An Interference Mitigation Technique for Automotive Millimeter Wave Radars in the Tunable Q-Factor Wavelet Transform Domain

With the widespread use of millimeter-wave radar in advanced driver assistance systems (ADASs) and higher level autonomous driving, the probability of mutual interference increases. Radar echoes are distorted by the presence of mutual interferences, leading to the increase of the noise level and even producing ghost targets. Therefore, mitigating interference is one of the most important and pressing challenges for automotive radars today. This article presents an interference mitigation technique in the tunable
$Q$
-factor wavelet transform (TQWT) domain. It is discovered that the target’s signal exhibits sparsity in the high
$Q$
-factor wavelet transform. Conversely, the interference signal has better representation in the low
$Q$
-factor wavelet transform. Then, using morphological components analysis and
$l_{1}$
-norm penalized least squares, one sparsity-based nonlinear signal separation model is further derived to reduce the interferences. Finally, the split augmented Lagrangian shrinkage algorithm is applied to optimize the proposed model. Experiments demonstrate that the performance of the proposed algorithm is comparable to three state-of-the-art methods.