针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别...针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别进行SVD完成对伪码序列集合规模数的估计、数据降噪、粗分类以及初始聚类中心的选取。最后通过K-means算法优化分类结果,得到伪码序列的估计值。该算法在聚类之前事先确定聚类数目,大大减少了迭代次数。同时实验结果表明,该算法在信息码元分组小于5 bit,信噪比大于-10 dB时可以准确估计出软扩频信号的伪码序列,性能较同类算法有所提升。展开更多
This paper proposes the retrieval method of ocean wave spectrum for airborne radar observations at small incidence angles, which is slightly modified from the method developed by Hauser. Firstly, it makes use of integ...This paper proposes the retrieval method of ocean wave spectrum for airborne radar observations at small incidence angles, which is slightly modified from the method developed by Hauser. Firstly, it makes use of integration method to estimate total mean square slope instead of fitting method, which aims to reduce the affects of fluctuations superposed on normalized radar cross-section by integration. Secondly, for eliminating the noise spectrum contained in signal spectrum, the method considers the signal spectrum in certain look direction without any long wave components as the assumed noise spectrum, which would be subtracted from signal spectrum in any look direction for linear wave spectrum retrieval. Estimated v from the integration method are lower than the one from fitting method and have a standard deviation of 0.004 between them approximately. The assumed noise spectrum energy almost has no big variations along with the wave number and is slightly lower to the high wave number part of signal spectrum in any look direction, which follows that the assumption makes sense. The retrieved directional spectra are compared with the buoy records in terms of peak wavelength, peak direction and the significant wave height. Comparisons show that the retrieved peak wavelength and significant wave height are slightly higher than the buoy records but don＇t differs significantly （error less than 10%）. For peak direction, the swell waves in first case basically propagate in the wind direction 6 hours ago and the wind-generated waves in second case also propagate in the wind direction, but the 180° ambiguity remains. Results show that the modified method can carry out the retrieval of directional wave spectrum.展开更多
With the aim to the quantification of anomaly identification and extraction for observed or analyzed records, we present two statistical methods of earthquake corresponding relevancy spectrum （ECRS） and sliding mean...With the aim to the quantification of anomaly identification and extraction for observed or analyzed records, we present two statistical methods of earthquake corresponding relevancy spectrum （ECRS） and sliding mean relevancy （SMR）. With ECRS method, we can obtain the abnormal confidence attribute of data in different value ranges. Based on the relevancy spectrum in different studied time-intervals, we convert the original time sequence into relevancy time sequence, and can obtain the SMR time series by using the multi-point cumulative sliding mean method. Then we can identify the seismic precursor anomaly. We test this method by taking the time sequence of r/-value in the northern Tianshan region as original data. The result shows that when the studied time-interval is 18 months, the precursor anomaly can be identified bet- ter from sliding mean relevancy. The anomaly corresponding rate is 83 percent, the earthquake corresponding rate is 86 per- cent, and the anomaly is characteristic of the middle term. To try the research on multi-parameter comprehensive application, we take the Kalpin tectonic block in Xinjiang as our studied region, and analyze the spatial and temporal abnormal characters of multi-parameter sliding extreme-value relevancy （MSER） before mid-strong earthquakes in the Kalpin block. The result indicates that ECRS and SMR sequence in different time-intervals can not only be used to identify the precursor anomaly of single-item data, but also offer the data of quantitative single-item anomaly for comprehensive earthquake analysis and prediction.展开更多
Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, d...Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.展开更多
In the study by Baliarsingh and Dutta [Internat. J.Anal., Vol.2014（2014）, Article ID 786437], the authors computed the spectrum and the fine spectrum of the product operator G （u, v; A） over the sequence space e1....In the study by Baliarsingh and Dutta [Internat. J.Anal., Vol.2014（2014）, Article ID 786437], the authors computed the spectrum and the fine spectrum of the product operator G （u, v; A） over the sequence space e1. The product operator G （u, v; △） over l1 is defined by （G（u,v;△）x）k=^k∑i=0ukvi（xi- xi-1） with xk = 0 for all k 〈 0, where x = （xk）∈e1,and u and v axe either constant or strictly decreasing sequences of positive real numbers satisfying certain conditions. In this article we give some improvements of the computation of the spectrum of the operator G （u, v; △） on the sequence space gl.展开更多
