20 June 2014 Hybrid multi-Bernoulli CPHD filter for superpositional sensors
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Abstract
We propose, for the super-positional sensor scenario, a hybrid between the multi-Bernoulli filter and the cardinal­ized probability hypothesis density (CPHD) filter. We use a multi-Bernoulli random finite set (RFS) to model existing targets and we use an independent and identically distributed cluster (IIDC) RFS to model newborn targets and targets with low probability of existence. Our main contributions are providing the update equa­tions of the hybrid filter and identifying computationally tractable approximations. We achieve this by defining conditional probability hypothesis densities (PHDs), where the conditioning is on one of the targets having a specified state. The filter performs an approximate Bayes update of the conditional PHDs. In parallel, we perform a cardinality update of the IIDC RFS component in order to estimate the number of newborn targets. We provide an auxiliary particle filter based implementation of the proposed filter and compare it with CPHD and multi-Bernoulli filters in a simulated multitarget tracking application
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Santosh Nannuru, Santosh Nannuru, Mark Coates, Mark Coates, } "Hybrid multi-Bernoulli CPHD filter for superpositional sensors", Proc. SPIE 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 90910D (20 June 2014); doi: 10.1117/12.2050158; https://doi.org/10.1117/12.2050158
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