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Proceedings Paper

Divergence detectors for multitarget tracking algorithms
Author(s): Ronald Mahler
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Paper 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.

Paper Details

Date Published: 23 May 2013
PDF: 11 pages
Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87450F (23 May 2013); doi: 10.1117/12.2015450
Show Author Affiliations
Ronald Mahler, Lockheed Martin Corp. (United States)

Published in SPIE Proceedings Vol. 8745:
Signal Processing, Sensor Fusion, and Target Recognition XXII
Ivan Kadar, Editor(s)

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