论文信息 - Target Classification and Pattern Recognition Using Micro-Doppler Radar Signatures

Target Classification and Pattern Recognition Using Micro-Doppler Radar Signatures

Micro-motions, such as vibrations or rotations of an object or structures on the object, induce additional frequency modulations on returned radar signal, which generates sidebands about the object's Doppler frequency, called micro-Doppler by V.C. Chen et al. (2002). In this paper, we investigated statistical classification methods for target classification using their micro-Doppler signatures. At this stage only simulated data are studied, and two models are used to generate simulation data: point scatter model and RCS model. Both models are tested and compared for their performance on target classification

[1] J. C. BurgesChristopher. A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .

[2] Erkki Oja,et al. Independent component analysis: algorithms and applications , 2000, Neural Networks.

[3] Bassem Mahafza,et al. Radar Systems Analysis and Design Using MATLAB , 2000 .

[4] Robert P.W. Duin,et al. PRTools3: A Matlab Toolbox for Pattern Recognition , 2000 .

[5] E. Oja,et al. Independent Component Analysis , 2013 .

[6] Qun Zhao,et al. Support vector machines for SAR automatic target recognition , 2001 .

[7] Hao Ling,et al. Time-Frequency Transforms for Radar Imaging and Signal Analysis , 2002 .

[8] Khaled H. Hamed,et al. Time-frequency analysis , 2003 .

[9] H. Wechsler,et al. Analysis of micro-Doppler signatures , 2003 .

[10] T. Thayaparan,et al. Micro-Doppler Radar Signatures for Itelligent Target Recognition , 2004 .

[11] Jiajin Lei,et al. Target classification based on micro-Doppler signatures , 2005, IEEE International Radar Conference, 2005..

[12] Armin Eberlein,et al. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing , 2009, Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing.