Based on the high positioning accuracy,low cost and low-power consumption,the ultra-wide-band(UWB)is an ideal solution for indoor unmanned aerial vehicle(UAV)localization and navigation.However,the UWB signals are eas...Based on the high positioning accuracy,low cost and low-power consumption,the ultra-wide-band(UWB)is an ideal solution for indoor unmanned aerial vehicle(UAV)localization and navigation.However,the UWB signals are easy to be blocked or reflected by obstacles such as walls and furniture.A resilient tightly-coupled inertial navigation system(INS)/UWB integration is proposed and implemented for indoor UAV navigation in this paper.A factor graph optimization(FGO)method enhanced by resilient stochastic model is established to cope with the indoor challenging scenarios.To deal with the impact of UWB non-line-of-sight(NLOS)signals and noise uncertainty,the conventional neural net-works(CNNs)are introduced into the stochastic modelling to improve the resilience and reliability of the integration.Based on the status that the UWB features are limited,a‘two-phase'CNNs structure was designed and implemented:one for signal classification and the other one for measurement noise prediction.The proposed resilient FGO method is tested on flighting UAV platform under actual indoor challenging scenario.Compared to classical FGO method,the overall positioning errors can be decreased from about 0.60 m to centimeter-level under signal block and reflection scenarios.The superiority of resilient FGO which effectively verified in constrained environment is pretty important for positioning accuracy and integrity for indoor navigation task.展开更多
To improve the accuracy of the Ultra-Wide Band(UWB)based quadrotor aircraft localization,a Finite Impulse Response(FIR)flter aided with an integration of the predictive model and Extreme Learning Machine(ELM)is propos...To improve the accuracy of the Ultra-Wide Band(UWB)based quadrotor aircraft localization,a Finite Impulse Response(FIR)flter aided with an integration of the predictive model and Extreme Learning Machine(ELM)is proposed in this work.The FIR flter estimates the quad-rotor aircraft’s position by fusing the positions measured with the UWB and Inertial Navigation System respectively.When the UWB dada are unavailable,both the ELM and the predictive model are used to provide the measurements,replacing those unavailable UWB data,for the FIR flter.The ELM estimates the measurement via the mapping between the one step prediction of state vector and the measurement built when the UWB data are available.For the predictive model,we mathematically describe the missing UWB data.Then,both the measurements estimated with the ELM and predictive model are employed to estimate the observations via Mahalanobis distance.The test results show that the FIR flter aided by the predictive model/ELM integrated can reduce the Cumulative Distribution Function and position Root Mean Square Error efectively when the UWB is unavailable.Compared with the ELM assisted FIR flter,the proposed FIR flter can reduce the localization error by about 48.59%,meanwhile,the integrated method is close to the method with a better solution.展开更多
基金National Natural Science Foundation of China(Grant No.62203111)the Open Research Fund of State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University(Grant No.21P01)the Foundation of Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology,Ministry of Education,China(Grant No.SEU-MIAN-202101)to provide fund for conducting experiments。
文摘Based on the high positioning accuracy,low cost and low-power consumption,the ultra-wide-band(UWB)is an ideal solution for indoor unmanned aerial vehicle(UAV)localization and navigation.However,the UWB signals are easy to be blocked or reflected by obstacles such as walls and furniture.A resilient tightly-coupled inertial navigation system(INS)/UWB integration is proposed and implemented for indoor UAV navigation in this paper.A factor graph optimization(FGO)method enhanced by resilient stochastic model is established to cope with the indoor challenging scenarios.To deal with the impact of UWB non-line-of-sight(NLOS)signals and noise uncertainty,the conventional neural net-works(CNNs)are introduced into the stochastic modelling to improve the resilience and reliability of the integration.Based on the status that the UWB features are limited,a‘two-phase'CNNs structure was designed and implemented:one for signal classification and the other one for measurement noise prediction.The proposed resilient FGO method is tested on flighting UAV platform under actual indoor challenging scenario.Compared to classical FGO method,the overall positioning errors can be decreased from about 0.60 m to centimeter-level under signal block and reflection scenarios.The superiority of resilient FGO which effectively verified in constrained environment is pretty important for positioning accuracy and integrity for indoor navigation task.
基金the Natural Science Foundation of Shandong Province(ZR2020KF027,ZR2020MF067).
文摘To improve the accuracy of the Ultra-Wide Band(UWB)based quadrotor aircraft localization,a Finite Impulse Response(FIR)flter aided with an integration of the predictive model and Extreme Learning Machine(ELM)is proposed in this work.The FIR flter estimates the quad-rotor aircraft’s position by fusing the positions measured with the UWB and Inertial Navigation System respectively.When the UWB dada are unavailable,both the ELM and the predictive model are used to provide the measurements,replacing those unavailable UWB data,for the FIR flter.The ELM estimates the measurement via the mapping between the one step prediction of state vector and the measurement built when the UWB data are available.For the predictive model,we mathematically describe the missing UWB data.Then,both the measurements estimated with the ELM and predictive model are employed to estimate the observations via Mahalanobis distance.The test results show that the FIR flter aided by the predictive model/ELM integrated can reduce the Cumulative Distribution Function and position Root Mean Square Error efectively when the UWB is unavailable.Compared with the ELM assisted FIR flter,the proposed FIR flter can reduce the localization error by about 48.59%,meanwhile,the integrated method is close to the method with a better solution.
