Design of adaptive robust square-root cubature Kalman filter with noise statistic estimator

Zhao, Liqiang; Wang, Jianlin; Yu, Tao; Jian, Huan; Liu, Tangjiang

doi:10.1016/j.amc.2014.12.036

Cited by 47 publications

(35 citation statements)

References 14 publications

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“…The CKF has been widely applied to navigation, guidance, signal processing, biology and so on. In recent years, many improved CKF algorithms have been proposed for precision and applicability . However, there are few literatures concerned with fusion CKF for multisensor nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, many improved CKF algorithms have been proposed for precision and applicability. [15][16][17][18][19][20] However, there are few literatures concerned with fusion CKF for multisensor nonlinear systems. In literature, 21 a distributed fusion CKF was proposed based on Jacobian determinant, which provided an effective way for distributed fusion estimation of nonlinear systems, but the use of approximate linearization may lead to bias or even divergence in some cases.…”

Section: Introductionmentioning

confidence: 99%

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Distributed fusion cubature Kalman filters for nonlinear systems

Hao

Sun

2019

Intl J Robust & Nonlinear

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Summary This paper is concerned with the distributed fusion estimation problem for multisensor nonlinear systems. Based on the Kalman filtering framework and the spherical cubature rule, a general method for calculating the cross‐covariance matrices between any two local estimators is presented for multisensor nonlinear systems. In the linear unbiased minimum variance sense, based on the cross‐covariance matrices, a distributed fusion cubature Kalman filter weighted by matrices (MW‐CKF) is presented. The proposed MW‐CKF has better accuracy and robustness. An example verifies the effectiveness of the proposed algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed fusion cubature Kalman filters for nonlinear systems

Hao

Sun

2019

Intl J Robust & Nonlinear

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“…However, the noise statistics of a practical system are often difficult to obtain [13,26,27], especially, for the considered system, as the measurement of the vision-based navigation sensor is sensitive to the jitter of the spacecraft and the illumination condition, and the characteristics of the measurement noise may be time-variant in the space mission. However, the noise statistics of a practical system are often difficult to obtain [13,26,27], especially, for the considered system, as the measurement of the vision-based navigation sensor is sensitive to the jitter of the spacecraft and the illumination condition, and the characteristics of the measurement noise may be time-variant in the space mission.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Iterated Extended KALMAN Filter for Relative Spacecraft Attitude and Position Estimation

Xiong¹,

Wei²

2017

Asian Journal of Control

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This paper presents a novel adaptive iterated extended Kalman filter (AIEKF) for relative position and attitude estimation, taking into account the influence of model uncertainty. Considering a nonlinear stochastic discrete-time system with unknown disturbance, the AIEKF algorithm adopts the Gauss-Newton iterative optimization steps to implement a maximum a posteriori (MAP) estimation, and the switch-mode combination technique is used to achieve the adaptive capability. The mean-square estimation error (MSE) of the state estimate is derived. It is proved that the AIEKF can yield a smaller MSE than that of the traditional extended Kalman filter (EKF) or iterated extended Kalman filter (IEKF). The performance advantage of the AIEKF is illustrated via Monte Carlo simulations on a typical relative position and attitude estimation application. Through comparisons in different scenarios, the presented algorithm is shown to improve adaptability and ensure estimation accuracy.

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“…The ISTCKF is tested via a target tracking model [6]. The non-linear process equation of the target tracking is modeled as below:…”

Section: Simulationmentioning

confidence: 99%

“…The process model uncertainty will affect the filtering performance or even lead to divergence [5]. Covariance matching techniques have been proved to CKF and UKF to build adaptive CKF [6] and adaptive UKF, which possess better adaptability and robustness. Strong tracking filter [7] is an innovation covariance matching based adaptive EKF with a time-varying suboptimal fading factor.…”

Section: Introductionmentioning

confidence: 99%

Improved Strong Tracking Cubature Kalman Filter for Target Tracking

Bao¹,

Zhang²

2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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Abstract-Cubature Kalman filter (CKF) is a very popular nonlinear filter algorithm recently. CKF obtains better numerical stability and accuracy in high dimensional situation compared to UKF. However, in case of process model uncertainty, the performance of CKF will greatly degrade or even provoke divergence. An improved strong tracking CKF (ISTCKF) is proposed to keep the numerical stability and improve the robustness. First, the theoretical framework of strong tracking filter (STF) is combined with CKF. Then, an enhanced fault detection and isolation technique is established to overcome the drawback in STCKF. ISTCKF only performs correction phase when the process model uncertainty is detected and isolated. The ISTCKF is tested and validated via a target tracking model.

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Design of adaptive robust square-root cubature Kalman filter with noise statistic estimator

Cited by 47 publications

References 14 publications

Distributed fusion cubature Kalman filters for nonlinear systems

Distributed fusion cubature Kalman filters for nonlinear systems

Adaptive Iterated Extended KALMAN Filter for Relative Spacecraft Attitude and Position Estimation

Improved Strong Tracking Cubature Kalman Filter for Target Tracking

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