Confidence field-based temporal alignment and positioning for vehicles using multiple sensors

Various vehicle applications in the future will require reliable and accurate vehicle positioning techniques. Nowadays, hybrid schemes combining multiple sensors have been promising solutions for high precision positioning. However, positioning error can be remarkably affected by the temporal alignment and fusion algorithms in practice. In this paper, we propose a decentralized fusion structure containing an inertial navigation system (INS), a GPS receiver, a RFID reader, and an odometer. The update rates of the sensors are different, and the INS/GPS integration presents severe performance degradation in urban area. To achieve an effective alignment and fusion of the sensors, we propose a concept of confidence field to indicate the confidence levels of subsystems for changing driving environments. A confidence field-based alignment and fusion algorithm and its simplification are proposed when we use the weighted least squares curve method. Time biases of the sensors are also considered in local adaptive filters. Simulation results demonstrates the performance of the proposed scheme with the proposed algorithms, especially in GPS-denied environments.