Radar Target Detection Framework Based on False Discovery Rate

Abstract

A new framework is presented for the detection of targets in clutter and noise. Conventional radar detection techniques involve testing each test cell separately at a predefined probability of false alarm. In this paper, we formulate a novel surveillance paradigm that controls the false discovery rate (FDR) for a specified surveillance area (SA), which consists of a number of test cells. FDR is defined as the expectation of the ratio of the number of false alarms to the total number of cells classified as targets over the entire SA. It is observed that control of FDR results in an increase in the probabilities of detection and false alarm with an increase in the number of targets in the SA. This is achieved without the actual knowledge of the number of targets in the SA. To control the increase of the probability of false alarm and to confine it within a prespecified range, we propose a hybrid detection strategy involving both SA-based testing and per-cell testing. Our proposed hybrid approach, based on the concept of algorithm fusion, provides substantial improvement in detection performance in target-rich environments at the cost of a controlled increase in the false alarm rate. Analytical and simulation results are provided to demonstrate the performance of the proposed approach.