Maximum Correntropy Unscented Kalman Filter for Ballistic Missile Navigation System based on SINS/CNS Deeply Integrated Mode

Hou, Bowen; He, Zhangming; Liu, Dong; Zhou, Hongyan; Wang, Jiongqi

doi:10.3390/s18061724

Cited by 35 publications

(25 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering only one fixed point iteration, the Equation (36) can be solved by using the modified measurement covariance matrix

in the update process as [ 34 , 35 ]:

…”

Section: Derivation Of Mcupfmentioning

confidence: 99%

“…The existing PF [ 14 ], UPF [ 14 ], Huber unscented particle filter (HRUPF) [ 24 ], MCUKF [ 34 ], iterated VB based cubature Kalman filter (IVBCKF) [ 21 ], and the proposed MCUPF are compared, where the simple random resampling is employed in PF and UPF. In order to obtain a better performance of MCUKF, the kernel bandwidth in MCUKF and MCUPF are set as

following the advice of [ 34 , 35 ]. Besides, the turning parameter and iterated times in HRUPF are commonly used

and

[ 24 , 27 ], respectively.…”

Section: Lake-water Filed Trialmentioning

confidence: 99%

“…Besides, the maximum particle number must be less than starting number in MCUPF and the maximum particle number is set as

. Following the advice of [ 4 , 24 , 35 , 36 ], the parameters of above filters are presented in Table 6 .…”

Section: Lake-water Filed Trialmentioning

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 3 more Smart Citations

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

“…Considering only one fixed point iteration, the Equation (36) can be solved by using the modified measurement covariance matrix

in the update process as [ 34 , 35 ]:

…”

Section: Derivation Of Mcupfmentioning

confidence: 99%

following the advice of [ 34 , 35 ]. Besides, the turning parameter and iterated times in HRUPF are commonly used

and

[ 24 , 27 ], respectively.…”

Section: Lake-water Filed Trialmentioning

confidence: 99%

“…Besides, the maximum particle number must be less than starting number in MCUPF and the maximum particle number is set as

. Following the advice of [ 4 , 24 , 35 , 36 ], the parameters of above filters are presented in Table 6 .…”

Section: Lake-water Filed Trialmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

“…Being highly precise and highly reliable, a star sensor is one of the main devices of a celestial navigation system [ 1 ]. It is widely used in spacecraft attitude control [ 2 ], missile guidance [ 3 ], and ship and aircraft navigation [ 4 , 5 ], and is small in size and light in weight [ 6 , 7 ]. By executing star centroid extraction, star identification, and attitude estimation, the attitude information can be determined [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Motion Blurred Star Image Restoration Based on MEMS Gyroscope Aid and Blur Kernel Correction

Wang

Zhang

Ning

et al. 2018

Sensors

View full text Add to dashboard Cite

Under dynamic conditions, motion blur is introduced to star images obtained by a star sensor. Motion blur affects the accuracy of the star centroid extraction and the identification of stars, further reducing the performance of the star sensor. In this paper, a star image restoration algorithm is investigated to reduce the effect of motion blur on the star image. The algorithm includes a blur kernel calculation aided by a MEMS gyroscope, blur kernel correction based on the structure of the star strip, and a star image reconstruction method based on scaled gradient projection (SGP). Firstly, the motion trajectory of the star spot is deduced, aided by a MEMS gyroscope. Moreover, the initial blur kernel is calculated by using the motion trajectory. Then, the structure information star strip is extracted by Delaunay triangulation. Based on the structure information, a blur kernel correction method is presented by utilizing the preconditioned conjugate gradient interior point algorithm to reduce the influence of bias and installation deviation of the gyroscope on the blur kernel. Furthermore, a speed-up image reconstruction method based on SGP is presented for time-saving. Simulated experiment results demonstrate that both the blur kernel determination and star image reconstruction methods are effective. A real star image experiment shows that the accuracy of the star centroid extraction and the number of identified stars increase after restoration by the proposed algorithm.

show abstract

Distributed maximum correntropy unscented Kalman filter under hybrid attacks in non‐Gaussian environment

Liu,

Ma,

Huang

et al. 2024

Asian Journal of Control

View full text Add to dashboard Cite

This paper addresses a distributed nonlinear filtering issue based on maximum correntropy for dealing with randomly occurring hybrid cyber‐attacks in non‐Gaussian environment. The types of cyber‐attacks include denial of service attacks and deception attacks. First, a modified distributed unscented Kalman filter is proposed, using Cauchy kernel‐based maximum correntropy criterion instead of the traditional mean square error criterion, against cyber‐attacks and non‐Gaussian noise. Then based on fixed‐point iterative rules and information fusion strategy, the update equations of state estimates and covariance matrices of the proposed filter are obtained. Furthermore, the sufficient condition guaranteeing its convergence and computational complexity are also given. Finally, an illustrative example is presented to verify the effectiveness of the proposed filter under hybrid cyber‐attacks in non‐Gaussian environment.

show abstract

Maximum Correntropy Unscented Kalman Filter for Ballistic Missile Navigation System based on SINS/CNS Deeply Integrated Mode

Cited by 35 publications

References 51 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

Motion Blurred Star Image Restoration Based on MEMS Gyroscope Aid and Blur Kernel Correction

Distributed maximum correntropy unscented Kalman filter under hybrid attacks in non‐Gaussian environment

Contact Info

Product

Resources

About