%0 Journal Article
%A HAN Qiu-ting
%A ZHANG Hui
%A ZHENG Ji-ping
%T Sensor Data Processings Based on Gaussian Sum Particle Filters
%D 2013
%R 10.13190/jbupt.201304.116.zhengjp
%J Journal of Beijing University of Posts and Telecommunications
%P 110-115
%V 36
%N 4
%X <p>Sensor data is modeled and Gaussian sum particle filter method is used for probabilistic inference based on model-driven methods for saving energy. First, the correlations on different sensor nodes and constructed probabilistic models from historical data are exploited. Then, the particle filter method is adopted to infer values for one sensor node from values acquired from real world by other sensor nodes, which saves energy of sensors efficiently. Finally, according to the fact that the sensor data generally satisfies Gaussian distribution, Gaussian particle filters and Gaussian sum particle filters are utilized for probabilistic inference respectively. Experiments show that the proposed Gaussian sum particle filter method is of high accuracy and efficiency.</p>
%U https://journal.bupt.edu.cn/EN/10.13190/jbupt.201304.116.zhengjp