<title>Distributed detection of Rayleigh targets in K-distributed clutter</title>

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ABSTRACT Detection of target in the presence of Gaussian clutter using multiple radars have been studied in the recent literature. The algorithms considered include pure decision based fusion rules such as OR and AND and tests involving thefusion of partial information, such as normalized test statistic (NTS). However, based on experimental evidence, in manysituations, the compound K-distributed model seems to be a good fit for the clutter envelope. In this paper we study throughsimulation the performances of the NTS, AND, and OR tests for the detection of Rayleigh target in the presence of K-distributed clutter. The results show that for large signal to clutter power ratio and for large shape parameter values, the K-NTS significantly outperforms both the OR and the AND rules.Keywords: Distributed detection, CFAR detection, K-distribution 1. INTRODUCTION For the past several years a considerable amount of work [1-4] on single sensor (for example, radar) constant falsealarm rate (CFAR) signal detection has been done. The detection of signals becomes complex when radar returns are fromnonstationary background noise (or noise plus clutter). The probability of false alarm increases intolerably when a detectionscheme employing a fixed threshold is used. Therefore, adaptive threshold techniques are required in order to maintain anearly constant false alarm rate. Because of the diversity of the radar search environment (multiple target, abrupt changesin clutter, etc.) there exists no universal CFAR scheme. Typically the adaptive threshold of a CFAR scheme is the productof two terms, one is a fixed scaling factor to adjust the probability of false alarm, and the other is an estimate of the totalunknown noise (plus clutter) power of the test cell. The sample in the test cell is compared to this threshold in order todecide the presence or the absence of a target. A variety of CFAR techniques are developed according to the logic used toestimate the unknown noise power level. Some examples are, Cell Averaging CFAR (CA-CFAR), Ordered Statistics CFAR(OS-CFAR), Greatest Of GEAR, Smallest Of CFAR [3], and Selection and Estimation test [4].Distributed signal detection schemes are needed when system perfonnance factors such as speed, reliability, andconstraint over the communication bandwidth are taken into account. In distributed detection techniques, each sensor sendseither a binary decision or a condensed form of information (statistics) about the observations available at the sensor to thefusioii center, where a final decision about the presence of a target is made. Such techniques have been applied to CA-