Sensors, Vol. 20, Pages 3832: SVM+KF Target Tracking Strategy Using the Signal Strength in Wireless Sensor Networks

Sensors, Vol. 20, Pages 3832: SVM+KF Target Tracking Strategy Using the Signal Strength in Wireless Sensor Networks Sensors doi: 10.3390/s20143832 Authors: SVMKF Target Tracking Strategy Using the Signal Strength in Wireless Sensor Wang Liu Wang Li Wu Target Tracking (TT) (DBSCNA: density-based spatial clustering of application with noise; DPF: distributed particle filter; ELM: extreme learning machine; EKF: extended Kalman filter (KF); GRNN: generalized regression neural network; KF: Kalman filter; LBE: learning-by-example; MLE: maximum likelihood estimator; NN: neural network; PF: particle filter; RSSI: received signal strength indication; RR: ridge regression; RMSE: root-mean-square-error; SVM: support vector machine; TT: target tracking; UKF: unscented KF; WSNs: wireless sensor networks) is a fundamental application of wireless sensor networks. TT based on received signal strength indication (RSSI) is by far the cheapest and simplest approach, but suffers from a low stability and precision owing to multiple paths, occlusions, and decalibration effects. To address this problem, we propose an innovative TT algorithm, known as the SVM+KF method, which combines the support vector machine (SVM) and an improved Kalman filter (KF). We first use the SVM to obtain an initial estimate of the target’s position based on the RSSI. This enhances the ability of our algorithm to process nonlinear data. We then apply an improved KF to modify this estimate...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research