We propose a method that uses linear chirp modulated Gaussian functions as the elementary functions, by adaptively adjusting variances, time frequency centers and sweep rates, to decompose signals. By taking WVD, an ...We propose a method that uses linear chirp modulated Gaussian functions as the elementary functions, by adaptively adjusting variances, time frequency centers and sweep rates, to decompose signals. By taking WVD, an improved adaptive time frequency distribution is developed, which is non negative, free of cross term interference, and of better time frequency resolution. The paper presents an effective numerical algorithm to estimate the optimal parameters of the basis. Simulations indicate that the proposed approach is effective in analyzing signal's time frequency behavior.展开更多
基于心里声学客观参量的GA-BP声品质预测模型能够准确的预测稳态排气噪声声品质。对于非稳态噪声研究,引入正则化非稳态回归技术(RNR)优化计算维格纳-威尔分布(WVD)的时频方法,建立新的声品质参量SQP-RW(Sound Quality Parameter Base o...基于心里声学客观参量的GA-BP声品质预测模型能够准确的预测稳态排气噪声声品质。对于非稳态噪声研究,引入正则化非稳态回归技术(RNR)优化计算维格纳-威尔分布(WVD)的时频方法,建立新的声品质参量SQP-RW(Sound Quality Parameter Base on RNR-WVD),用此参量替换掉与满意度相关性较小的客观参量。同时,以Morlet小波基函数作为隐含层结点的传递函数构建小波神经网络(Wavelet Neural Network,WNN),并用GA优化小波神经网络层间的权值和阈值,构造出GA-WNN并用于非稳态排气噪声声品质预测。结果表明:GA-WNN在非稳态排气噪声声品质预测上比GA-BP神经网络更加准确;引入SQP-RW参量,模型具有更高的精度,更能体现出非稳态信号特征及声品质特点。展开更多
该文提出了一种基于魏格纳分布(Wigner-Ville Distributed,WVD)和重构时间采样(Reconstruction time sample,RTS)的空中机动目标检测和参数估计方法。该方法首先利用雷达回波的空域采样来重构时域采样,相当于增加了单个阵元的脉冲采样点...该文提出了一种基于魏格纳分布(Wigner-Ville Distributed,WVD)和重构时间采样(Reconstruction time sample,RTS)的空中机动目标检测和参数估计方法。该方法首先利用雷达回波的空域采样来重构时域采样,相当于增加了单个阵元的脉冲采样点数,然后再对重构后的数据进行WVD变换来估计目标的参数。该方法能够在方位信息未知,脉冲数较少的情况下有效地实现对机动目标的检测与参数估计。仿真结果验证了该方法的有效性。展开更多
为识别铝合金板孔损伤位置及区域,以Lamb波为研究基础,提出基于魏格纳-威利分布(WVD,WignerVille distribution)和到达时间差值法(ATDM,arrival time difference method)的损伤识别技术。首先,采集实验铝合金板健康和有损模型的Lamb信号...为识别铝合金板孔损伤位置及区域,以Lamb波为研究基础,提出基于魏格纳-威利分布(WVD,WignerVille distribution)和到达时间差值法(ATDM,arrival time difference method)的损伤识别技术。首先,采集实验铝合金板健康和有损模型的Lamb信号,对其差值信号进行WVD分析,准确提取损伤反射信号到达时间;其次,通过ATDM建立各传感器间的距离差值关系,确定孔损伤位置中心并预测最大损伤半径,从而实现对孔损伤关键指标的识别;最后,通过数值模拟进一步验证该方法,结果表明,基于WVD/ATDM的损伤识别技术不仅能准确识别出孔损伤位置,而且能够有效地识别损伤区域面积。展开更多
文摘We propose a method that uses linear chirp modulated Gaussian functions as the elementary functions, by adaptively adjusting variances, time frequency centers and sweep rates, to decompose signals. By taking WVD, an improved adaptive time frequency distribution is developed, which is non negative, free of cross term interference, and of better time frequency resolution. The paper presents an effective numerical algorithm to estimate the optimal parameters of the basis. Simulations indicate that the proposed approach is effective in analyzing signal's time frequency behavior.
文摘基于心里声学客观参量的GA-BP声品质预测模型能够准确的预测稳态排气噪声声品质。对于非稳态噪声研究,引入正则化非稳态回归技术(RNR)优化计算维格纳-威尔分布(WVD)的时频方法,建立新的声品质参量SQP-RW(Sound Quality Parameter Base on RNR-WVD),用此参量替换掉与满意度相关性较小的客观参量。同时,以Morlet小波基函数作为隐含层结点的传递函数构建小波神经网络(Wavelet Neural Network,WNN),并用GA优化小波神经网络层间的权值和阈值,构造出GA-WNN并用于非稳态排气噪声声品质预测。结果表明:GA-WNN在非稳态排气噪声声品质预测上比GA-BP神经网络更加准确;引入SQP-RW参量,模型具有更高的精度,更能体现出非稳态信号特征及声品质特点。
文摘该文提出了一种基于魏格纳分布(Wigner-Ville Distributed,WVD)和重构时间采样(Reconstruction time sample,RTS)的空中机动目标检测和参数估计方法。该方法首先利用雷达回波的空域采样来重构时域采样,相当于增加了单个阵元的脉冲采样点数,然后再对重构后的数据进行WVD变换来估计目标的参数。该方法能够在方位信息未知,脉冲数较少的情况下有效地实现对机动目标的检测与参数估计。仿真结果验证了该方法的有效性。
文摘为识别铝合金板孔损伤位置及区域,以Lamb波为研究基础,提出基于魏格纳-威利分布(WVD,WignerVille distribution)和到达时间差值法(ATDM,arrival time difference method)的损伤识别技术。首先,采集实验铝合金板健康和有损模型的Lamb信号,对其差值信号进行WVD分析,准确提取损伤反射信号到达时间;其次,通过ATDM建立各传感器间的距离差值关系,确定孔损伤位置中心并预测最大损伤半径,从而实现对孔损伤关键指标的识别;最后,通过数值模拟进一步验证该方法,结果表明,基于WVD/ATDM的损伤识别技术不仅能准确识别出孔损伤位置,而且能够有效地识别损伤区域面积。
