GNSS signal processing in GPU

• Autorzy: Roule P. , Jakubov O. , Ková P. , Kamaík P. , Vejražka F.

Identyfikatory

DOI

10.2478/arsa-2013-0005

• Konferencja: European Navigation Conference 2012, Gdansk, Poland
• Języki publikacji: EN

Abstrakty

EN

Signal processing of the global navigation satellite systems (GNSS) is a computationally demanding task due to the wide bandwidth of the signals and their complicated modulation schemes. The classical GNSS receivers therefore utilize tailored digital signal processors (DSP) not being flexible in nature. Fortunately, the up-to-date parallel processors or graphical processing units (GPUs) dispose sufficient computational power for processing of not only relatively narrow band GPS L1 C/A signal but also the modernized GPS, GLONASS, Galileo and COMPASS signals. The performance improvement of the modern processors is based on the constantly increasing number of cores. This trend is evident not only from the development of the central processing units (CPUs), but also from the development of GPUs that are nowadays equipped with up to several hundreds of cores optimized for video signals. GPUs include special vector instructions that support implementation of massive parallelism. The new GPUs, named as general-purpose computation on graphics processing units (GPGPU), are able to process both graphic and general data, thus making the GNSS signal processing possible. Application programming interfaces (APIs) supporting GPU parallel processing have been developed and standardized. The most general one, Open Computing Language (Open CL), is now supported by most of the GPU vendors. Next, Compute Unified Device Architecture (CUDA) language was developed for NVidia graphic cards. The CUDA language features optimized signal processing libraries including efficient implementation of the fast Fourier transform (FFT). In this paper, we study the applicability of the GPU approach in GNSS signal acquisition. Two common parallel DSP methods, parallel code space search (PCSS) and double-block zero padding (DBZP), have been investigated. Implementations in the C language for CPU and the CUDA language for GPU are discussed and compared with respect to the acquisition time. It is shown that the GPU implementation was approximately sixteen times faster than the CPU’s for signals with long ranging codes (with 10230 number of chips - Galileo E5, GPS L5 etc.).

Słowa kluczowe

EN

GNSS; GPU; signal acquisition

• Wydawca: Centrum Badań Kosmicznych PAN ; De Gruyter
• Czasopismo: Artificial Satellites : Journal of Planetary Geodesy
• Rocznik: 2013
• Tom: Vol. 48, No. 2
• Strony: 51--61
• Opis fizyczny: Bibliogr. 6 poz., rys., tab.

Twórcy

autor

Roule P.

• Czech Technical University in Prague, Faculty of Electrical Engineering

autor

Jakubov O.

• Czech Technical University in Prague, Faculty of Electrical Engineering

autor

Ková P.

• Czech Technical University in Prague, Faculty of Electrical Engineering

autor

Kamaík P.

• Czech Technical University in Prague, Faculty of Electrical Engineering

autor

Vejražka F.

• Czech Technical University in Prague, Faculty of Electrical Engineering

Bibliografia

• Hobiger T., Gotoh T., et al. GPU based real-time GPS software receiver,GPS SOLUTIONS (2010), pp. 208-216.
• Mistry P., Schaa D., et al. OpenCL University Kit, [Cited 20-04-2012], AMD 2011, Available at: http://developer.amd.com/zones/OpenCLZone/universities/Pages/default.aspx
• NVidia: CUDA C, Programming Guide, Version 4.1, 2011, Available at: http://www.nvidia.com
• Kai B. Dennis M. Akos, et al.A Software-Defined GPS and Galileo Receiver, Boston, Birkhauser 2007.
• Chao-jun Wei, Song-lin Sun et al. An Enhanced Spectrum Method for GPS Weak Signal Acquisition, in Proceedings of 2nd IITA International Conference on Geoscience and Remote Sensing 2010, Qingdao (China), pp. 502-505.
• T. Hobiger, et al., A real-time GNSS-R system based on software-defined radio and graphics processing units, Advances in Space Research, Volume 49, Issue 7, 1 April 2012, Pages 1180-1190, 10.1016/j.asr.2012.01.009, 2012.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).