Simultaneous spatiotemporal bias and state estimation for asynchronous multi-sensor system

Abstract

Elimination of spatiotemporal bias of asynchronous multiple sensors is the foundation of accurate multi-sensor data fusion. In this article, a sequential processing simultaneous spatiotemporal bias and state estimation algorithm for asynchronous multi-sensor system is proposed. The spatiotemporal bias is combined with target state to obtain an augmented state vector, and the augmented state model is formulated. For asynchronous multi-sensor with known but different sampling periods, a sequential processing method is proposed to handle the multiple measurements. The relationship of sensor measurements, target state and spatiotemporal bias is analysed, and the corresponding measurement model is formulated. A novel algorithm, in which the unscented Kalman filter is used to handle the nonlinearity between the augmented measurement and state vectors, is proposed to jointly estimate the spatiotemporal bias and target state. Simulation results verify the effectiveness of the proposed algorithm.

Keywords

• target tracking
• state estimation
• nonlinear filters
• Kalman filters
• sensor fusion
• target state
• asynchronous multisensor system
• asynchronous multiple sensors
• accurate multisensor data fusion
• sequential processing simultaneous spatiotemporal bias
• state estimation algorithm
• augmented state vector
• augmented state model
• sensor measurements
• augmented measurement
• state vectors