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General Heterogeneous Sensor Estimation Fusion-System Fusion Method

机译：一般异构传感器估计融合系统融合方法

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摘要

This paper provides a summary of general weighted sensor estimate Mean Square Error (MSE) fusion and recently established Minimizing Euclidean Error Estimation (MEEE) fusion. Based on the MEEE setting, we propose a general heterogeneous sensor estimation fusion method. Unlike the previous estimation fusion method, the statistical correlations between sensor estimation errors are not needed, as well as, a quantitative joint function relationship between the multiple heterogeneous sensor estimates also is not needed in the new method. In practice, it is so hard to get the both information. This is why so difficult to fuse heterogeneous sensor data before. All we need are the sensor estimates and their error bounds. Obviously, a rough estimation error bound can be derived much easier than an accurate statistical correlation or a quantitative joint relationship between sensor estimate errors. Instead, a reasonable way to establish their organical connection between the multiple heterogeneous sensor estimates is to remodel all sensor estimates and their error bounds to be a group of measurement equations for the given estimation problem. While the MEEE for this remodelled system is derived, the heterogeneous sensor estimation fusion can be completed by using MEEE. Hence, we call it System Fusion Method (SFM) to differentiate from the most popular distributed weighted fusion method. Since the estimate of an algorithm can be reviewed as a measurement of a soft/nominal sensor, the proposed method can be used to fuse the results from multiple heterogeneous algorithms. It is easy to see that this method is quite general and independent of specific heterogeneous sensor data, and builded on solid theoretical basis of optimality since MEEE is founded on contemporary convex optimization and intersection fusion of estimation coverage sets of true value as given in [1]. Besides, we present some suggestions how to choose better error bounds and apply a multi-error-bound method to handle lack of the error bound knowledge.
机译：本文提供了一般加权传感器估计均方误差（MSE）融合的概要，最近建立了最小化欧几里德误差估计（MEEE）融合。基于MEEE设置，我们提出了一般的异构传感器估计融合方法。与先前的估计融合方法不同，不需要传感器估计误差之间的统计相关性，以及在新方法中也不需要多个异构传感器估计之间的定量关节功能关系。在实践中，难以获得这两个信息。这就是如此难以以前融合异构传感器数据的原因。我们所需要的只是传感器估计和错误界限。显然，粗略估计误差绑定可以比准确的统计相关或传感器估计误差之间的定量联合关系更容易得多。相反，在多个异构传感器估计之间建立其有机连接的合理方法是改造所有传感器估计，并且它们的误差界限为给定估计问题的一组测量方程。虽然导出了用于该重建系统的MEEE，但是通过使用MEEE可以完成异构传感器估计融合。因此，我们称之为系统融合方法（SFM）以区分最流行的分布式加权融合方法。由于可以审查算法的估计作为软/标称传感器的测量，因此该方法可用于熔断来自多个异构算法的结果。很容易看出，这种方法非常一般，独立于特定的异构传感器数据，并且在最佳的稳定性理论基础上建立，因为MEEE成立于当代凸优化和估计覆盖范围的真实值的估计覆盖集的融合，如[1 ]。此外，我们提出了一些建议如何选择更好的错误界限并应用多错误绑定的方法来处理缺少错误绑定知识。

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