Adaptive Robust Unscented Kalman Filter via Fading Factor and Maximum Correntropy Criterion

Deng, Zhihong; Yin, Lijian; Huo, Bo; Xia, Yuanqing

doi:10.3390/s18082406

Cited by 15 publications

(9 citation statements)

References 33 publications

(53 reference statements)

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“…However, under the conditions of inaccurate filtering model and noise statistics, the estimation error of KF will be increased or even divergent. To solve the problem of filtering divergence caused by inaccurate state equation or non-Gaussian system noise, strong tracking Kalman filter (STKF) was proposed, which is based on the orthogonality of innovation sequence [ 52 , 53 , 54 ].…”

Section: The Algorithms Of Strong Tracking Strategy and Fuzzy Adapmentioning

confidence: 99%

A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS

Zhao

Yan

Yong-yuan

et al. 2020

Sensors

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In order to achieve the fine alignment of strapdown inertial navigation (SINS) under large misalignment angles, a novel filtering alignment method is proposed based on the second-order extended Kalman filter (EKF2) and adaptive fuzzy inference system (AFIS). Firstly, the quaternion is employed to represent the attitude errors of SINS. A second-order nonlinear state equation is made based on the nonlinear velocity error model and attitude error model, and the linear measurement equation is based on the velocity outputs from SINS. Then, the filtering scheme is designed based on EKF2 and AFIS. The error estimation and fine alignment can be achieved by using the proposed filtering scheme. The results of Monte Carlo Simulation show that the errors of pitch, roll and yaw misalignment angles quickly decrease to about 14″, 15″ and 7.62′ respectively in 350 s under the condition of any misalignment angles with pitch error from −40° to 40°, roll error from −40° to 40°, and yaw error from −50° to 50°. Even when the initial misalignment angles are all very large such as (80°, 120°, 170°), the proposed nonlinear alignment method still can converge normally by utilizing the adaptive fuzzy inference system (AFIS) to adjust the covariance matrix Pk/k−1. Finally, the turntable experiment was performed, and the effectiveness and superiority of the proposed method were further verified by compared with other nonlinear methods.

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Section: The Algorithms Of Strong Tracking Strategy and Fuzzy Adapmentioning

confidence: 99%

A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS

Zhao

Yan

Yong-yuan

et al. 2020

Sensors

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“…Deng et a. [36] developed a new adaptive robust UKF (AUKF) scheme that is referred to as adaptive maximum correntropy UKF (AMUKF), which is based on both a fading factor and the maximum correntropy criterion (MCC). Compared with other existing KFs, the proposed filter has a better adaptive ability to balance the contribution between the process model information and the measurements of the state variables.…”

Section: Algorithm Improvementmentioning

confidence: 99%

“…A step-by-step expression of the state equations for KF is presented in subsection IV-C. The algorithms of the adaptive UKF [36] [30] [32] are discussed in subsection IV-D to estimate the true geomagnetic signal and the disturbing magnetic field. The filtering results and a comparison are presented in subsection IV-E.…”

Section: Introductionmentioning

confidence: 99%

Online Correction Method for Ratio Methods by Using Geomagnetic Sensors Based on Kalman Filter

Wang

Liu

2019

IEEE Access

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By eliminating the data offset caused by a disturbing magnetic field during the flying condition of a spinning projectile, an adaptive unscented Kalman filter is applied to estimate the actual data of a geomagnetic field and the false value introduced by a disturbing magnetic field. For the ratio methods related to geomagnetic attitude measurement, the physical significance of the ratios obtained by all ratio methods is clarified. A new definition for the ratio is presented to ensure online estimation and numerical stability. The variation in the sensor outputs along the lateral directions is suggested to be a harmonic motion with the assumption that the spindle is fixed or in slow motion. The true data employed as a reference for comparison is generated by a dynamics model for spinning objects and by projecting the geomagnetic field to the measurement frame. Signal gathering and data preprocessing are considered. A signal-disturbing ratio is introduced to indicate the proportions of the real geomagnetic signal and the interference signal in the data acquisition process. The algorithm of the adaptive unscented Kalman filter is discussed. Simulations are performed using a 1 kHz sample rate and a duration of 60 seconds. The filtering results supported by a conventional unscented Kalman filter and adaptive unscented Kalman filters are compared with actual data for the conditions of different signal-disturbing ratios and 10 shifts of the disturbing magnetic field. The results of the simulation indicate that the proposed adaptive unscented Kalman filter with the introduced state equation achieves better performance than other filters mentioned in the paper. The filter has the ability to provide clean data from the noisy data flow measured from a spinning projectile for the ratio methods based on the geomagnetic measurements. INDEX TERMS Geomagnetic measurement, attitude estimation, Kalman filter, data correction, ratio methods.

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“…So they are not suitable for space target tracking because the orbital maneuvers are usually implemented by impulsive thrust which cannot be modeled. Another effective method to reduce the impact of dynamical model error is to inflate the predicted state covariance through a fading factor [29,30]. This method has been applied in the unscented Kalman filter to track a space maneuvering target [31].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Consensus-Based Unscented Information Filter for Tracking Target with Maneuver and Colored Noise

Wang

Zheng

2019

Sensors

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Distributed state estimation plays a key role in space situation awareness via a sensor network. This paper proposes two adaptive consensus-based unscented information filters for tracking target with maneuver and colored measurement noise. The proposed filters can fulfill the distributed estimation for non-linear systems with the aid of a consensus strategy, and can reduce the impact of colored measurement noise by employing the state augmentation and measurement differencing methods. In addition, a fading factor that shrinks the predicted information state and information matrix can suppress the impact of dynamical model error induced by target maneuvers. The performances of the proposed algorithms are investigated by considering a target tracking problem using a space-based radar network. This shows that the proposed algorithms outperform the traditional consensus-based distributed state estimation method in aspects of tracking stability and accuracy.

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Adaptive Robust Unscented Kalman Filter via Fading Factor and Maximum Correntropy Criterion

Cited by 15 publications

References 33 publications

A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS

A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS

Online Correction Method for Ratio Methods by Using Geomagnetic Sensors Based on Kalman Filter

Adaptive Consensus-Based Unscented Information Filter for Tracking Target with Maneuver and Colored Noise

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