A NEW MULTI-SCALE SEQUENTIAL DATA FUSION SCHEME

摘要：

Researches on multi-scale data fusion have become a hot topic in data fusion field. However, limited by the constraint that signal to implement wavelet transform must have the length of 2, data fusion problem involved non-2 sampled observation data still hasnt been efficiently solved. In this paper, we aim to develop a new sequential fusion scheme by designing the stacked observation model for hybrid wavelet-Kalman filter based sequential data fusion method for the fusion of non-2 sampled multi-sensor dynamic system by analyzing the possible observation structure of non- 2 sampled sensor. Simulation of three sensors with sampling interval 1, 2 and 3 shows the efficiency of this scheme.

关键词： Hybrid wavelet-Kalman filter Sequential fusion Non- 2 sample

作者: FU-NA ZHOU TIAN-HAO TANG CHENG-LIN WEN

作者单位: Logistis School, Shanghai Marintime University, Shanghai 200135, China Computer&Information school, Logistis School, Shanghai Marintime University, Shanghai 200135, China Automatic Control School, Hangdian University, Hangzhou, 310018, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 4029-4033

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）