Wang Ling-Huan, Ma Hong-Guang, Zhang Xin-Yu, Zhang Ge-Xiang. Multi-component Linear FM Signal Detection Based on Support Vector Clustering[J]. Journal of Electronics & Information Technology, 2007, 29(11): 2661-2664. doi: 10.3724/SP.J.1146.2006.00617
Citation:
Wang Ling-Huan, Ma Hong-Guang, Zhang Xin-Yu, Zhang Ge-Xiang. Multi-component Linear FM Signal Detection Based on Support Vector Clustering[J]. Journal of Electronics & Information Technology, 2007, 29(11): 2661-2664. doi: 10.3724/SP.J.1146.2006.00617
Wang Ling-Huan, Ma Hong-Guang, Zhang Xin-Yu, Zhang Ge-Xiang. Multi-component Linear FM Signal Detection Based on Support Vector Clustering[J]. Journal of Electronics & Information Technology, 2007, 29(11): 2661-2664. doi: 10.3724/SP.J.1146.2006.00617
Citation:
Wang Ling-Huan, Ma Hong-Guang, Zhang Xin-Yu, Zhang Ge-Xiang. Multi-component Linear FM Signal Detection Based on Support Vector Clustering[J]. Journal of Electronics & Information Technology, 2007, 29(11): 2661-2664. doi: 10.3724/SP.J.1146.2006.00617
The Support Vector Clustering (SVC) algorithm is introduced to get the number of the pinnacles in the result of the time-frequency analysis and Radon transform of the multi-component Linear FM (LFM) signal, and to finish the detection of the components of the LFM signal. Meanwhile, the preprocessing to reduce the points number of the input data-set for SVC is proposed to improve the computation efficiency. And a novel cluster labeling method is developed to improve the SVC algorithm. The simulation results depict that the SVC-Radon-time-frequency approach is efficient for the detection and parameter estimation of the multi-components LFM signal with low SNR.
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