Robust predetection data fusion for enhanced target detection

Stelios C.A. Thomopoulos; Nickens N. Okello

doi:10.1117/12.172531

1 February 1994 Robust predetection data fusion for enhanced target detection

Stelios C.A. Thomopoulos, Nickens N. Okello

Proceedings Volume 2093, Substance Identification Analytics; (1994) https://doi.org/10.1117/12.172531
Event: Substance Identification Technologies, 1993, Innsbruck, Austria

Abstract

A robust Constant False Alarm Rate (CFAR) distributed detection system that operates in heavy clutter with unknown distribution is presented. The system is designed to provide CFARness under clutter power fluctuations and robustness under unknown clutter and noise distributions. The system is also designed to operate successfully under different-power sensors and exhibit fault-tolerance in the presence of sensor power fluctuations. The test statistic at each sensor is a robust CFAR t-statistic. In addition to the primary binary decisions, confidence levels are generated with each decision and used in the fusion logic to robustify the fusion performance and eliminate weaknesses of the Boolean fusion logic. The test statistic and the fusion logic are analyzed theoretically for Weibull and lognormal clutter. The theoretical performance is compared against Monte-Carlo simulations that verify that the system exhibits the desired characteristics of CFARness, robustness, insensitivity to power fluctuations and fault-tolerance. The system is tested with experimental target-in-clear and target-in-clutter data and its experimental performance agrees with the theoretically predicted behavior.

Citation Download Citation

Stelios C.A. Thomopoulos and Nickens N. Okello "Robust predetection data fusion for enhanced target detection", Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); https://doi.org/10.1117/12.172531

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