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�������պ����ѧѧ�� 2008, Vol. 34 Issue (11) :1331-1334    DOI:
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ԭ Ȫ, ������, �� ��*
�������պ����ѧ �Զ�����ѧ���������ѧԺ, ���� 100191
Adaptive fusion algorithm based on wavelet neural networks for multisensor measurement
Yuan Quan, Dong Chaoyang, Wang Qing*
School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

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Abstract�� To solve the problems of nonlinear and uncertain fusion systems, an adaptive fusion algorithm based on wavelet neural networks(WNNs) for multisensor measurement was proposed. The fusion system consisted of extended Kalman filters(EKFs), WNNs, knowledge base(KB) and track-to-track fusion algorithms. Based on the distributed fusion method, sensor precision values, sensor states and the local estimation errors were transferred from sensors to WNNs to deduce the relevant sensor confidence degrees in the real-time process of data fusion. In order to obtain the sensor confidence degrees, contextual information theory and normalized variable method were introduced to WNNs and the experimental data were implemented to train WNNs. According to the rules about the sensor confidence degrees, KB made decisions to select suitable track-to-track fusion algorithms. Simulation results show that the algorithm can effectively adjust the system to adapt contextual changes and has strong fusion capability in resisting uncertain information.
Keywords�� data fusion   wavelet neural networks   contextual information   multisensor     
Received 2007-11-22;


About author: ԭ Ȫ(1978-),��,��������������,��ʿ��,yuanquan@asee.buaa.edu.cn.
ԭ Ȫ, ������, �� ��.����С��������Ķഫ��������Ӧ�ں��㷨[J]  �������պ����ѧѧ��, 2008,V34(11): 1331-1334
Yuan Quan, Dong Chaoyang, Wang Qing.Adaptive fusion algorithm based on wavelet neural networks for multisensor measurement[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2008,V34(11): 1331-1334
http://bhxb.buaa.edu.cn//CN/     ��     http://bhxb.buaa.edu.cn//CN/Y2008/V34/I11/1331
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