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Sensors 2018, 18(5), 1586;

Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)

School of Aeronautics and Space Engineering-Technical University of Madrid (UPM), Plaza Cardenal Cisneros 3, 28040 Madrid, Spain
Authors to whom correspondence should be addressed.
Received: 28 April 2018 / Revised: 9 May 2018 / Accepted: 11 May 2018 / Published: 16 May 2018
(This article belongs to the Special Issue GNSS and Fusion with Other Sensors)
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A significant challenge in global navigation satellite system (GNSS) signal processing is a requirement for a very high sampling rate. The recently-emerging compressed sensing (CS) theory makes processing GNSS signals at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on CS and SVD theories, an algorithm for sampling GNSS signals at a rate much lower than the Nyquist rate and reconstructing the compressed signal is proposed in this research, which is validated after the output from that process still performs signal detection using the standard fast Fourier transform (FFT) parallel frequency space search acquisition. The sparse representation of the GNSS signal is the most important precondition for CS, by constructing a rectangular Toeplitz matrix (TZ) of the transmitted signal, calculating the left singular vectors using SVD from the TZ, to achieve sparse signal representation. Next, obtaining the M-dimensional observation vectors based on the left singular vectors of the SVD, which are equivalent to the sampler operator in standard compressive sensing theory, the signal can be sampled below the Nyquist rate, and can still be reconstructed via 1 minimization with accuracy using convex optimization. As an added value, there is a GNSS signal acquisition enhancement effect by retaining the useful signal and filtering out noise by projecting the signal into the most significant proper orthogonal modes (PODs) which are the optimal distributions of signal power. The algorithm is validated with real recorded signals, and the results show that the proposed method is effective for sampling, reconstructing intermediate frequency (IF) GNSS signals in the time discrete domain. View Full-Text
Keywords: GNSS; GPS; compressed Sensing; sparse approximation GNSS; GPS; compressed Sensing; sparse approximation

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Bermúdez Ordoñez, J.C.; Arnaldo Valdés, R.M.; Gómez Comendador, F. Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD). Sensors 2018, 18, 1586.

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