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Proceedings Paper

Detection and classification of land-mine-like targets in a non-Gaussian noise environment
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Paper Abstract

Many statistical signal processing approaches to target detection and classification assume the measurement is corrupted by independent, identically distributed white Gaussian noise. This common assumption often results in simpler, and less computationally, intense, mathematically realization for the processor. However, in many instances it is not clear if this assumptions regarding the statistics of the noise is valid. In this paper, the effects of assuming i.i.d. white Gaussian noise on the performance of likelihood ratio detectors and maximum likelihood classifiers implemented in a non-Gaussian noise environment are discussed. If the assumptions regarding the noise distribution are accurate, the resulting likelihood ratio detector and classifier are optimal. However, if those assumptions are inaccurate, performance may be degraded. We present simulation result illustrating the effects of mismatch between the assumed and actual noise distributions on detection and classification performance for likelihood ratio processors derived under several assumptions regarding the noise distribution. Specifically, target detection and classification utilizing electromagnetic induction sensors is considered.

Paper Details

Date Published: 22 August 2000
PDF: 10 pages
Proc. SPIE 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, (22 August 2000); doi: 10.1117/12.396317
Show Author Affiliations
Stacy L. Tantum, Duke Univ. (United States)
Leslie M. Collins, Duke Univ. (United States)

Published in SPIE Proceedings Vol. 4038:
Detection and Remediation Technologies for Mines and Minelike Targets V
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Regina E. Dugan, Editor(s)

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