Passive Localization Using TDOA Based on Improved Zebra Optimization Algorithm
Yanping Liao, Yongpeng Wang · 2024
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 include deterministic iterative methods and stochastic optimization methods. The deterministic iterative methods suffer from the risk of not converging and require initial values. In this paper, a stochastic optimization algorithm named Zebra Optimization Algorithm (ZOA) is used to solve the TDOA localization problem. Improvements are made to the defensive strategies of zebras in the improved ZOA (IZOA), adjusting the proportion of predators at different stages of the algorithm to optimize the balance between exploration and exploitation. Simulation results show that IZOA performs excellently in TDOA localization, exhibiting rapid convergence. It efficiently and accurately solves the TDOA equations to obtain the source position, outperforming comparative algorithms in terms of localization accuracy.