基于支持向量聚类的多分量线性调频信号检测
doi: 10.3724/SP.J.1146.2006.00617
Multi-component Linear FM Signal Detection Based on Support Vector Clustering
-
摘要: 为了精确获取多分量线性调频(Linear FM, LFM)信号中分量的数量,该文引入支持向量聚类(Support Vector Clustering, SVC)算法对LFM信号的Radon-时频分析结果进行聚类分析,完成多个分量的检测;并通过减少SVC算法中输入集样本数量和改进聚类标识方法为直接聚类标识法,提高了SVC算法的计算效率。仿真结果表明:在较低信噪比条件下,Radon-时频分析和SVC结合的方法可有效地检测多分量LFM信号中分量数和进行参数估计。Abstract: 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.
-
Wood J C and Barry D T. Linear signal synthesis using the Radon-Wigner transform[J].IEEE Trans. on Signal Processing.1994, 42(8):2105-2111[2]Li Y X and Yi M. Recursive filtering Radon-Ambiguity trans- form algorithm for multi-LFM signals detection. Proc. Of IEEE 2002 International Conference on Communications, Circuits and Systems and West Sino Expositions, Chengdu, China, 2002, (2): 1050-1053.[3]郭汉伟, 王岩. 基于小波Radon变换检测线性调频信号. 国防 科技大学学报, 2005, 25(1): 91-94. Guo H W and Wang Yan. Linear chirp signals detection by wavelet Radon transform. Journal of National University of Defense Technology, 2005, 25(1): 91-94.[4]章步云, 刘爱芳. 基于Radon-STFT的多分量线性调频信号检测与参数估计. 探测与控制学报, 2003, 25(3): 30-33. Zhang B Y and Liu A F. Multicomponent LFM signal detection and parameter estimationbased on Radon-STFT. Journal of Detection Control, 2003, 25(3): 30-33.[5]Ben-Hur A and Horn D. Support vector clustering. Journal of Machine Learning Research, 2001, (2): 125-137.[6]Ben-Hur A and Horn D. Support vector method for hierarc- hical clustering. Advances in Neural Information Processing Systems, 2001, (13): 367-373.[7]Tax D M J and Duin R P W. Support vector domain descrip- tion[J].Pattern Recognition Letters.1999, 20(11~13):1191-1199[8]Yang J.[J].Estivill-Castro V, and Chalup S K. Support vector clustering through proximity gaph modelling. Proc. Ninth Intl Conf. Neural Information Processing.2002,:-[9]张贤达, 保铮. 非平稳信号分析与处理[M]. 北京: 国防工业出版社, 1998: 1-180.[10]向敬成, 张明友. 雷达系统. 北京: 电子工业出版社, 2001. 5: 112-130.
计量
- 文章访问数: 3264
- HTML全文浏览量: 80
- PDF下载量: 731
- 被引次数: 0