Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributio...Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributions,and it is difficult to identify such signals using traditional time-frequency analysis methods.To solve this problem,this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and time-frequency analysis.First of all,fractional Fourier transform and the Wigner-Ville distribution(WVD)are used to determine the number of main ridgelines and the tilt angle of the target component in WVD.Next,the standard deviation of the target component's width in the signal's WVD is calculated.Finally,an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features.Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17%under 0 dB.When the training data set and the test data set are mixed with noise,the recognition rate reaches 89.93%.The best recognition accuracy is achieved when the size of the training set is taken as 400.The algorithm complexity can meet the requirements of real-time recognition.展开更多
This paper proposes a new method for estimating the parameter of maneuvering targets based on sparse time-frequency transform in over-the-horizon radar（OTHR）. In this method, the sparse time-frequency distribution o...This paper proposes a new method for estimating the parameter of maneuvering targets based on sparse time-frequency transform in over-the-horizon radar（OTHR）. In this method, the sparse time-frequency distribution of the radar echo is obtained by solving a sparse optimization problem based on the short-time Fourier transform. Then Hough transform is employed to estimate the parameter of the targets. The proposed algorithm has the following advantages： Compared with the Wigner-Hough transform method, the computational complexity of the sparse optimization is low due to the application of fast Fourier transform（FFT）. And the computational cost of Hough transform is also greatly reduced because of the sparsity of the time-frequency distribution. Compared with the high order ambiguity function（HAF） method, the proposed method improves in terms of precision and robustness to noise. Simulation results show that compared with the HAF method, the required SNR and relative mean square error are 8 dB lower and 50 dB lower respectively in the proposed method. While processing the field experiment data, the execution time of Hough transform in the proposed method is only 4% of the Wigner-Hough transform method.展开更多
大地震能够同时激发出许多的地球自由振荡简正模,且地球的椭率、自转和内部的各向异性也会引起简正模的分裂,使各单线态之间的频率更接近(仅为几个μHz),这对地球自由振荡模型的检测提出更高的要求。本文以标准时频变换为基础,推导并验...大地震能够同时激发出许多的地球自由振荡简正模,且地球的椭率、自转和内部的各向异性也会引起简正模的分裂,使各单线态之间的频率更接近(仅为几个μHz),这对地球自由振荡模型的检测提出更高的要求。本文以标准时频变换为基础,推导并验证一种自由振荡模型检测的新方法。以3 S 1模型的检测为例,与经典的FT谱方法和最新的OSE方法相比,该方法具有更高的频率分辨率。展开更多
液压设备在运行过程中伴随着多域间的能量转换,尤其在变工况下呈现出非平稳性及非线性等特征,为状态监测与故障诊断带来难度。为了提高非平稳工况轴向柱塞泵故障诊断的性能,该研究提出采用既是运行参数又是状态参量的瞬时转速信号作为...液压设备在运行过程中伴随着多域间的能量转换,尤其在变工况下呈现出非平稳性及非线性等特征,为状态监测与故障诊断带来难度。为了提高非平稳工况轴向柱塞泵故障诊断的性能,该研究提出采用既是运行参数又是状态参量的瞬时转速信号作为轴向柱塞泵故障诊断的信息源。通过理论分析得出瞬时转速信号的波动成分中蕴含着元件健康状态信息。提出采用同步提取标准S变换(synchro-extracting of normal S transform,SNST)对其进行线通滤波处理。利用K-medoids方法将滤波重构后的瞬时转速波动信号角度域特征值进行聚类分析,并在机电液一体化平台上进行了变转速和变负载工况试验,实现了轴向柱塞泵配流盘在正常、轻微、严重磨损时的故障诊断。研究成果可为液压设备的运行状态监测与故障诊断提供新的方法。展开更多
基金This work was supported by the National Natural Science Foundation of China(91538201)the Taishan Scholar Project of Shandong Province(ts201511020)the project supported by Chinese National Key Laboratory of Science and Technology on Information System Security(6142111190404).
文摘Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributions,and it is difficult to identify such signals using traditional time-frequency analysis methods.To solve this problem,this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and time-frequency analysis.First of all,fractional Fourier transform and the Wigner-Ville distribution(WVD)are used to determine the number of main ridgelines and the tilt angle of the target component in WVD.Next,the standard deviation of the target component's width in the signal's WVD is calculated.Finally,an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features.Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17%under 0 dB.When the training data set and the test data set are mixed with noise,the recognition rate reaches 89.93%.The best recognition accuracy is achieved when the size of the training set is taken as 400.The algorithm complexity can meet the requirements of real-time recognition.
基金supported by the National Natural Science Foundation of China(611011726137118461301262)
文摘This paper proposes a new method for estimating the parameter of maneuvering targets based on sparse time-frequency transform in over-the-horizon radar（OTHR）. In this method, the sparse time-frequency distribution of the radar echo is obtained by solving a sparse optimization problem based on the short-time Fourier transform. Then Hough transform is employed to estimate the parameter of the targets. The proposed algorithm has the following advantages： Compared with the Wigner-Hough transform method, the computational complexity of the sparse optimization is low due to the application of fast Fourier transform（FFT）. And the computational cost of Hough transform is also greatly reduced because of the sparsity of the time-frequency distribution. Compared with the high order ambiguity function（HAF） method, the proposed method improves in terms of precision and robustness to noise. Simulation results show that compared with the HAF method, the required SNR and relative mean square error are 8 dB lower and 50 dB lower respectively in the proposed method. While processing the field experiment data, the execution time of Hough transform in the proposed method is only 4% of the Wigner-Hough transform method.
文摘大地震能够同时激发出许多的地球自由振荡简正模,且地球的椭率、自转和内部的各向异性也会引起简正模的分裂,使各单线态之间的频率更接近(仅为几个μHz),这对地球自由振荡模型的检测提出更高的要求。本文以标准时频变换为基础,推导并验证一种自由振荡模型检测的新方法。以3 S 1模型的检测为例,与经典的FT谱方法和最新的OSE方法相比,该方法具有更高的频率分辨率。
文摘液压设备在运行过程中伴随着多域间的能量转换,尤其在变工况下呈现出非平稳性及非线性等特征,为状态监测与故障诊断带来难度。为了提高非平稳工况轴向柱塞泵故障诊断的性能,该研究提出采用既是运行参数又是状态参量的瞬时转速信号作为轴向柱塞泵故障诊断的信息源。通过理论分析得出瞬时转速信号的波动成分中蕴含着元件健康状态信息。提出采用同步提取标准S变换(synchro-extracting of normal S transform,SNST)对其进行线通滤波处理。利用K-medoids方法将滤波重构后的瞬时转速波动信号角度域特征值进行聚类分析,并在机电液一体化平台上进行了变转速和变负载工况试验,实现了轴向柱塞泵配流盘在正常、轻微、严重磨损时的故障诊断。研究成果可为液压设备的运行状态监测与故障诊断提供新的方法。
