Quantum-Inspired Bio-Optimization for Robust Received Signal Strength-Based Underwater Target Localization in Noisy Stratified Acoustic Environments

Robust underwater target localization is essential for marine sensing and monitoring. However, RSS-based localization is affected by ray bending caused by sound-speed variations, anchor-node position perturbations, and non-line-of-sight (NLOS) propagation biases, resulting in a nonlinear and noise-sensitive estimation problem. To address these challenges, this paper proposes a Quantum-Inspired Animated Oat Optimization (QIAOO)-based localization algorithm for stratified underwater acoustic environments. First, a multi-error-coupled RSS localization model is established by jointly considering acoustic ray propagation effects, anchor-node perturbations, and NLOS propagation biases. Subsequently, the QIAOO algorithm is developed to solve the resulting nonlinear optimization problem, where quantum-inspired initialization, elite opposition-based learning, and a dynamic Cauchy–Gaussian mutation mechanism are incorporated to enhance search diversity and convergence performance. In addition, the Cramér–Rao Lower Bound (CRLB) is derived under the same localization model to provide a theoretical performance benchmark. Simulation results demonstrate that the proposed method outperforms benchmark algorithms in terms of localization accuracy and robustness under various scenarios.