基于随机不可靠量测的事件触发多传感器系统融合估计方法研究

项目名称： 基于随机不可靠量测的事件触发多传感器系统融合估计方法研究

项目编号： No.61773063

项目类型： 面上项目

立项/批准年度： 2018

项目学科： 自动化技术、计算机技术

项目作者： 闫莉萍

作者单位： 北京理工大学

项目金额： 16万元

中文摘要： 在复杂网络通信环境下，多传感器数据通过网络进行传输，数据量庞大、类型多样，且存在丢包、多速率采样、随机不可靠等问题。如何在减少网络负载的同时提高数据融合精度是当前网络化多传感器数据融合领域面临的首要科学问题。本项目基于多传感器非可靠量测，以时间触发数据融合、基于模型的故障检测与事件触发估计为基础，开展事件触发多传感器系统容错融合估计方法研究，旨在利用最少的通信传输率，达到最优的状态估计结果。通过对概率模型和通信数据的匹配性分析，建立事件触发多传感器系统框架；基于对观测的统计分析，给出事件触发数据可靠性评估方法，有效避免故障传播；提出分布式事件触发多传感器容错融合估计算法，在有限通信条件下，提高复杂网络环境下的状态估计精度。项目成果丰富了多传感器最优估计理论，并为多传感器网络系统信息融合与控制相关应用提供理论和技术支持。

中文关键词： 多传感器信息融合；卡尔曼滤波；最小方差估计；异步融合；多速率估计

英文摘要： In the complex network communication environment, the multisensor data is transmitted through the network. The amount of data is huge and the type is various. There are many problems, such as packet loss, multirate sampling, random unreliable and so on. How to reduce the network load and improve the accuracy of data fusion is the most important problem in the field of networked multi-sensor data fusion. Based on unreliable measurements, by combining the traditional time triggered multisensor data fusion, model based fault detection, event-triggered estimation, this project aimed to research event-triggered multisensor fault tolerant fusion estimation algorithms. The aim of this project is to achieve the optimal state estimation by using the minimum transmission rate. Through the analysis of the matching probability model and communication data, the event triggered multisensor system is to be established. Through the statistical analysis of observations, we will present the event triggered data reliability evaluation method to effectively avoid the fault propagation. This project will put forward series of distributed event triggered multisensor fault tolerant fusion estimation algorithms, to improve the estimation precision in limited communication conditions in complex network environment. The results enrich the theory of multi-sensor optimal estimation, and provide theoretical and technical support for the application of information fusion and control in multisensor network systems.

英文关键词： Multisensor information fusion;Kalman filter;Minimum variance estimation;Asynchronous fusion;Multirate estimation