Abstract
Present land vehicle navigation relies mostly on the Global Positioning System (GPS) that may be interrupted or deteriorated in urban areas. In order to obtain continuous positioning services in all environments, GPS can be integrated with inertial sensors and vehicle odometer using Kalman filtering (KF). For car navigation, low-cost positioning solutions based on MEMS-based inertial sensors are utilized. To further reduce the cost, a reduced inertial sensor system (RISS) consisting of only one gyroscope and speed measurement (obtained from the car odometer) is integrated with GPS. The MEMS-based gyroscope measurement deteriorates over time due to different errors like the bias drift. These errors may lead to large azimuth errors and mitigating the azimuth errors requires robust modeling of both linear and nonlinear effects. Therefore, this paper presents a solution based on Parallel Cascade Identification (PCI) module that models the azimuth errors and is augmented to KF. The proposed augmented KF-PCI method can handle both linear and nonlinear system errors as the linear parts of the errors are modeled inside the KF and the nonlinear and residual parts of the azimuth errors are modeled by PCI. The performance of this method is examined using road test experiments in a land vehicle.
Highlights
Over the past couple of decades, we are heading towards dense, layered, and complex road systems with increasingly heavy traffic that demands modern navigation systems
In order to apply Parallel Cascade Identification (PCI) to reduced inertial sensor system (RISS)/Global Positioning System (GPS) integration, this research proposes a Kalman filtering (KF)-PCI method where the role of PCI is to model the residual azimuth error not modeled by KF; this includes nonlinear parts as well as other residuals due to mismodeling, while the linear parts of the errors are modeled inside the KF
This paper proposed a KF-PCI method to curtail both linear and nonlinear errors in azimuth of MEMS-grade gyroscope for 2D RISS integrated with GPS using loosely coupled integration approach
Summary
Over the past couple of decades, we are heading towards dense, layered, and complex road systems with increasingly heavy traffic that demands modern navigation systems. In order to lower the cost further, recent research [8,9,10] has explored the applicability of replacing a full IMU with a reduced number of MEMS-based inertial sensors, while still maintaining adequate overall performance This two-dimensional (2D) reduced inertial sensor system (RISS) computes the vehicle heading and position using low-cost MEMS-based gyroscope integrated with the vehicle odometer exploiting the nonholonomic constraints on land vehicles and integrates these sensors with GPS. If linearized system models are utilized for navigation error states estimation with these sensors they can introduce significant errors These limitations, in turn, may result in suboptimal performance of the integration filter as the assumption of local linearity is violated [11]. The estimated state vector is a vector of RISS positions, velocities, and azimuth errors augmented with the sensors stochastic errors (for both gyroscope and the odometer-derived acceleration). Parallel Cascade Identification, a nonlinear modeling technique, is used to augment KF in order to overcome its limitations and enhance the accuracy of MEMSgrade RISS solutions
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