Gridless Parameter Estimation for One-Bit MIMO Radar with Time-Varying Thresholds

TL;DR

This paper introduces a gridless parameter estimation method for one-bit MIMO radar with time-varying thresholds, reducing hardware costs and data while maintaining high accuracy in angle and Doppler estimation.

Contribution

It develops a novel gridless estimation approach using atomic-norm minimization tailored for one-bit radar with time-varying thresholds, along with an efficient iterative solution method.

Findings

High-resolution parameter estimation achieved with significantly less data.

The proposed method approaches the theoretical CRB performance.

Numerical results validate the effectiveness of the approach.

Abstract

We investigate the one-bit MIMO (1b-MIMO) radar that performs one-bit sampling with a time-varying threshold in the temporal domain and employs compressive sensing in the spatial and Doppler domains. The goals are to significantly reduce the hardware cost, energy consumption, and amount of stored data. The joint angle and Doppler frequency estimations from noisy one-bit data are studied. By showing that the effect of noise on one-bit sampling is equivalent to that of sparse impulsive perturbations, we formulate the one-bit $\ell_1$-regularized atomic-norm minimization (1b-ANM-L1) problem to achieve gridless parameter estimation with high accuracy. We also develop an iterative method for solving the 1b-ANM-L1 problem via the alternating direction method of multipliers. The Cram$\acute{\text{e}}$r-Rao bound (CRB) of the 1b-MIMO radar is analyzed, and the analytical performance of one-bit sampling with two different threshold strategies is discussed. Numerical experiments are presented to show that the 1b-MIMO radar can achieve high-resolution parameter estimation with a largely reduced amount of data.