Fast Algorithm for Automotive Radar Target Detection in Mutual Interference

As one of the important sensors for driving assistance, millimeter wave radar is of great significance for automotive safety driving. However, the problem of mutual interference between automotive radar signals is becoming a non negligible threat. Mutual interference can cause a significant performance decrease of radar target detection, and this artide proposes a simple and efficient target detection method to address this issue. The proposed method utilizes the distribution difference between target echoes and interference signals in the time-frequency domain to obtain a accumulation vector of spectral peaks, which follow a Poisson distribution. Based on that, the Poisson constant false alarm detection method is proposed to detect target echos effectively at low signal-to-interference ratios. The experimental results show that the detection accuracy of the method is above 95% when the signal-to-interference ratio is below -20dB. In addition, this algorithm does not require interference compression before detecting the target, and its computational complexity is only slightly greater than that of the short time Fourier transform, so it is a fast anti interference detection method.