A Multisensor Multi-Bernoulli Filter

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Authors
  1. Saucan, A-A.
  2. Coates, M.J.
  3. Rabbat, M.
Corporate Authors
Defence Research and Development Canada, Atlantic Research Centre, Halifax NS (CAN)
Abstract
In this paper,we derive a multisensor multi-Bernoulli (MS-MeMBer) filter for multitarget tracking. Measurements from multiple sensors are employed by the proposed filter to update a set of tracks modeled as a multi-Bernoulli random finite set. An exact implementation of the MS-MeMBer update procedure is computationally intractable. We propose an efficient approximate implementation by using a greedy measurement partitioning mechanism. The proposed filter allows for Gaussian mixture or particle filter implementations. Numerical simulations conducted for both linear-Gaussian and nonlinear models highlight the improved accuracy of the MS-MeMBer filter and its reduced computational load with respect to the multisensor cardinalized probability hypothesis density filter and the iterated-corrector cardinality-balanced multi-Bernoulli filter especially for low probabilities of detection.
Keywords
Random finite sets;multi-sensor multi-Bernoulli filter;multi-sensor tracking;multi-target tracking
Report Number
DRDC-RDDC-2018-P079 — External Literature
Date of publication
01 Jun 2018
Number of Pages
15
Reprinted from
IEEE Transactions on Signal Processing, 65(20), October 15, 2017, pp. 5495 5509
DSTKIM No
CA046183
CANDIS No
806787
Format(s):
Electronic Document(PDF)

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