Multi-rate Hierarchical Fusion Estimation of Nearest Neighbor Clusters in Sensor Networks Based on Target Tracking

Abstract

In order to overcome the shortcomings of low precision and high speed target tracking in wireless sensor networks, multi-rate hierarchical fusion estimation method based on target tracking is proposed. Sensors are divided into several clusters, and only the nearest cluster is used for target tracking. Each sensor detects a local estimate of the data, and then uploads it to the cluster head node for fusion to get the accurate information of the target. In order to improve positioning accuracy, strong tracking filtering (STF) estimator is used to identify time-varying structural parameters. By orthogonalizing the residual after filtering updating, the fading factor is obtained to correct the filter covariance matrix in real time, which guarantees the tracking ability of the filter to the change of structural parameters. The cluster head node uses the matrix weighted data fusion algorithm to obtain the final fusion results. The simulation results verify the effectiveness of the method.