针对到达时间差(time difference of arrival,TDOA)方法对全球卫星导航系统(GNSS)较弱的干扰进行定位时难以估计时差而导致定位失败的问题,提出一种基于信号噪声分离的差方均值函数拟合的时差估计方法。该方法首先利用干扰信号与噪声不...针对到达时间差(time difference of arrival,TDOA)方法对全球卫星导航系统(GNSS)较弱的干扰进行定位时难以估计时差而导致定位失败的问题,提出一种基于信号噪声分离的差方均值函数拟合的时差估计方法。该方法首先利用干扰信号与噪声不同的时频分布特性采用奇异值差分谱将信号和噪声分离,然后通过差分和拟合的方法估计出干扰信号到达不同位置接收机的时差。通过仿真对比了该方法与传统的改进直接互相关、相位变换、经验模式分解等方法的性能,结果表明在相关噪声存在和较低信噪比的情况下该方法表现出了优越的性能。展开更多
Semiparametric regression models and estimating covariance functions are very useful for longitudinal study. To heed the positive-definiteness constraint, we adopt the modified Cholesky decomposition approach to decom...Semiparametric regression models and estimating covariance functions are very useful for longitudinal study. To heed the positive-definiteness constraint, we adopt the modified Cholesky decomposition approach to decompose the covariance structure. Then the covariance structure is fitted by a semiparametric model by imposing parametric within-subject correlation while allowing the nonparametric variation function. We estimate regression functions by using the local linear technique and propose generalized estimating equations for the mean and correlation parameter. Kernel estimators are developed for the estimation of the nonparametric variation function. Asymptotic normality of the the resulting estimators is established. Finally, the simulation study and the real data analysis are used to illustrate the proposed approach.展开更多
文摘针对到达时间差(time difference of arrival,TDOA)方法对全球卫星导航系统(GNSS)较弱的干扰进行定位时难以估计时差而导致定位失败的问题,提出一种基于信号噪声分离的差方均值函数拟合的时差估计方法。该方法首先利用干扰信号与噪声不同的时频分布特性采用奇异值差分谱将信号和噪声分离,然后通过差分和拟合的方法估计出干扰信号到达不同位置接收机的时差。通过仿真对比了该方法与传统的改进直接互相关、相位变换、经验模式分解等方法的性能,结果表明在相关噪声存在和较低信噪比的情况下该方法表现出了优越的性能。
基金supported by National Natural Science Foundation of China (GrantNos.10931002,10911120386)
文摘Semiparametric regression models and estimating covariance functions are very useful for longitudinal study. To heed the positive-definiteness constraint, we adopt the modified Cholesky decomposition approach to decompose the covariance structure. Then the covariance structure is fitted by a semiparametric model by imposing parametric within-subject correlation while allowing the nonparametric variation function. We estimate regression functions by using the local linear technique and propose generalized estimating equations for the mean and correlation parameter. Kernel estimators are developed for the estimation of the nonparametric variation function. Asymptotic normality of the the resulting estimators is established. Finally, the simulation study and the real data analysis are used to illustrate the proposed approach.
