Lie group based nonlinear state errors for MEMS-IMU/GNSS/magnetometer integrated navigation

The error propagation of traditional strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) is not autonomous because its error state model is trajectorydependence. Recently, the invariant error defined on Lie group has raised much attention due to its trajectory-independent and autonomous error propagation. In this paper, the invariant errorbased Kalman filter for SINS/DVL integration solution is investigated with main focus on its extension and comparison. The contributions of this study are threefold. First, the invariant error defined on matrix Lie group (group of double direct isometrics) are extended to model the non-group-affine traditional SINS mechanism and the group-affine transformed SINS mechanism, both of them are derived in Earth frame and augmented with the drift and bias of the inertial measurement units (IMUs). Then, the observation equations for different invariant error-based state models are derived for SINS/DVL application, theoretical analysis is performed and comprehensive evaluation are conducted under different maneuvering condition by lake field trial. Finally, the variational Bayesian approach is introduced into invariant errorbased Kalman filter to inferring the inaccurate process noise covariance matrix (PNCM) and time-varying measurement noise covariant matrix (MNCM) from a practical perspective, experimental results demonstrate that it can improve the navigation accuracy significantly. This study is expected to facilitate the selection of appropriate invariant error to SINS/DVL application.

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“…The derivation above is similar to (28), but it can clearly conclude that the error dynamic is independent to estimated global states while (28)…”

Section: Invariant Error State Model Within Group Affinementioning

confidence: 85%

Invariant Error-Based Integrated Solution for SINS/DVL in Earth Frame: Extension and Comparison

Tang

Chang

et al. 2023

IEEE Trans. Instrum. Meas.

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“…At present, SINS/GNSS is the most commonly used navigation mode of land vehicle navigation system, which can provide highprecision output with low error for a long time and meet the requirements of real-time positioning. [1][2][3][4][5][6] However, SINS/ GNSS still has shortcomings in some challenging environments. For example, the signal blocking caused by buildings, bridges, and trees in urban environment and the multipath effect in canyon and tunnel will lead to the GNSS equipment unable to output effective navigation information.…”

Section: Introductionmentioning

confidence: 99%

State transformation extended Kalman filter–based tightly coupled strapdown inertial navigation system/global navigation satellite system/laser Doppler velocimeter integration for seamless navigation of unmanned ground vehicle in urban areas

Wang

et al. 2023

International Journal of Advanced Robotic Systems

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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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“…Meanwhile, IMU measurements can provide high-rate velocity and attitude to integrated navigation systems and perform dead reckoning to acquire short-term positioning solutions when GNSS measurements are unavailable. GNSS/IMU integration techniques have been widely developed such as by adding auxiliary sensors such as magnetic sensors [13], odometers [14], Light Detection and Ranging (LiDAR) [15], or cameras [9] and by improving integration algorithms [10,11]. The integration algorithm improvements are commonly carried out in low-cost GNSS and IMU sensors research to obtain optimal navigation solutions without additional sensors.…”

Section: Introductionmentioning

confidence: 99%

GNSS/IMU Sensor Fusion Performance Comparison of a Car Localization in Urban Environment Using Extended Kalman Filter

Erfianti

Asfihani

Suhandri

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) are popular navigation sensor for position fixing technique and dead reckoning system that complement each other. GNSS can provide accurate position and velocity information when it establishes a Line of Sight (LOS) with a minimum of four satellites. However, this accuracy can decrease due to signal outage, jamming, interference, and multipath effects. On the other hand, the IMU has the advantage of measuring the platform’s orientation with a high-frequency update and is not affected by environmental conditions. However, a drift effect causes the measurement errors to accumulate. Several studies have demonstrated the fusion of both sensors in terms of the Extended Kalman Filter (EKF). This study conduct sensor fusion for car localization in an urban environment based on the loosely coupled integration scheme. In order to improve the sensor fusion performance, pre-processing GNSS and IMU data were applied. The result shows that pre-processing DGNSS and IMU filtering can increase the accuracy of the integrated navigation solution up to 80.02% in the east, 80.13% in the north, and 89.45% in the up direction during the free outage period.

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Lie group based nonlinear state errors for MEMS-IMU/GNSS/magnetometer integrated navigation

Cited by 33 publications

References 24 publications

Invariant Error-Based Integrated Solution for SINS/DVL in Earth Frame: Extension and Comparison

Invariant Error-Based Integrated Solution for SINS/DVL in Earth Frame: Extension and Comparison

State transformation extended Kalman filter–based tightly coupled strapdown inertial navigation system/global navigation satellite system/laser Doppler velocimeter integration for seamless navigation of unmanned ground vehicle in urban areas

GNSS/IMU Sensor Fusion Performance Comparison of a Car Localization in Urban Environment Using Extended Kalman Filter

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