Detection Boosting of Low Signal-to-Noise Ratio Targets Using A Proposed Adaptive Filter Technique

Radar systems are essential for various applications such as military surveillance, weather monitoring, and autonomous vehicles. However, these systems are often susceptible to various types of interference; e.g. noise, jamming, and clutter, which may degrade the accuracy and reliability of radar measurements and, consequently, its probability of detection and probability of false alarm. This paper presents a study on the use of adaptive filters, combined with matched filters, for noise cancellation in radar systems to enhance its performance, particularly for low Signal-to-Noise Ratio (SNR) backscattered echoes. The research evaluates the efficacy of adaptive filters specifically the Normalized Least Mean Square (NLMS) and the Recursive Least Square (RLS) - across various noise scenarios and compares their effectiveness with conventional noise cancellation methods. The results shows that the NLMS algorithm exhibits superior noise reduction capabilities in radar applications due to its reduced complexity and enhanced stability when compared to the RLS algorithm. By integrating adaptive filters with matched filters, the proposed technique shows promising results in improving radar performance by mitigating noise interference and refining signal quality. Consequently, this advancement contributes to more precise target detection, enhanced tracking capabilities, and an overall elevation in radar system efficiency.