一种基于鲸鱼优化的TOA/AOA最优化定位算法

A TOA/AOA optimization localization algorithm based on whale optimization

针对非视距(NLOS)环境下,基于最优化原理的无线定位算法在迭代运算后期容易陷入局部最优,进而导致定位精度下降的问题,提出一种基于非线性、自适应、动态惯性权重的鲸鱼优化到达时间(TOA)/到达角(AOA)最优化定位算法:基于双基站定位场景建立TOA/AOA混合定位模型;并且在对种群位置进行初始化时,在混合线性定位算法中增加角度约束条件避免种群在定位初期陷入局部最优;然后在搜索更新最优个体时,引入自适应动态惯性权重系数对鲸鱼优化算法进行改进,以更好地协调优化算法的全局随机搜索与局部寻优能力。仿真结果表明,与现有的粒子群优化(PSO)、鲸鱼优化(WOA)和改进的鲸鱼优化(MWOA)等智能优化算法相比,所提算法在迭代寻优过程中具有更快的收敛速度和更优的收敛精度;相较于WOA算法,其平均消耗时间可降低约50%,平均定位误差可降低约41%,定位性能更优。

Aiming at the problem that wireless localization algorithm based on optimization theory in non-line-of-sight(NLOS)environment is prone to fall into local optimum at the late stage of iterative operation,which leads to the degradation of localization accuracy,the paper proposed a whale-optimized time of arrival(TOA)/angle of arrival(AOA)optimal localization algorithm based on nonlinear,adaptive dynamic inertia weights:the hybrid TOA/AOA localization model based on double base stations localization scenario was built;and in the localization process,a hybrid linear localization algorithm with angle constraint condition was used to initialize the population position for avoiding the population from falling into local optimum at the early localization stage;then,when searching and updating the optimal individuals,adaptive dynamic inertia weight coefficient was introduced to improve the performance of whale optimization algorithm,in order to better coordinate and optimize the global stochastic search and local optimization-seeking capability of the algorithm.Simulational results showed that compared with existing intelligent optimization algorithms such as particle swarm optimization(PSO),whale optimization algorithm(WOA)and modified whale optimization algorithm(MWOA),the proposed algorithm would exhibit faster convergence speed and better convergence accuracy in the iterative optimization search process;moreover,compared with WOA algorithm,the average consumption time could be reduced by about 50%,the average positioning error could be reduced by about 41%,indicating a better localization performance.