SVM+KF target tracking strategy using the signal strength in wireless sensor networks

Abstract

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 estimated position. Our improved KF adds the threshold value of the innovation update in the traditional KF. This value changes dynamically according to the target speed and network parameters to ensure the stability of the results. Simulations and real experiments in different scenarios demonstrate that our algorithm provides a superior tracking accuracy and stability compared to similar algorithms.