Source Localization using TDOA Based on Improved Snake Optimizer

In passive localization, the time-difference-of-arrival (TDOA) measurement model is commonly used for source location estimation. Methods for TDOA-based estimation can be categorized into two main groups: closed-form algebraic solutions and iterative approaches. Algebraic solutions circumvent convergence issues and achieve global optima, but are usually sensitive to TDOA measurement inaccuracies. Iterative methods optimize the objective function through multiple iterations, including deterministic iterative methods that require an iterative initial value and stochastic optimization methods reliant on optimization algorithms. In this paper, a stochastic optimization algorithm named snake optimization (SO) is used to solve the TDOA localization problem and improved to meet the localization requirements. Initially, a chaotic system is utilized to generate three random sequences to establish the initial snake population. The search strategy in the exploration phase is subsequently improved to enhance the early-stage convergence speed of the algorithm. Moreover, an adaptive evolutionary stage threshold is introduced to adaptively handle various noise conditions and source locations in TDOA localization scenarios. Lastly, a snake oviposition strategy, inspired by genetic principles, is proposed. Simulations show that the improved SO algorithm can converge to the source position quickly and stably and has better positioning accuracy.