| 李燕,何怡刚,于文新,尹柏强.广义S变换多分量LFM信号检测及参数估计[J].电子测量与仪器学报,2017,31(12):2056-2062 |
| 广义S变换多分量LFM信号检测及参数估计 |
| Detection and parameter estimation of multi component LFM signals based on GST |
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| DOI：10.13382/j.jemi.2017.12.025 |
| 中文关键词: 多分量LFM信号 广义S变换 奇异值分解 时频滤波 |
| 英文关键词:multi component LFM signals generalized S transform singular value decomposition time frequency filtering |
| 基金项目:国家自然科学基金(51577046)、国家自然科学基金重点项目(51637004)、国家重点研发计划“重大科学仪器设备开发”项目(2016YFF0102200)、国家自然科学基金青年科学基金（61501162 ）、中国博士后科学基金（2015M571926）、湖南省教育厅科研项目(16C0639)资助 |
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| Author | Institution |
| Li Yan | College of Electrical and Information Engineering, Hunan University, Changsha 410082,China |
| He Yigang | 1.College of Electrical and Information Engineering, Hunan University, Changsha 410082,China; 2.School of Electrical Engineering, Wuhan University, Wuhan 430072, China |
| Yu Wenxin | College of Electrical and Information Engineering, Hunan University, Changsha 410082,China |
| Yin Baiqiang | School of Electrical Engineering, Wuhan University, Wuhan 430072, China |
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| 中文摘要: |
| 针对多分量线性调频信号(LFM)信号在低信噪比状况下信号检测出现漏检、参数估计精度不高等问题，提出在广义S变换(GST)基础上，进行奇异值分解(SVD)滤波的方法。在S变换基础上，导出了广义S变换及逆变换公式，对离散后得到的广义S变换矩阵进行奇异值求解，通过选取合适的奇异值个数，实现多分量信号时频滤波。仿真结果表明，该方法在低信噪比状况下能有效滤除噪声，避免因噪声或者各分量信号强弱相差较大而出现漏检现象，同时信号参数估计精度也得到了提高。 |
| 英文摘要: |
| In order to solve some problems that the signal is undetected in the low signal to noise ratio(SNR), and the accuracy is not high of parameter estimation, the singular value decomposition (SVD) filtering is proposed on the basis of generalized S transform (GST) for multi component chirp signal (MLFM). On the basis of S transform, the generalized S transform and inverse transformation formula are derived in the paper. The singular value of the generalized S transform matrix is obtained by discrete singular value, and the multi component signal Time frequency filtering is realized by selecting the appropriate singular value. The simulation results show that the method can effectively filter out the noise in the low SNR, and avoids the phenomenon of missed detection when the amplitude of each component signal is quite difference, the accuracy of the signal parameter estimation is optimized. |
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