A Backward Automatic Censored Cell Averaging Detector for Multiple Target Situations in Log-normal Clutter

A challenging problem in radar signal processing is to achieve reliable target detection in the presence of interferences. In this paper, we propose a novel algorithm for automatic censoring of radar interfering targets in log-normal clutter. The proposed algorithm, timed the backward automatic censored cell averaging detector (B-ACCAD), consists of two steps: removing the corrupted reference cells (censoring) and the actual detection. Both steps are performed dynamically by using a suitable set of ranked cells to estimate the unknown background level and set the adaptive thresholds accordingly. The B-ACCAD algorithm does not require any prior information about the clutter parameters nor does it require the number of interfering targets. The effectiveness of the B-ACCAD algorithm is assessed by computing, using Monte Carlo simulations, the probability of censoring and the probability of detection in different background environments. Keywords:: CFAR, Log-normal clutter, Censoring, Probability of detection, Probability of false alarm, Probability of false censorin