针对现有稀疏信号功率迭代算法对方位相近目标分辨概率与估计精度较低问题,提出了一种稀疏信号功率迭代补偿的矢量传感器阵列波达方向(Direction of Arrival, DOA)估计方法。基于稀疏信号补偿原理和加权协方差矩阵拟合准则,构建了关于...针对现有稀疏信号功率迭代算法对方位相近目标分辨概率与估计精度较低问题,提出了一种稀疏信号功率迭代补偿的矢量传感器阵列波达方向(Direction of Arrival, DOA)估计方法。基于稀疏信号补偿原理和加权协方差矩阵拟合准则,构建了关于稀疏信号功率与补偿权重的目标函数。推导了稀疏信号功率迭代更新表达式的闭式解。通过对稀疏信号功率进行谱峰搜索获得DOA估计值。理论分析表明,所提算法通过对离散网格点上的信号功率进行补偿提高了方位相近目标的分辨率概率与估计精度。仿真结果表明,相较于经典子空间算法与现有稀疏功率迭代算法,所提算法对方位相近目标具有较高的分辨概率与估计精度。展开更多
For analyzing correlated binary data with high-dimensional covariates,we,in this paper,propose a two-stage shrinkage approach.First,we construct a weighted least-squares(WLS) type function using a special weighting sc...For analyzing correlated binary data with high-dimensional covariates,we,in this paper,propose a two-stage shrinkage approach.First,we construct a weighted least-squares(WLS) type function using a special weighting scheme on the non-conservative vector field of the generalized estimating equations(GEE) model.Second,we define a penalized WLS in the spirit of the adaptive LASSO for simultaneous variable selection and parameter estimation.The proposed procedure enjoys the oracle properties in high-dimensional framework where the number of parameters grows to infinity with the number of clusters.Moreover,we prove the consistency of the sandwich formula of the covariance matrix even when the working correlation matrix is misspecified.For the selection of tuning parameter,we develop a consistent penalized quadratic form(PQF) function criterion.The performance of the proposed method is assessed through a comparison with the existing methods and through an application to a crossover trial in a pain relief study.展开更多
文摘针对现有稀疏信号功率迭代算法对方位相近目标分辨概率与估计精度较低问题,提出了一种稀疏信号功率迭代补偿的矢量传感器阵列波达方向(Direction of Arrival, DOA)估计方法。基于稀疏信号补偿原理和加权协方差矩阵拟合准则,构建了关于稀疏信号功率与补偿权重的目标函数。推导了稀疏信号功率迭代更新表达式的闭式解。通过对稀疏信号功率进行谱峰搜索获得DOA估计值。理论分析表明,所提算法通过对离散网格点上的信号功率进行补偿提高了方位相近目标的分辨率概率与估计精度。仿真结果表明,相较于经典子空间算法与现有稀疏功率迭代算法,所提算法对方位相近目标具有较高的分辨概率与估计精度。
基金supported by National Natural Science Foundation of China(Grant No.11201306)the Innovation Program of Shanghai Municipal Education Commission(Grant No.13YZ065)+2 种基金the Fundamental Research Project of Shanghai Normal University(Grant No.SK201207)the scholarship under the State Scholarship Fund by the China Scholarship Council in 2011the Research Grant Council of Hong Kong, Hong Kong,China(Grant No.#HKBU2028/10P)
文摘For analyzing correlated binary data with high-dimensional covariates,we,in this paper,propose a two-stage shrinkage approach.First,we construct a weighted least-squares(WLS) type function using a special weighting scheme on the non-conservative vector field of the generalized estimating equations(GEE) model.Second,we define a penalized WLS in the spirit of the adaptive LASSO for simultaneous variable selection and parameter estimation.The proposed procedure enjoys the oracle properties in high-dimensional framework where the number of parameters grows to infinity with the number of clusters.Moreover,we prove the consistency of the sandwich formula of the covariance matrix even when the working correlation matrix is misspecified.For the selection of tuning parameter,we develop a consistent penalized quadratic form(PQF) function criterion.The performance of the proposed method is assessed through a comparison with the existing methods and through an application to a crossover trial in a pain relief study.
