Hybrid Sequential Fusion Estimation for Asynchronous Sensor Network-Based Target Tracking

Abstract

This brief presents a sequential fusion estimation method for maneuvering target tracking in asynchronous wireless sensor networks. The modeling error caused by asynchronous sampling and communication uncertainties is considered and compensated for by introducing a time-varying fading factor into the unscented Kalman filter (UKF). A square root form of the unscented strong tracking filter (SR-USTF) based on QR decomposition is proposed to improve the stability and performance of the USTF. Moreover, a hybrid sequential fusion estimation method is presented to estimate the state of the target, and the proposed sequential fusion estimation method combines the superiorities of both the SR-USTF and the conventional UKF, and is able to deal with communication uncertainties such as time delay and packet loss in a unified framework. Both simulations and experiments of an E-puck robot tracking example are provided to demonstrate the effectiveness and superiorities of the proposed sequential fusion estimation method.