基于多策略蜜獾算法的TDOA定位

TDOA localization based on multi-strategy honey badger algorithm

针对超宽带传感器TDOA定位估计的非线性最优问题,文章提出一种多策略改进的蜜獾优化的TDOA定位算法,通过引入Tent混沌映射函数、正余弦策略和Levy飞行多种策略进行改进,解决了传统蜜獾优化算法存在的局限性问题。文章首先建立TDOA算法适应度函数,通过改进的蜜獾算法得到定位信息,其次将定位信息作为Taylor算法的初始值,通过Taylor级数展开算法的进一步迭代,减小非视距误差,获得更高精度的定位结果。仿真实验结果表明,改进的多策略蜜獾算法与其他多种智能算法相比,具有更高的定位精度。

In this paper,a positioning algorithm with multi-strategy improved honey badger optimization is proposed for the nonlinear optimal problem of TDOA positioning estimation for ultra-wideband sensors.Aiming at the partial limitations from the traditional honey badger optimization algorithm,it is improved by introducing multiple strategies,such as tent chaotic mapping function,cosine strategy,and Levy flight.Firstly,the fitness function for TDOA algorithms is established,and the positioning information is obtained by the improved honey badger algorithm.Secondly,the positioning information is used as the initial value of Taylor algorithm,and the NLOS error is reduced by iterating the Taylor expansion algorithm,so that a more accurate positioning result is obtained.Results of simulation experiments show that the improved multi-strategy honey badger algorithm has more higher positioning accuracy than other intelligent algorithms.