SIA OpenIR  > 机器人学研究室
Navigation system design for RUAVs based on a novel KALMAN filter
Wu C(吴冲); Song DL(宋大雷); Qi JT(齐俊桐); Han JD(韩建达); Shi ZL(史泽林)
Department机器人学研究室
Conference NameAUVSI Unmanned Systems North America Conference 2011
Conference DateAugust 16-19, 2011
Conference PlaceWashington, DC, United states
Source PublicationAUVSI Unmanned Systems North America Conference 2011
PublisherAssociation for Unmanned Vehicle Systems International
Publication PlaceArlington, VA
2011
Pages583-590
Indexed ByEI
EI Accession number20120914803978
Contribution Rank1
ISBN978-1-61839-348-7
KeywordAccelerometers Algorithms Data Fusion Extended Kalman Filters Kalman Filters Navigation Navigation Systems Systems Analysis Time Delay Velocity Measurement
AbstractGPS/INS navigation system is necessary for realizing the accurate and autonomous control of the rotorcraft unmanned aerial vehicles (RUAVs). However, because of the intrinsic time delay of the velocity measurement from the GPS and the noisy acceleration measurement caused by the strong vibration on the RUAV, the traditional navigation system's estimation often cannot be applied for online flight controllers as feedback value on RUAVs. In this paper, a new algorithm of data fusion is proposed to eliminate the time delay of velocity and the noise of acceleration. Based on the simplified reference model of GPS/INS system, a two-step kalman filter is used to estimate the acceleration bias and fusion the INS and GPS data to obtain the accurate measurement for the body moving of the RUAVs. Based on the ServoHeli-40 RUAV platform, flight tests have been done to verify the validity of the data fusion algorithm.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/8803
Collection机器人学研究室
Corresponding AuthorWu C(吴冲)
Affiliation1.Shenyang Institute and Automation, Chinese Academy of Sciences, Shenyang, Liaoning, 110016, China
2.Graduate School of the Chinese Academy of Sciences, Beijing, 100080, China
Recommended Citation
GB/T 7714
Wu C,Song DL,Qi JT,et al. Navigation system design for RUAVs based on a novel KALMAN filter[C]. Arlington, VA:Association for Unmanned Vehicle Systems International,2011:583-590.
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