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.

我们将给出的不确定信息的组合视为概率密度函数（PDFS）。信息通常来自通过通信网络连接并提供其对环境的推断的自主估计器。信息处理是通过通过网络传播本地估计并执行本地融合来以分散的方式完成的。有关估计值之间依赖关系的全局信息不是或仅是近似维护的，以保持计算，通信和存储。观测和状态的不确定性以概率密度函数为特征，在实际目的中，采用有限维参数化。更具体地说，我们主要关注高斯混合物和狄拉克混合物。对于系统地融合本地估计，有必要考虑其常见信息，由于上述原因，该信息已故意（部分）被丢弃。结果，不可能区分已经使用的新信息和信息，从而导致估计依赖性时过度自信的估计。为了避免这种所谓的“数据乱伦”问题，即对相同的数据进行双重计数，融合估计值必须至少与真实估计值一样不确定。 PDF保守性描述融合估计的财产在其余部分被称为“一致性”。尽管在线性系统的背景下保证高斯密度一致性的程序是众所周知的，但是这些概念不能在非线性信息处理中出现的任意密度转移到任意密度。几乎存在困难和基本挑战已被确定为该建议的基础：首先，融合的一致性也必须适当定义用于递归处理的PDF形式。然后必须开发局部融合的程序，以考虑本地估计之间的未知依赖性，以提供一致的结果。总之，我们为任意密度的融合算法框架提出了提供一致估计的任意密度。这些算法在整合依赖信息，准确性和计算工作的方式上会有所不同。希望这将导致进一步的进展，以解决可估算质量的大型问题的可进行估算方法。