Caballero-Águila Raquel, Hu Jun, Linares-Pérez Josefa
Department of Statistics and Operations Research, University of Jaén, Campus Las Lagunillas, 23071 Jaén, Spain.
Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China.
Sensors (Basel). 2022 Nov 4;22(21):8505. doi: 10.3390/s22218505.
Due to its great importance in several applied and theoretical fields, the signal estimation problem in multisensor systems has grown into a significant research area. Networked systems are known to suffer random flaws, which, if not appropriately addressed, can deteriorate the performance of the estimators substantially. Thus, the development of estimation algorithms accounting for these random phenomena has received a lot of research attention. In this paper, the centralized fusion linear estimation problem is discussed under the assumption that the sensor measurements are affected by random parameter matrices, perturbed by time-correlated additive noises, exposed to random deception attacks and subject to random packet dropouts during transmission. A covariance-based methodology and two compensation strategies based on measurement prediction are used to design recursive filtering and fixed-point smoothing algorithms. The measurement differencing method-typically used to deal with the measurement noise time-correlation-is unsuccessful for these kinds of systems with packet losses because some sensor measurements are randomly lost and, consequently, cannot be processed. Therefore, we adopt an alternative approach based on the direct estimation of the measurement noises and the innovation technique. The two proposed compensation scenarios are contrasted through a simulation example, in which the effect of the different uncertainties on the estimation accuracy is also evaluated.
由于其在多个应用领域和理论领域的重要性，多传感器系统中的信号估计问题已发展成为一个重要的研究领域。众所周知，网络系统会遭受随机缺陷，如果不能妥善解决，这些缺陷会严重降低估计器的性能。因此，考虑这些随机现象的估计算法的开发受到了很多研究关注。本文在传感器测量受到随机参数矩阵影响、受到与时间相关的加性噪声干扰、遭受随机欺骗攻击以及在传输过程中出现随机数据包丢失的假设下，讨论了集中式融合线性估计问题。基于协方差的方法和基于测量预测的两种补偿策略被用于设计递归滤波和定点平滑算法。通常用于处理测量噪声时间相关性的测量差分方法，对于这类存在数据包丢失的系统并不适用，因为一些传感器测量会随机丢失，因此无法进行处理。因此，我们采用一种基于测量噪声直接估计和创新技术的替代方法。通过一个仿真示例对比了所提出的两种补偿方案，其中还评估了不同不确定性对估计精度的影响。
