Low Computational Signal Acquisition for GNSS Receivers Using a Resampling Strategy and Variable Circular Correlation Time

Zhang, Yeqing; Wang, Meiling; Li, Yafeng

doi:10.3390/s18020678

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…Finally, in [50], the reduction of the sampling frequency below the Nyquist rate was reviewed based on band-pass theory. It concerns superheterodyne receivers with an Intermediate Frequency (IF) stage, yet the study also shows improvements in baseband signals datasets.…”

Section: A Gnss Signal Acquisitionmentioning

confidence: 99%

A Survey on Low-Power GNSS

Grenier

Lohan

Ometov

et al. 2023

IEEE Commun. Surv. Tutorials

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With the miniaturization of electronics, Global Navigation Satellite Systems (GNSS) receivers are getting more and more embedded into devices with harsh energy constraints. This process has led to new signal processing challenges due to the limited processing power on battery-operated devices and to challenging wireless environments, such as deep urban canyons, tunnels and bridges, forest canopies, increased jamming and spoofing. The latter is typically tackled via new GNSS constellations and modernization of the GNSS signals. However, the increase in signal complexity leads to higher computation requirements to recover the signals; thus, the trade-off between precision and energy should be evaluated for each application. This paper dives into low-power GNSS, focusing on the energy consumption of satellite-based positioning receivers used in battery-operated consumer devices and Internet of Things (IoT) sensors. We briefly overview the GNSS basics and the differences between legacy and modernized signals. Factors dominating the energy consumption of GNSS receivers are then reviewed, with special attention given to the complexity of the processing algorithms. Onboard and offloaded (Cloud/Edge) processing strategies are explored and compared. Finally, we highlight the current challenges of today's research in low-power GNSS.

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Section: A Gnss Signal Acquisitionmentioning

confidence: 99%

A Survey on Low-Power GNSS

Grenier

Lohan

Ometov

et al. 2023

IEEE Commun. Surv. Tutorials

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“…Figure 7 (a) shows the waveform of MIT-BIH ECG signal including noise and artifact value considered as SNR = 5 dB in blue color and the corresponding reconstructed ECG signal in red color is described in Figure 7 (b). For comparing between the MIT-BIH ECG signal with the noise and artifact and the reconstructed one, we can look at their power spectrum density (PSD) versus frequency as shown in Figure 8, in which for being easy to view, the ratio between Figure 8 [25]. In particular, one Lab ECG signal and one the reconstructed one after using the WDFR algorithm with the wavelet function "dmey" are presented in Figure 9 (a) (the raw Lab ECG signal) and Figure 9 (b) (the reconstructed signal).…”

Section: Ecg Signal Reconstructionmentioning

confidence: 99%

Artifact elimination in ECG signal using wavelet transform

Hải

Ngo

2020

TELKOMNIKA

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Electrocardiogram signal is the electrical actvity of the heart and doctors can diagnose heart disease based on this electrocardiogram signal. However, the electrocardiogram signals often have noise and artifact components. Therefore, one electrocardiogram signal without the noise and artifact plays an important role in heart disease diagnosis with more accurate results. This paper proposes a wavelet transform with three stages of decomposition, filter, and reconstruction for eliminating the noise and artifact in the electrocardiogram signal. The signal after decomposing produces approximation and detail coefficients, which contains the frequency ranges of the noise and artifact components. Hence, the approximation and detail coefficients with the frequency ranges corresponding to the noise and artifact in the electrocardiogram signal are eliminated by filters before they are reconstructed. For the evaluation of the proposed algorithm, filter evaluation metrics are applied, in which signal-to-noise ratio and mean squared error along with power spectral density are employed. The simulation results show that the proposed wavelet algorithm at level 8 is effective, in which the with the "dmey" wavelet function was selected be the best based power spectrum density.

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“…Time-domain algorithms seek to improve detection requirements like success rate and acquisition time mainly by increasing the length of the observed signal and then folding or sub-sampling it [19,20]. The longer signal length improves accuracy at lower C/N0 ratios because the averaging process reduces noise influence.…”

Section: Related Workmentioning

confidence: 99%

Variance-Triggered Two-Step GPS Acquisition

Costa

Albuquerque

Silveira

et al. 2019

Sensors

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The acquisition is the most time-consuming step performed by a Global Navigation Satellite System (GNSS) receiver. The objective is to detect which satellites are transmitting and what are the phase and Doppler frequency shift of the signal. It is the step with the highest computational complexity, especially for signals subjected to large Doppler shifts. Improving acquisition performance has a large impact on the overall performance of the GNSS reception. In this paper, we present a two-step Global Positioning System (GPS) acquisition algorithm whose first step performs an incremental correlation to find a coarse pair of phase and frequency and the second step, triggered by the variance of the largest correlation values, refines the first step. The proposed strategy, based on the conventional time-domain serial algorithm, reduces the average execution time of the acquisition process to about 1/5 of the conventional acquisition while keeping the same modest logic hardware requirements and slightly better success and false-positive rates. Additionally, the new method reduces memory usage by a factor that is proportional to the signal’s sampling frequency. All these advantages over conventional acquisition contribute together to significantly improve the overall performance and cost of GPS receivers.

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Low Computational Signal Acquisition for GNSS Receivers Using a Resampling Strategy and Variable Circular Correlation Time

Cited by 11 publications

References 31 publications

A Survey on Low-Power GNSS

A Survey on Low-Power GNSS

Artifact elimination in ECG signal using wavelet transform

Variance-Triggered Two-Step GPS Acquisition

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