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dc.contributor.authorMahler, Ronald
dc.identifier.citationMahler, R. 2013. Divergence detectors for multitarget tracking algorithms.

Single-target tracking filters will typically diverge when their internal measurement or motion models deviate too much from the actual models. Niu, Varshney, Alford, Bubalo, Jones, and Scalzo have proposed a metric- the normalized innovation squared (NIS)-that recursively estimates the degree of nonlinearity in a single-target tracking problem by detecting filter divergence. This paper establishes the following: (1) NIS can be extended to generalized NIS (GNIS), which addresses more general nonlinearities; (2) NIS and GNIS are actually anomaly detectors, rather than filter-divergence detectors; (3) NIS can be heuristically generalized to a multitarget NIS (MNIS) metric; (4) GNIS also can be rigorously extended to multitarget problems via the multitarget GNIS (MGNIS); (5) explicit, computationally tractable formulas for MGNIS can be derived for use with CPHD and PHD filters; and thus (6) these formulas can be employed as anomaly detectors for use with these filters. © 2013 SPIE.

dc.titleDivergence detectors for multitarget tracking algorithms
dc.typeConference Paper
dcterms.source.titleProceedings of SPIE - The International Society for Optical Engineering
dcterms.source.seriesProceedings of SPIE - The International Society for Optical Engineering
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available

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