An Optimal Fusion Method of Multiple Inertial Measurement Units Based on Measurement Noise Variance Estimation

Huang, Hongliang; Zhang, Hai; Jiang, Lanyue

doi:10.1109/jsen.2022.3229475

Cited by 14 publications

(7 citation statements)

References 21 publications

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“…where ε GN k and ε I N k are the bias terms that can be assumed to be Gaussian random walk process [23], k represents the kth epoch, and v GN k and v I N k are uncorrelated discrete noise:…”

Section: The Mnce and The Filter Performance Evaluationmentioning

confidence: 99%

A Multi-Step Pseudo-Measurement Adaptive Kalman Filter Based on Filtering Performance Evaluation and Its Application in the INS/GNSS Navigation System

Wang,

Zhang

2024

Remote Sensing

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The objective of this paper is to tackle the issue of the degraded navigation accuracy of the inertial navigation system/global navigation satellite system (INS/GNSS) integrated navigation system in urban applications, especially under complex environments. This study utilizes historical state estimates and proposes a multi-step pseudo-measurement adaptive Kalman filter (MPKF) algorithm based on the filter performance evaluation. First, taking advantage of the independence between INS and GNSS, the enhanced second-order mutual difference (SOMD) algorithm is utilized for estimating the noise variance of the GNSS, which is decoupled from the estimate error of state and used as a module for filter performance evaluation. Then, the construction of the proposed method is presented, together with the analysis of the noise variance of multi-step pseudo-measurement. Ultimately, the efficacy of the MPKF is confirmed through a real-world vehicle experiment involving a tightly-coupled INS/GNSS integrated navigation application, demonstrating a noteworthy enhancement in navigation precision within densely wooded and built-up areas. Compared to the standard EKF and enhanced redundant measurement-based adaptive Kalman filter (ERMAKF), the proposed algorithm improves the positioning accuracy by 48% and 34%, velocity accuracy by 50% and 35%, and attitude accuracy by 38% and 48%, respectively, in the urban building segment.

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Section: The Mnce and The Filter Performance Evaluationmentioning

confidence: 99%

A Multi-Step Pseudo-Measurement Adaptive Kalman Filter Based on Filtering Performance Evaluation and Its Application in the INS/GNSS Navigation System

Wang,

Zhang

2024

Remote Sensing

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“…According to the model, a Kalman filter is used for data fusion. The system noise matrix and measurement noise matrix in the Kalman filter are set according to the noise parameters obtained by Allan variance [20][21][22][23], as shown in Figure 5 below: The output error model of the MEMS gyroscope is shown as follows [24][25][26][27]:…”

Section: Data Fusion Of Redundant Mems-imumentioning

confidence: 99%

Redundant Configuration Method of MEMS Sensors for Bottom Hole Assembly Attitude Measurement

Zheng,

Wang,

Zhang

et al. 2024

Micromachines

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Micro-electro-mechanical systems inertial measurement units (MEMS-IMUs) are increasingly being employed for measuring the attitude of bottom hole assemblies (BHAs). However, the reliability and measurement precision of a single MEMS-IMU may not meet drilling’s stringent needs. Redundant MEMS-IMU systems can effectively enhance the reliability and precision. This paper proposes a redundant configuration method for MEMS sensors tailored to BHA attitude measurement. Firstly, based on reliability theory and a cost-benefit analysis, considering factors such as cost, size, and reliability, the optimal number of sensors in the redundant system was determined to be six. Considering the structural characteristics of the BHA, a hollow hexagonal prism-shaped redundant configuration scheme was proposed, ensuring the circulation of drilling fluid within the drill pipe. Next, by employing Kalman filtering to integrate the output data from the six sensors, a virtual IMU (VIMU) was formed. Finally, experimental verification was carried out. The results confirmed that, after redundancy implementation, the velocity random walk of the accelerometer decreased by an average of 58% compared to a single MEMS-IMU, and bias instability was reduced by an average of 54%. The angular random walk of the gyroscope decreased by an average of 58%, and bias instability was reduced by an average of 37%. This research provides a theoretical foundation for enhancing the precision and reliability of BHA attitude measurements.

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“…The VIMU observation fusion method probabilistically combines measurements from all the IMUs into the functional equivalent of a single sensor, such as in [7]. The VIMU fusion method is relatively lightweight computationally and can easily be incorporated into existing VIO systems built around a single IMU.…”

Section: Related Workmentioning

confidence: 99%

Online Multi Camera-IMU Calibration

Hartzer¹,

Saripalli²

2022

2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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This work presents a centralized multi-IMU filter framework with online intrinsic and extrinsic calibration for unsynchronized inertial measurement units that is robust against changes in calibration parameters. The novel Extended Kalman Filter-based method estimates the positional and rotational offsets of the system of sensors as well as their intrinsic biases without the use of rigid body geometric constraints. Additionally, the filter is flexible in the total number of sensors used while leveraging the commonly used Multi-State Constraint Kalman Filter framework for camera measurements. The filter framework has been validated using Monte Carlo simulation as well as experimentally. In both simulations and experiments, using multiple IMU measurement streams within the proposed filter framework outperforms the use of a single IMU in a filter prediction step while also producing consistent and accurate estimates of initial calibration errors. Compared to current state-of-the-art optimizers, the filter produces similar intrinsic and extrinsic calibration parameters for each sensor. Finally, an open source repository has been provided at https://github.com/unmannedlab/ekf-cal containing both the online estimator and the simulation used for testing and evaluation.

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An Optimal Fusion Method of Multiple Inertial Measurement Units Based on Measurement Noise Variance Estimation

Cited by 14 publications

References 21 publications

A Multi-Step Pseudo-Measurement Adaptive Kalman Filter Based on Filtering Performance Evaluation and Its Application in the INS/GNSS Navigation System

A Multi-Step Pseudo-Measurement Adaptive Kalman Filter Based on Filtering Performance Evaluation and Its Application in the INS/GNSS Navigation System

Redundant Configuration Method of MEMS Sensors for Bottom Hole Assembly Attitude Measurement

Online Multi Camera-IMU Calibration

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