Real-Time Interference Mitigation for Automotive Radar Sensor

Abstract

Automotive radar sensor plays a crucial role in advanced driver assistance systems. As radar technology becomes increasingly common in vehicles, radar-to-radar interference poses a significant challenge, leading to a reduction in target detection performance. It is essential for an interference mitigation algorithm to effectively reduce this interference under dynamic driving conditions while adhering to strict processing time requirements. In this article, we present Soteria—a real-time interference mitigation algorithm for frequency modulated continuous wave radar systems, leveraging compressed sensing techniques. Soteria identifies interference by exploiting the sparsity of signals in the frequency-time domain, then separates the desired signal from interference using the orthogonal matching pursuit (OMP) algorithm. Additionally, Soteria utilizes the inherent correlation between input data from neighboring time frames to reduce the search space for the OMP algorithm. To further enhance processing speed, Soteria is implemented using a GPU-based parallel computing approach. Simulation results indicate that Soteria can achieve $\sim$1 ms processing time, outperforming state-of-the-art methods in target detection accuracy.