Sparse Parameter Estimation for PMCW MIMO Radar Using Few-Bit ADCs

Abstract

In this work, we consider target parameter estimation of phase-modulated continuous-wave (PMCW) multiple-input multiple-output (MIMO) radars with few-bit analog-to-digital converters (ADCs). We formulate the estimation problem as a sparse signal recovery problem and modify the fast iterative shrinkage-thresholding algorithm (FISTA) to solve it. The 2,1 -norm is adopted to promote the sparsity in the range-Doppler-angle domain. Simulation results show that using few-bit ADCs can achieve comparable performance to many-bit ADCs when targets are widely separated. However, if targets are spaced closely, performance losses can occur when 1-bit ADCs are applied.

Sparse Bayesian learning-based parameter estimation for PMCW-MIMO radars with few-bit quantization

“…When dealing with coarsely quantized observations, sparse recovery is considered as an effective approach for parameter estimation. In study 9 , the FISTA algorithm is utilized to tackle the issue of estimating sparse parameter in PMCW MIMO radar systems that have limited-precision ADCs. References 10,11 discuss a sparse recovery problem and modify the sparse learning via iterative minimization (SLIM), which requires high signal sparsity and is sensitive to the geometric shape of the array.…”