Developing Signal Processing Algorithms in Radar Systems to Improve Detection Accuracy

Abstract

Radar systems are used in many applications such as navigation, surveillance and weather observation. Nonetheless, noise used in radar signals is also a giant drawback, which makes target detection less precise and reliable. Improving detectability of signals in noisy environments is hence an important radar issue in signal processing. Matched filtering and cross-correlation detection are two well-known detection algorithms, which this paper shall investigate and compare in a bid to evaluate their usefulness in different signal-to-noise ratio (SNR) scenarios. A simulation framework was produced in Python and designed to simulate the behavior of radar signals when adopting an additive noise model, and subsequently perform a systematic performance analysis of both techniques. As an established technique that maximizes the signal-to-noise-ratio in known-waveform scenarios, matched filtering was compared to cross-correlation detection, which evidences computational efficiency and flexibility to any improper signal correlations. Findings indicate that the Matched Filter is more successful in detection performance with a 4.97×10⁵ to 5.29×10⁵ detections and an average first detection time of 1-6 µs, whereas Cross-Correlation Detection has 4.56×10⁵ to 4.85×10⁵ detections, but with less computing requirements. The results show that periodic waves (sine and triangle) have the best noise immunity, and the best clarity of envelope, and amplitude-modulated waveforms give the best trade-off between bandwidth utilization and information capacity. The hybrid idea, which is amplitude-modulated and triangular waveforms, has the advantage of providing better noise resilience and scalability to adaptive radar designs. Theoretically, the matched filter maximizes convolutional energy to reach optimum SNR and this is why it performs better in poor detecting situations under noisy conditions. This study explains the feasible performance limits of the traditional detection algorithms and forms a pedagogical basis of subsequent hybrid radar architecture that incorporates the efficiency and the accuracy.