Performance of Distributed CFAR Test Under Various Clutter Amplitudes

We evaluate the performances of several distributed constant false-alarm rate (CFAR) tests operating in different background clutter conditions. The analysis considers the detection of Rayleigh target in various clutters with the possibility of differing clutter power levels in the test cells of distributed radars. Numerical results studied for a two-radar system show how the false-alarm rate of the maximum order statistic (MOS) test changes with differences in the clutter power levels of the test cells. The analysis for the detection of Rayleigh target in Rayleigh clutter indicates that, with the power levels of differing test cells, the OR fusion rule can be quite competitive with the new normalized test statistic (NTS). However, for the detection of Rayleigh target in Weibull or K-distributed clutter, the results show that NTS outperforms both the OR and the AND rules under the condition of large signal-to-clutter power ratio and moderate shape parameter values

Robustness of Decentralized Tests with ε-Contamination Prior

We consider a decentralized detection problem where the prior density is not completely known, but is assumed to belong to an ε-contamination class. The expressions for the infimum and the supremum of the posterior probability that the parameter under question is in a given region, as the prior varies over the ε-contamination class, are derived. Numerical results are obtained for a specific case of an exponentially distributed observation and an exponentially distributed nominal prior. Asymptotic (as number of sensors tends to a large value) results are also obtained. The results illustrate the degree of robustness achieved with quantized observations as compared to unquantized observations