A CPHD approximation based on a discrete-Gamma cardinality model

De Melo, flavio and Maskell, S ORCID: 0000-0003-1917-2913 (2019) A CPHD approximation based on a discrete-Gamma cardinality model. IEEE Transactions on Signal Processing, 67 (2). pp. 336-350.

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Abstract

The cardinalized probability hypothesis density (CPHD) filter has become one of the most acclaimed algorithms for multi-Target Bayesian filtering due to its ability to accurately estimate the number of objects and the object states in tracking scenarios affected by clutter. The CPHD filter generalizes the probabilistic hypothesis density filter by jointly propagating the first-order multi-Target moment (intensity function) along with the entire probability distribution on the number of targets (cardinality distribution). In general, the CPHD recursion is computationally intractable, however successful approximations have been devised with reported computational complexity dominated by O(m3) operations per filtering iteration, where m is the number of measurements. Room for improvement was originally acknowledged by Mahler, who conceived the idea of approximating the cardinality distribution by two-parameter distributions. In this paper, we further explore this idea to provide an efficient approximation of the CPHD filter where the cardinality distribution is modeled as a discretized Gamma distribution. Experiments show that the resulting filter is less computationally complex than the standard implementation of the CPHD filter but shows similar cardinality accuracy and variance.

Item Type:	Article
Uncontrolled Keywords:	CPHD, cardinality model, discrete Gamma distribution, finite set statistics, multi-target tracking, PHD
Depositing User:	Symplectic Admin
Date Deposited:	05 Nov 2018 09:44
Last Modified:	15 Mar 2024 08:52
DOI:	10.1109/TSP.2018.2881659
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3028320