Computationally Efficient Implementation of SLIM for Parameter Estimation in Few-Bit PMCW MIMO Radar Systems

Parameter estimation is an important task for few-bit phase-modulated continuous-wave (PMCW) multiple-input multiple-output (MIMO) radar systems. To estimate the targets’ parameters, sparse learning via iterative minimization (SLIM) has been adopted since it can achieve a finer resolution than conventional matched filtering. However, directly applying SLIM to estimate the targets’ time delays, Doppler shifts, directions, and amplitudes faces implementational challenges. The computational cost is high and a large memory space is required to store a dictionary matrix. To overcome this issue, in this work, we propose a computationally efficient scheme for parameter estimation using SLIM. In the proposed approach, initial estimates of the targets’ time delays and directions are obtained by using a small portion of the observations with SLIM. Then, the Doppler shifts are estimated and the estimates of time delays and directions are redetermined by SLIM with the entire observations. A debiasing process is also proposed to refine the estimates of the targets’ amplitudes. Simulation results and complexity analysis show that the proposed scheme can make SLIM practical to handle large scale parameter estimation problems.