Based on the maximunl-entropy （ME） principle, a new power spectral estimator for random waves is derived in the form of S（ω）=a/8H^2^-（2π）^（d＋2）exp[-b（2π/ω）^n],1）y solving a variational problem subject ...Based on the maximunl-entropy （ME） principle, a new power spectral estimator for random waves is derived in the form of S（ω）=a/8H^2^-（2π）^（d＋2）exp[-b（2π/ω）^n],1）y solving a variational problem subject to some quite general constraints. This robust method is comprehensive enough to describe the wave spectra even in extreme wave conditions and is superior to periodogranl method that is not suit＇able to process comparatively short or intensively unsteady signals for its tremendous boundary effect and some inherent defects of FKF. Fortunately, the newly derived method for spectral estimation works fairly well, even though the sample data sets are very short and unsteady, and the reliability and efficiency of this spectral estimator have been preliminarily proved.展开更多
Daily return series of Dow Jones Industrial Average Index (DJIA) and Shanghai Conposite Index are investigated using spectral analysis methods. The day of the week effect is found in the frequency domain in both ...Daily return series of Dow Jones Industrial Average Index (DJIA) and Shanghai Conposite Index are investigated using spectral analysis methods. The day of the week effect is found in the frequency domain in both stock markets. Time domain performances of the daily returns are also studied. Although both markets have a clear weekly component in the frequency domain, they show some different behaviors with respect to the day of the week effects.展开更多
To estimate the period of a periodic point process from noisy and incomplete observations, the classical periodogram algorithm is modified. The original periodogram algorithm yields an estimate by performing grid sear...To estimate the period of a periodic point process from noisy and incomplete observations, the classical periodogram algorithm is modified. The original periodogram algorithm yields an estimate by performing grid search of the peak of a spectrum, which is equivalent to the periodogram of the periodic point process, thus its performance is found to be sensitive to the chosen grid spacing. This paper derives a novel grid spacing formula, after finding a lower bound of the width of the spectral mainlobe. By employing this formula, the proposed new estimator can determine an appropriate grid spacing adaptively, and is able to yield approximate maximum likelihood estimate （MLE） with a computational complexity of O（n2）. Experimental results prove that the proposed estimator can achieve better trade-off between statistical accuracy and complexity, as compared to existing methods. Simulations also show that the derived grid spacing formula is also applicable to other estimators that operate similarly by grid search.展开更多
研究宽带信噪比(RSNW)和有效功率谱宽度对最大似然(ML)法多普勒频率估计结果的影响,采用探测概率和正确估计频率标准差来描述ML算法对仿真信号和实验数据的处理性能,并与周期图最大值(PM)法所得结果进行比较。经理论仿真和对中国科学院...研究宽带信噪比(RSNW)和有效功率谱宽度对最大似然(ML)法多普勒频率估计结果的影响,采用探测概率和正确估计频率标准差来描述ML算法对仿真信号和实验数据的处理性能,并与周期图最大值(PM)法所得结果进行比较。经理论仿真和对中国科学院上海光学精密机械研究所1.5μm全光纤相干测风激光雷达实测数据的反演处理,结果表明,高谱宽度下,ML算法的正确估计频率标准差与相比PM可降低0.5 MHz,达到90%的探测概率所需宽带信噪比ML算法与PM算法相比低2 d B。表明ML算法的正确估计频率标准差能与PM算法相比不高于1.1 MHz,探测概率高9%。要获得风速精度小于1 m/s同时探测概率在80%以上,所需宽带信噪比大于-14 d B。展开更多
Preliminary results of the wind velocity estimation using the Maximum Entropy Method (MEM) to MU radar observation data sets are presented. The comparison of the results from the periodogram method and the MEM shows t...Preliminary results of the wind velocity estimation using the Maximum Entropy Method (MEM) to MU radar observation data sets are presented. The comparison of the results from the periodogram method and the MEM shows that the MEM estimation is reliable, and has higher accuracy, resolution and detectability than the estimation from periodogram method. The high accuracy power spectrum obtained by the MEM is very useful to studying the atmospheric turbulence structure. However. the MEM needs the longer computing time for obtaining the high accuracy spectrum. Particularly, the estimation of MEM will bring serious devia- tion at lower signal-to-noise ratio.展开更多
基金This research was financially supported by the National Natural Science Foundation of China(Grant No.50479028)a Research Fundfor Doctoral Programs of Higher Education of China(Grant No.20060423009)
文摘Based on the maximunl-entropy （ME） principle, a new power spectral estimator for random waves is derived in the form of S（ω）=a/8H^2^-（2π）^（d＋2）exp[-b（2π/ω）^n],1）y solving a variational problem subject to some quite general constraints. This robust method is comprehensive enough to describe the wave spectra even in extreme wave conditions and is superior to periodogranl method that is not suit＇able to process comparatively short or intensively unsteady signals for its tremendous boundary effect and some inherent defects of FKF. Fortunately, the newly derived method for spectral estimation works fairly well, even though the sample data sets are very short and unsteady, and the reliability and efficiency of this spectral estimator have been preliminarily proved.
文摘Daily return series of Dow Jones Industrial Average Index (DJIA) and Shanghai Conposite Index are investigated using spectral analysis methods. The day of the week effect is found in the frequency domain in both stock markets. Time domain performances of the daily returns are also studied. Although both markets have a clear weekly component in the frequency domain, they show some different behaviors with respect to the day of the week effects.
基金supported by the National Natural Science Foundation of China (No. 61002026)
文摘To estimate the period of a periodic point process from noisy and incomplete observations, the classical periodogram algorithm is modified. The original periodogram algorithm yields an estimate by performing grid search of the peak of a spectrum, which is equivalent to the periodogram of the periodic point process, thus its performance is found to be sensitive to the chosen grid spacing. This paper derives a novel grid spacing formula, after finding a lower bound of the width of the spectral mainlobe. By employing this formula, the proposed new estimator can determine an appropriate grid spacing adaptively, and is able to yield approximate maximum likelihood estimate （MLE） with a computational complexity of O（n2）. Experimental results prove that the proposed estimator can achieve better trade-off between statistical accuracy and complexity, as compared to existing methods. Simulations also show that the derived grid spacing formula is also applicable to other estimators that operate similarly by grid search.
文摘研究宽带信噪比(RSNW)和有效功率谱宽度对最大似然(ML)法多普勒频率估计结果的影响,采用探测概率和正确估计频率标准差来描述ML算法对仿真信号和实验数据的处理性能,并与周期图最大值(PM)法所得结果进行比较。经理论仿真和对中国科学院上海光学精密机械研究所1.5μm全光纤相干测风激光雷达实测数据的反演处理,结果表明,高谱宽度下,ML算法的正确估计频率标准差与相比PM可降低0.5 MHz,达到90%的探测概率所需宽带信噪比ML算法与PM算法相比低2 d B。表明ML算法的正确估计频率标准差能与PM算法相比不高于1.1 MHz,探测概率高9%。要获得风速精度小于1 m/s同时探测概率在80%以上,所需宽带信噪比大于-14 d B。
文摘Preliminary results of the wind velocity estimation using the Maximum Entropy Method (MEM) to MU radar observation data sets are presented. The comparison of the results from the periodogram method and the MEM shows that the MEM estimation is reliable, and has higher accuracy, resolution and detectability than the estimation from periodogram method. The high accuracy power spectrum obtained by the MEM is very useful to studying the atmospheric turbulence structure. However. the MEM needs the longer computing time for obtaining the high accuracy spectrum. Particularly, the estimation of MEM will bring serious devia- tion at lower signal-to-noise ratio.
