In the integrated navigation system using extended Kalman filter (EKF), the state error conventionally uses linear approximation to tackle the commonly nonlinear problem. However, this error definition can diverge the filter in some adverse situations due to significant distortion of the linear approximation. By contrast, the nonlinear state error defined in the Lie group satisfies the autonomous equation, which thus has distinctively better convergence property. This work proposes a novel strapdown inertial navigation system (SINS) nonlinear state error defined in the Lie group and derives the SINS equations of the Lie group EKF (LG-EKF) for the MIMU/GNSS/magnetometer integrated navigation system. The corresponding measurement equations are also derived. A land vehicle field test has been conducted to evaluate the performance of EKF, ST-EKF (state transformation extended Kalman filter) and LG-EKF, which verifies LG-EKF's superior estimation accuracy of the heading angle as well as the other two horizontal angles (pitch and roll). The LG-EKF proposed in this paper is unlimited in the choice of sensors, which means it can be applied with both high-end and low-end inertial sensors.
With the rapid development of unmanned ground vehicle industry, how to achieve continuous, reliable, and high-accuracy navigation becomes very important. At present, the integrated navigation with global navigation satellite system and strapdown inertial navigation system is the most mature and effective navigation technology for unmanned ground vehicle. However, this technique depends on the signal accuracy of global navigation satellite system. When the receiver cannot capture four or more satellite signals for a long time or the satellite completely invalid, it cannot provide accurate navigation and positioning information for the unmanned ground vehicle. Therefore, this article combine the observation information of strapdown inertial navigation system, global navigation satellite system, and laser Doppler velocimeter to propose a high-precision seamless navigation technique of unmanned ground vehicle based on state transformation extended Kalman filter. Under different land vehicle driving environments and global navigation satellite system signal quality conditions, the seamless navigation technique is evaluated through global navigation satellite system interruption simulation and land vehicle experiments. The experimental results show that the strapdown inertial navigation system/global navigation satellite system/laser Doppler velocimeter tightly coupled integration seamless navigation has good environmental adaptability and reliability and can maintain high navigation accuracy under high frequency global navigation satellite system–signal blockage conditions in urban areas.
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