9 citations · 11 across the 9 of their papers we have counts for
10 papers
FlowCritic: Bridging Value Estimation with Flow Matching in Reinforcement Learning
Shan Zhong, Shutong Ding, He Diao +3
Reliable value estimation serves as the cornerstone of reinforcement learning (RL) by evaluating long-term returns and guiding policy improvement, significantly influencing the con…
A Covariance Adaptive Student's t Based Kalman Filter
Benyang Gong, Jiacheng He, Gang Wang +1
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 predic…
Interactive Model Fusion-Based GM-PHD Filter
Jiacheng He, Shan Zhong, Bei Peng +2
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…
Cubature Kalman filter Based on generalized minimum error entropy with fiducial point
Jiacheng He, Gang Wang, Zhenyu Feng +2
In real applications, non-Gaussian distributions are frequently caused by outliers and impulsive disturbances, and these will impair the performance of the classical cubature Kalma…
Distributed fusion filter over lossy wireless sensor networks with the presence of non-Gaussian noise
Jiacheng He, Bei Peng, Zhenyu Feng +3
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 environmenta…
Quantized criterion-based kernel recursive least squares adaptive filtering for time series prediction
Jiacheng He, Gang Wang, Kun Zhang +2
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…
A Model Fusion Distributed Kalman Filter For Non-Gaussian Observation Noise
Xuemei Mao, Gang Wang, Bei Peng +4
Wireless sensor networks (WSNs) represent a critical research domain within the Internet of Things (IoT) technology. The distributed Kalman filter (DKF) has garnered significant at…
Minimum Error Entropy Rauch-Tung-Striebel Smoother
Jiacheng He, Hongwei Wang, Gang Wang +2
Outliers and impulsive disturbances often cause heavy-tailed distributions in practical applications, and these will degrade the performance of Gaussian approximation smoothing alg…
State Estimation of Wireless Sensor Networks in the Presence of Data Packet Drops and Non-Gaussian Noise
Jiacheng He, Gang Wang, Xuemei Mao +2
Distributed Kalman filter approaches based on the maximum correntropy criterion have recently demonstrated superior state estimation performance to that of conventional distributed…
Generalized Minimum Error Entropy for Adaptive Filtering
Jiacheng He, Gang Wang, Bei Peng +2
Error entropy is a important nonlinear similarity measure, and it has received increasing attention in many practical applications. The default kernel function of error entropy cri…