A Novel Millimeter-Wave Radar Interference Suppression Method Based on VMD and VI-CFAR Algorithms

With the increasing use of millimeter-wave radar in automobiles, mutual interference between vehicle-mounted millimeter-wave radar systems is becoming increasingly serious. Mutual interference between vehicle-mounted millimeter-wave radars significantly reduces the accuracy of target parameter estimation and the reliability of target detection. In view of this, this study proposes an interference suppression method that combines the Variational Mode Decomposition (VMD) algorithm and Variation Index Constant False Alarm Rate (VI-CFAR). First, the method performs adaptive decomposition of the intermediate frequency (IF) signals of an interfered vehicle-mounted millimeter-wave radar by VMD in order to obtain several different intrinsic modal functions (IMFs). Then, the relevant IMFs containing target information are identified based on the spectrograms of each IMF, followed by signal reconstruction of the relevant IMFs using VI-CFAR. Finally, the relevant IMFs are superimposed to complete the signal reconstruction. In the case of simultaneous interference by several different interference radars, the results of simulation experiments show that the method can improve the Signal-to-Interference Ratio (SIR) of the target and increase the detection of the interfered target to a greater extent than the Adaptive Noise Cancellation (ANC), Empirical Modal Decomposition (EMD), and Iterative Zeroing methods. According to the SIR results of the simulation experiments, it can be seen that under the static interference of several slightly weak interference sources, the SIR of each target is improved by 8.1, 8.1, 5.8, and 7.9 dB, respectively, after suppressing the interference by the method, whereas in the dynamic cases of simultaneous interference of several strong interference sources and detection of several weak targets, the SIR of each target is improved by 4.6, 2.2, 7.9, and 6.8 dB, respectively. Therefore, the method has some performance advantages.