Riemann–Langevin Particle Filtering in Track-Before-Detect

Abstract

Track-before-detect (TBD) is a powerful approach that consists in providing the tracker directly with the sensor measurements without any predetection. Due to the measurement model nonlinearities, online state estimation in TBD is most commonly solved via particle filtering. Existing particle filters for TBD do not incorporate measurement information in their proposal distribution. The Langevin Monte Carlo (LMC) is a sampling method whose proposal is able to exploit all available knowledge of the posterior (that is, both prior and measurement information). This letter synthesizes recent advances in differential-geometric LMC-based filtering to introduce its application to TBD. The benefits of LMC filtering in TBD are illustrated in a challenging low-noise scenario.