典型文献
Improved Spatial Registration Algorithm for Sensors on Multiple Mobile Platforms
文献摘要:
This paper focuses on the spatial registration algorithm under the earth-centered earth-fixed(ECEF)coordinate system for multiple mobile platforms.The sensor measurement biases are discussed with the platform attitude information taken into consideration.First,the biased measurement model is constructed.Besides,the maximum likelihood registration(MLR)algorithm is discussed to simultaneously estimate the measurement biases and the target state.Finally,an improved online MLR(IMLR)algorithm is proposed through a sliding window of adaptive size.Simulation results demonstrate that the proposed IMLR algorithm effectively improves the real-time ability of the system and can approach similar estimation accuracy to the conventional MLR algorithm.
文献关键词:
中图分类号:
作者姓名:
Lü Runyan;PENG Na;WU Yi;CAI Yunze
作者机构:
Department of Automation,Key Laboratory of System Control and Information Processing of Ministry of Education,Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education,Shanghai Jiao Tong University,Shanghai 200240,China;Shanghai Aerospace Electronic Technology Institute,Shanghai Academy of Spaceflight Technology,Shanghai 201109,China;Alibaba(China)Co.,Ltd.,Hangzhou 311121,China
文献出处:
引用格式:
[1]Lü Runyan;PENG Na;WU Yi;CAI Yunze-.Improved Spatial Registration Algorithm for Sensors on Multiple Mobile Platforms)[J].上海交通大学学报(英文版),2022(05):638-648
A类:
ECEF,IMLR
B类:
Improved,Spatial,Registration,Algorithm,Sensors,Multiple,Mobile,Platforms,This,paper,focuses,spatial,registration,algorithm,under,earth,centered,fixed,coordinate,system,multiple,mobile,platforms,sensor,measurement,biases,are,discussed,attitude,information,taken,into,consideration,First,biased,model,constructed,Besides,maximum,likelihood,simultaneously,estimate,target,state,Finally,improved,online,proposed,through,sliding,window,adaptive,size,Simulation,results,demonstrate,that,effectively,improves,real,ability,can,approach,similar,estimation,accuracy,conventional
AB值:
0.642695
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