Aiming at the problem of poor observability of measurement information in the loosely-coupled integration of the inertial navigation system （INS） and the wireless sensor network （WSN）, this paper presents a tightl...Aiming at the problem of poor observability of measurement information in the loosely-coupled integration of the inertial navigation system （INS） and the wireless sensor network （WSN）, this paper presents a tightly-coupled integration based on the Kalman filter （KF）. When the WSN is available, the difference between the distances from the blind node（BN） to the reference nodes （RNs） measured by the INS and those measured by the WSN are used as measurement information for the KF due to its better observability and independence, which can effectively improve the accuracy of the KF. Simulations show that the proposed approach reduces the mean error of the position by about 50% compared with loosely-coupled integration, while the mean error of the velocity is a little higher than that of loosely-coupled integration.展开更多
In view of the failure of GNSS signals,this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network(RNN).This proposed method utilizes the calculation principle of INS and the mem...In view of the failure of GNSS signals,this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network(RNN).This proposed method utilizes the calculation principle of INS and the memory function of the RNN to estimate the errors of the INS,thereby obtaining a continuous,reliable and high-precision navigation solution.The performance of the proposed method is firstly demonstrated using an INS/GNSS simulation environment.Subsequently,an experimental test on boat is also conducted to validate the performance of the method.The results show a promising application prospect for RNN in the field of positioning for INS/GNSS integrated navigation in the absence of GNSS signal,as it outperforms extreme learning machine(ELM)and EKF by approximately 30%and 60%,respectively.展开更多
Inertial navigation system/visual navigation system(INS/VNS) integrated navigation is a commonly used autonomous navigation method for planetary rovers. Since visual measurements are related to the previous and curren...Inertial navigation system/visual navigation system(INS/VNS) integrated navigation is a commonly used autonomous navigation method for planetary rovers. Since visual measurements are related to the previous and current state vectors(position and attitude) of planetary rovers, the performance of the Kalman filter(KF) will be challenged by the time-correlation problem. A state augmentation method, which augments the previous state value to the state vector, is commonly used when dealing with this problem. However, the augmenting of state dimensions will result in an increase in computation load. In this paper, a state dimension reduced INS/VNS integrated navigation method based on coordinates of feature points is presented that utilizes the information obtained through INS/VNS integrated navigation at a previous moment to overcome the time relevance problem and reduce the dimensions of the state vector. Equations of extended Kalman filter(EKF) are used to demonstrate the equivalence of calculated results between the proposed method and traditional state augmented methods. Results of simulation and experimentation indicate that this method has less computational load but similar accuracy when compared with traditional methods.展开更多
With the rapid development of autopilot technology,a variety of engi-neering applications require higher and higher requirements for navigation and positioning accuracy,as well as the error range should reach centimet...With the rapid development of autopilot technology,a variety of engi-neering applications require higher and higher requirements for navigation and positioning accuracy,as well as the error range should reach centimeter level.Single navigation systems such as the inertial navigation system(INS)and the global navigation satellite system(GNSS)cannot meet the navigation require-ments in many cases of high mobility and complex environments.For the purpose of improving the accuracy of INS-GNSS integrated navigation system,an INS-GNSS integrated navigation algorithm based on TransGAN is proposed.First of all,the GNSS data in the actual test process is applied to establish the data set.Secondly,the generator and discriminator are constructed.Borrowing the model structure of generator transformer,the generator is constructed by multi-layer transformer encoder,which can obtain a wider data perception ability.The generator and discriminator are trained and optimized by the production countermeasure network,so as to realize the speed and position error compensa-tion of INS.Consequently,when GNSS works normally,TransGAN is trained into a high-precision prediction model using INS-GNSS data.The trained Trans-GAN model is emoloyed to compensate the speed and position errors for INS.Through the test analysis offlight test data,the test results are compared with the performance of traditional multi-layer perceptron(MLP)and fuzzy wavelet neural network(WNN),demonstrating that TransGAN can effectively correct the speed and position information when GNSS is interrupted,with the high accuracy.展开更多
Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobil...Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.展开更多
In orderto furtherstudy theperform ance oftightly integrated navigation system ofGPS/ INS, a sem i-physicalsim ulation oftightly coupled system has been done based on the data gathered from the experim entof integra...In orderto furtherstudy theperform ance oftightly integrated navigation system ofGPS/ INS, a sem i-physicalsim ulation oftightly coupled system has been done based on the data gathered from the experim entof integrated system ofGPSand INS. The closed-loop Kalm an Filter and U-D discom pose algorithm have been used. The sim ulation results associated to four integrated m odels of pseudo-range, delta-range, pseudo-range and delta-range alternation, and pseudo-range and delta- range synthesis have been provided, and the actualeffects of variously integrated m odels have been analyzed. The results show that the pseudo-range and delta-range synthesis coupled m odelis the m osteffective to im provethe coupled system perform anceand the individualdelta-rangecoupled m od- elhad betterbe avoided in application.展开更多
The method of integrated data processing for GPS and INS(inertial navigation system) field test over the Rocky Mountains using the adaptive Kalman filtering technique is presented. On the basis of the known GPS output...The method of integrated data processing for GPS and INS(inertial navigation system) field test over the Rocky Mountains using the adaptive Kalman filtering technique is presented. On the basis of the known GPS outputs and the offset of GPS and INS, state equations and observations are designed to perform the calculation and improve the navigation accuracy. An example shows that with the method the reliable navigation parameters have been obtained.展开更多
基金The National Basic Research Program of China(973 Program)(No.2009CB724002)the National Natural Science Foundation of China(No.50975049)+3 种基金the Specialized Research Fund for the Doctoral Program of Higher Education of China(No.20110092110039)the Aviation Science Foundation(No.20090869008)the Six Peak Talents Foundation in Jiangsu Province(No.2008143)Program of Scientific Innovation Research of College Graduate in Jiangsu Province(No.CXLX_0101)
文摘Aiming at the problem of poor observability of measurement information in the loosely-coupled integration of the inertial navigation system （INS） and the wireless sensor network （WSN）, this paper presents a tightly-coupled integration based on the Kalman filter （KF）. When the WSN is available, the difference between the distances from the blind node（BN） to the reference nodes （RNs） measured by the INS and those measured by the WSN are used as measurement information for the KF due to its better observability and independence, which can effectively improve the accuracy of the KF. Simulations show that the proposed approach reduces the mean error of the position by about 50% compared with loosely-coupled integration, while the mean error of the velocity is a little higher than that of loosely-coupled integration.
