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23 May 2013 Divergence detectors for multitarget tracking algorithms
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Abstract
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) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ronald Mahler "Divergence detectors for multitarget tracking algorithms", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87450F (23 May 2013); https://doi.org/10.1117/12.2015450
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