Efficient non homogeneous CFAR processing

Abstract

In this work a new radar detection method is proposed, the Cell Average Neural Network Constant false Alarm Rate (CANN CFAR), which can be used with Weibull distributed non homogeneous radar returns. This processor combines Maximum Likelihood estimation method with Neural Networks for the clutter parameter estimation, resolving homogeneity and determining clutter bank transition points and size. To characterize its performance, probability of detection is evaluated using Monte Carlo simulations and compared to other efficient CFAR schemes. As a result, CANN CFAR detection has better performance than conventional CFAR processors, especially when detecting targets located near clutter heterogeneities. An additional advantage of the proposed technique is its efficiency when determining clutter transition points, bank size and threshold setting. This efficiency translates in lower computation time than other CFAR algorithms, mostly considering real time processing.