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
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
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
The integration of GNSS （Global Navigation Satellite System） and INS （Inertial Navigation System） using IMU （Inertial Measurement Unit） is now widely used for MMS （Mobile Mapping System） and navigation applica...The integration of GNSS （Global Navigation Satellite System） and INS （Inertial Navigation System） using IMU （Inertial Measurement Unit） is now widely used for MMS （Mobile Mapping System） and navigation applications to seamlessly determine position, velocity and attitude of the mobile platform. With low cost, small size, ligh weight and low power consumtion, the MEMS （Micro-Electro-Mechanical System） IMU and low cost GPS （Global Positioning System） receivers are now the trend in research and using for many applications. However, researchs in the literature indicated that the the performance of the low cost INS/GPS systems is still poor, particularly, in case of GNSS-noise environment. To overcome this problem, this research applies analytic contrains including non-holonomic constraint and zero velocity update in the data fusion engine such as Extended Kalman Filter to improve the performance of the system. The benefit of the proposed method will be demonstrated through experiments and data analysis.展开更多
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.
文摘The integration of GNSS （Global Navigation Satellite System） and INS （Inertial Navigation System） using IMU （Inertial Measurement Unit） is now widely used for MMS （Mobile Mapping System） and navigation applications to seamlessly determine position, velocity and attitude of the mobile platform. With low cost, small size, ligh weight and low power consumtion, the MEMS （Micro-Electro-Mechanical System） IMU and low cost GPS （Global Positioning System） receivers are now the trend in research and using for many applications. However, researchs in the literature indicated that the the performance of the low cost INS/GPS systems is still poor, particularly, in case of GNSS-noise environment. To overcome this problem, this research applies analytic contrains including non-holonomic constraint and zero velocity update in the data fusion engine such as Extended Kalman Filter to improve the performance of the system. The benefit of the proposed method will be demonstrated through experiments and data analysis.
