@Article{iasc.2023.035876,
AUTHOR = {Linxuan Wang, Xiangwei Kong, Hongzhe Xu, Hong Li},
TITLE = {INS-GNSS Integrated Navigation Algorithm Based on TransGAN},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {37},
YEAR = {2023},
NUMBER = {1},
PAGES = {91--110},
URL = {http://www.techscience.com/iasc/v37n1/52636},
ISSN = {2326-005X},
ABSTRACT = {With the rapid development of autopilot technology, a variety of engineering 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 requirements in many cases of high mobility and complex environments. For the purpose
of improving the accuracy of INS-GNSS integrated navigation system, an INSGNSS 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 multilayer 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 compensation of INS. Consequently, when GNSS works normally, TransGAN is trained
into a high-precision prediction model using INS-GNSS data. The trained TransGAN model is emoloyed to compensate the speed and position errors for INS.
Through the test analysis of flight 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.},
DOI = {10.32604/iasc.2023.035876}
}