IMIA: Interference Mitigation via Iterative Approaches for Automotive Radar

Interference mitigation for automotive radar is becoming increasingly important due to its increasing popularity in automobiles. In this paper, we tackle this problem and propose two iterative algorithms to suppress mutual interferences among Frequency Modulated Continuous Wave (FMCW) radars. We first exploit the angular feature of the interferences and use a high-resolution compressed sensing algorithm to single out the interferences in the spatial domain. In order to avoid the influence between the interference segments, we extend the traditional 2-D Constant False Alarm Rate (CFAR) algorithm in an iterative manner to enhance its interference detection performance. In practice, the weak interference is usually difficult to detect, especially when its power is close to that of the strong target, and its existence will mask the weak targets, resulting in missed detection. To tackle this problem, we design a novel iterative interference mitigation algorithm. In each iteration, we estimate the parameters of the targets and subtract out the estimated targets signals from the original time-domain signal. After subtracting out the strong targets, the weak interference segments become more obvious, and thus can be detected more easily. Finally, both the numerical and experimental examples are provided to demonstrate the effectiveness of the proposed iterative approaches for interference mitigation.