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Study on Target Tracking Algorithm in Wireless Sensor Network

Author: GaoHuanPing
Tutor: DuYongGui；LiuMei
School: Taiyuan University of Technology
Course: Pattern Recognition and Intelligent Control
Keywords: Wireless sensor networks Target tracking Non-parametric belief propagation Particle filter
CLC: TN929.5
Type: Master's thesis
Year: 2010
Downloads: 171
Quote: 1
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Abstract


Wireless sensor networks is one of the most influential technology of the 21st century , target tracking is an important application of wireless sensor networks . Wireless sensor network - based target tracking without fixed network support to expand rapidly , high fault tolerance , and survivability features , has been widely used in the field of military , traffic control , environmental monitoring , medical health , smart home track . Wireless sensor network - based target tracking studies have important academic value and practical significance . Target tracking widespread nonlinear problems . Particle filter method is widely applied because of the flexibility to deal with the nonlinear non- Gaussian system . In this paper, the weights of elementary particle filtering method has degradation and the resampling particle loss of the lack of diversity , to study particle filter target tracking method based on non-parametric belief propagation . Summary wireless sensor network target tracking research status and with issues related to the mathematical theory based on mainly the following four aspects : ( 1) based on fuzzy fusion particle filter multi- target tracking algorithm . First using fuzzy C-means algorithm to determine the target of measurement vesting and optimize linear fusion , and then use sampling importance resampling particle filter estimated target location , multi- target tracking . (2) based on non-parametric belief propagation algorithm presents a distributed target tracking method . First, the target node detected by kernel density estimation pretreatment testing data , estimated by the target information , and then send the information to the cluster head node , cluster head by information multiplied Gibbs sampling integration of information , the last regular particles filtering , accurate tracking of the target . (3) on the basis of the previously discussed non-parametric belief propagation algorithm is extended to multi - target tracking . Correct multi - target data association and tracking through the establishment of multi- target tracking probabilistic graphical models and nonparametric belief propagation . (4 ) papers in the target tracking algorithm based on the development of ZigBee wireless location system preliminary experiments show that the effectiveness and feasibility of the algorithm .

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