Research On WSN Positioning Algorithm Based On Swarm Intelligence Optimizatio

Localization technology as an important element of wireless sensor network(WSN)has played an important role in military,disaster relief,transportation and other fields.Along with the application of WSN technology,the non-line-of-sight(NLOS)errors in mountainous,underwater and indoor scenes have become an important obstacle to the application of WSN localization technology,and therefore,the accuracy and adaptability of localization algorithms are more demanding.To this end,this thesis combines swarm intelligence optimization algorithm and particle filtering to study the localization algorithm of WSN,and the main work is as follows:(1)A Hybrid Bald Eagle Search(HBES)algorithm with a hybrid strategy is proposed to address the problems of slow convergence and insufficient accuracy of the bald eagle algorithm.First,a chaotic mapping strategy is used for population initialization to improve the diversity of the initial population;then,the strategies of Levy flight and backward learning are used to improve the global optimization-seeking ability of the algorithm;finally,the strategy of the positive cosine algorithm is applied to coordinate the global exploration and local exploitation ability,which facilitates the algorithm to jump out of the local optimum.HBES is compared with the comparison algorithm in 23 test functions for the optimization-seeking experiments,and the results show that the convergence speed,optimization-seeking accuracy and stability of HBES have been improved.(2)To address the problems of the traditional method of Time Difference of Arrival(TDOA)localization algorithm,the objective function is established based on the hyperbolic localization principle of TDOA,and the HBES algorithm is introduced into the coordinate solving stage.The experimental results show that HBES and the compared algorithms can obtain better localization accuracy with less number of populations and iterations,indicating that HBES algorithm can effectively improve the localization accuracy.(3)To address the particle depletion problem of the traditional particle filtering algorithm,the distribution of particles is optimized by the HBES algorithm through continuous iterations,so that most particles are distributed in the high-likelihood region while some particles are distributed in the low-likelihood region,which improves the diversity of particle distribution and the filtering accuracy of the algorithm.The simulation analysis of TDOA localization in line-of-sight and non-line-of-sight environments with HBES optimized particle filtering overcomes the problem of large localization error of traditional TDOA localization algorithm in NLOS environment,and improves the localization accuracy and stability of the algorithm compared with standard particle filtering for localization.