Bernoulli Forward-Backward Smoothing for Joint Target Detection and Tracking
MetadataShow full item record
In this correspondence, we derive a forward-backward smoother for joint target detection and estimation and propose a sequential Monte Carlo implementation. We model the target by a Bernoulli random finite set since the target can be in one of two “present” or “absent” modes. Finite set statistics is used to derive the smoothing recursion. Our results indicate that smoothing has two distinct advantages over just using filtering: First, we are able to more accurately identify the appearance and disappearance of a target in the scene, and second, we can provide improved state estimates when the target exists.
Showing items related by title, author, creator and subject.
Nadarajah, Nandakumaran; Tharmarasa, R.; McDonald, M.; Kirubarajan, T. (2012)The interacting multiple model (IMM) estimator has been proven to be effective in tracking agile targets. Smoothing or retrodiction, which uses measurements beyond the current estimation time, provides better estimates ...
Nadarajah, Nandakumaran; Kirubarajan, T.; Lang, T.; McDonald, M.; Punithakumar, K. (2011)In general, for multitarget problems where the number of targets and their states are time varying, the optimal Bayesian multitarget tracking is computationally demanding. The Probability Hypothesis Density (PHD) filter, ...
Mahler, R.; Vo, Ba Tuong; Vo, Ba-Ngu (2012)A forward-backward probability hypothesis density (PHD) smoother involving forward filtering followed by backward smoothing is proposed. The forward filtering is performed by Mahler's PHD recursion. The PHD backward ...