摘要翻译：
提出了一种新的多传感器数据融合方法。这表明，当考虑传感器的不同特征信息时，可以更有效地聚合来自多个传感器的数据。与大多数有效传感器特性的研究类似，特别是在控制系统中，我们的重点是传感器的精度和频率响应。提出了一种基于规则的模糊系统，用于对在精度和带宽上具有互补特性的传感器所获得的原始数据进行融合。在此基础上，提出了一种模糊预测系统，该系统的目标是在高灵敏度应用中普遍需要的极高精度。通过对一个利用融合系统进行输出估计的控制系统的仿真，说明了我们提出的传感器融合系统的优点。
---
英文标题：
《Multi-Sensor Fuzzy Data Fusion Using Sensors with Different
  Characteristics》
---
作者：
Mohammad Amin Ahmad Akhoundi, Ehsan Valavi
---
最新提交年份：
2019
---
分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Systems and Control        系统与控制
分类描述：This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
--
一级分类：Computer Science        计算机科学
二级分类：Systems and Control        系统与控制
分类描述：cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
cs.sy是eess.sy的别名。本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
--

---
英文摘要：
  This paper proposes a new approach to multi-sensor data fusion. It suggests that aggregation of data from multiple sensors can be done more efficiently when we consider information about sensors' different characteristics. Similar to most research on effective sensors' characteristics, especially in control systems, our focus is on sensors' accuracy and frequency response. A rule-based fuzzy system is presented for fusion of raw data obtained from the sensors that have complement characteristics in accuracy and bandwidth. Furthermore, a fuzzy predictor system is suggested aiming for extreme accuracy which is a common need in highly sensitive applications. Advantages of our proposed sensor fusion system are shown by simulation of a control system utilizing the fusion system for output estimation.
---
PDF链接：
https://arxiv.org/pdf/1010.6096