TDOA/AOA Hybrid Localization Based on Improved Dandelion Optimization Algorithm for Mobile Location Estimation Under NLOS Simulation Environment

To improve the tracking accuracy of moving targets in Non-Line-of-Sight (NLOS) environments and reduce positioning errors, a hybrid TDOA/AOA positioning method is proposed using the Time Difference of Arrival (TDOA) and Angle of Arrival (AOA) joint localization. The method is based on an improved multi-objective dandelion optimization algorithm. In the optimization process of the fitness function for the positioning model using the dandelion optimization algorithm, a fast non-dominated sorting method is used to determine the core dandelions, and an approximation ideal solution sorting method is employed to achieve global optimization, thereby enhancing the speed and accuracy of the model solution. By introducing a multi-objective mechanism, the optimal individual of one population guides another population, accelerating the convergence speed, balancing the exploration and exploitation abilities to a certain extent, and enhancing the optimization performance. Simulation results show that compared to TDOA/AOA, Taylor, Chan, PSO, and DOA algorithms, the proposed algorithm outperforms TDOA/AOA algorithm and other similar algorithms. The algorithm exhibits excellent performance in terms of node positioning accuracy, convergence speed, and robustness.