针对车载雷达多参数联合超分辨计算复杂度高、无法快速实现参数估计的问题，提出了基于频域波束降维的多参数联合超分辨算法。所提算法通过快速傅里叶变换(fast Fourier transform，FFT)将空时多参数域联合数据变换到频域，处理感兴趣区域的多维频域数据，完成空时波束空间降维和基于频域数据的多参数联合超分辨，实现目标信息的快速联合估计。推导了频域子空间正交性及频域波束降维超分辨算法理论。仿真研究了算法的分辨率和估计性能与信噪比的关系。仿真结果表明，所提算法的精度和分辨率远超传统FFT算法，相对于传统多重信号分类(multiple signal classification，MUSIC)算法，所提算法计算量大幅降低。
Frequency-domain beam dimension reduction based multi-parameter joint super-resolution algorithm for vehicle radar
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.
frequency domainbeam-spacejoint super-resolutionmultiple signal classification(MUSIC)
NETL
NSTL
万方数据
