TDOA localization based on multi-strategy honey badger algorithm
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.
TDOA localization algorithmhoney badger algorithmtaylor series expansionLevy flies
万方数据
维普
