Data association probability and measurement density function of tracking in clutter with strongest-neighbor measurements

X. Rong Li

doi:10.1117/12.283971

29 October 1997 Data association probability and measurement density function of tracking in clutter with strongest-neighbor measurements

X. Rong Li

Author Affiliations +

Proceedings Volume 3163, Signal and Data Processing of Small Targets 1997; (1997) https://doi.org/10.1117/12.283971
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

When tracking a target in clutter, a measurement may have originated from either the target, clutter, or some other source. The measurement with the strongest intensity (amplitude) in the neighborhood of the predicted target measurement is known as the 'strongest neighbor' (SN) measurement. A simple and commonly used method for tracking in clutter is the so-called strongest neighbor filter (SNF), which uses the SN measurement at each time as if it were the true one. This paper presents analytic results, along with discussions, for the SN measurement, including the a priori and a posteriori probabilities of data association events and the conditional probability density functions. These results provide theoretical foundation for performance prediction and development of improved tracking filters.

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X. Rong Li "Data association probability and measurement density function of tracking in clutter with strongest-neighbor measurements", Proc. SPIE 3163, Signal and Data Processing of Small Targets 1997, (29 October 1997); https://doi.org/10.1117/12.283971

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