Stratification-Aware RSS-Based Localization in Underwater Environments: CRLB Analysis and Perturbation-Resilient Solutions

This work investigates target localization in underwater environments using methods based on Received Signal Strength (RSS), accounting for stratified acoustic propagation and multiple perturbed parameters, including Perturbed Anchor positions (PA) and Uncertain Path Loss Exponent (UPLE). Starting from a stratification-aware propagation model, we derive the corresponding RSS-based Cramér–Rao Lower Bound (CRLB), whose analysis is then extended to incorporate the effects of PA and UPLE, arising from the dynamic and uncertain nature of the underwater environment. Inspired by the Banachiewicz–Schur theorem, an asymmetric block matrix inversion approach is employed to obtain analytical expressions for the CRLB under multiple perturbations. Furthermore, a closed-form localization solution is developed based on a Multiple Linearization Algorithm (MLA) that mitigates the errors induced by the perturbed parameters step-by-step. Simulation results validate the analytical findings and the proposed method across various scenarios compared to the state-of-the-art methods.