文摘针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别进行SVD完成对伪码序列集合规模数的估计、数据降噪、粗分类以及初始聚类中心的选取。最后通过K-means算法优化分类结果,得到伪码序列的估计值。该算法在聚类之前事先确定聚类数目,大大减少了迭代次数。同时实验结果表明,该算法在信息码元分组小于5 bit,信噪比大于-10 dB时可以准确估计出软扩频信号的伪码序列,性能较同类算法有所提升。
基金The Key Technologies Research on HY-1B Satellite Data Applications:JT0805the Composite Investigation and Evaluation on China Offshore Ocean:908-03-02-08
文摘This paper proposes the retrieval method of ocean wave spectrum for airborne radar observations at small incidence angles, which is slightly modified from the method developed by Hauser. Firstly, it makes use of integration method to estimate total mean square slope instead of fitting method, which aims to reduce the affects of fluctuations superposed on normalized radar cross-section by integration. Secondly, for eliminating the noise spectrum contained in signal spectrum, the method considers the signal spectrum in certain look direction without any long wave components as the assumed noise spectrum, which would be subtracted from signal spectrum in any look direction for linear wave spectrum retrieval. Estimated v from the integration method are lower than the one from fitting method and have a standard deviation of 0.004 between them approximately. The assumed noise spectrum energy almost has no big variations along with the wave number and is slightly lower to the high wave number part of signal spectrum in any look direction, which follows that the assumption makes sense. The retrieved directional spectra are compared with the buoy records in terms of peak wavelength, peak direction and the significant wave height. Comparisons show that the retrieved peak wavelength and significant wave height are slightly higher than the buoy records but don＇t differs significantly （error less than 10%）. For peak direction, the swell waves in first case basically propagate in the wind direction 6 hours ago and the wind-generated waves in second case also propagate in the wind direction, but the 180° ambiguity remains. Results show that the modified method can carry out the retrieval of directional wave spectrum.
文摘With the aim to the quantification of anomaly identification and extraction for observed or analyzed records, we present two statistical methods of earthquake corresponding relevancy spectrum （ECRS） and sliding mean relevancy （SMR）. With ECRS method, we can obtain the abnormal confidence attribute of data in different value ranges. Based on the relevancy spectrum in different studied time-intervals, we convert the original time sequence into relevancy time sequence, and can obtain the SMR time series by using the multi-point cumulative sliding mean method. Then we can identify the seismic precursor anomaly. We test this method by taking the time sequence of r/-value in the northern Tianshan region as original data. The result shows that when the studied time-interval is 18 months, the precursor anomaly can be identified bet- ter from sliding mean relevancy. The anomaly corresponding rate is 83 percent, the earthquake corresponding rate is 86 per- cent, and the anomaly is characteristic of the middle term. To try the research on multi-parameter comprehensive application, we take the Kalpin tectonic block in Xinjiang as our studied region, and analyze the spatial and temporal abnormal characters of multi-parameter sliding extreme-value relevancy （MSER） before mid-strong earthquakes in the Kalpin block. The result indicates that ECRS and SMR sequence in different time-intervals can not only be used to identify the precursor anomaly of single-item data, but also offer the data of quantitative single-item anomaly for comprehensive earthquake analysis and prediction.
文摘Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.
文摘In the study by Baliarsingh and Dutta [Internat. J.Anal., Vol.2014（2014）, Article ID 786437], the authors computed the spectrum and the fine spectrum of the product operator G （u, v; A） over the sequence space e1. The product operator G （u, v; △） over l1 is defined by （G（u,v;△）x）k=^k∑i=0ukvi（xi- xi-1） with xk = 0 for all k 〈 0, where x = （xk）∈e1,and u and v axe either constant or strictly decreasing sequences of positive real numbers satisfying certain conditions. In this article we give some improvements of the computation of the spectrum of the operator G （u, v; △） on the sequence space gl.
