Sequential Spatiotemporal Bias Compensation and Data Fusion for Maneuvering Target Tracking

Abstract

In multisensor tracking systems, bias compensation is essential for accurate data fusion. It is challenging to obtain correct fusion results in the presence of both spatial and temporal biases, as well as target maneuver. In this article, a sequential spatiotemporal bias compensation and data fusion method is proposed for maneuvering target tracking with asynchronous multisensor measurements. The spatiotemporal biases of the sensors are augmented into the state vector to be estimated. The state equations are presented to formulate the nearly coordinated turn motion and the nearly constant acceleration motion, respectively, without exactly known measurement interval. In each target motion, the measurements are formulated as functions of both spatiotemporal bias and target state based on the time difference between the measurements and the states to be estimated. Furthermore, the interacting multiple model estimator is incorporated with the unscented Kalman filter to achieve simultaneous sequential estimation of spatiotemporal biases and target states in the presence of target maneuvers. Finally, the posterior Cramer–Rao lower bound for spatiotemporal bias and state estimation is provided. Simulation experiments are performed to demonstrate the effectiveness of the proposed method.