C/N0 estimation: design criteria and reliability analysis under global navigation satellite system (GNSS) weak signal scenarios

Satyanarayana, S.; Borio, Daniele; Lachapelle, G.

doi:10.1049/iet-rsn.2011.0164

Cited by 11 publications

(9 citation statements)

References 7 publications

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“…The result of this operation can be described by the well-established receiver signal model. Under the assumption of perfect carrier frequency tracking in a GPS receiver, the baseband signal from the correlator output can be expressed as [23]:…”

Section: Receiver Snr Modeling and Estimation Under Noisementioning

confidence: 99%

“…This assumption is reasonable if the antenna height is low and the ground is flat (surface height variation is less than one wavelength). Navigation bit modulation is assumed to be known in advance through methods such as external aiding [23], and thus, long-time coherent accumulation is allowed to improve SNR estimation. According to [23], the SNR of the base-band GNSS signal can be expressed as follows:…”

Section: Receiver Snr Modeling and Estimation Under Noisementioning

confidence: 99%

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A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data

et al. 2018

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Abstract:The Global Navigation Satellite System-Interferometry and Reflectometry (GNSS-IR) technique on soil moisture remote sensing was studied. A semi-empirical Signal-to-Noise Ratio (SNR) model was proposed as a curve-fitting model for SNR data routinely collected by a GNSS receiver. This model aims at reconstructing the direct and reflected signal from SNR data and at the same time extracting frequency and phase information that is affected by soil moisture as proposed by K. M. Larson et al. This is achieved empirically through approximating the direct and reflected signal by a second-order and fourth-order polynomial, respectively, based on the well-established SNR model. Compared with other models (K. M. Larson et al., T. Yang et al.), this model can improve the Quality of Fit (QoF) with little prior knowledge needed and can allow soil permittivity to be estimated from the reconstructed signals. In developing this model, we showed how noise affects the receiver SNR estimation and thus the model performance through simulations under the bare soil assumption. Results showed that the reconstructed signals with a grazing angle of 5 • -15 • were better for soil moisture retrieval. The QoF was improved by around 45%, which resulted in better estimation of the frequency and phase information. However, we found that the improvement on phase estimation could be neglected. Experimental data collected at Lamasquère, France, were also used to validate the proposed model. The results were compared with the simulation and previous works. It was found that the model could ensure good fitting quality even in the case of irregular SNR variation. Additionally, the soil moisture calculated from the reconstructed signals was about 15% closer in relation to the ground truth measurements. A deeper insight into the Larson model and the proposed model was given at this stage, which formed a possible explanation of this fact. Furthermore, frequency and phase information extracted using this model were also studied for their capability to monitor soil moisture variation. Finally, phenomena such as retrieval ambiguity and error sensitivity were stated and discussed.

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Section: Receiver Snr Modeling and Estimation Under Noisementioning

confidence: 99%

Section: Receiver Snr Modeling and Estimation Under Noisementioning

confidence: 99%

A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data

et al. 2018

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“…This represented the error in the run-time SNR estimate. A plot of the measured standard deviation of the error is shown in Figure 10, along with the Cramér-Rao Lower Bound standard deviation for non-data-aided BPSK SNR estimation [22,23].…”

Section: A Note On Snr Estimationmentioning

confidence: 99%

“…As SNR is reduced, (14) approaches a uniform distribution over the interval [− /2, /2] and reaches a maximum variance of 2 /12. Once again, approximate solutions to(22) and(23) are provided in the Appendix.…”

mentioning

confidence: 99%

Enhancing Weak-Signal Carrier Phase Tracking in GNSS Receivers

Curran

2015

International Journal of Navigation and Observation

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Examining the performance of the GNSS PLL, this paper presents novel results describing the statistical properties of four popular phase estimators under both strong- and weak-signal conditions when subject to thermal noise, deterministic dynamics, and typical pedestrian motion. Design routines are developed which employ these results to enhance weak-signal performance of the PLL in terms of transient response, steady-state errors, and cycle-slips. By examining both single and data-pilot signals, it is shown that appropriate design and tuning of the PLL can significantly enhance tracking performance, in particular when used for pedestrian applications.

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“…Several NRTK techniques exist and the most commonly used at present are, for instance, the Master Auxiliary Concept (MAC) [4,5], the Virtual Reference Station (VRS) concept [6], and the FKP techniques [7], as well as the Network Adjustment (NetAdjust) concept developed by Raquet and Lachapelle [8,9]. Multistate system reliability theory has been a research topic for many years, for instance, extension of the system from two-state to multistate reliability [10,11] to compute the mean performance level at any given time and stochastic evaluation and bound computation of multistate coherent systems [12], further, studies on application of reliability analysis to GNSS data processing [2], a comparative GNSS reliability analysis [13], reliability analysis under GNSS weak signals [14], accuracy and reliability of multi-GNSS real-time precise positioning [15], and robust reliability testing in case of signal degradation environment [16,17].…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of Network Real-Time Kinematic

Ouassou

Natvig²,

Jensen

2018

Journal of Electrical and Computer Engineering

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The multistate reliability theory was applied to the network real-time kinematic (NRTK) data processing chain, where the qualities of the network corrections, baseline residuals, and the associated variance-covariance matrices are considered as the system state vectors. The state vectors have direct influence on the rover receiver position accuracy. The penalized honored stochastic averaged standard deviation (PHSASD) is used to map the NRTK sensitive data, represented by the states vectors to different levels of performance. The study shows that the improvement is possible by identification of critical components in the NRTK system and implementation of some parallelism that makes the system more robust.

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C/N0 estimation: design criteria and reliability analysis under global navigation satellite system (GNSS) weak signal scenarios

Cited by 11 publications

References 7 publications

A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data

A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data

Enhancing Weak-Signal Carrier Phase Tracking in GNSS Receivers

Reliability Analysis of Network Real-Time Kinematic

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