Using Tobit Kalman filtering in order to improve the Motion recorded by Microsoft Kinect

In this paper, we analyze data from Microsoft Kinect v2 camera using Kalman Tobit and Kalman filters so as to minimize noise. The data concern three-dimensional spatial coordinates recording movements of a persons’A joints, which are subject to measurement errors. The noise variances of the process and the measurements are estimated using the maximum likelihood function. In order to include into the model restrictive conditions based on anthropometric data (e.g. the distances between various joints) we apply the Tobit Kalman Filter. Additionally, restrictions for the joints displacements per fame based on real data can be used in order to get better results. Finally simulations of skeleton before and after using Kalman filtering are presented

Robust Channel Estimation in Multiuser Downlink 5G Systems Under Channel Uncertainties

In wireless communication, the performance of the network highly relies on the accuracy of channel state information (CSI). On the other hand, the channel statistics are usually unknown, and the measurement information is lost due to the fading phenomenon. Therefore, we propose a channel estimation approach for downlink communication under channel uncertainty. We apply the Tobit Kalman filter (TKF) method to estimate the hidden state vectors of wireless channels. To minimize the maximum estimation error, a robust minimax minimum estimation error (MSE) estimation approach is developed while the QoS requirements of wireless users is taken into account. We then formulate the minimax problem as a non-cooperative game to find an optimal filter and adjust the best behavior for the worst-case channel uncertainty. We also investigate a scenario in which the actual operating point is not exactly known under model uncertainty. Finally, we investigate the existence and characterization of a saddle point as the solution of the game. Theoretical analysis verifies that our work is robust against the uncertainty of the channel statistics and able to track the true values of the channel states. Additionally, simulation results demonstrate the superiority of the model in terms of MSE value over related techniques

Unreliable networks with random parameter matrices and time-correlated noises: distributed estimation under deception attacks

This paper examines the distributed filtering and fixed-point smoothing problems for networked systems, considering random parameter matrices, time-correlated additive noises and random deception attacks. The proposed distributed estimation algorithms consist of two stages: the first stage creates intermediate estimators based on local and adjacent node measurements, while the second stage combines the intermediate estimators from neighboring sensors using least-squares matrix-weighted linear combinations. The major contributions and challenges lie in simultaneously considering various network-induced phenomena and providing a unified framework for systems with incomplete information. The algorithms are designed without specific structure assumptions and use a covariance-based estimation technique, which does not require knowledge of the evolution model of the signal being estimated. A numerical experiment demonstrates the applicability and effectiveness of the proposed algorithms, highlighting the impact of observation uncertainties and deception attacks on estimation accuracy

Locally Minimum-Variance Filtering of 2-D Systems over Sensor Networks with Measurement Degradations: A Distributed Recursive Algorithm

10.13039/501100012166-National Key Research and Development Program of China (Grant Number: 2018AAA0100202); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61673110, 61873148, 61933007, 61903082 and 61973080); 10.13039/501100002858-China Postdoctoral Science Foundation (Grant Number: 2018M640443); Jiangsu Planned Projects for Postdoctoral Research Funds of China (Grant Number: 2019K192); 10.13039/100005156-Alexander von Humboldt Foundation of German

Diabetes Monitoring System

This is the author accepted manuscript. The final version is available from CRC Press via the DOI in this recordThis chapter reviews the current technologies for monitoring and intervention of diabetes. Especially various blood glucose concentration estimation, online signal monitoring and adaptive control mechanisms are discussed. Recent research has proposed many control engineering approaches for Type 1 diabetes and many algorithms of artificial pancreas have been proposed. This book chapter reviews the current state of the art and industrial standards on diabetes monitoring and control

Recursive Set-Membership State Estimation Over a FlexRay Network

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61873148, 61873169, 61903253 and 61933007); Royal Society of the U.K.; Alexander von Humboldt Foundation of Germany