Adaptive censored cell-averaging CFAR detection in distributed sensor networks

Abstract

This paper present a new distributed CFAR (constant false alarm rate) detector based on the adaptive censored cell-averaging CFAR technique. In the scheme, every local decision of individual detector, resulting from the comparison between its sample in test cell and the estimate of clutter power level of its reference samples, takes the value zero or one. In local processor, the CCA-CFAR (censored cell-averaging) technique is utilized to get the local decision, Then, the fusion center makes the global decision based on the total local decisions, which are transmitted from each local sensor. The overall decision, which is zero or one, is obtained at the data fusion center grounded on k/N fusion rule. The results show that for the nonhomogeneous background caused by multiple interfering targets, this approach is more reality. Particularly in multiple target situations, it exhibits robustness than MOS (maximum order statistic), mOS (minimum order statistic), and OSOR (ordered statistics), ORAND in distributed sensor networks. Under Swerling 2 assumption, the analytic expression of false alarm and detection probability are derived.