基于改进变异粒子群算法的TDOA/AOA定位研究

Research on TDOA/AOA Location Algorithm Based on Improve Mutational Particle Swarm Optimization

针对GPS盲点区域的定位问题,蜂窝定位技术能够有效解决。蜂窝定位技术中的混合定位算法能够有效地提高定位精度和定位可靠性,但是算法中的信号测量产生的误差和定位估计遇到的非线性优化问题严重影响了混合定位算法的性能。针对上述算法问题,文章提出了一种基于改进的变异粒子群算法(IMPSO)的目标定位策略。该算法是以TDOA/AOA混合定位算法为对象,首先用最大似然法得到移动台的估计函数,将估计函数作为适应度函数产生初始种群,然后对粒子群(PSO)算法中适应度方差进行变异操作,同时改进惯性权重,达到PSO算法在对适应度函数进行寻优处理时不会出现陷入局部最优的目的,最后用IMPSO算法对种群进行寻优,得到最优的估计位置。仿真实验结果表明,IMPSO算法的应用相对传统的Chan算法和TDOA/AOA混合定位算法,在视距的环境下,能有效减小测量误差的影响,并提高定位系统的稳定性。

In view of the location problem of GPS blind spot area, the cellular location technology could solve effectively. The hybrid location algorithm in cellular positioning technology could effectively improve the positioning accuracy and location reliability. However, the error generated in signal measurement and the nonlinear optimization problems encountered in location estimation seriously affected the performance of the hybrid location algorithm. Aiming at the above algorithm problem, a target location strategy based on improved mutation particle swarm optimization(IMPSO) was proposed in this paper. The algorithm used the TDOA/AOA hybrid location algorithm as the object. Firstly, the estimation function of the mobile station was obtained by the maximum likelihood method, and the estimation function was used as the fitness function to generate the initial population. Then the inertia weight in the PSO algorithm with operation was improved, so that the PSO algorithm did not fall into the local optimum when optimizing the fitness function. At last, we used Improve Particle Swarm Optimization to optimize population and obtain the best estimation location. The simulation results show that the IMPSO algorithm can effectively reduce the influence of the measurement error and improve the stability of the positioning system in the sight distance environment, compared with the traditional Chan algorithm and the TDOA/AOA hybrid location algorithm.