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 no...
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| Format: | Conference Paper |
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2013
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| Online Access: | http://hdl.handle.net/20.500.11937/55142 |
| _version_ | 1848759545524912128 |
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| author | Mahler, Ronald |
| author_facet | Mahler, Ronald |
| author_sort | Mahler, Ronald |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T10:01:35Z |
| format | Conference Paper |
| id | curtin-20.500.11937-55142 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:01:35Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-551422017-09-13T16:10:40Z Divergence detectors for multitarget tracking algorithms Mahler, Ronald 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. 2013 Conference Paper http://hdl.handle.net/20.500.11937/55142 10.1117/12.2015450 restricted |
| spellingShingle | Mahler, Ronald Divergence detectors for multitarget tracking algorithms |
| title | Divergence detectors for multitarget tracking algorithms |
| title_full | Divergence detectors for multitarget tracking algorithms |
| title_fullStr | Divergence detectors for multitarget tracking algorithms |
| title_full_unstemmed | Divergence detectors for multitarget tracking algorithms |
| title_short | Divergence detectors for multitarget tracking algorithms |
| title_sort | divergence detectors for multitarget tracking algorithms |
| url | http://hdl.handle.net/20.500.11937/55142 |