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dc.contributor.authorNannuru, S.
dc.contributor.authorCoates, M.
dc.contributor.authorMahler, Ronald
dc.identifier.citationNannuru, S. and Coates, M. and Mahler, R. 2013. Computationally-tractable approximate PHD and CPHD filters for superpositional sensors. IEEE Journal on Selected Topics in Signal Processing. 7 (3): pp. 410-420.

In this paper we derive computationally-tractable approximations of the Probability Hypothesis Density (PHD) and Cardinalized Probability Hypothesis Density (CPHD) filters for superpositional sensors with Gaussian noise. We present implementations of the filters based on auxiliary particle filter approximations. As an example, we present simulation experiments that involve tracking multiple targets using acoustic amplitude sensors and a radio-frequency tomography sensor system. Our simulation study indicates that the CPHD filter provides promising tracking accuracy with reasonable computational requirements. © 2007-2012 IEEE.

dc.publisherInstitute of Electrical and Electronic Engineers
dc.titleComputationally-tractable approximate PHD and CPHD filters for superpositional sensors
dc.typeJournal Article
dcterms.source.titleIEEE Journal on Selected Topics in Signal Processing
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available

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