Application of Updated Sage–Husa Adaptive Kalman Filter in the Navigation of a Translational Sprinkler Irrigation Machine

Liu, Kenan; Zhao, Wuyun; Sun, Bugong; Wu, Pute; Zhu, Delan; Zhang, Peng

doi:10.3390/w11061269

Cited by 18 publications

(20 citation statements)

References 17 publications

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“…The adaption process is based on the residual vector and its covariance. The residual vector ðV k Þ, which follows a white noise with zero mean, and its theoretic covariance ðS k Þ are defined as shown in [6,13,24].…”

Section: R K Adaption Based On the Fismentioning

confidence: 99%

INS/GPS Sensor Fusion based on Adaptive Fuzzy EKF with Sensitivity to Disturbances

Sabzevari

Chatraei

2021

IET Radar Sonar & Navi

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The estimation accuracy of the inertial navigation system integrated with the global positioning system (GPS) through multiple kinds of Kalman filters (KFs) has been widely considered. Since the classical KFs could not overcome environmental disturbances and noises, adaptive and robust structures are utilised in sensor fusion techniques. Here, different types of adaptive structures have been assumed. The fuzzy inference system benefits the adaption of the measurement covariance matrix, a scale factor employed to tune the process covariance matrix and the Chi-square algorithm to detect and bound the disturbances. The estimation accuracy and robustness of the adaptive fuzzy extended Kalman filter (AFEKF) are compared with the unscented Kalman filter (UKF) and extended Kalman filter (EKF) structure in various scenarios involving position, velocity, attitude, accelerometer and gyroscope bias estimation errors. Likewise, in order to evaluate the AFEKF approach practically, an embedded electronic board is designed involving an ARM microcontroller, an inertial measurement unit sensor, and a GPS receiver, which was installed on a land vehicle. The results demonstrated the superiority of the AFEKF over the UKF and EKF in the case of lower estimation error and higher robustness against disturbances and outliers.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: R K Adaption Based On the Fismentioning

confidence: 99%

INS/GPS Sensor Fusion based on Adaptive Fuzzy EKF with Sensitivity to Disturbances

Sabzevari

Chatraei

2021

IET Radar Sonar & Navi

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“…It effectively reduces the undesirable influence of the historical sequence. The general equation of EWMA is (Sun et al., 2016; Narasimhappa et al., 2018): where k is the time step, d k is the forgetting factor, b is a constant, b ∈(0⋅9,1].…”

Section: Akfmentioning

confidence: 99%

“…As a covariance matching estimation strategy, the Sage-Husa AKF (SHAKF) was proposed and has become widely utilised (Sage and Husa, 1969;Liu et al, 2019). It estimates the real-time measurement noise covariance by the statistical information of historical epochs.…”

Section: Introductionmentioning

confidence: 99%

“…proved that with the same filter performance RAE is more reliable than IAE (Almagbile et al., 2010). To simplify computation and improve estimation accuracy, the moving windows (MW) method was proposed to estimate Q and R (Yang and Xu, 2003), and the exponential weighted moving average (EWMA) method was also introduced (Narasimhappa et al., 2018; Franzen and Fingscheidt, 2019; Xu et al., 2019). However, the optimal window width in MW and the forgetting factor in EWMA have to be determined empirically.…”

Section: Introductionmentioning

confidence: 99%

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Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

Hou

Yao

et al. 2020

J. Navigation

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The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

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“…The existing solution assumes the noise satisfies the white Gaussian noise distribution and realizes the state estimation through an adaptive filter. Sage Husa [ 20 , 21 ], H∞ filter [ 22 ], the maximum likelihood estimation method [ 23 , 24 ], and variational Bayesian (VB) [ 25 , 26 ] are the primary adaptive algorithms at present. The adaptive filter could obtain the state estimation with approximate error covariance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle

2021

Sensors

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Precise navigation is essential for autonomous underwater vehicles (AUVs). The measurement deviation of the navigation sensors, especially the microelectromechanical systems (MEMS) sensors, is a crucial factor that affects the localization accuracy. Deep learning is a novel method to solve this problem. However, the calculation cycle and robustness of the deep learning method may be insufficient in practical application. This paper proposes an adaptive navigation algorithm with deep learning to address these questions and realize accurate navigation. Firstly, this algorithm uses deep learning to generate low-frequency position information to correct the error accumulation of the navigation system. Secondly, the χ2 rule is selected to judge if the Doppler velocity log (DVL) measurement fails, which could avoid interference from DVL outliers. Thirdly, the adaptive filter, based on the variational Bayesian (VB) method, is employed to estimate the navigation information simultaneous with the measurement covariance, improving navigation accuracy even more. The experimental results, based on AUV field data, show that the proposed algorithm could realize robust navigation performance and significantly improve position accuracy.

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Application of Updated Sage–Husa Adaptive Kalman Filter in the Navigation of a Translational Sprinkler Irrigation Machine

Cited by 18 publications

References 17 publications

INS/GPS Sensor Fusion based on Adaptive Fuzzy EKF with Sensitivity to Disturbances

INS/GPS Sensor Fusion based on Adaptive Fuzzy EKF with Sensitivity to Disturbances

Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle

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