A Novel Hybrid Location Algorithm Based on Chaotic Particle Swarm Optimization for Mobile Position Estimation

Location estimation is significant in mobile and ubiquitous computing systems. Considering the influence of measurement error caused by time difference of arrival (TDOA)/angle of arrival (AOA) hybrid location algorithm and the nonlinear optimization problem encountered in the location estimation, in this paper, a particle swarm optimization (PSO) algorithm based on the chaos theory is proposed for the hybrid location of mobile location estimation. Taking the TDOA/AOA hybrid location algorithm for mobile location estimation as the object, the proposed algorithm greatly improves the location performance and accuracy of mobile location estimation. First, the estimation function of the mobile station is obtained by the maximum likelihood method, and then the initial population of PSO is generated by using the estimation function of the mobile station as a fitness function. The chaotic optimized particle swarm optimization algorithm (CPSO) is used to solve the optimal solution of the optimal position of the population and obtain the optimal mobile location position estimation, which makes the TDOA/AOA location algorithm have better location performance. The simulation results have demonstrated that the performance of the proposed method compared with the traditional Chan algorithm, the Taylor algorithm, and the TDOA/AOA hybrid location algorithm, the proposed algorithm can reduce the impact of error on the location accuracy, achieve a balance of global and local search capabilities, and have a faster convergence speed and more accurate positioning accuracy.