2 research outputs found
Riemann-Langevin Particle Filtering in Track-Before-Detect
Track-before-detect (TBD) is a powerful approach that consists in providing
the tracker with sensor measurements directly without pre-detection. Due to the
measurement model non-linearities, 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 LMC-based
filtering to describe the Riemann-Langevin particle filter and introduces its
novel application to TBD. The benefits of our approach are illustrated in a
challenging low-noise scenario.Comment: Minor grammatical update
Langevin Monte Carlo filtering for target tracking
This paper introduces the Langevin Monte Carlo Filter (LMCF), a particle filter with a Markov chain Monte Carlo algorithm which draws proposals by simulating Hamiltonian dynamics. This approach is well suited to non-linear filtering problems in high dimensional state spaces where the bootstrap filter requires an impracticably large number of particles. The simulation of Hamiltonian dynamics is motivated by leveraging more model knowledge in the proposal design. In particular, the gradient of the posterior energy function is used to draw new samples with high probability of acceptance. Furthermore, the introduction of auxiliary variables (the so-called momenta) ensures that new samples do not collapse at a single mode of the posterior density. In comparison with random-walk Metropolis, the LMC algorithm has been proven more efficient as the state dimension increases. Therefore, we are able to verify through experiments that our LMCF is able to attain multi-target tracking using small number of particles when other MCMC-based particle filters relying on random-walk Metropolis require a considerably larger particle number. As a conclusion, we claim that performing little additional work for each particle (in our case, computing likelihood energy gradients) turns out to be very effective as it allows to greatly reduce the number of particles while improving tracking performance