A novel robust Kalman filter on AHRS in the magnetic distortion environment

Gao, Nan; Wang, M.Y.; Zhao, Luodi

doi:10.1016/j.asr.2017.04.012

Cited by 7 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the methods from these works do not easily extend to full 3DOF roll, pitch, and yaw estimation and require multiple magnetometers. References [ 7 , 8 , 9 , 10 , 11 ] and others mitigated the effect of disturbances by adjusting the credibility of the magnetometer’s or accelerometer’s measurements when distortions in either were detected. Such works proposed different methods of detecting disturbances, but generally assumed that the disturbance was brief and the magnetic field would return back to some expected value.…”

Section: Related Workmentioning

confidence: 99%

“…The methods of representing the magnetic field tend to differ based on the size of p . Note that the works that tried to detect and mitigate perturbations in Earth’s magnetic field did not create local magnetic field maps [ 4 , 5 , 6 , 7 , 8 , 9 , 10 ].…”

Section: Related Workmentioning

confidence: 99%

“…There are two opposing methods of using indoor magnetic fields in the literature. When the goal is to estimate attitude or heading using an indoor magnetic field, some works aim to remove or mitigate anomalies in the magnetic field [ 4 , 5 , 6 ] or reduce the impact of the magnetometer’s effect on attitude estimates if disturbances are detected [ 7 , 8 , 9 , 10 , 11 ]. In this paradigm, Earth’s magnetic field is seen as the signal, while any disturbances or anomalies are treated as noisy perturbations to be rejected.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving Attitude Estimation Using Gaussian-Process-Regression-Based Magnetic Field Maps

Kuevor

Cutler

Atkins

2021

Sensors

View full text Add to dashboard Cite

Magnetometers measure the local magnetic field and are present in most modern inertial measurement units (IMUs). Readings from magnetometers are used to identify Earth’s Magnetic North outdoors, but are often ignored during indoor experiments since the magnetic field does not behave how most expect. This paper presents methods to create, validate, and visualize three-dimensional magnetic field maps to expand the use of magnetic fields as a sensing modality for navigation. The utility of these maps is measured in their ability to accurately represent the magnetic field and to enable dynamic attitude estimation. In experiments with motion capture truth data, a small multicopter with three-axis inertial measurements, including magnetometer, traversed five flight profiles distinctly exciting roll, pitch, and yaw motion to provide interesting trajectories for attitude estimation. Indoor experimental results were compared to those outdoors to emphasize how spatial variation in the magnetic field drives the need for our mapping techniques. Our work presents a new way of visualizing 3D magnetic fields, which allows users to better reason about the magnetic field in their workspace. Next, we show that magnetic field maps generated from coverage patterns are generally more accurate, but training such maps using observations from desired flight paths is sufficient in the vicinity of these paths. All training sets were interpolated using Gaussian process regression (GPR), which yielded maps with <1 μT of error when interpolating between and extrapolating outside of observed locations. Finally, we validated the utility of our GPR-based maps in enabling attitude estimates in regions of high magnetic field spatial variation with experimental data.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving Attitude Estimation Using Gaussian-Process-Regression-Based Magnetic Field Maps

Kuevor

Cutler

Atkins

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Several efforts have been made to improve the heading measurement accuracy so far, with magnetic distortion eliminating as one of the most common strategies [10]. Over recent years, many researchers have devoted themselves to eliminating the negative effects of the magnetic distortion [11], and existing methods for the magnetic disturbance rejection can be categorized into two groups, namely, threshold-based approach and model-based approach [12,13]. To be specific, thresholdbased approach can contribute to eliminating the magnetic distortion by comparing the measured magnetic field intensity with the threshold value chosen based on the prior knowledge available.…”

Section: Introductionmentioning

confidence: 99%

MCTLS-Assisted Completed SINS/GPS Integrated and Applied to Low-Cost Attitude and Heading Reference System

Tao

Song

Weng

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this paper, a robust heading determination method is proposed for low-cost attitude and heading reference system (AHRS) aided by the global positioning system (GPS). As compared with the traditional GPS/SINS-integrated navigation-based heading determination method, in the proposed method, the heading information obtained from the GPS velocity outputs is first incorporated into the observation vector, which constructs a novel completed GPS/SINS integration framework and greatly improves the observability of azimuthal misalignment in the Kalman filter. Moreover, a multivariate constrained total least square (MCTLS) method is proposed and integrated into the completed integration framework to deal with the measurement error in both input and output data of GPS velocity measurement model, which improves the accuracy of the observed heading information and yields a robust heading estimation at each time instant. Simulation and experiment results demonstrate that the proposed robust heading determination method can outperform the related state-of-the-art methods for the GPS-aided attitude and heading reference system.

show abstract

“…There are many methods to deal with unknown random interference, such as adaptive filters (Cai et al, 2017), robust control and so on (Liu et al, 2017; Qian et al, 2014). For random magnetic disturbance, a Kalman filter with a two-step observation update has been proposed (Gao et al, 2017), but the disturbance was not analysed in detail and only sudden disturbances were considered. A novel online magnetic disturbance estimator using nonlinear programming has been proposed (Wu, 2019), however, it mainly solves the problem of static estimation of unknown outer magnetic disturbances, rather than variable random magnetic disturbances in dynamic motion.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Anti-Disturbance Method for Magnetometer and INS Integration in a Road Vehicle

Wang

2019

J. Navigation

View full text Add to dashboard Cite

The integration of magnetometers and Inertial Navigation Systems (INS) is widely used in low-cost navigation systems. However, even if the system has been calibrated, random magnetic disturbances still appear in practical applications, which lead to large heading errors. To solve this problem, an adaptive anti-disturbance method to overcome random magnetic disturbance is proposed. First, disturbances are classified and analysed in detail based on actual road vehicle driving data. Then an Adaptive Robust Extend Kalman Filter (AREKF) is designed to resist sudden disturbances. However, an AREKF may accumulate errors slowly when a long-term disturbance exists. Considering this situation, this paper proposes that AREKF is used to maintain accuracy in the early stages, at the same time as the magnetometer is quickly calibrated with a Kalman filter. Then, the new magnetometer parameters are put into the AREKF to suppress long-term disturbances. Finally, cascading these two modules, not only the sudden disturbance can be overcome, but the situation of long-term disturbances can be suppressed. The results of simulation and an actual driving test show that the proposed method can effectively overcome random magnetic disturbances in both the short and long term.

show abstract

A novel robust Kalman filter on AHRS in the magnetic distortion environment

Cited by 7 publications

References 19 publications

Improving Attitude Estimation Using Gaussian-Process-Regression-Based Magnetic Field Maps

Improving Attitude Estimation Using Gaussian-Process-Regression-Based Magnetic Field Maps

MCTLS-Assisted Completed SINS/GPS Integrated and Applied to Low-Cost Attitude and Heading Reference System

Adaptive Anti-Disturbance Method for Magnetometer and INS Integration in a Road Vehicle

Contact Info

Product

Resources

About