典型文献
Distributed state estimation for heterogeneous mobile sensor networks with stochastic observation loss
文献摘要：
The problem of distributed fusion and random observation loss for mobile sensor net-works is investigated herein.In 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 con-structed.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 sup-press the negative effects of measurement noise.To derive a consistent and accurate estimation result,a novel weighted average consensus-based filtering approach is put forward.For the sensor that suffers from observation loss,its local prediction information vector is fused with the informa-tion contribution vectors of the neighbors to obtain the local estimation.Then the consensus weight matrix is designed for consensus-based distributed collaborative information fusion.The boundness of the estimation errors is proved by employing the stochastic stability theory.In the end,two numerical examples are offered to assert the validity of the presented method.
文献关键词：
中图分类号：
作者姓名：
Yingrong YU;Jianglong YU;Yishi LIU;Zhang REN
作者机构：
Science and Technology on Aircraft Control Laboratory,School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China
文献出处：
引用格式：
[1]Yingrong YU;Jianglong YU;Yishi LIU;Zhang REN-.Distributed state estimation for heterogeneous mobile sensor networks with stochastic observation loss)[J].中国航空学报（英文版）,2022(02):265-275
A类：
boundness
B类：
Distributed,state,estimation,heterogeneous,mobile,networks,stochastic,observation,loss,problem,distributed,fusion,random,investigated,herein,In,view,fact,that,measured,values,sampling,frequency,noise,various,sensors,are,different,model,structed,binary,variable,introduced,describe,drop,component,topology,switching,caused,by,complete,also,considered,cubature,information,filtering,algorithm,adopted,local,filters,each,observer,sup,press,negative,effects,measurement,To,derive,consistent,accurate,result,novel,weighted,average,consensus,approach,put,forward,For,suffers,from,its,prediction,fused,contribution,vectors,neighbors,obtain,Then,matrix,designed,collaborative,errors,proved,employing,stability,theory,end,numerical,examples,offered,assert,validity,presented,method
AB值：
0.580168
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