Distributed state estimation for heterogeneous mobile sensor networks with stochastic observation loss

作     者：Yingrong YU Jianglong YU Yishi LIU Zhang REN Yingrong YU;Jianglong YU;Yishi LIU;Zhang REN

作者机构：Science and Technology on Aircraft Control LaboratorySchool of Automation Science and Electrical EngineeringBeihang.UniversityBeijing 100191China 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2022年第35卷第2期

页      面：265-275页

核心收录：

学科分类：08[工学] 0825[工学-航空宇航科学与技术] 

基　　金：supported by the Science and Technology Innovation 2030-Key Project of“New Generation Artificial Intelligence”of China(No.2020AAA0108200) the National Natural Science Foundation of China(Nos.61873011,61922008,61973013,61803014) the Innovation Zone Project of China(No.18-163-00-TS-001-001-34) the Defense Industrial Technology Development Program of China(No.JCKY2019601C106) the Special Research Project of Chinese Civil Aircraft,China 

主　　题：Consistency theorem Heterogeneous sensor networks Information fusion State estimation Stochastic boundedness 

摘      要：The problem of distributed fusion and random observation loss for mobile sensor networks is investigated *** view of the fact that the measured values,sampling frequency and noise of various sensors are different,the observation model of a heterogeneous network is constructed.A binary random variable is introduced to describe the drop of observation component and the topology switching problem caused by complete observation loss is also considered.A cubature information filtering algorithm is adopted to design local filters for each observer to suppress the negative effects of measurement *** derive a consistent and accurate estimation result,a novel weighted average consensus-based filtering approach is put *** the sensor that suffers from observation loss,its local prediction information vector is fused with the information contribution vectors of the neighbors to obtain the local *** the consensus weight matrix is designed for consensus-based distributed collaborative information *** boundness of the estimation errors is proved by employing the stochastic stability *** the end,two numerical examples are offered to assert the validity of the presented method.