Forty-six Years of Statistical Signal Processing

统计信号处理四十六年统计信号处理中几何思想的影响可以追溯到该领域的诞生。线性子空间的思想产生了巨大的影响，它们的成功导致了越来越复杂的几何结构的开发，例如线性子空间的Grassman流形，作为关于信号检测、参数估计和推理的统计推理的领域。在最近的统计信号处理研究(以及相关领域，如统计学习)中，有一种趋势是利用在高维环境空间中呈现的数据中的低维结构。这一发展继续为该领域的基础带来新的几何机械。在华盛顿大学完成博士学业后的46年里，路易斯·沙尔夫将几何学的见解带到了统计信号处理的几乎每一个方面。由于他在该学科的工作非常广泛，几乎对该领域的每一个主要主题都做出了重大贡献，所以围绕他职业生涯的主题举办这次研讨会？S对统计信号处理的影响为我们提供了一个自然的框架，让我们能够快速了解该领域的历史，以及关键的几何见解如何继续塑造该领域的历史。组织者预计，此次研讨会将促进人们对沙尔夫？S提出的几何透视在塑造当今S研究前沿方面的力量的认识。他们认为，在超越线性子空间的几何思想在信号处理中变得越来越重要的时候，这对该领域即将到来的研究人员和实践者来说是一个特别重要的信息。与Asilomar信号、系统和计算机会议(Asilomar Conference on Signals，Systems，and Computers)合作举办研讨会，许多来自美国西部的统计信号处理博士生和早期研究人员定期参加，为这些受众提供了研讨会机会。

Forty-Six Years of Statistical Signal ProcessingThe influence of geometrical ideas in statistical signal processing dates to the inception of the field. Linear subspace ideas have had enormous impact and their success has led to exploitation of increasingly sophisticated geometrical structures, such as Grassmannian manifolds of linear subspaces, as domains for statistical reasoning about signal detection, parameter estimation, and inference. There is a trend in recent statistical signal processing research (and in related areas, such as statistical learning) toward exploitation of low-dimensional structure in data presented in ambient spaces of much higher dimension. This development continues to bring new geometrical machinery into the foundations of the field. In the forty-six years since completing his doctoral studies at the University of Washington, Louis Scharf has brought geometrical insights to almost every aspect of statistical signal processing. Because his work in the discipline has been so widespread, contributing significantly to almost every major topic within the field, building this workshop around the theme of his career?s influence on statistical signal processing provides a natural framework for a snapshot of the history of the field and how key geometrical insights continue to shape it. The organizers anticipate that this workshop will promote awareness of the power of the geometrical perspective advanced by Scharf?s work in shaping the forefront of today?s research. They believe this is a particularly important message to upcoming researchers and practitioners in the field at a time when geometrical ideas beyond linear subspaces are becoming increasingly relevant in signal processing. And holding the workshop in coordination with the Asilomar Conference on Signals, Systems, and Computers, where many Ph.D. students and early-career researchers in statistical signal processing from the western U.S. attend regularly, provides the workshop access to this audience.