Two-Filter Smoothing for Accurate INS/GPS Land-Vehicle Navigation in Urban Centers

摘要

Currently, the concept of multisensor system integration is implemented in land-vehicle navigation (LVN) applications. The most common LVN multisensor configuration incorporates an integrated Inertial Navigation System/Global Positioning System (INS/GPS) system based on the Kalman filter (KF). For LVN, the demand is directed toward low-cost inertial sensors such as microelectromechanical systems (MEMS). Due to the combined problem of frequent GPS signal loss during navigation in urban centers and the rapid time-growing inertial navigation errors when the INS is operated in stand-alone mode, some methodologies should be applied to improve the LVN accuracy in these cases. One of these approaches is to apply smoothing algorithms such as the Rauch–Tung–Striebel smoother (RTSS), which uses only the output of the forward KF. In this paper, the development of the two-filter smoother (TFS) algorithm and its implementation in LVN applications is introduced. Two different LVN INS/GPS data sets that include tactical-grade and MEMS inertial measuring units are utilized to validate the TFS algorithm and to compare its performance with the RTSS.