基于谱分解的降阶求根MUSIC算法

求根多重信号分类(Root-MUSIC)算法以多项式求根代替谱峰搜索,降低了波达方向(DOA)估计的计算量,但当阵元数较大时,其计算量依然很大。为进一步降低计算量,该文提出一种降阶Root-MUSIC(RD-Root-MUSIC)算法。该算法基于谱分解将Root-MUSIC多项式的阶次降低一半,再根据矩阵特征多项式与求根多项式的关系构造友阵,采用Arnoldi迭代计算得到友阵的L个大特征值(L为信号数)并估计DOA。仿真结果表明,RD-Root-MUSIC估计精度与Root-MUSIC相近,但其在大阵元下具有比Root-MUSIC更低的计算量。     

基于方差分量估计的GPS和BD组合单点定位

讨论了GPS/BD组合单点定位相关问题,利用北京某站的实测双模数据分析了基于方差分量估计的验后定权方案对定位结果的影响。实验结果表明,和等权的GPS/BD组合单点定位相比,基于验后权的组合单点定位可以改善站心坐标系中E方向和U方向的定位精度,但N方向的定位精度没有得到提高。     

任意分布的均值的渐近有效估计

讨论一般随机分布的均值的区间估计问题,给出了一种求任意分布均值的置信区间的抽样方案：证明此抽样方案渐近有效,得出了关于任意分布均值的置信区间的新结果。     

核ICA在电流传感器相位差监测中的应用

介绍了核独立分量分析(ICA)的基本原理和算法,并将其用于对电流传感器输出的混合信号进行分离,通过比较分离出的单频测试信号输入前后的相位差,来标定传感器本身的相位差对其检测对象的影响。此外,还采用最大似然法对核ICA的分离效果进行评价。实验证明:在输入信号的信噪比为18.73dB的情况下,核ICA分离出的信号与源信号相位差在0.002 rad以内,达到了实际应用中所要求的误差范围。     

PAC learning in non‐linear FIR models

The PAC learning theory creates a framework to assess the learning properties of static models for which the data are assumed to be independently and identically distributed (i.i.d.). The present paper first extends the idea of PAC learning to cover the learning of modelling tasks with m‐dependent data, and then applies the resulting framework to evaluate learning of non‐linear FIR models. Also, the learning properties of FIR modelling with radial basis function networks are further specified. These results include an upper bound on the size of the data set required to train an FIR radial basis function network, provided that the input data are uniformly distributed. Copyright © 2001 John Wiley & Sons, Ltd.     

纯方位单目标定位与跟踪的线性时变模型

给出了纯方位系统（第二类系统）单目标定位与跟踪的１种线性时变模型,讨论了它的 可测性．给出了滤波解、离散系统及其解析解．     

Observability theorems for some linear hyperbolic systems

In this paper the observability of systems of first-order hyperbolic partial differential equations is investigated. Both systems with boundary measurements and systems with distributed measurements are considered. In the case of boundary measurements a sufficient condition is found for observability. This condition is shown also to be necessary for systems defined on a one-dimensional space domain, as well as in other special cases. In the case of linear distributed measurements, a necessary and sufficient condition for observability is derived. The theorems are applicable for example to linearized hydrodynamical flow equations and to industrial flow system equations.     

Weighted least squares estimation of background in EELS imaging

In quantitative Electron Energy Loss Spectrometry, a weighted least squares estimation should theoretically be used to estimate the background law below core edge energy, since the variances of the data vary. However, it is found that proper weighting makes the above edge signal-to-noise ratio decrease rather than increase. This result is discussed, and the influence of the bias introduced by the logarithmic transformation of the data is quantified.     

A new approach for improved identification of measurement bias

This work presents a technique that can completely and accurately identify measurement bias in cases where it is not possible to use the method of Rollins and Davis (1992, 1993) and where the method of Narasimhan and Mah (1987) fail to perform accurately. This technique makes use of information contained in the relationship between individual measurements and the corresponding nodal imbalance. The performance of this method is demonstrated on a problem from the literature that has been difficult for other methods to handle. In addition, this article discusses how the new technique can be used as a visual monitoring tool for identifying biased measured variables.