A Multiple Radar Approach for Automatic Target Recognition of Aircraft using Inverse Synthetic Aperture Radar

TL;DR

This paper presents a multi-radar CNN approach using simulated complex SAR/ISAR data for aircraft recognition, achieving higher accuracy and faster training than traditional methods.

Contribution

Introduces a multi-radar approach with complex-valued CNN trained on simulated data, improving aircraft recognition accuracy in SAR/ISAR ATR tasks.

Findings

Multi-radar approach increases recognition accuracy.

Simulated complex data enhances CNN training.

Optimal number of radars improves detection performance.

Abstract

Along with the improvement of radar technologies, Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR) has come to be an active research area. SAR/ISAR are radar techniques to generate a two-dimensional high-resolution image of a target. Unlike other similar experiments using Convolutional Neural Networks (CNN) to solve this problem, we utilize an unusual approach that leads to better performance and faster training times. Our CNN uses complex values generated by a simulation to train the network; additionally, we utilize a multi-radar approach to increase the accuracy of the training and testing processes, thus resulting in higher accuracies than the other papers working on SAR/ISAR ATR. We generated our dataset with 7 different aircraft models with a radar simulator we developed called RadarPixel; it is a Windows GUI program implemented using Matlab and Java programming, the simulator is capable of accurately replicating a real SAR/ISAR configurations. Our objective is to utilize our multi-radar technique and determine the optimal number of radars needed to detect and classify targets.