Departamento de Estadística, Universidad de Jaén, Paraje Las Lagunillas, 23071 Jaén, Spain.
Departamento de Estadística, Universidad de Granada, Avda. Fuentenueva, 18071 Granada, Spain.
Sensors (Basel). 2018 Aug 16;18(8):2697. doi: 10.3390/s18082697.
This paper is concerned with the least-squares linear centralized estimation problem in multi-sensor network systems from measured outputs with uncertainties modeled by random parameter matrices. These measurements are transmitted to a central processor over different communication channels, and owing to the unreliability of the network, random one-step delays and packet dropouts are assumed to occur during the transmissions. In order to avoid network congestion, at each sampling time, each sensor's data packet is transmitted just once, but due to the uncertainty of the transmissions, the processing center may receive either one packet, two packets, or nothing. Different white sequences of Bernoulli random variables are introduced to describe the observations used to update the estimators at each sampling time. To address the centralized estimation problem, augmented observation vectors are defined by accumulating the raw measurements from the different sensors, and when the current measurement of a sensor does not arrive on time, the corresponding component of the augmented measured output predictor is used as compensation in the estimator design. Through an innovation approach, centralized fusion estimators, including predictors, filters, and smoothers are obtained by recursive algorithms without requiring the signal evolution model. A numerical example is presented to show how uncertain systems with state-dependent multiplicative noise can be covered by the proposed model and how the estimation accuracy is influenced by both sensor uncertainties and transmission failures.
本文研究了具有不确定性随机参数矩阵的多传感器网络系统中基于测量输出的最小二乘线性集中估计问题。这些测量通过不同的通信信道传输到中央处理器，由于网络的不可靠性，在传输过程中假设会随机出现一步延迟和数据包丢失。为了避免网络拥塞，在每个采样时刻，每个传感器的数据仅传输一次，但由于传输的不确定性，处理中心可能只收到一个数据包、两个数据包或什么都没有。引入不同的白序列伯努利随机变量来描述用于在每个采样时刻更新估计器的观测值。为了解决集中估计问题，通过累积来自不同传感器的原始测量值来定义扩充观测向量，并且当传感器的当前测量值未按时到达时，扩充测量输出预测器的相应分量将被用作估计器设计中的补偿。通过创新方法，通过递归算法获得了包括预测器、滤波器和平滑器在内的集中融合估计器，而无需信号演化模型。通过一个数值例子来说明如何通过所提出的模型来涵盖具有状态相关乘性噪声的不确定系统，以及传感器不确定性和传输故障如何影响估计精度。
