid, occupancy). Updated to the actual API (ping, lowpass, highpass, dft_magnitude, etc.).

  5. No LICENSE file — README claimed MIT but no LICENSE file existed. Added LICENSE and updated pyproject.toml.

Real Gaps and Surprising Behavior

  1. Simple tracker cannot resolve close crossings. During early manual exploration, two targets placed on an intersecting path merged into a single track once they came within the association gate. This is expected behavior for a nearest-neighbour constant-velocity tracker, but it is a real limitation for dense target environments. The test was adjusted to use a ~12 m miss distance, which the tracker handles correctly.

  2. No explicit probability of detection model. The current detector is deterministic: a target either exceeds the soft SNR threshold or it does not. Real sonar has a probabilistic detection curve near threshold. The scenario demonstrates intermittent detection only via beam-edge bearing noise; a richer model would add range-dependent detection probability.

  3. Absorption coefficient is not frequency-dependent. The code calls absorption_loss "frequency-dependent" but takes a constant absorption_db_km parameter. The docstring was corrected to describe the simplified model. A production-grade physics model would use Thorp's formula or similar.

  4. No doppler / target aspect effects. Target strength is constant, and moving targets do not produce Doppler shifts. These are acceptable for a pure-Python simulation toolkit but should be noted as out of scope.

Verification Status

pytest is not installed in this environment, so the new test file was verified by:

  • python3 -m py_compile tests/test_simulation_scenarios.py (syntax check).
  • Manual execution of the scenario logic with Python 3.12, confirming the tracker produces the expected number of tracks and the map accumulates occupied/free cells as designed.

The final verification will be performed by the repository's GitHub Actions CI workflow (.github/workflows/ci.yml).