Yu Yihua, Liang Yuan
School of Mathematical Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.
School of Economics, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2025 Jun 3;25(11):3526. doi: 10.3390/s25113526.
This paper addresses multisensor multitarget tracking where the sensor network can potentially be compromised by false data injection (FDI) attacks. The existence of the targets is not known and time-varying. A tracking algorithm is proposed that can detect the possible FDI attacks over the networks. First, a local estimate is generated from the measurements of each sensor based on the labeled multi-Bernoulli (LMB) filter. Then, a detection method for FDI attacks is derived based on the Kullback-Leibler divergence (KLD) between LMB random finite set (RFS) densities. The statistical characteristics of the KLD are analyzed when the measurements are secure or compromised by FDI attacks, from which the value of the threshold is selected. Finally, the global estimate is obtained by minimizing the weighted sum of the information gains from all secure local estimates to itself. A set of suitable weight parameters is selected for the information fusion of LMB densities. An efficient Gaussian implementation of the proposed algorithm is also presented for the linear Gaussian state evolution and measurement model. Experimental results illustrate that the proposed algorithm can provide reliable tracking performance against the FDI attacks.
本文研究了多传感器多目标跟踪问题，其中传感器网络可能会受到虚假数据注入（FDI）攻击的影响。目标的存在是未知且随时间变化的。提出了一种跟踪算法，该算法可以检测网络上可能的FDI攻击。首先，基于标记多伯努利（LMB）滤波器，从每个传感器的测量值生成局部估计。然后，基于LMB随机有限集（RFS）密度之间的库尔贝克-莱布勒散度（KLD），推导了一种FDI攻击检测方法。分析了测量值安全或受到FDI攻击影响时KLD的统计特性，据此选择阈值。最后，通过最小化所有安全局部估计到自身的信息增益的加权和来获得全局估计。为LMB密度的信息融合选择了一组合适的权重参数。针对线性高斯状态演化和测量模型，还给出了所提算法的一种高效高斯实现。实验结果表明，所提算法能够针对FDI攻击提供可靠的跟踪性能。
