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Vol. 22, Special Issue, June 2013, pp. 126-132




Data Fusion of Multi Model with One Sensor
J. J. Yin, and J. Q. Zhang

State Key Laboratory of ASIC & System (Fudan University), 825 Zhang Heng Rd., Shanghai, 201203, China

Tel.: 8621-55664227, 8621-55664226

Department of Electronic Engineering, Fudan University, 220 Han Dan RD., Shanghai, 200433, China

E-mail: yinjianjun@fudan.edu.cn


Received: 15 April 2013   /Accepted: 20 June 2013   /Published: 28 June 2013

Digital Sensors and Sensor Sysstems


Abstract: In this letter, a novel data fusion method, called the single sensor data fusion filter (SSDFF) was proposed. It differed from the existed multi sensor fusion algorithms. In the proposed SSDFF, a new process model using polynomial predictive filter by expanding the dimension of the state, was firstly constructed. Then the local estimation results based on the original model and the proposed model were fused to get the global estimation. It was shown that the new process model could be presented whether the original state transition density was known exactly or not and the fusion result was better than the local ones. The demonstration results verified the effectiveness of the proposed method.


Keywords: Data fusion, Kalman filter, Particle filter.


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