Physics Maths Engineering
Non-Linear Signal Processing Methods for UAV Detections from a Multi-Function X-Band Radar
Mohit Kumar,
P. Keith Kelly
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 increases 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.
Key Questions
What is the focus of the study?
The study explores the application of non-linear processing techniques—such as Compressed Sensing (CS), Principal Component Analysis (PCA), Iterative Adaptive Approach (IAA), and Multiple-Input-Multiple-Output (MIMO)—to enhance unmanned aerial vehicle (UAV) detection using portable radar systems.
What are the advantages of combining these non-linear processing techniques?
Combining these techniques can improve detection and classification accuracy of UAVs. For instance, CS and IAA are effective in micro-Doppler processing of radar returns due to the sparse nature of target Doppler frequencies, which can reduce dwell time and increase the rate of volume interrogation.
What platform is used to demonstrate these techniques?
The study utilizes the novel portable phased array radar (PWR) developed by Agile RF Systems (ARS), which offers quadrant outputs, to demonstrate the effectiveness of these combined non-linear processing techniques.
How do advancements in hardware contribute to the applicability of these techniques?
The advent of GPU-based graphics processing hardware enables real-time processing of target information using iterative and non-linear solutions, making the application of these techniques more feasible in practical scenarios.
What do the simulation results indicate?
Simulations conducted in the study show promising results, indicating that the combined application of these non-linear processing techniques can effectively enhance UAV detection capabilities in portable radar systems.
