Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estima...Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.展开更多
基金supported by the National Natural Science Foundation of China (61302188)the Nanjing University of Science and Technology Research Foundation (2010ZDJH05)
文摘Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.
文摘为了有效检测轨道波磨故障,提出一种基于参数优化变分模态分解(VMD,VariableMode Decomposition)和平滑伪维格纳分布(SPWVD,SmoothPseudo Wigner VilleDistribution)的轨道波磨辨识方法。采用变步长最小均方(VSSLMS,Variable Step Size Least Mean Square)算法对列车轴箱振动加速度原始信号滤波;对滤波后的信号进行变分模态分解,将分解信号包络熵作为轨道波磨辨识的指标;采用平滑伪维格纳分布对分解后的信号进行时频分析,确定波磨发生的位置及波长;通过仿真信号与实例验证方法的有效性。验证结果表明,该方法可提高轨道波磨辨识的准确性,辅助轨道维修和养护。
