Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

Widodo, Romy Budhi; Wada, Chikamune

doi:10.1155/2016/6943040

Cited by 27 publications

(12 citation statements)

References 14 publications

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“…Interestingly, the discrepancy of the accuracy of several state of art fusion algorithms is very small in magnetic disturbances free environment [ 8 , 13 ]. Therefore, the trend is not to proposed a new algorithm outperforming previous algorithms, but to place efforts on dealing with special situations, such as magnetic disturbances [ 22 ], high speed motion [ 33 ] and longtime monitoring [ 34 ]. Focusing on improving the estimated accuracy in the presence of magnetic disturbances, this paper proposes a novel adaptive method based on existing fusion algorithms.…”

Section: Discussionmentioning

confidence: 99%

An Adaptive Orientation Estimation Method for Magnetic and Inertial Sensors in the Presence of Magnetic Disturbances

Fan

Wang

et al. 2017

Sensors

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Magnetic and inertial sensors have been widely used to estimate the orientation of human segments due to their low cost, compact size and light weight. However, the accuracy of the estimated orientation is easily affected by external factors, especially when the sensor is used in an environment with magnetic disturbances. In this paper, we propose an adaptive method to improve the accuracy of orientation estimations in the presence of magnetic disturbances. The method is based on existing gradient descent algorithms, and it is performed prior to sensor fusion algorithms. The proposed method includes stationary state detection and magnetic disturbance severity determination. The stationary state detection makes this method immune to magnetic disturbances in stationary state, while the magnetic disturbance severity determination helps to determine the credibility of magnetometer data under dynamic conditions, so as to mitigate the negative effect of the magnetic disturbances. The proposed method was validated through experiments performed on a customized three-axis instrumented gimbal with known orientations. The error of the proposed method and the original gradient descent algorithms were calculated and compared. Experimental results demonstrate that in stationary state, the proposed method is completely immune to magnetic disturbances, and in dynamic conditions, the error caused by magnetic disturbance is reduced by 51.2% compared with original MIMU gradient descent algorithm.

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Section: Discussionmentioning

confidence: 99%

An Adaptive Orientation Estimation Method for Magnetic and Inertial Sensors in the Presence of Magnetic Disturbances

Fan

Wang

et al. 2017

Sensors

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“…Nilai Q dan R dapat diubah-ubah untuk menentukan pembobotan atas nilai keluaran beberapa sensor dalam proses penggabungan data sensor atau data fusion sebagaimana Gambar 2. Meski demikian, kebanyakan peneliti memilih adanya perubahan nilai R daripada Q, karena pengubahan nilai Q dapat mengakibatkan adanya overshoot pada nilai Kalman gain [12][13].…”

Section: Gambar 1 Proses DI Kalman Filterunclassified

“…Banyaknya data terakhir yang digunakan untuk menentukan nilai kovarian didefinisikan sebagai . Nilai total moving covariance ketiga sumbu dapat dihitung menggunakan Persamaan (12).…”

Section: Penentuan Kondisi Sensorunclassified

Penentuan Orientasi dan Translasi Gerakan UAV menggunakan Data Fusion berbasis Kalman Filter

Kurniawan¹,

Nasution²,

Dinaryanto³

et al. 2021

Jurnal Teknik Elektro AVITEC

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In an unmanned aircraft vehicle, a navigation system is needed to calculate its orientation and translation. The navigation system can utilize data from the accelerometer, gyroscope, magnetometer, and GPS. The orientation can be precisely calculated from the accelerometer and magnetometer data when the sensor is in a static state. Meanwhile, under dynamic conditions, the orientation can be more precisely calculated from the gyroscope data. In order to obtain the robust navigation system, a data fusion based on Kalman filter is built to calculate the orientation from the accelerometer, gyroscope, and magnetometer. The Kalman filter trusts more in the data from the accelerometer and magnetometer when the UAV is static and trusts more in to the gyroscope data when the UAV is in dynamic conditions. Meanwhile, the UAV translation is obtained by performing data fusion of the accelerometer data with location data from the GPS sensor. The Kalman filter combines data from the accelerometer and GPS when available, otherwise trusts in data from the accelerometer only. The trust level shifting is done by changing the measurement noise covariance. The data fusion based on Kalman filter provides more accurately the orientation and translation data. The orientation as a result of the calculation from the gyroscope has an average error of 18.12%, while the orientation as a result of the accelerometer and magnetometer has an error of 1.3%. By using Kalman filter-based data fusion, the error of the orientation decreases to 0.87%

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“…In order to reduce the noise and compensate for the drift of the Micro Electro Mechanical Systems (MEMS) gyroscope during usage, the authors of [ 17 ] proposed a Kalman filtering method based on information fusion. In [ 18 ], the authors proposed an algorithm based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the EKF. In general, the noise matrices expressing the covariance of the measurements and of the process can be fine-tuned to reduce as much as possible the noise influence.…”

Section: Related Workmentioning

confidence: 99%

DANAE++: A Smart Approach for Denoising Underwater Attitude Estimation

Russo

Ciaccio

Troisi

2021

Sensors

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One of the main issues for the navigation of underwater robots consists in accurate vehicle positioning, which heavily depends on the orientation estimation phase. The systems employed to this end are affected by different noise typologies, mainly related to the sensors and to the irregular noise of the underwater environment. Filtering algorithms can reduce their effect if opportunely configured, but this process usually requires fine techniques and time. This paper presents DANAE++, an improved denoising autoencoder based on DANAE (deep Denoising AutoeNcoder for Attitude Estimation), which is able to recover Kalman Filter (KF) IMU/AHRS orientation estimations from any kind of noise, independently of its nature. This deep learning-based architecture already proved to be robust and reliable, but in its enhanced implementation significant improvements are obtained in terms of both results and performance. In fact, DANAE++is able to denoise the three angles describing the attitude at the same time, and that is verified also using the estimations provided by an extended KF. Further tests could make this method suitable for real-time applications in navigation tasks.

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Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

Cited by 27 publications

References 14 publications

An Adaptive Orientation Estimation Method for Magnetic and Inertial Sensors in the Presence of Magnetic Disturbances

An Adaptive Orientation Estimation Method for Magnetic and Inertial Sensors in the Presence of Magnetic Disturbances

Penentuan Orientasi dan Translasi Gerakan UAV menggunakan Data Fusion berbasis Kalman Filter

DANAE++: A Smart Approach for Denoising Underwater Attitude Estimation

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