基金supported in part by the National Natural Science Foundation of China(No.41876222)。
文摘In view of the failure of GNSS signals,this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network(RNN).This proposed method utilizes the calculation principle of INS and the memory function of the RNN to estimate the errors of the INS,thereby obtaining a continuous,reliable and high-precision navigation solution.The performance of the proposed method is firstly demonstrated using an INS/GNSS simulation environment.Subsequently,an experimental test on boat is also conducted to validate the performance of the method.The results show a promising application prospect for RNN in the field of positioning for INS/GNSS integrated navigation in the absence of GNSS signal,as it outperforms extreme learning machine(ELM)and EKF by approximately 30%and 60%,respectively.
基金supported by the National Natural Science Foundation of China (Nos. 61233005 and 61503013)the National Basic Research Program of China (No. 2014CB744202)+2 种基金Beijing Youth Talent ProgramFundamental Science on Novel Inertial Instrument & Navigation System Technology LaboratoryProgram for Changjiang Scholars and Innovative Research Team in University (IRT1203) for their valuable comments
文摘Inertial navigation system/visual navigation system(INS/VNS) integrated navigation is a commonly used autonomous navigation method for planetary rovers. Since visual measurements are related to the previous and current state vectors(position and attitude) of planetary rovers, the performance of the Kalman filter(KF) will be challenged by the time-correlation problem. A state augmentation method, which augments the previous state value to the state vector, is commonly used when dealing with this problem. However, the augmenting of state dimensions will result in an increase in computation load. In this paper, a state dimension reduced INS/VNS integrated navigation method based on coordinates of feature points is presented that utilizes the information obtained through INS/VNS integrated navigation at a previous moment to overcome the time relevance problem and reduce the dimensions of the state vector. Equations of extended Kalman filter(EKF) are used to demonstrate the equivalence of calculated results between the proposed method and traditional state augmented methods. Results of simulation and experimentation indicate that this method has less computational load but similar accuracy when compared with traditional methods.
文摘With the rapid development of autopilot technology,a variety of engi-neering applications require higher and higher requirements for navigation and positioning accuracy,as well as the error range should reach centimeter level.Single navigation systems such as the inertial navigation system(INS)and the global navigation satellite system(GNSS)cannot meet the navigation require-ments in many cases of high mobility and complex environments.For the purpose of improving the accuracy of INS-GNSS integrated navigation system,an INS-GNSS integrated navigation algorithm based on TransGAN is proposed.First of all,the GNSS data in the actual test process is applied to establish the data set.Secondly,the generator and discriminator are constructed.Borrowing the model structure of generator transformer,the generator is constructed by multi-layer transformer encoder,which can obtain a wider data perception ability.The generator and discriminator are trained and optimized by the production countermeasure network,so as to realize the speed and position error compensa-tion of INS.Consequently,when GNSS works normally,TransGAN is trained into a high-precision prediction model using INS-GNSS data.The trained Trans-GAN model is emoloyed to compensate the speed and position errors for INS.Through the test analysis offlight test data,the test results are compared with the performance of traditional multi-layer perceptron(MLP)and fuzzy wavelet neural network(WNN),demonstrating that TransGAN can effectively correct the speed and position information when GNSS is interrupted,with the high accuracy.
基金Project(2013AA06A411)supported by the National High Technology Research and Development Program of ChinaProject(CXZZ14_1374)supported by the Graduate Education Innovation Program of Jiangsu Province,ChinaProject supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.
文摘In orderto furtherstudy theperform ance oftightly integrated navigation system ofGPS/ INS, a sem i-physicalsim ulation oftightly coupled system has been done based on the data gathered from the experim entof integrated system ofGPSand INS. The closed-loop Kalm an Filter and U-D discom pose algorithm have been used. The sim ulation results associated to four integrated m odels of pseudo-range, delta-range, pseudo-range and delta-range alternation, and pseudo-range and delta- range synthesis have been provided, and the actualeffects of variously integrated m odels have been analyzed. The results show that the pseudo-range and delta-range synthesis coupled m odelis the m osteffective to im provethe coupled system perform anceand the individualdelta-rangecoupled m od- elhad betterbe avoided in application.
基金Supported by the Scientific Research Foundation for ROCS,SEMJiangxi Education Bureau Project(No.200525) .
文摘The method of integrated data processing for GPS and INS(inertial navigation system) field test over the Rocky Mountains using the adaptive Kalman filtering technique is presented. On the basis of the known GPS outputs and the offset of GPS and INS, state equations and observations are designed to perform the calculation and improve the navigation accuracy. An example shows that with the method the reliable navigation parameters have been obtained.
