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Receiver bandwidth compression for multi-GNSS signal processing

: Rossouw van der Merwe, J.; Garzia, F.; Saad, M.; Kreh, B.; Rügamer, A.; Plata, R.M.G.; Felber, W.


Institute of Navigation -ION-, Manassas/Va.; Institute of Navigation -ION-, Satellite Division, Washington/DC:
33rd International Technical Meeting of the Satellite Division of The Institute of Navigation, ION GNSS+ 2020. Proceedings : September 21 - 25, 2020, Virtual
Fairfax/Va.: ION, 2020
ISBN: 0-936406-26-7
ISBN: 978-0-936406-26-8
Institute of Navigation, Satellite Division (ION GNSS International Technical Meeting) <33, 2020, Online>
Fraunhofer IIS ()

Global navigation satellite system (GNSS) receivers require a substantially large bandwidth to be able to use all available GNSS signals in the L1 frequency band (i.e. GPS, Galileo, BeiDou and GLONASS). Consequently, high throughput, processing, and storage requirements exist. It is especially problematic for software-defined radio (SDR) receivers, where most processing is done in software. Similarly, higher required sample-rates also lead to higher power consumption in hardware based receivers. Through spectrum compression, the unused parts of the spectrum are omitted, such that a lower sample-rate can be used. The high-rate DFT-based data manipulator (HDDM) infrastructure, which is already used for interference mitigation, is re-purposed for spectrum compression. The algorithm separates each signal from the L1 band through filtering, then translates the signals to be spectrally closer to each other. Lastly, the sample-rate of the signal is reduced. As a proof-of-concept, both the original signal and the spectrum compressed one are digitally replayed on an open interface GNSS receiver. The performance for both cases is similar, hence, no degradation is visible, proving the feasibility and effectiveness of this solution. The proposed method shows that significant data reductions can be achieved for the GNSS L1 frequency band, saving both effort (processing time and energy by lower sample-rates) as well as digital data throughput and potential memory requirements.