Abstract:

Accurate detection and tracking of low-altitude targets, such as drones, is a cornerstone of security in smart cities but is profoundly challenged by multipath propagation and atmospheric distortions in low-angle scenarios. This paper introduces a novel radar waveform design to overcome these limitations. Our method focuses on optimizing an Orthogonal Frequency Division Multiplexing (OFDM) waveform by shaping its Wideband Ambiguity Function (WAF) to approach an ideal, high-resolution profile. The optimization is driven by a Genetic Algorithm (GA) that efficiently navigates the complex parameter space to minimize a defined cost function. Simulation results demonstrate that our designed waveform achieves a significant performance leap over existing techniques. Key improvements include a 22 dB suppression of the first ambiguity sidelobe and a dramatic reduction in tracking error. Quantitative analysis shows our method achieves a 41.66% greater improvement in RMSE versus SNR and a 66.66% greater improvement in RMSE versus Range compared to a leading benchmark. This work establishes that GA-optimized OFDM waveforms are a powerful tool for enhancing radar resolution and tracking precision, directly addressing critical safety needs in modern urban surveillance systems.