Sensor Placement Strategies for Target Localization via 3-D TOA Measurements in Underwater Acoustic Sensor Networks

This article investigates sensor placement strategies for 3-D time-of-arrival (TOA)-based target localization in underwater acoustic sensor networks (UASNs). To prevent the overestimation of localization performance common in idealized marine models, we derive an exact acoustic propagation time and estimate the TOA measurement variance using a non-linear ray acoustic model. Leveraging this formulation, we establish a realistic 3-D TOA measurement model that incorporates depth-dependent sound speed profiles (SSP) and spatially heterogeneous noise, where the trace of the Cramér-Rao lower bound (CRLB) serves as the optimization criterion. To solve the resulting non-convex optimization problem, we propose a MinMax k-Means algorithm to determine the optimal sensor configuration by minimizing the average of the trace of CRLB. Extensive numerical simulations demonstrate that the proposed placement strategy significantly enhances localization accuracy and robustness compared to conventional benchmarks, proving its effectiveness in complex underwater environments.