Paper
4 October 2005 Multiple-model detection of target maneuvers
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Abstract
This paper proposes a multiple-model (MM) hypothesis testing approach for detection of unknown target maneuvers that may have several possible prior distributions. An MMmaneuver detector based on sequential hypothesis testing is developed. Simulation results that compare the performance of the proposed MM detector to that of traditional maneuver detectors are presented. They demonstrate that the new sequential MM detector outperforms traditional multiple hypothesis testing based detectors when the prior acceleration distributions are unknown.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jifeng Ru, X. Rong Li, and Vesselin P. Jilkov "Multiple-model detection of target maneuvers", Proc. SPIE 5913, Signal and Data Processing of Small Targets 2005, 59130A (4 October 2005); https://doi.org/10.1117/12.621779
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CITATIONS
Cited by 19 scholarly publications.
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KEYWORDS
Sensors

Target detection

Monte Carlo methods

Detection and tracking algorithms

Motion models

Filtering (signal processing)

Binary data

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