Research on Tdoa Localization Model Based on Particle Swarm Optimization and Quasi-Newton Method

This paper focuses on the problem of satellite-based Time Difference of Arrival (TDOA) passive location estimation and proposes a solution method combining the Particle Swarm Optimization (PSO) algorithm and the Quasi-Newton method. By constructing a TDOA-based localization model, the study delves into resolving the nonlinear optimization issues inherent in passive location estimation. Addressing the challenge of determining the initial point for the Quasi-Newton method, this paper innovatively suggests using the PSO algorithm to find the initial point. This approach effectively mitigates the difficulties associated with initial point determination and the tendency to fall into local optima, as validated by experiments demonstrating its superior optimization capabilities compared to traditional algorithms. Furthermore, the paper analyzes the scenario of estimating target positions with four base stations and explores the application of intelligent algorithms in solving traditional nonlinear problems, providing new theoretical support and practical guidance for solving TDOA localization models.