Application of Improved Snake Localization Algorithm with Extended Kalman Filter in WSN
In order to address the impact of environmental factors on the Received Signal Strength Index(RSSI)for localization,a based RSSI Extended Kalman Filter Improved Snake Optimization Localization Algorithm(RSSI-EISL)is proposed.This algorithm utilizes the Extended Kalman Filter(EKF)model to smooth the RSSI signal values and suppress the influence of noise and outliers on the estimation results,thereby improving the accuracy and robustness of distance measurement.By introducing the Improved Snake Optimization Algorithm(ISO)with Levy flight and nonlinear convergence factor,the ability of the Snake Optimization Algorithm(SO)is enhanced,enabling more accurate calculation of the coordinates of the nodes to be measured.According to simulation results,the proposed RSSI-EISL improves the localization accuracy by about 26.4％,8.75％,and 5.6％compared to RSSI Ordinary Least Squares Localization Algorithm(ROL),RSSI Extended Kalman Filter-based Grey Wolf Optimization Algorithm(REGL),and RSSI EKF-based Snake Optimization Localization Algorithm(RESL)algorithms,respectively,while the convergence speed and global search capability of the algorithm are also improved.
wireless sensor networkreceived signal strengthSOEKFLevy flightnonlinear convergence factor
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