Parameter estimation of the attributed scattering center(ASC) model is significant for automatic target recognition(ATR). Sparse representation based parameter estimation methods have developed rapidly. Construction o...Parameter estimation of the attributed scattering center(ASC) model is significant for automatic target recognition(ATR). Sparse representation based parameter estimation methods have developed rapidly. Construction of the separable dictionary is a key issue for sparse representation technology. A compressive time-domain dictionary(TD) for ASC model is presented. Two-dimensional frequency domain responses of the ASC are produced and transformed into the time domain. Then these time domain responses are cutoff and stacked into vectors. These vectored time-domain responses are amalgamated to form the TD. Compared with the traditional frequency-domain dictionary(FD), the TD is a matrix that is quite spare and can markedly reduce the data size of the dictionary. Based on the basic TD construction method, we present four extended TD construction methods, which are available for different applications. In the experiments, the performance of the TD, including the basic model and the extended models, has been firstly analyzed in comparison with the FD. Secondly, an example of parameter estimation from SAR synthetic aperture radar(SAR) measurements of a target collected in an anechoic room is exhibited. Finally, a sparse image reconstruction example is from two apart apertures. Experimental results demonstrate the effectiveness and efficiency of the proposed TD.展开更多
滚动轴承是旋转机械系统的关键部件之一,其振动性能对机械设备的运行质量有重要影响。为确定轴承性能演变趋势,对轮毂轴承的全寿命周期振动时间序列进行了研究。首先,按照是否超过标定值,将轴承振动时间序列分为性能未退化和性能退化序...滚动轴承是旋转机械系统的关键部件之一,其振动性能对机械设备的运行质量有重要影响。为确定轴承性能演变趋势,对轮毂轴承的全寿命周期振动时间序列进行了研究。首先,按照是否超过标定值,将轴承振动时间序列分为性能未退化和性能退化序列,并且将其按照时间顺序等分为10个子序列;然后,采用了最大熵原理模型逐一分析了子序列,寻找了参数最早发生明显变化的序列;最后,按照时间顺序继续细分了该子序列,分析了每个子序列的参数变化趋势,并对比了参数变化序列对应的轴承服役时间。研究结果表明:通过对比映射参数、各阶原点矩、拉格朗日乘子与轴承性能变化之间的规律,可验证映射参数表征轴承性能演变的可行性;第8个子序列映射参数a减小了53.3%,映射参数b增加了43.2%;第8个子序列继续细分时,第9个子序列映射参数a减小了44.5%,映射参数b增加了38.7%;与标定失效时间(189 h 41 min)相比,采用最大熵模型可提前175 min实现对轴承的性能失效预报。采用最大熵模型可以对轴承性能进行失效预报。展开更多
基金Project(NCET-11-0866)supported by Education Ministry's new Century Excellent Talents Supporting Plan,China
文摘Parameter estimation of the attributed scattering center(ASC) model is significant for automatic target recognition(ATR). Sparse representation based parameter estimation methods have developed rapidly. Construction of the separable dictionary is a key issue for sparse representation technology. A compressive time-domain dictionary(TD) for ASC model is presented. Two-dimensional frequency domain responses of the ASC are produced and transformed into the time domain. Then these time domain responses are cutoff and stacked into vectors. These vectored time-domain responses are amalgamated to form the TD. Compared with the traditional frequency-domain dictionary(FD), the TD is a matrix that is quite spare and can markedly reduce the data size of the dictionary. Based on the basic TD construction method, we present four extended TD construction methods, which are available for different applications. In the experiments, the performance of the TD, including the basic model and the extended models, has been firstly analyzed in comparison with the FD. Secondly, an example of parameter estimation from SAR synthetic aperture radar(SAR) measurements of a target collected in an anechoic room is exhibited. Finally, a sparse image reconstruction example is from two apart apertures. Experimental results demonstrate the effectiveness and efficiency of the proposed TD.
文摘滚动轴承是旋转机械系统的关键部件之一,其振动性能对机械设备的运行质量有重要影响。为确定轴承性能演变趋势,对轮毂轴承的全寿命周期振动时间序列进行了研究。首先,按照是否超过标定值,将轴承振动时间序列分为性能未退化和性能退化序列,并且将其按照时间顺序等分为10个子序列;然后,采用了最大熵原理模型逐一分析了子序列,寻找了参数最早发生明显变化的序列;最后,按照时间顺序继续细分了该子序列,分析了每个子序列的参数变化趋势,并对比了参数变化序列对应的轴承服役时间。研究结果表明:通过对比映射参数、各阶原点矩、拉格朗日乘子与轴承性能变化之间的规律,可验证映射参数表征轴承性能演变的可行性;第8个子序列映射参数a减小了53.3%,映射参数b增加了43.2%;第8个子序列继续细分时,第9个子序列映射参数a减小了44.5%,映射参数b增加了38.7%;与标定失效时间(189 h 41 min)相比,采用最大熵模型可提前175 min实现对轴承的性能失效预报。采用最大熵模型可以对轴承性能进行失效预报。
