Compressed Sensing-aided Vector Tracking Algorithm for GNSS Receivers

Jumin Zhao, Xiaofang Zhao, Dengao Li, Doudou Deng, Chong Han

Abstract:	The vector tracking algorithm has become a better method to ensure the accuracy of positioning navigation under serious condition, but the drawback of vector tracking algorithm is that its handling load and complexity are high. The sampling rate of the compression sensing algorithm is no longer depend on the signal bandwidth, it is determined by the amount of information contained in the signal, this method can reduce the cost of signal acquisition effectively and alleviate the processing pressure of receiver. Although compressed sensing algorithm can reduce the sampling rate and data rate effectively, but the drawback of the algorithm is high computational complexity so it consumed a large amount of computing resources, therefore, we propose Compressed Sensing-aided vector tracking loop. In this method, the code phase, carrier frequency and carrier phase information can be directly extracted from the received signal, it realizes high-precision carrier, pseudo-code delay tracking and then the output from discriminators used as the input of the navigation Kalman filter, it is not only bring down the data rate, sampling rate and computational complexity effectively, but also the compressed sensing-aided vector tracking algorithm can obtain a higher positioning accuracy compared to traditional vector tracking algorithm.
Published in:	Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017) September 25 - 29, 2017 Oregon Convention Center Portland, Oregon
Pages:	3639 - 3647
Cite this article:	Zhao, Jumin, Zhao, Xiaofang, Li, Dengao, Deng, Doudou, Han, Chong, "Compressed Sensing-aided Vector Tracking Algorithm for GNSS Receivers," Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 3639-3647. https://doi.org/10.33012/2017.15401
Full Paper:	ION Members/Non-Members: 1 Download Credit Sign In