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Consistent Fusion in Networked Estimation Systems

网络估计系统中的一致融合

基本信息

批准号：
232171657
负责人：
Professor Dr.-Ing. Uwe D. Hanebeck
金额：
--
依托单位：
Department of Cybernetics
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2013
资助国家：
德国
起止时间：
2012-12-31 至 2015-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/232171657?language=en
关键词：
Consistent Fusion Networked Estimation Systems

项目摘要

We consider the combination of uncertain information given as probability density functions (pdfs). The information typically arises from autonomous estimators that are connected by a communication network and provide their inferences about the environment.Information processing is done in a decentralized fashion by propagating local estimates through the network and performing local fusion. Global information about the dependencies between the estimates is not or only approximately maintained in order to keep computation, communication, and storage tractable. Uncertainty in observations and states is characterized by probability density functions, where for practical purposes, finite-dimensional parameterizations are employed. More specifically, we primarily focus on Gaussian mixtures and Dirac mixtures.For systematically fusing local estimates, it is necessary to consider their common information, which has been deliberately (partially) discarded for the reasons stated above. As a result, it is not possible to make a distinction between new information and information already used, which leads to overconfident estimates when the dependencies are neglected. In order to avoid this so called "data incest" problem, i.e., double-counting the same data, the fused estimate must be at least as uncertain as the true estimate. This property of conservativeness of the pdf for describing the fused estimate is called "consistency" in the remainder. Although procedures for guaranteeing consistency for Gaussian densities in the context of linear systems are well-known, these concepts cannot be transferred to arbitrary densities as they appear in nonlinear information processing.Several difficult and fundamental challenges have been identified as the basis for this proposal: First of all, consistency of fusion results in the form of pdfs has to be properly defined also for recursive processing. Procedures for local fusion then have to be developed that consider the unknown dependencies between local estimates in order to provide consistent results. In summary, we propose a framework of fusion algorithms for arbitrary densities that provides consistent estimates. These algorithms will differ in their way of incorporating dependency information, in their accuracy, and in their computational effort. This will hopefully result in further progress towards tractable estimation methods for large problems with guaranteed estimation quality.

我们考虑不确定性信息的组合作为概率密度函数（pdf）。这些信息通常来自通过通信网络连接的自主估计器，并提供关于环境的推断。信息处理以分散的方式进行，通过网络传播本地估计并执行本地融合。关于估计之间的依赖关系的全局信息不被维护或仅被近似地维护，以便保持计算、通信和存储易于处理。观测和状态的不确定性的特征在于概率密度函数，其中出于实用目的，采用有限维参数化。更具体地说，我们主要关注高斯混合和狄拉克混合，为了系统地融合局部估计，有必要考虑它们的公共信息，这些信息由于上述原因而被故意（部分）丢弃。因此，不可能区分新信息和已经使用的信息，这导致在忽略依赖关系时过度自信的估计。为了避免这种所谓的“数据乱伦”问题，即，如果重复计算相同的数据，则融合估计值必须至少与真实估计值一样不确定。这种描述融合估计的概率密度函数的保守性在余项中被称为“一致性”。虽然在线性系统的背景下，保证高斯密度的一致性的程序是众所周知的，这些概念不能转移到任意密度，因为它们出现在非线性信息processing.Several困难和根本的挑战已被确定为这个建议的基础：首先，PDF格式的融合结果的一致性必须适当定义也递归处理。然后，必须开发考虑局部估计之间的未知依赖关系的局部融合程序，以提供一致的结果。总之，我们提出了一个框架的融合算法为任意密度，提供一致的估计。这些算法将在其结合依赖信息的方式、其准确性和其计算工作量方面有所不同。这将有希望导致进一步的进展，以保证估计质量的大问题的易处理的估计方法。