Parallax Trackingבס״ד

Multi-target tracking, sensor fusion and estimation

Interactive tutorial · with Claude AI assistance

Parallax Tracking — every figure below is computed as you read it

Table IResult at the current epoch
Live: the running scenario's own numbers, not a recorded benchmark. For reproducible medians see Table III.
Fig. 2aMean 2σ major axis, recent history
Step changes are sensors being added or removed; the sawtooth is track birth and death.
Table IIIThe four algorithms, five-seed medians
Method2 sen.3 sen.4 sen. ms/stepvalidation
OSPA at cutoff 200 m and clutter 2, in metres; lower is better. Every cell is the median of five seeds — the same configuration has been measured spanning 4.5 to 162.9 across seeds, so a single-seed figure is noise. Tuned on one seed set, reported on a disjoint one. Timing measured solo at three sensors. Click a row to open that article.
Fig. 2. The Cramér–Rao position bound as a body. One target at the origin, its sensors around it, and the 2σ covariance ellipsoids of a converged EKF and of a batch Gauss–Newton solution, both normalised by σ · reff so the absolute noise level cancels. Drag to orbit; the sensor list and both estimators are behind the gear. Three-dimensional because with a single target there is nothing to associate, hence no filter and nothing that can diverge.
Table IIGeometry and accuracy for the constellation above

Appendix — common to all four methods

References

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