Resource constrained particle filtering for real-time multi-target tracking in sensor networks

2009 ◽  
Author(s):  
Lee-Ling Ong ◽  
Wendong Xiao ◽  
Chen-Khong Tham ◽  
Marcelo H. Ang
Keyword(s):  
Sensor Networks ◽  
Target Tracking ◽  
Real Time ◽  
Particle Filtering ◽  
Resource Constrained ◽  
Multi Target Tracking

Research of Localization Issues in Underwater Sensor Networks

2012 ◽  
Vol 157-158 ◽  
pp. 424-427
Author(s):  
Xian Min Wei
Keyword(s):  
Sensor Networks ◽  
Target Tracking ◽  
Real Time ◽  
Development Trend ◽  
Underwater Sensor Networks ◽  
Localization Problem ◽  
Advantages And Disadvantages ◽  
Localization Algorithms ◽  
Underwater Localization ◽  
The Development Trend

Localization problem has great significance in the applications of underwater sensor networks (UWSN), such as real-time monitoring, target tracking and navigation. This paper analized a series of challenges in underwater localization problem, introduced exsiting localization algorithms and discussed their advantages and disadvantages, and then the development trend of localization problem is discussed. Finally, we give some suggestions in the design of localization algoprithms.

Target Tracking Based Adaptive Particle Filter in Binary Wireless Sensor Networks

2013 ◽  
Vol 756-759 ◽  
pp. 2281-2287
Author(s):  
Ling Dong Su ◽  
Ming Yue Zhai ◽  
Zhi Yu Zhu
Keyword(s):  
Wireless Sensor Network ◽  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Sensor Network ◽  
Particle Filtering ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Local Estimate ◽  
Real Time Tracking

Since real-time and communication amount is crucial for the wireless sensor network target tracking, the performance of target tracking in the wireless sensor network is critically depended on real-time and communication amount reduction. This paper presents a target tracking method based on distributed adaptive particle filtering in binary wireless sensor network. Based on dynamic clustering, the adaptive particle filter receives the observations from children nodes and formulates the local estimate with the cluster head as the processing center. Simulation results show that the method can effectively improve the real-time tracking and reduce communication amount.

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