11,271 research outputs found
Poisson multi-Bernoulli mixture trackers: continuity through random finite sets of trajectories
The Poisson multi-Bernoulli mixture (PMBM) is an unlabelled multi-target
distribution for which the prediction and update are closed. It has a Poisson
birth process, and new Bernoulli components are generated on each new
measurement as a part of the Bayesian measurement update. The PMBM filter is
similar to the multiple hypothesis tracker (MHT), but seemingly does not
provide explicit continuity between time steps. This paper considers a recently
developed formulation of the multi-target tracking problem as a random finite
set (RFS) of trajectories, and derives two trajectory RFS filters, called PMBM
trackers. The PMBM trackers efficiently estimate the set of trajectories, and
share hypothesis structure with the PMBM filter. By showing that the prediction
and update in the PMBM filter can be viewed as an efficient method for
calculating the time marginals of the RFS of trajectories, continuity in the
same sense as MHT is established for the PMBM filter
Multi-Target Tracking in Distributed Sensor Networks using Particle PHD Filters
Multi-target tracking is an important problem in civilian and military
applications. This paper investigates multi-target tracking in distributed
sensor networks. Data association, which arises particularly in multi-object
scenarios, can be tackled by various solutions. We consider sequential Monte
Carlo implementations of the Probability Hypothesis Density (PHD) filter based
on random finite sets. This approach circumvents the data association issue by
jointly estimating all targets in the region of interest. To this end, we
develop the Diffusion Particle PHD Filter (D-PPHDF) as well as a centralized
version, called the Multi-Sensor Particle PHD Filter (MS-PPHDF). Their
performance is evaluated in terms of the Optimal Subpattern Assignment (OSPA)
metric, benchmarked against a distributed extension of the Posterior
Cram\'er-Rao Lower Bound (PCRLB), and compared to the performance of an
existing distributed PHD Particle Filter. Furthermore, the robustness of the
proposed tracking algorithms against outliers and their performance with
respect to different amounts of clutter is investigated.Comment: 27 pages, 6 figure
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