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《Acta Armamentarii》 2017-06
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Multi-feature Compressive Sensing Target Tracking Algorithm Based on Redundant Dictionary

ZHU Su;BO Yu-ming;HE Liang;School of Automation,Nanjing University of Science and Technology;School of Electronic Information and Optoelectronic Technology,Zijin College,Nanjing University of Science and Technology;  
In consideration that the basis of signal sparse representation is an orthogonal matrix in the multi-feature compressed sensing algorithm,the multi-features of infrared and visible images are extracted to construct a sparse representation in a subspace of redundant dictionary,and the selection of sensing matrix and the reconstruction of sparse signal in the algorithm are analyzed. A redundant dictionary-based target tracking algorithm of multi-feature compressed sensing in the framework of particle filter is proposed by reconstructing the signal sparse representation,which can automatically detect dynamic targets in complex environment. Experimental results show that,compared with other classical algorithms,the proposed algorithm has better robustness and real-time in complex environment like illumination change and interference object occlusion.
【Fund】: 国家自然科学基金项目(U1330133)
【CateGory Index】: TP391.41
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3 ZHU Su;BO Yu-ming;HE Liang;School of Automation,Nanjing University of Science and Technology;School of Electronic Information and Optoelectronic Technology,Zijin College,Nanjing University of Science and Technology;;Multi-feature Compressive Sensing Target Tracking Algorithm Based on Redundant Dictionary[J];兵工学报;2017-06
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