Robust Filtering Algorithm for Uncertain Systems with Observation Losses in Sensor Network

2013 ◽  
Author(s):  
Baofeng Wang ◽  
Xiue Gao
Keyword(s):  
Sensor Network ◽  
Uncertain Systems ◽  
Robust Filtering ◽  
Filtering Algorithm

Robust Filtering for Interconnected Uncertain Systems Under Sampled Measurements

1997 ◽  
Vol 119 (2) ◽  
pp. 337-340 ◽  
Author(s):  
Peng Shi ◽  
Youyi Wang ◽  
Lihua Xie
Keyword(s):  
Parameter Uncertainty ◽  
Continuous Time ◽  
Uncertain Systems ◽  
Infinite Horizon ◽  
Riccati Equations ◽  
Robust Filtering ◽  
Sampled Measurements ◽  
Initial States

This paper presents the results of robust filtering for a class of interconnected uncertain systems under sampled measurements. We address the problem of designing filters, using sampled measurements, which would guarantee a prescribed H∞ performance in the continuous-time context, irrespective of the parameter uncertainty and unknown initial states. Both the cases of finite and infinite horizon filtering are investigated in terms of N pairs of Riccati equations with finite discrete jumps.

Robust filtering for uncertain systems

2001 ◽  
Vol 81 (4) ◽  
pp. 809-817 ◽  
Author(s):  
Ph. Neveux ◽  
G. Thomas
Keyword(s):  
Uncertain Systems ◽  
Robust Filtering

Study on WSN Localization Algorithm and Simulation Model Based on Kalman Filtering Algorithm

2014 ◽  
Vol 945-949 ◽  
pp. 2380-2385
Author(s):  
Lian Zhou Gao
Keyword(s):  
Wireless Sensor Network ◽  
Simulation Model ◽  
Sensor Network ◽  
Kalman Filtering ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm

This paper studies on the algorithm to improve the location of Wireless Sensor Network (WSN) in Intelligent Transportation System (ITS). Considering multi-path effect in the localization, an improved RSSI algorithm is introduced in the localization algorithm. The localization results are analyzed under different density of beacon nodes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, to test the algorithm based on Kalman filtering algorithm, a simulation model of ITS is developed, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application.

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