Distributed GOSCA-CFAR Detection Based on Automatic Censoring Technique

Abstract

This paper present a new distributed constant false alarm rate (CFAR) detector based on automatic censoring technique. In the scheme, every local decision of individual detector, resulting from the comparison between its sample level and the estimation of the background based on GOSCA-CFAR (generalized order statistic, cell averaging-CFAR) algorithm, takes the value zero or one. Then, the fusion center makes the final decision utilizing the total local decisions, which are transmitted from each local detector. The overall decision, which is zero or one, is obtained at the data fusion center based on “AND”, “OR”, and “k/N” fusion rule. The results show that for the nonhomogeneous background caused by multiple interfering targets, particularly in multiple target situation, it exhibits good robustness in distributed sensor network. Under Swerling 2 assumption, the analytic expression of detection probability and false alarm probability are derived.