Dual-Domain Feature-Oriented Interference Suppression for FMCW Automotive Radar

Frequency-modulated continuous wave (FMCW) radar is an excellent sensor; however, the widespread popularity and application of millimeter-wave (mmW) automotive radar, greatly increases the risk of mutual interference between vehicles, weakening the abilities of automotive radar to sense the environment. As a result, efficiently suppressing interference has been a challenge. To address this issue, this article presents a modified block coordinate descent optimization with dual-domain sparsities named BCD-DS, to synchronously suppress mutual interference between automotive FMCW radars by jointly leveraging the priors in time and frequency domains. First, the sparsities of the target and the interference on multiple transformation domains are analyzed. Then, the interference suppression problem is modified into an optimization framework with regularizers being sparse priors in dual domains subject to the
${L}_{{1}}$
norm constraint. Based on the joint optimization model, the target and interference are cooperatively estimated by the alternating direction method of multipliers (ADMM) scheme. One iteration consists of two phases, with the solution obtained at each phase updating the echoes, promoting a good convergence performance. BCD-DS balances interference suppression and target signal power. Both simulated and measured experiments validate the effectiveness of the proposed algorithm. Compared to the state-of-the-art algorithms, the results show that BCD-DS achieves better interference suppression performance and increases the signal-to-interference and noise ratio (SINR) by at least 20 dB, simultaneously retrieving the speeds and ranges of multiple targets with less than 1% error.