Joint Angle and Doppler Frequency Estimation for MIMO Radar with One-Bit Sampling: A Maximum Likelihood-Based Method

We consider a multiple-input multiple-output (MIMO) radar that works through one-bit sampling of received radar echoes. The application of one-bit sampling significantly reduces the hardware cost, energy consumption, and systematic complexity, but it also poses serious challenges to extracting highly accurate target information from one-bit quantized data. In this article, we propose a maximum likelihood (ML)-based method that first iteratively maximizes the likelihood function to recover a virtual array data matrix and then jointly estimates the angle and Doppler parameters from the recovered matrix. Because the ML problem is convex, we can successfully apply a computationally efficient gradient descent algorithm to solve it. Based on our analysis of the Cram$\acute{\text{e}}$r-Rao bound of the ML-based method, a pre-estimation-assisted threshold (PET) strategy is developed to improve the estimation performance. Numerical experiments demonstrate that the proposed ML-based method, combined with the PET strategy, can provide highly accurate parameter estimation performance, close to that of the classic MIMO radar.