一种用于稳健融合可能有害第三方数据的框架及其统计意义
A Framework for Robust Assimilation of Potentially Malign Third-Party Data, and its Statistical Meaning
AI总结：
本文提出了一种基于模型的数据融合方法，结合假设检验组件来拒绝可能有故障或有害的数据。该框架基于经典粒子滤波算法的扩展，用于实时估计具有非线性动态和部分噪声观测的不确定系统的状态。通过将Fisher显著性检验和Neyman-Pearson假设检验框架应用于蒙特卡洛和传感器融合场景，提出了一种稳健的蒙特卡洛状态估计方案，能够有效处理故障或异常数据。
摘要：
本文提出了一种基于模型的数据融合方法，结合假设检验组件来拒绝可能有故障或有害的数据。该框架基于经典粒子滤波算法的扩展，用于实时估计具有非线性动态和部分噪声观测的不确定系统的状态。该扩展基于经典统计理论，利用统计检验对抗系统观测模型。我们讨论了将两种主要统计检验框架，即Fisher显著性检验和Neyman-Pearson假设检验，应用于蒙特卡洛和传感器融合设置。我们开发的蒙特卡洛Neyman-Pearson检验适用于具有可靠故障数据模型的情况，而Fisher检验适用于可能没有故障模型的情况，这在处理第三方数据（如交通运输系统用户的GNSS数据）时可能发生。这些统计检验可以与粒子滤波结合，以获得一种稳健于故障或异常数据的蒙特卡洛状态估计方案。我们提出一个合成的高速公路交通状态估计问题，其中滤波器能够拒绝模拟的故障GNSS测量。故障模型无关的Fisher滤波器在后者具有准确故障模型时表现较差，但在假设的故障模型不正确时表现更好。
英文摘要：
This paper presents a model-based method for fusing data from multiple sensors with a hypothesis-test-based component for rejecting potentially faulty or otherwise malign data. Our framework is based on an extension of the classic particle filter algorithm for real-time state estimation of uncertain systems with nonlinear dynamics with partial and noisy observations. This extension, based on classical statistical theories, utilizes statistical tests against the system's observation model. We discuss the application of the two major statistical testing frameworks, Fisherian significance testing and Neyman-Pearsonian hypothesis testing, to the Monte Carlo and sensor fusion settings. The Monte Carlo Neyman-Pearson test we develop is useful when one has a reliable model of faulty data, while the Fisher one is applicable when one may not have a model of faults, which may occur when dealing with third-party data, like GNSS data of transportation system users. These statistical tests can be combined with a particle filter to obtain a Monte Carlo state estimation scheme that is robust to faulty or outlier data. We present a synthetic freeway traffic state estimation problem where the filters are able to reject simulated faulty GNSS measurements. The fault-model-free Fisher filter, while underperforming the Neyman-Pearson one when the latter has an accurate fault model, outperforms it when the assumed fault model is incorrect.
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