TY - JOUR
AU1 - Hu, Zhongyi
AU2 - Lu, Dongwan
AU3 - Xu, Minghai
AU4 - Tong, Yinsheng
AU5 - Ma, Hanjie
AU6 - Yue, Yinggao
AB - To address the difficulty in calculating the nonlinear equation of time difference of arrival (TDOA) positioning, as well as the problem of measurement error in the hybrid time difference of arrival/angle of arrival (TDOA/AOA) positioning algorithm, an improved sparrow search algorithm is proposed to optimize positioning, and the optimization mechanism is retained on the basis of improving the performance of the original algorithm. The maximum likelihood estimation method is used to calculate the objective function, and then, the estimated function of the mobile station is used as the fitness function to generate the initial population of sparrows. Then, using particle swarm optimization, optimize the sparrow search algorithm and obtain the population’s optimal solution in order to obtain the optimal position. The simulation results show that, when compared to the existing algorithm, increasing the number of base stations increases the average accuracy of the sparrow search algorithm (SSA) positioning method by 18.54% and 4.5%, respectively, and, when compared to the proposed particle swarm optimization (PSO) positioning method, by 13.79% and 11.6% as the radius increases. The SSA hybrid positioning algorithm performs better in terms of positioning accuracy, convergence speed, and robustness.
TI - A TDOA/AOA Hybrid Positioning Based on Improved Sparrow Search Algorithm for Mobile Position Estimation
JF - Wireless Communications and Mobile Computing
DO - 10.1155/2023/6579699
DA - 2023-04-14
UR - https://www.deepdyve.com/lp/wiley/a-tdoa-aoa-hybrid-positioning-based-on-improved-sparrow-search-0cyb24Dc0H
VL - 2023
IS - 
DP - DeepDyve
ER -