Abstract:

In order to address the issues of low angle resolution in elevation and azimuth dimensions of the 4D vehicle-mounted millimeter wave radar,as well as the biased angle measurement when the array includes amplitude and phase defects.A robust two-dimensional super-resolution angle estimation method based on fast sparse Bayesian Learning(FSBL) is suggested as a solution to this issue.First,a two-dimensional super-resolution angle signal model with amplitude and phase errors is built by using grids to split the angle domain space depending on spatial sparsity.Then,the two-dimensional angle estimation for spatial proximity targets is obtained using the fixed-point updated based MacKay SBL reconstruction algorithm,with the phase error and biased angle compensation calibrated using the self-correcting algorithm based on vector dot product.Finally,the computational complexity of the proposed algorithm is analyzed,and the Cramer-Rao Lower Bound(CRB) for two-dimensional angle estimation under MIMO non-uniform sparse arrays is provided.By comparing six distinct categories of super-resolution algorithms,simulation results demonstrate that the proposed method has a high angle resolution and a low root mean square error(RMSE) in a low SNR and few snapshot numbers under the actual layout of 12 transmitting and 16 receiving antennas for the continental ARS548 radar.

Key words: super-resolution, multiple-input multiple-output(MIMO) array, millimeter wave radar, sparse Bayesian learning, amplitude and phases error