Adaptive cubature Kalman filter based on the variance-covariance components estimation

Zhang, Ya; Wang, Jianguo; Sun, Qian; Gao, Wei

doi:10.1186/s41445-017-0006-z

Cited by 10 publications

(5 citation statements)

References 21 publications

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“…It assumes that the predictive density of the combined state measurement follows a Gaussian distribution. The CKF employs the third-degree spherical-radial cubature rule to numerically compute integrals, scaling points linearly with the state vector dimension [ 15 ]. It effectively addresses complex nonlinear problems with high dimensions [ 26 ].…”

Section: Cubature Kalman Filter With Missing Measurementsmentioning

confidence: 99%

“…The CKF utilizes a third-degree cubature rule and offers advantages such as reduced parameters [ 11 , 12 ], improved stability, and accuracy compared to the UKF [ 13 , 14 ]. It is widely used to handle nonlinear problems [ 15 ], but applying the CKF to a nonlinear system requires knowledge of the mathematical model and noise statistics, which can be challenging to obtain in practical applications [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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Robust Cubature Kalman Filter for Moving-Target Tracking with Missing Measurements

Sahl,

Song,

Niu

2024

Sensors

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Handling the challenge of missing measurements in nonlinear systems is a difficult problem in various scientific and engineering fields. Missing measurements, which can arise from technical faults during observation, diffusion channel shrinking, or the loss of specific metrics, can bring many challenges when estimating the state of nonlinear systems. To tackle this issue, this paper proposes a technique that utilizes a robust cubature Kalman filter (RCKF) by integrating Huber’s M-estimation theory with the standard conventional cubature Kalman filter (CKF). Although a CKF is often used for solving nonlinear filtering problems, its effectiveness might be limited due to a lack of knowledge regarding the nonlinear model of the state and noise-related statistical information. In contrast, the RCKF demonstrates an ability to mitigate performance degradation and discretization issues related to track curves by leveraging covariance matrix predictions for state estimation and output control amidst dynamic disruption errors—even when noise statistics deviate from prior assumptions. The performance of extended Kalman filters (EKFs), unscented Kalman filters (UKFs), CKFs, and RCKFs was compared and evaluated using two numerical examples involving the Univariate Non-stationary Growth Model (UNGM) and bearing-only tracking (BOT). The numerical experiments demonstrated that the RCKF outperformed the EKF, EnKF, and CKF in effectively handling anomaly errors. Specifically, in the UNGM example, the RCKF achieved a significantly lower ARMSE (4.83) and ANCI (3.27)—similar outcomes were observed in the BOT example.

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Section: Cubature Kalman Filter With Missing Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Cubature Kalman Filter for Moving-Target Tracking with Missing Measurements

Sahl,

Song,

Niu

2024

Sensors

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“…Zhang et al [19] have proposed a novel adaptive filter based on a nonlinear cubature Kalman filter and an estimate of variance-covariance components (VCE). Cubature Kalman filter was used to solve nonlinearity, but VCE was used for the nonlinear system's real-time estimation noise correlation matrix.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Kalman Filter Navigation Algorithm Based on Correntropy and Fixed-Point Update

Pagoti

Vemuri²,

Mohiddin

2022

Int. j. eng. technol. innov.

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The accuracy of position estimation plays a key role in many of the precise positioning applications such as category I (CAT-I) aircraft landings, survey work, etc. To improve the accuracy of position estimation, a novel kinematic positioning algorithm designated as correntropy Kalman filter (CKF) is proposed in this study. Instead of minimum mean square error (MMSE), correntropy criterion (CC) is used as the optimality criterion of CKF. The prior estimates of the state and covariance matrix are computed in CKF and a novel fixed-point algorithm is then used to update the posterior estimates. The data of a dual-frequency global positioning system (GPS) receiver located at Indian Institute of Science (IISc), Bangalore (13.021°N/77.5°E) is collected from Scripps Orbit and Permanent Array Centre (SOPAC) to implement the proposed algorithm. The results of the proposed CKF algorithm are promising and exhibit significant improvement in position estimation compared to the conventional methods.

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“…The Kalman filter has become one of the most widely used recursive methods that estimate the states of a process. 9 If the system, observation model, input and measurement values, and the observation noise covariance matrix N (t) are known, the Kalman filter produces an optimal solution. In practice, the a priori N (t) is unknown or approximated by applying the best available knowledge.…”

Section: Adaptive Kalman Filtermentioning

confidence: 99%

Estimation of baseball bat trajectory during a practice swing using a Kalman filter for velocity compensation

Shibata¹,

Hirose²,

Naruo³

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part P:

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This study aimed to (a) develop an algorithm that could estimate a baseball bat trajectory from the beginning of the swing to the follow-through phase during a practice swing without a ball and (b) evaluate the accuracy of the proposed method using a three-dimensional motion capture system. The sensor fusion using the adaptive Kalman filter for compensating velocity decreased the error of acceleration integration during the follow-through phase. Further, the three-dimensional bat trajectory in a global coordinate was estimated by combining the sensor fusion and compensation by motion characteristics. The three-dimensional bat trajectory from the swing beginning to the follow-through phase estimated by the proposed method was compared with the three-dimensional bat trajectory obtained by the three-dimensional motion capture system. The proposed method achieved a root mean square of the error of 7.72 km/h for velocity, which was less than the root mean square of the error (8.91 km/h) obtained by simple time integration of forward direction. These results indicate that the error by acceleration integration during the follow-through phase is compensated. The proposed method is, thus, deemed effective and can be used to evaluate baseball swing, including the follow-through phase, with high accuracy.

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Adaptive cubature Kalman filter based on the variance-covariance components estimation

Cited by 10 publications

References 21 publications

Robust Cubature Kalman Filter for Moving-Target Tracking with Missing Measurements

Robust Cubature Kalman Filter for Moving-Target Tracking with Missing Measurements

Enhanced Kalman Filter Navigation Algorithm Based on Correntropy and Fixed-Point Update

Estimation of baseball bat trajectory during a practice swing using a Kalman filter for velocity compensation

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