Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation

Liu, Xi; Qu, Hua; Zhao, Jihong; Yue, Pengcheng; Wang, Meng

doi:10.3390/s16091530

Cited by 86 publications

(58 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…:

where

represents

- norm of vector,

denotes the inverse matrix of one-step predicted covariance matrix

and

denotes the i -th component of

is Gaussian kernel which is defined as follows [ 34 ]:

…”

Section: Derivation Of Mcupfmentioning

confidence: 99%

“…The correntropy can capture higher-order statistical information of data directly not only the usual second-order statistical information, which has the potential to achieve better estimation performance [ 31 , 32 ]. Based on the maximum correntropy criterion (MCC), several robust filters including maximum correntropy Kalman filter (MCKF) and maximum correntropy unscented Kalman filter (MCUKF) have been proposed to suppress the impulsive noises [ 31 , 32 , 33 , 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises

Ying

Zhang

Wang

et al. 2018

Sensors

View full text Add to dashboard Cite

In this paper, a novel robust particle filter is proposed to address the measurement outliers occurring in the multiple autonomous underwater vehicles (AUVs) based cooperative navigation (CN). As compared with the classic unscented particle filter (UPF) based on Gaussian assumption of measurement noise, the proposed robust particle filter based on the maximum correntropy criterion (MCC) exhibits better robustness against heavy-tailed measurement noises that are often induced by measurement outliers in CN systems. Furthermore, the proposed robust particle filter is computationally much more efficient than existing robust UPF due to the use of a Kullback-Leibler distance-resampling to adjust the number of particles online. Experimental results based on actual lake trial show that the proposed maximum correntropy based unscented particle filter (MCUPF) has better estimation accuracy than existing state-of-the-art robust filters for CN systems with heavy-tailed measurement noises, and the proposed MCUPF has lower computational complexity than existing robust particle filters.

show abstract

“…:

where

represents

- norm of vector,

denotes the inverse matrix of one-step predicted covariance matrix

and

denotes the i -th component of

is Gaussian kernel which is defined as follows [ 34 ]:

…”

Section: Derivation Of Mcupfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises

Ying

Zhang

Wang

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Recently, information‐theoretic learning (ITL) has enlightened some new optimization criteria, such as the maximum correntropy criterion (MCC), which uses the information‐theoretic quantities directly estimated from data as the optimization cost function, and has been successfully applied in many areas in impulsive‐noise environments . ITL costs can capture higher‐order statistics of data and stay intensive to large outliers, thus offering better performance than conventional second‐order statistical measures, such as variance and mean square error.…”

Section: Introductionmentioning

confidence: 99%

A robust blind adaptive multiuser detection based on maximum correntropy criterion in satellite CDMA systems

Yue

Zhao

et al. 2019

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

Satellite code‐division multiple‐access systems use multiuser detection (MUD) methods to deal with multiple‐access interferences. Among the different kinds of MUD, blind adaptive MUD regarding the system as a state‐space model is widely used for its tracking capability and independence of training sequences. The innovation process noise of the state‐space model is usually assumed to follow the Gaussian distribution, and the minimum mean‐square error criterion is the default criterion for most conventional blind adaptive MUD methods. In the past few years, the impulsive noise of the innovation process has attracted attention, declaring that a Gaussian distribution is not suitable for simulating the impulsive noise. Thus, more criteria are proposed to enhance the robustness of the blind adaptive MUD. This paper improves the robustness of MUD methods under impulsive noise by bringing forward a new cost function based on the maximum correntropy criterion and deduces the expression of the blind adaptive detector. Simulation results show that the proposed method achieves superior performance in terms of bit error rate, convergence, and robustness compared with other methods.

show abstract

“…EKF also involves a complicated calculation process of solving Jacobian matrix. The unscented Kalman filter (UKF) avoids the linearization error of EKF by approximating the probability density of state distribution using unscented transformation (UT) [5,6]. It does not need to calculate Jacobian matrix.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Square-Root Unscented Particle Filtering Algorithm for Dynamic Navigation

Wei

Gao

Zhong

et al. 2018

Sensors

View full text Add to dashboard Cite

This paper presents a new adaptive square-root unscented particle filtering algorithm by combining the adaptive filtering and square-root filtering into the unscented particle filter to inhibit the disturbance of kinematic model noise and the instability of filtering data in the process of nonlinear filtering. To prevent particles from degeneracy, the proposed algorithm adaptively adjusts the adaptive factor, which is constructed from predicted residuals, to refrain from the disturbance of abnormal observation and the kinematic model noise. Cholesky factorization is also applied to suppress the negative definiteness of the covariance matrices of the predicted state vector and observation vector. Experiments and comparison analysis were conducted to comprehensively evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm exhibits a strong overall performance for integrated navigation systems.

show abstract

Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation

Cited by 86 publications

References 34 publications

Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises

Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises

A robust blind adaptive multiuser detection based on maximum correntropy criterion in satellite CDMA systems

Adaptive Square-Root Unscented Particle Filtering Algorithm for Dynamic Navigation

Contact Info

Product

Resources

About