Reliable Positioning Algorithm Using Two-Stage Adaptive Filtering in GPS-Denied Environments

Abstract

To overcome the disadvantages of RFID application for outdoor vehicle positioning in completely GPS-denied environment, a fusion vehicle positioning strategy based on the integration of RFID and in-vehicle sensors is proposed. To obtain higher performance, both preliminary and fusion positioning algorithms are studied. First, the algorithm for preliminary positioning is developed in which only RFID is adopted. In the algorithm, through using the received signal strength, range from RFID tags to the reader is estimated by implementing the extreme learning machine algorithm, and then, the first-level adaptive extended Kalman filter (AEKF) which can accommodate the uncertainties in the observation noise description of RFID is employed to compute the vehicle's location. Further, to compensate the deficiencies of preliminary positioning, the in-vehicle sensors are introduced to fuse with RFID. The second-level adaptive decentralized information filtering (ADIF) is designed to achieve fusion. In the implementation process of ADIF, the improved vehicle motion model is established to accurately describe the motion of the vehicle. To isolate the RFID failure and fuse multiple observation sources with different sample rates, instead of the centralized EKF, the decentralized architecture is employed. Meanwhile, the adaptive rule is designed to judge the effectiveness of preliminary positioning result, deciding whether to exclude RFID observations. Finally, the proposed strategy is verified through field tests. The results validate that the proposed strategy has higher accuracy and reliability than traditional methods.