<title>Constant false alarm rate target detection in clutter: a neural processing algorithm</title>

Abstract

A serious degradation in detection probability of conventional Constant False Alarm Rate (CFAR) processors used in the automatic detection of radar targets results from a reduction in the number of available reference cells. Several factors such as any constraints on the radar system used (in terms of resolution and sampling time), presence of interfering targets and nonstationary clutter may contribute to the reduction in the number of reference cells. This paper presents a novel neural network-based CFAR detection scheme (referred to as NN- CFAR scheme) that offers robust performance in the face of loss of reference cells. This scheme employs a multilayer feedforward neural network trained by error backpropagation approach using the optimal detector as the teacher. The excellent pattern classification capabilities of trained neural networks are exploited in this application to effectively counter performance degradations due to reduced reference window sizes. In particular it is demonstrated that a neural network implementation of the CFAR detection scheme provides an efficient approach for accommodating more input parameters without increasing design complexity for countering the information loss due to reduced reference window size. Precise quantitative performance evaluation of the NN-CFAR scheme are conducted in a variety of situations that include both homogeneous and nonhomogeneous clutter backgrounds and the target detection performance is compared with that of the traditional CA-CFAR scheme to highlight the benefits.