A neural network framework for binary classification of radar detections

Jabran Akhtar

doi:10.1186/s13634-021-00801-y

Abstract

A desired objective in radar target detection is to satisfy two very contradictory requirements: offer a high probability of detection with a low false alarm rate. In this paper, we propose the utilization of artificial neural networks for binary classification of targets detected by a depreciated detection process. It is shown that trained neural networks are capable of identifying false detections with considerable accuracy and can to this extent utilize information present in guard cells and Doppler profiles. This allows for a reduction in the false alarm rate with only moderate loss in the probability of detection. With an appropriately designed neural network, an overall improved system performance can be achieved when compared against traditional constant false alarm rate detectors for the specific trained scenarios.

Highlights

Discriminating targets from background noise and interference is a fundamental task of all radar systems
In order to achieve a high probability of correct target detection, we show that using the constant false alarm rate (CFAR) sliding window samples is not sufficient rather the target spread in Doppler contains important discriminatory information and contributes positively if integrated by the neural network
Parts of a pulsed radar system are simulated in slow-time to train neural networks under the proposed methodology, and the performance is evaluated against traditional SO-CFAR and GO-CFAR detectors alongside Censored mean smallest of (CMSO)-CFAR

Summary

Introduction

Discriminating targets from background noise and interference is a fundamental task of all radar systems. One particular class of algorithms, who sufficiently satisfy the constant false alarm rate (CFAR) property, include CA (cell averaging), GO (greatest of ) and SO (smallest of ) CFAR sliding window methods with several proposed variants [1,2,3,4,5,6,7,8] These detectors aim to provide an adaptive mean to calculate the detection threshold as fixed thresholds are inadequate in case of complex and dynamic surroundings. A wide variety of alternative detection methods have been proposed for specific environmental conditions where the detectors are tailored with respect to assorted target and clutter distributions and applicable secondary data are made use of [9,10,11,12,13]. These methods often rely on estimation of distribution parameters, the covariance matrices and are dependent upon accurate estimation of these figures

Methods

Results

Conclusion