Autonomous radar interference detection and mitigation using neural network and signal decomposition

Autonomous radar interference is a challenging problem in autonomous vehicle systems. Interference signals can decrease the signal-to-interference noise ratio (SINR), and this condition decreases the performance detection of autonomous radar. This paper exploits a neural network (NN) and signal decomposition to detect and mitigate radar interference in autonomous vehicle applications. A NN with four inputs, one hidden layer, and one output is trained with various signal-to-noise ratio (SNR), interference radar bandwidth, and sweep time of autonomous radar. Four inputs of NN represent SNR, mean, total harmonic distortion (THD), and root means square (RMS) of the received radar signal. Variational mode decomposition (VMD) and zeroing based on a constant false alarm rate (CFAR-Z) are used to mitigate radar interference. VMD algorithm is applied to decompose interference signals into multi-frequency sub-band. As a result, the proposed NN can detect radar interference, and NN-VMD-CFAR-Z can increase SINR up to 2 dB higher than the NN-CFAR-Z algorithm.