Mobility Prediction Based Tracking of Moving Objects in Wireless Sensor Networks

Abstract

This paper investigates the multi-sensor fused localization of moving targets in a wireless sensor network. Each ultra-wide band (UWB) sensor is assigned a stability weight according to its survival time prediction. The measurement accuracy of each sensor into the constraints of the weight distribution based on the interactive multi-model method, a double weight distribution algorithm that considers measurement accuracy and stability is proposed. Based on the double weight algorithm, the measurement information of each UWB sensor, the inertial measurement unit (IMU)-based state vector and the UWB-based state vector by federated Kalman filter are integrated to realize the correction of the IMU. Finally, several numerical simulations are performed to show that the proposed algorithm can effectively suppress the measurement dropout when tracking moving targets in a wireless sensor network, and it can also automatically adjust the weight of each sensor based on the measurement error covariance to improve the tracking accuracy.