To enable precise target positioning in constrained underwater platforms with limited storage, we propose a 1-bit quantized frequency diverse array multiple-input multiple-output (FDA-MIMO) sonar system. The FDA-MIMO sonar significantly enhances target localization by providing both range and angular resolution. However, the coupling between range and angle in traditional FDA-MIMO processing leads to parameter estimation ambiguity, and grid-based compressed sensing (CS) methods suffer from grid mismatch, resulting in reduced accuracy. To overcome these challenges, we present a 1-bit gridless CS-based target positioning algorithm for accurate underwater localization. First, we develop an array expansion model to decouple the range and angle parameters. Then, leveraging gridless CS with 1-bit quantization, we reformulate the joint estimation as an atomic norm minimization problem. Using convex relaxation, we convert this into an asymmetric cone programming problem and propose an efficient, fast interior-point method for rapid optimization. Numerical simulations show that the proposed algorithm excels in both estimation accuracy and computational speed.
GAO et al. (Sun,) studied this question.