Hybrid multi-Bernoulli CPHD filter for superpositional sensors

Santosh Nannuru; Mark Coates

doi:10.1117/12.2050158

20 June 2014 Hybrid multi-Bernoulli CPHD filter for superpositional sensors

Santosh Nannuru, Mark Coates

Proceedings Volume 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII; 90910D (2014) https://doi.org/10.1117/12.2050158
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

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

Citation Download Citation

Santosh Nannuru and 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); https://doi.org/10.1117/12.2050158

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