The Application of Compressed Sensing in Automotive Radar Signal Processing for the Target Location

Millimeter wave (MMW) automotive radar plays an important role in the advanced driving assistance system (ADAS), which detects vehicles, pedestrians and other obstacles. In the adaptive cruise control (ACC) and the automatic emergency brake (AEB) system, the target needs to be oriented. One of the automotive radar’s task is to get the direction information which includes the range, speed, azimuth and height of the target by high intermediate frequency (IF) signal sampling rate. In order to solve the problem of high sampling rate for the MMW radar caused by the traditional Nyquist sampling theorem when the target is located, a new method based on the compressed sensing (CS) for the target location is proposed in this paper. This paper presents the linear frequency modulated continuous wave (LFMCW) model and simulates the sampling and reconstruction of the radar’s IF signal via CS technique by using MATLAB. Taking into account the IF signal is compressible in the frequency domain, CS technique can reconstruct the IF signal from the sub-Nyquist sampled data that can reduce the sampling rate. The simulation results show that CS can effectively relieve the storage and transmission pressure of the hardware by reducing the sampling rate in the IF signal processing and provide accurate orientation information of the target, which has a promising prospect of the automotive radar signal processing.