Parameter Estimation in PMCW MIMO Radar Systems With Few-Bit Quantized Observations

IEEE Transactions on Signal Processing(2022)

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We consider the problem of target parameter estimation in phase modulated continuous wave (PMCW) multiple-input multiple-output (MIMO) radar systems with quantized observations. We derive the Cramér-Rao bound (CRB) for jointly estimating targets’ amplitudes, time delays, Doppler shifts, and directions. The derived bound provides an efficient method to analyze the estimation performance achieved by different quantization schemes. We also devise the maximum likelihood (ML) estimator for the considered parameters, and a direct grid-based method (DGM) is introduced to obtain the ML estimates. To obtain the ML estimates more efficiently, a two-stage scheme is proposed. In the proposed scheme, we first formulate the estimation problem as a sparse signal recovery problem and modify the sparse learning via iterative minimization (SLIM) approach to solve it. Next, a RELAX-based iterative algorithm is proposed to refine the estimates. Simulation results show that the proposed scheme can approach the CRB. Simulation results also show that using quantized observations does not affect the estimation performance significantly when the targets’ amplitudes are similar.
Millimeter wave,phase modulated continuous wave radar,multiple-input multiple-output,target parameter estimation,sparse signal recovery,Cramér-Rao bound
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