A Monte Carlo simulation for two novel automatic censoring techniques of radar interfering targets in log-normal clutter

In this paper, we present two novel algorithms for automatic censoring of radar interfering targets in log-normal clutter. The proposed algorithms consist 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 proposed detectors do not require any prior information about the clutter parameters nor do they require the number of interfering targets. The effectiveness of the proposed algorithms is assessed by computing, using Monte Carlo simulations, the probability of censoring and the probability of detection in different background environments.