By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets ...By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets of multi-measurements of the same ESG in different noise environments are ＂mapped＂ into a sensor network,and DKF with embedded consensus filters is then used to preprocess the data sets. After transforming the preprocessed results into the trained input and the desired output of neural network,BPNN with the learning rate and the momentum term is further utilized to identify the ESG bias. As demonstrated in the experiment,the proposed approach is effective for the model identification of the ESG bias.展开更多
Reliable and accurate cooperative positioning is vital to intelligent connected vehicles(ICVs),in which vehicle-vehicle relative measurements are integrated to provide stable locationaware services.However,in zero-tru...Reliable and accurate cooperative positioning is vital to intelligent connected vehicles(ICVs),in which vehicle-vehicle relative measurements are integrated to provide stable locationaware services.However,in zero-trust autonomous driving environments,the possibility of measurement failures and malicious communication attacks tends to reduce positioning performance.With this in mind,this paper presents an ultra-wide bandwidth(UWB)based cooperative positioning system with the specific objective of ICV localization in zero-trust driving environments.Firstly,to overcome measurement degradation under non-line-ofsight(NLOS)propagation conditions,this study proposes a decentralized 3D cooperative positioning method based on a distributed Kalman filter(DKF)by integrating relative rangeazimuth-elevation measurements,unlike the state-of-the-art methods that rely on only one single relative range information to update motion states.More specifically,in contrast to pioneering studies that mainly focus on the positioning problem arising from only one single type of communication attack(either false data injection(FDI)or denial of service(DoS)),we consider a more challenging case of secure cooperative state estimation under mixed FDI and DoS attacks.To this end,a singular-value decomposition(SVD)-assisted decoupled DKF algorithm is proposed in this work,in which a novel update-triggered inter-vehicular communication mechanism is introduced to ensure robust positioning performance against communication attacks while maintaining low transmission load between individuals.To verify the effectiveness in practical 3D NLOS scenarios,we design an intelligent connected multi-robot platform based on a robot operating system(ROS)and UWB technology.Consequently,extensive experimental results demonstrate its superiority and feasibility by achieving a high positioning accuracy of 0.68 m under adverse attacks,especially in the case of hybrid FDI and DoS attacks.In addition,several critical discussions,including the impact of attack parameters,resilience assessment,and a comparison with event-triggered methods,are provided in this work.Moreover,a demo video has been uploaded in the supplementary materials for a detailed presentation.展开更多
文摘By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets of multi-measurements of the same ESG in different noise environments are ＂mapped＂ into a sensor network,and DKF with embedded consensus filters is then used to preprocess the data sets. After transforming the preprocessed results into the trained input and the desired output of neural network,BPNN with the learning rate and the momentum term is further utilized to identify the ESG bias. As demonstrated in the experiment,the proposed approach is effective for the model identification of the ESG bias.
基金supported in part by the National Natural Science Foundation of China(62273065,62003064,62303386)the Natural Science Foundation of Chongqing(CSTB2023NSCQ-LZX0014)+1 种基金the Science and Technology Research Program of Chongqing Municipal Education Commission(KJZDK201800701,KJQN202000717)Sichuan Science and Technology Program(2024NSFSC0525).
文摘Reliable and accurate cooperative positioning is vital to intelligent connected vehicles(ICVs),in which vehicle-vehicle relative measurements are integrated to provide stable locationaware services.However,in zero-trust autonomous driving environments,the possibility of measurement failures and malicious communication attacks tends to reduce positioning performance.With this in mind,this paper presents an ultra-wide bandwidth(UWB)based cooperative positioning system with the specific objective of ICV localization in zero-trust driving environments.Firstly,to overcome measurement degradation under non-line-ofsight(NLOS)propagation conditions,this study proposes a decentralized 3D cooperative positioning method based on a distributed Kalman filter(DKF)by integrating relative rangeazimuth-elevation measurements,unlike the state-of-the-art methods that rely on only one single relative range information to update motion states.More specifically,in contrast to pioneering studies that mainly focus on the positioning problem arising from only one single type of communication attack(either false data injection(FDI)or denial of service(DoS)),we consider a more challenging case of secure cooperative state estimation under mixed FDI and DoS attacks.To this end,a singular-value decomposition(SVD)-assisted decoupled DKF algorithm is proposed in this work,in which a novel update-triggered inter-vehicular communication mechanism is introduced to ensure robust positioning performance against communication attacks while maintaining low transmission load between individuals.To verify the effectiveness in practical 3D NLOS scenarios,we design an intelligent connected multi-robot platform based on a robot operating system(ROS)and UWB technology.Consequently,extensive experimental results demonstrate its superiority and feasibility by achieving a high positioning accuracy of 0.68 m under adverse attacks,especially in the case of hybrid FDI and DoS attacks.In addition,several critical discussions,including the impact of attack parameters,resilience assessment,and a comparison with event-triggered methods,are provided in this work.Moreover,a demo video has been uploaded in the supplementary materials for a detailed presentation.
