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Structure of robust signal detector for multiple sensors with unknown signal-to-clutter ratio

Xiang‐Yang Liu, Jia Xu, Jin Pan, Yingning Peng

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Abstract

In practical mutisensor system, the signal-to-clutter ratio of the returns of the same target at different sensors may be unknown and variant, which leads to the nonexistence of the uniformly most powerful test. By investigating the demonstration of the nonexistence, it was found that the canonical detector based on the likelihood ratio test can not possess robust performance of detection. Therefore, a structure of robust detector was proposed that synthesize the results of several canonical detectors to form a robust one. Finally, an example was given to illustrate the effectiveness of the structure. (4 pages)

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What this paper is about

In practical mutisensor system, the signal-to-clutter ratio of the returns of the same target at different sensors may be unknown and variant, which leads to the nonexistence of the uniformly most powerful test. By investigating the demonstration of the nonexistence, it was found that the canonical detector based on the likelihood ratio test can not possess robust performance of detection. Therefore, a structure of robust detector was proposed that synthesize the results of several canonical detectors to form a robust one. Finally, an example was given to illustrate the effectiveness of the structure. (4 pages)

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Available abstract

In practical mutisensor system, the signal-to-clutter ratio of the returns of the same target at different sensors may be unknown and variant, which leads to the nonexistence of the uniformly most powerful test. By investigating the demonstration of the nonexistence, it was found that the canonical detector based on the likelihood ratio test can not possess robust performance of detection. Therefore, a structure of robust detector was proposed that synthesize the results of several canonical detectors to form a robust one. Finally, an example was given to illustrate the effectiveness of the structure. (4 pages)

Key concepts: Clutter, Detector, Likelihood-ratio test, SIGNAL (programming language), Detection theory, Computer science, Signal-to-noise ratio (imaging), Algorithm

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