  With the development of autonomous driving technology, automotive radar has
received unprecedented attention due to its day-and-night and all-weather
working capability. It is worthwhile to note that more and more vehicles are
equipped with automotive radars, resulting in mutual interference between
radars. The interference reduces radar target detection performance, making
perception information unreliable. In this paper, a novel interference
mitigation method based on power-weighted Hough transform is proposed for
solving the radar mutual interference and improving the safety of autonomous
driving systems. Firstly, the frequency modulation characteristics of
interference signals and target echo signals are analyzed, and differences
between the two signals are introduced. Secondly, based on the straight line
detection technique, the power of the mutual interference signal in
time-frequency domain is accumulated, and the accurate position of the
interference is located. Finally, the target echo is recovered by
autoregressive model. Compared with existing state-of-the-art methods, the
proposed method has the ability to retain more useful signals after the
interference mitigation, and achieve better interference detection robustness
under low signal-to-noise ratio conditions. Simulation experiments and real
scenario experiments verify the effectiveness of the proposed method and show
its superiority.
