Spectral Theory of Large Dimensional Random Matrices and Its Applications

大维随机矩阵谱理论及其应用

• 批准号: 9408799
• 负责人: Zhi-Dong Bai
• 金额: $ 6.12万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-07-01 至 1995-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9408799&HistoricalAwards=false
• 关键词: Spectral; Theory; Large; Dimensional; Random

The principal investigators (Z.D. Bai and Jack W. Silverstein) plan to study several remaining questions concerning the eigenvalues of a class of random matrices of sample covariance type, where the numbers of variables and observations are proportionally large. Theoretical problems include the convergence and convergence rates of the empirical spectral distributions to some nonrandom limits, limits of extreme eigenvalues, separation between eigenvalues when the population ones are separated, and analogues when the underlying samples are dependent, such as stationary ergodic. The principal investigators also plan to apply the theory of spectral analysis of large dimensional random matrices to the detection problem in array signal processing when the numbers of (unknown) sources and the sensors are both large. Recent work has shown that, when applying known results, the number of measurements needed to estimate the proportion of the number of sources to the number of sensors can be much smaller than what is required when using classical multivariate analysis. However, extensive simulations reveal an interesting phenomenon: the exact number of sources can be detected with the same relatively low number of samples. Intensive investigation of these problems is of great interest in both probability theory and signal processing. Some other application problems are also proposed. The principal investigators (Z.D. Bai and Jack W. Silverstein) plan to study certain properties of random matrices of high dimension used in modeling multivariate random phenomena. The motivation stems from the detection problem in array signal processing. For example, when determining the number of sources impinging on a bank of sensors in the presence of noise when the number of sources is sizable, known results on large dimensional random matrices can be used to reliably estimate the proportion of the number of sources to the number of sensors with a number of measurements much smaller than what is needed according to standard multivariate analysis. However, extensive simulations reveal that, with high probability, the exact number of sources can be detected with the same relatively low number of samples. The principal investigators intend to mathematically analyze the observed phenomena which allows for exact detection, and its dependence on the number of sensors and the sample size. Several other remaining questions on large dimensional random matrices important to applications will also be studied.

主要研究者(Z.D.Bai和Jack W.Silverstein)计划研究一类样本协方差型随机矩阵的特征值的几个剩余问题，其中变量和观测值的数量成比例地大。理论问题包括经验谱分布对某些非随机极限的收敛和收敛速度，极值特征值的极限，总体分离时特征值之间的分离，以及基础样本相依时的类似问题，如平稳遍历。主要研究人员还计划将高维随机矩阵谱分析理论应用于阵列信号处理中的检测问题，当(未知)源和传感器的数量都很大时。最近的工作表明，当应用已知结果时，估计源数量与传感器数量的比例所需的测量数量可以比使用经典多变量分析时所需的测量数量少得多。然而，广泛的模拟揭示了一个有趣的现象：在样本数量相对较少的情况下，可以检测到确切数量的源。对这些问题的深入研究是概率论和信号处理领域的研究热点。文中还提出了其他一些应用问题。主要研究人员(Z.D.Bai和Jack W.Silverstein)计划研究用于模拟多变量随机现象的高维随机矩阵的某些性质。其动机源于阵列信号处理中的检测问题。例如，当确定在噪声存在的情况下撞击传感器组的源的数量时，当源的数量相当大时，可以使用关于大维随机矩阵的已知结果来可靠地估计源的数量与具有比标准多变量分析所需的测量数量小得多的传感器的数量的比例。然而，大量的仿真表明，在很高的概率下，可以在相同的相对较少的样本数量下检测到准确的信源数量。主要研究人员打算对观察到的现象进行数学分析，以实现准确的检测，并分析其对传感器数量和样本大小的依赖。关于大维随机矩阵的其他几个对应用很重要的问题也将被研究。