<title>Experimental validation of likelihood ratio and CFAR detectors for multifrequency radar data fusion: part 2</title>

Abstract

This paper, which is the second part of a two-paper series, summarizes some key theoretical results in distributed decision fusion and provides new experimental evidence that validates these results. The experimental evidence is important in that it provides a guideline for designing and optimizing a distributed decision fusion system. Furthermore, it demonstrates that the performance of a distributed decision fusion system, when properly designed, may have performance which is comparable to the optimal centralized fusion. The objective of this paper is to benchmark centralized and distributed hypothesis testing algorithms and validate theoretical results from distributed decision fusion using the experimental multifrequency radar data from the Rome Lab Predetection Fusion Program. In a series of papers Thomopoulos et al. have designed and evaluated a robust CFAR detector (code-named RobCFAR) for the distributed fusion of multi-frequency radar data from the Rome Lab Predetection Fusion Program. In this paper the optimal centralized and distributed detectors for the same multi-frequency radar data are developed and their performance is compared with that of the RobCFAR detectors. Several problems that occur due to the necessity of on-line evaluation of the data statistics are addressed. The experimental results are used to validate several theoretical results from distributed decision fusion and benchmark the performance of a CFAR fusion design against the optimal centralized and distributed data fusion design.