基于测距-定位双阶段优化的RSSI定位算法研究OA

A two-stage optimization method is proposed to address the issues of environmental impact and low positioning accuracy in wireless sensor networks based on Received Signal Strength Indicator(RSSI)positioning technology.The RSSI positioning process is divided into a ranging stage and a positioning stage.In the ranging stage,the Kalman filter algorithm is improved to use the RSSI signal mean as the initial state estimation,combined with grid traversal search optimization process noise covariance and measurement noise co-variance parameters,to enhance the adaptability and filtering effect of the Kalman filter algorithm and reduce errors in the ranging stage.In the positioning stage,multiple strategies are used to improve the whale optimization node position estimation algorithm to solve for un-known node positions,further improving the positioning accuracy.This algorithm improves the global search ability and convergence speed of the original whale optimization algorithm through K-means clustering initialization strategy,elite reverse learning strategy,and random whale learning strategy,further enhancing the positioning accuracy.The experimental results show that the proposed two-stage optimization method is superior to traditional single-stage optimization strategies in positioning error control,with higher positioning accuracy and stronger environmental adaptability.It also demonstrates significant advantages in performance comparison with other com-parative algorithms.