基于模拟卡尔曼滤波的无线通信传感器网络覆盖优化方法OA

Aiming at the coverage optimization problem of wireless sensor networks(WSN),this paper proposes a solution based on simulated Kalman filter(SKF)algorithm.The main purpose of using SKF algorithm is to maximize the sensor cover-age in the interested area to improve the service quality of WSN.Specifically,the candidate solution of the network coverage is generated by random initialization,and it is predicted,measured,and estimated based on the Kalman filtering process in each iteration to continuously optimize the sensor position distribution.The experimental results show that the coverage of SKF al-gorithm is better than that of particle swarm optimization(PSO)algorithm and genetic algorithm(GA)in various network con-figurations,especially in medium and high-density networks,SKF algorithm can significantly improve coverage and show strong convergence performance.