Adaptive Radar Detection and Classification Algorithms for Multiple Coherent Signals

TL;DR

This paper introduces adaptive detection and classification algorithms for radar signals affected by multiple coherent interferences, improving detection accuracy and signal classification in complex scenarios.

Contribution

It proposes novel penalized likelihood-ratio detection architectures and a suboptimal angle estimation method for coherent signals, enhancing radar performance against interference.

Findings

Effective detection and classification in simulated environments

Comparable performance to exhaustive search methods

Improved detection probability over conventional detectors

Abstract

In this paper, we address the problem of target detection in the presence of coherent (or fully correlated) signals, which can be due to multipath propagation effects or electronic attacks by smart jammers. To this end, we formulate the problem at hand as a multiple-hypothesis test that, besides the conventional radar alternative hypothesis, contains additional hypotheses accounting for the presence of an unknown number of interfering signals. In this context and leveraging the classification capabilities of the Model Order Selection rules, we devise penalized likelihood-ratio-based detection architectures that can establish, as a byproduct, which hypothesis is in force. Moreover, we propose a suboptimum procedure to estimate the angles of arrival of multiple coherent signals ensuring (at least for the considered parameters) almost the same performance as the exhaustive search. Finally, the performance assessment, conducted over simulated data and in comparison with conventional radar detectors, highlights that the proposed architectures can provide satisfactory performance in terms of probability of detection and correct classification.