Open Access

ARTICLE

INS-GNSS Integrated Navigation Algorithm Based on TransGAN

Linxuan Wang^1,*, Xiangwei Kong¹, Hongzhe Xu², Hong Li¹

1 Chinese Flight Test Establishment, Xi’an, 710089, China
2 Xi’an Jiaotong University, Xi’an, 710049, China

* Corresponding Author: Linxuan Wang. Email:

Intelligent Automation & Soft Computing 2023, 37(1), 91-110. https://doi.org/10.32604/iasc.2023.035876

Received 08 September 2022; Accepted 08 October 2022; Issue published 29 April 2023

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.

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Keywords

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