A scalable belief-propagation track-before-detect method tracks multiple objects directly from correlated raw sensor data with fluctuating amplitudes and unknown noise, outperforming detect-then-track baselines.
Marginal multi-Bernoulli filters: RFS d erivation of MHT, JIPDA and association-based MeMBer,
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Message Passing for Track-Before-Detect
A scalable belief-propagation track-before-detect method tracks multiple objects directly from correlated raw sensor data with fluctuating amplitudes and unknown noise, outperforming detect-then-track baselines.