Generally, the extended Kalman filter (EKF) is used for sensor fusion in a land vehicle navigation system. However, defects of the first-order linearization of the nonlinear model in the EKF can introduce large estimated errors, and may lead to sub-optimal performance. In order to yield higher accuracy of navigation, in this paper, a novel particle filter (PF) for sensor fusion is proposed and the sampling importance resampling particle filter (SIR-PF) is applied to address the nonlinear measurement model and it shows better performances when compared with the EKF. The basic theories and application of the general PF and the SIR-PF for a global position system/dead reckoning (GPS/DR) integrated navigation system are discussed.This familiar question is asked countless times each day by mobile phone users attempting to improve their signal. Terrain, buildings, and foliage can block or seriously impede the propagation of cell-phone signals. Users of GPS receivers suffer the same problems. While there have been some advances in improving the sensitivity of GPS receivers and developing techniques such as assisted GPS that permit a GPS receiver to use attenuated signals, the antenna of a conventional receiver must have a direct line of sight to the GPS satellites. In urban canyons, it may not be able to "see" a sufficient number of satellites with good geometry to determine a three-dimensional position fix. And in tunnels or in parking garages, the receiver will see no satellites at all. Consequently, continuous navigation in many cities is impossible for conventional GPS-only navigation systems. © 2010 IEEE.
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