This study investigates the state estimation problem of multi-agent systems under a homologous sensor attack. A distributed filter is proposed to achieve a minimum variance unbiased (MVU) estimation of system states and attacks in the presence of measurement noise. A gain matrix selection method for implementing the MVU estimation is also provided. The proposed filter can be used for positioning corrections affected by global positioning system (GPS) meaconing attacks. This study treats the positioning offset caused by GPS meaconing attacks as a zero-mean white random variable and verifies the validity of this hypothesis through experiments with real GPS signals. Moreover, this study comprehensively analyses the integration of the filters into practical systems. Finally, the effectiveness of the proposed results is verified using simulation examples. Note to Practitioners—This study introduces a filter that can achieve GPS positioning calibration under meaconing attacks. This filter treats the true positioning of the system as a state and the deviation caused by meaconing attacks as a homologous attack. The filter employs a collaborative filter to reconstruct the state, thereby enabling positioning calibration. Additionally, the study explores the relationship between the homology of meaconing attacks and the estimation error of filters. This result reveals that as the homology of attacks increases, the performance of filters also improves.

中文翻译：

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同源传感器攻击下的分布式滤波器及其在GPS测量攻击中的应用


本研究研究同源传感器攻击下多智能体系统的状态估计问题。提出了一种分布式滤波器，以在存在测量噪声的情况下实现系统状态和攻击的最小方差无偏（MVU）估计。还提供了一种用于实现MVU估计的增益矩阵选择方法。所提出的滤波器可用于受全球定位系统（GPS）测量攻击影响的定位校正。本研究将GPS信标攻击引起的定位偏移视为零均值白色随机变量，并通过真实GPS信号的实验验证了该假设的有效性。此外，本研究全面分析了滤波器在实际系统中的集成。最后，通过仿真算例验证了所提结果的有效性。从业者须知——本研究介绍了一种可以在测量攻击下实现 GPS 定位校准的滤波器。该过滤器将系统的真实定位视为一种状态，将米康攻击造成的偏差视为同源攻击。该过滤器采用协同过滤器来重建状态，从而实现定位校准。此外，该研究还探讨了米康攻击的同源性与滤波器估计误差之间的关系。这个结果表明，随着攻击同源性的增加，过滤器的性能也会提高。