Interference Mitigation Method for Automotive Radar Systems

Automotive radar is one of the important components of advanced driver assistance systems(ADAS). With the rapid development of the automotive industry, the density of radars on the road has been increasing, and the problem of mutual interference between automotive radars has emerged. It has been demonstrated in existing work that interference between different modulated continuous wave (FMCW) radar systems not only leads to an increase in the noise base, resulting in missed detections, but also generates false targets, triggering false detection and making radar performance degrade dramatically. This paper presents an interference suppression method using particle swarm optimization-genetic algorithm(PSO-GA) and long short-term memory(LSTM) algorithm. Find the optimal parameters of automotive radar through PSO-GA, three -scale wavelet transformation to determine the relevant location of interference, LSTM neural network repair data. Our simulation results prove the effectiveness of our algorithm.