Direct target localization in USNs with hybrid quantized multi-snapshot measurements: A geometric structure-aided approach

In this article, a multi-snapshot hybrid quantization algorithm designed to enhance target localization accuracy is proposed for an underwater sensor network system, comprising an active acoustic source, multiple distributed passive sensors, and a fusion center. Within this framework, a direct target localization algorithm with particle dimension reduction is introduced. The proposed method considers channel transmission errors and allows for varying quantization depths at each sensor. The Cramer-Rao lower bound (CRLB) for the target localization with multi-snapshot hybrid quantization is derived, demonstrating that increasement of signal snapshots significantly reduces target localization error. The optimal quantization threshold is obtained by maximizing the objective function concerning the determinant of the Fisher information matrix, aiming to maximize localization performance. Leveraging the geometric structure of the model, a genetic algorithm embedded with particle dimension reduction (GA-PDR) is proposed to locate the target directly. Numerical results demonstrate that the proposed multi-snapshot hybrid quantization algorithm significantly improves overall localization performance, while the GA-PDR locates the target precisely and achieves convergence more quickly.