Zobrazeno 1 - 10
of 381
pro vyhledávání: '"He, Jiacheng"'
Mutual information has many applications in image alignment and matching, mainly due to its ability to measure the statistical dependence between two images, even if the two images are from different modalities (e.g., CT and MRI). It considers not on
Externí odkaz:
http://arxiv.org/abs/2407.07410
In the classical Kalman filter(KF), the estimated state is a linear combination of the one-step predicted state and measurement state, their confidence level change when the prediction mean square error matrix and covariance matrix of measurement noi
Externí odkaz:
http://arxiv.org/abs/2309.09565
In multi-target tracking (MTT), non-Gaussian measurement noise from sensors can diminish the performance of the Gaussian-assumed Gaussian mixture probability hypothesis density (GM-PHD) filter. In this paper, an approach that transforms the MTT probl
Externí odkaz:
http://arxiv.org/abs/2309.08088
The estimation of non-Gaussian measurement noise models is a significant challenge across various fields. In practical applications, it often faces challenges due to the large number of parameters and high computational complexity. This paper propose
Externí odkaz:
http://arxiv.org/abs/2309.02789
The information transmission between nodes in a wireless sensor networks (WSNs) often causes packet loss due to denial-of-service (DoS) attack, energy limitations, and environmental factors, and the information that is successfully transmitted can al
Externí odkaz:
http://arxiv.org/abs/2307.01445
The robustness of the kernel recursive least square (KRLS) algorithm has recently been improved by combining them with more robust information-theoretic learning criteria, such as minimum error entropy (MEE) and generalized MEE (GMEE), which also imp
Externí odkaz:
http://arxiv.org/abs/2307.01442
In real applications, non-Gaussian distributions are frequently caused by outliers and impulsive disturbances, and these will impair the performance of the classical cubature Kalman filter (CKF) algorithm. In this letter, a modified generalized minim
Externí odkaz:
http://arxiv.org/abs/2307.01438
The distributed Kalman filter (DKF) has attracted extensive research as an information fusion method for wireless sensor systems(WSNs). And the DKF in non-Gaussian environments is still a pressing problem. In this paper, we approximate the non-Gaussi
Externí odkaz:
http://arxiv.org/abs/2306.11476
Distributed Kalman filter approaches based on the maximum correntropy criterion have recently demonstrated superior state estimation performance to that of conventional distributed Kalman filters for wireless sensor networks in the presence of non-Ga
Externí odkaz:
http://arxiv.org/abs/2301.05867
Outliers and impulsive disturbances often cause heavy-tailed distributions in practical applications, and these will degrade the performance of Gaussian approximation smoothing algorithms. To improve the robustness of the Rauch-Tung-Striebel (RTS) sm
Externí odkaz:
http://arxiv.org/abs/2301.05813