基于混沌映射改进蝙蝠优化的DV-Hop定位算法OA

Aiming at the low localization accuracy of DV-Hop algorithm,an improved DV-Hop localization algorithm based on chaotic mapping is proposed.The algorithm is mainly optimized in two aspects,which are solving average hop distance and esti-mating node position.Firstly,the average hop distance is reconstructed according to the minimum mean square error criterion and the correction factor is set to reduce the ranging error.Secondly,logistic chaos mapping is used to change population distribution and increase population diversity.Two new position update strategies are adopted to enhance global search ability,formulate local search strategies,and set parameter factors to control search scope to improve search accuracy.Finally,chaotic mapping improved Bat algorithm(CEMBA)is used to solve the coordinates of unknown nodes.The simulation results show that the proposed ICEM-BADV-Hop algorithm has the minimum positioning error and superior positioning performance compared with DV-Hop algorithm,MDV-Hop algorithm,BADV-Hop algorithm and PSODV-Hop algorithm in both isotropic and anisotropic networks with the same parameters.