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Proceedings Paper

Performance evaluation of an asynchronous multisensor track fusion filter
Author(s): Ali T. Alouani; John E. Gray; D. Hugh McCabe
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Paper Abstract

Recently the authors developed a new filter that uses data generated by asynchronous sensors to produce a state estimate that is optimal in the minimum mean square sense. The solution accounts for communications delay between sensors platform and fusion center. It also deals with out of sequence data as well as latent data by processing the information in a batch-like manner. This paper compares, using simulated targets and Monte Carlo simulations, the performance of the filter to the optimal sequential processing approach. It was found that the new asynchronous Multisensor track fusion filter (AMSTFF) performance is identical to that of the extended sequential Kalman filter (SEKF), while the new filter updates its track at a much lower rate than the SEKF.

Paper Details

Date Published: 25 August 2003
PDF: 12 pages
Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); doi: 10.1117/12.487537
Show Author Affiliations
Ali T. Alouani, Tennessee Technological Univ. (United States)
John E. Gray, Naval Surface Warfare Ctr. (United States)
D. Hugh McCabe, Naval Surface Warfare Ctr. (United States)


Published in SPIE Proceedings Vol. 5096:
Signal Processing, Sensor Fusion, and Target Recognition XII
Ivan Kadar, Editor(s)

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