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The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations

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Authors
Vo, Ba Tuong
Vo, Ba-Ngu
Cantoni, Antonio
Date
2009
Type
Journal Article

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Vo, B.T. and Vo, B. and Cantoni, A. 2009. The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations. IEEE Transactions on Signal Processing. 57 (2): pp. 409-423.
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IEEE Transactions on Signal Processing
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Department of Electrical and Computer Engineering
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Copyright © 2009. IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Abstract

It is shown analytically that the multi-target multi- Bernoulli (MeMBer) recursion, proposed by Mahler, has a significant bias in the number of targets. To reduce the cardinality bias, a novel multi-Bernoulli approximation to the multi-target Bayes recursion is derived. Under the same assumptions as the MeMBer recursion, the proposed recursion is unbiased. In addition, a sequential Monte Carlo (SMC) implementation (for generic models) and a Gaussian mixture (GM) implementation (for linear Gaussian models) are proposed. The latter is also extended to accommodate mildly nonlinear models by linearization and the unscented transform.