基于频域波束降维的车载雷达多参数联合超分辨方法

Aiming at the problem that the multi-parameter joint super-resolution of vehicle radar has high computational complexity and cannot achieve parameter estimation quickly, a multi-parameter joint super-resolution algorithm based on frequency-domain beam dimension reduction is proposed. The proposed algorithm transforms the joint data of space-time multi-parameter domain to frequency domain by fast Fourier transform (FFT) , to process multi-dimensional frequency-domain data of the region of interest and complete the dimension reduction of the beam-space in space-time and the multi-parameter joint super-resolution based on the frequency-domain data, which achieve fast joint estimation of target information. The theory of frequency-domain subspace orthogonality and frequency-domain beam dimension reduction super-resolution is deduced. The relationship between the resolution, estimation performance of the algorithm and the signal to noise ratio (SNR) is investigated in simulation, and the simulation results show that compared with the traditional FFT, the accuracy and resolution of the proposed algorithm have been greatly improved, and the computational quantity is greatly reduced compared with that of the multiple signal classification (MUSIC) algorithm.