Department of Statistics and Operations Research, University of Jaén, Paraje Las Lagunillas, 23071 Jaén, Spain.
Department of Images and Signals, CNRS/GIPSA-Lab, CEDEX, 38402 Saint Martin d'Hères, France.
Sensors (Basel). 2023 Apr 17;23(8):4047. doi: 10.3390/s23084047.
This paper analyses the centralized fusion linear estimation problem in multi-sensor systems with multiple packet dropouts and correlated noises. Packet dropouts are modeled by independent Bernoulli distributed random variables. This problem is addressed in the tessarine domain under conditions of T1 and T2-properness, which entails a reduction in the dimension of the problem and, consequently, computational savings. The methodology proposed enables us to provide an optimal (in the least-mean-squares sense) linear fusion filtering algorithm for estimating the tessarine state with a lower computational cost than the conventional one devised in the real field. Simulation results illustrate the performance and advantages of the solution proposed in different settings.
本文分析了多传感器系统中存在多重数据包丢失和相关噪声时的集中融合线性估计问题。数据包丢失通过独立的伯努利分布随机变量进行建模。在 T1 和 T2 适当性条件下，该问题在 Tessarine 域中得到解决，这降低了问题的维度，从而节省了计算资源。所提出的方法使我们能够提供一种最优的（在最小均方意义下）线性融合滤波算法，用于以比传统的在实域中设计的算法更低的计算成本来估计 Tessarine 状态。仿真结果说明了在不同设置下所提出的解决方案的性能和优势。
