Present land vehicle navigation relies mostly on the Global Positioning System (GPS) that may be interrupted or deteriorated in\r\nurban areas. In order to obtain continuous positioning services in all environments, GPS can be integrated with inertial sensors and\r\nvehicle odometer using Kalman filtering (KF). For car navigation, low-cost positioning solutions based on MEMS-based inertial\r\nsensors are utilized. To further reduce the cost, a reduced inertial sensor system (RISS) consisting of only one gyroscope and speed\r\nmeasurement (obtained from the car odometer) is integrated with GPS. The MEMS-based gyroscope measurement deteriorates\r\nover time due to different errors like the bias drift. These errors may lead to large azimuth errors and mitigating the azimuth errors\r\nrequires robust modeling of both linear and nonlinear effects. Therefore, this paper presents a solution based on Parallel Cascade\r\nIdentification (PCI) module that models the azimuth errors and is augmented to KF. The proposed augmented KF-PCI method\r\ncan handle both linear and nonlinear system errors as the linear parts of the errors are modeled inside the KF and the nonlinear and\r\nresidual parts of the azimuth errors are modeled by PCI. The performance of this method is examined using road test experiments\r\nin a land vehicle.
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