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Proceedings Paper

Simultaneous position and number of source estimates using Random Set Theory
Author(s): Andreas M. Ali; Ralph E. Hudson; Flavio Lorenzelli; Kung Yao
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Paper Abstract

Joint estimation and detection for multi-sensor and multi-target algorithms are often hybrids of both analytical and ad-hoc approaches at various levels. The intricacies of the resulting solution formulation often obscures design intuition leaving many design choices to a largely trial and error based approach. Random Finite Set Theory (RFST)1,2 is a formal generalization of classical probability theory to the random set domain. By treating multi-target and multi-sensor jointly, RFST is able to provide a systematic theoretical framework for rigorous mathematical analysis. Because of its set theory domain, RFST is able to model the randomness of missed detection, sensor failure, target appearance and disappearance, clutter, jammer, ambiguous measurements, and other practical artifacts within its probability framework. Furthermore, a rigorous statistical framework, the Finite Set Statistics, has been developed for RFST that includes statistical operations such as: Maximum Likelihood, Bayesian prediction-correction filter, sensor fusion, and even the Cramer-Rao Lower Bound (CRB). In this paper we will apply RFST to jointly detect and locate a target in a power-aware wireless sensor network setting. We will further derive the CRB using both classical and RFST approaches as verification. Then we will use analytical results in conjunction with simulations to develop insights for choosing the design parameters.

Paper Details

Date Published: 3 September 2008
PDF: 10 pages
Proc. SPIE 7074, Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII, 70740G (3 September 2008); doi: 10.1117/12.793856
Show Author Affiliations
Andreas M. Ali, Univ. of California, Los Angeles (United States)
Ralph E. Hudson, Univ. of California, Los Angeles (United States)
Flavio Lorenzelli, Univ. of California, Los Angeles (United States)
Kung Yao, Univ. of California, Los Angeles (United States)

Published in SPIE Proceedings Vol. 7074:
Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII
Franklin T. Luk, Editor(s)

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