Non-Linear Signal Processing methods for UAV detections from a Multi-function X-band Radar

TL;DR

This paper explores advanced non-linear signal processing techniques like CS, PCA, IAA, and MIMO to improve UAV detection and classification using portable phased array radar systems, demonstrating promising simulation results.

Contribution

It introduces the application of non-linear processing methods to portable radar for UAV detection, highlighting their advantages and potential for real-time, accurate classification.

Findings

CS and IAA improve micro-Doppler processing accuracy

Non-linear methods reduce dwell time for detection

GPU-based processing enables real-time analysis

Abstract

This article develops the applicability of non-linear processing techniques such as Compressed Sensing (CS), Principal Component Analysis (PCA), Iterative Adaptive Approach (IAA) and Multiple-input-multiple-output (MIMO) for the purpose of enhanced UAV detections using portable radar systems. The combined scheme has many advantages and the potential for better detection and classification accuracy. Some of the benefits are discussed here with a phased array platform in mind, the novel portable phased array Radar (PWR) by Agile RF Systems (ARS), which offers quadrant outputs. CS and IAA both show promising results when applied to micro-Doppler processing of radar returns owing to the sparse nature of the target Doppler frequencies. This shows promise in reducing the dwell time and increase the rate at which a volume can be interrogated. Real-time processing of target information with iterative and non-linear solutions is possible now with the advent of GPU-based graphics processing hardware. Simulations show promising results.