Front-end filtering and quantisation effects on GNSS signal processing

Curran, James T.; Borio, Daniele; Murphy, Colin C.

doi:10.1109/wirelessvitae.2009.5172455

Cited by 15 publications

(10 citation statements)

References 8 publications

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“…Recently, closely related work has been published in [10] and [11]. The former, which was pointed out to the author after the model described herein was first presented in [9], provides an independently derived analytical model with many similarities.…”

Section: Introductionmentioning

confidence: 99%

“…The former, which was pointed out to the author after the model described herein was first presented in [9], provides an independently derived analytical model with many similarities. However, the model in [10] does not distinguish between the commensurate and incommensurate sampling cases, and also only addresses uniform quantizers with an even number of output levels. Interestingly, although the model in [10] only explicitly accounts for white noise at the receiver input, the derived model easily handles the case of noise plus non-white interference because it was designed to address filtering with an arbitrary transfer function prior to quantization.…”

Section: Introductionmentioning

confidence: 99%

“…However, the model in [10] does not distinguish between the commensurate and incommensurate sampling cases, and also only addresses uniform quantizers with an even number of output levels. Interestingly, although the model in [10] only explicitly accounts for white noise at the receiver input, the derived model easily handles the case of noise plus non-white interference because it was designed to address filtering with an arbitrary transfer function prior to quantization. The second reference [11] investigates losses in the presence of noise only through an entirely different analytical derivation using quantizer level crossing statistics.…”

Section: Introductionmentioning

confidence: 99%

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Analytical Model for GNSS Receiver Implementation Losses

Hegarty

2011

Navigation

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Global Navigation Satellite System (GNSS) receivers suffer signal‐to‐noise ratio (SNR) losses due to bandlimiting, quantization, and sampling. This paper presents an analytical model for GNSS receiver losses applicable to a wide variety of hardware configurations. The model addresses digitization of the received signal by a uniform quantizer with an arbitrary (even or odd) integer number of output levels. The model provides SNR loss values for GNSS signals in the presence of both additive white Gaussian noise and interference, provided that the interference can be accurately modeled as a non‐white, Gaussian wide sense stationary process.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analytical Model for GNSS Receiver Implementation Losses

Hegarty

2011

Navigation

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“…In an analytical model for evaluating receiver implementation losses is proposed in which incommensurate sampling and arbitrary number of output levels of quantizer are taken into account. Literature closely related to our work includes , in which the joint effects of front‐end filtering and quantization are deeply studied but the effect of sampling is overlooked.…”

Section: Introductionmentioning

confidence: 99%

Joint Determination of Precorrelation Bandwidth, Sampling Frequency and Quantization in Wideband Compass Receivers

Zhang

Zhan

2012

J Inst Navig

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Precorrelation filtering, sampling, and quantization are three fundamental functions for GNSS receivers. Analytical model and numerical simulation methods have been developed to evaluate the implementation losses due to different combinations of these parameters, first in the context of white noise and later non‐white interference. However, the performance metric was loss of effective carrier‐to‐noise density ratio (C/N0). This is indirect in predicting the performance degradation of a delay‐lock loop (DLL). This paper proposes an analytical model to predict the DLL performance with loss of root‐mean‐squared (RMS) tracking accuracy in meters. In the current project, a wideband Compass receiver capable of processing modern GNSS signals is required to form the core of the GNSS Vulnerabilities Assessment and Validation Platform (VAVE). The method proposed was used to determine the optimal combination of precorrelation bandwidth, sampling frequency, and quantization to guide the design of VAVE receiver hardware. Here optimality means highest possible performance with minimized implementation cost. Copyright © 2012 Institute of Navigation

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“…In [3,4] an analytical model for evaluating receiver implementation losses is proposed in which incommensurate sampling and arbitrary number of output levels of quantizer are taken into consideration. Literature closely related to our work includes [5], in which the joint effects of front-end filtering and quantization are deeply studied but the effect of sampling is neglected. This paper proposes a generic analytical model to predict DLL tracking accuracy when the combined effects of BSQ are studied with incommensurate sampling and arbitrary number of output levels of quantizer.…”

Section: Introductionmentioning

confidence: 99%

An analytical model of code-tracking performance for next-generation GNSS modulations

Zhang

2011

IEICE Electron. Express

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Precorrelation bandlimiting, sampling, and quantization (BSQ) are three fundamental functions of receivers for global navigation satellite system (GNSS) signal processing. Analytical models have been developed to evaluate the implementation losses due to different combinations of these parameters for legacy modulations such as BPSK. However, the performance metric was losses of effective carrier-to-noise ratio in dB, instead of performance degradation of a delay-locked loop (DLL) used for code-tracking. This paper proposes an analytical model to predict the DLL performance with the loss of RMS tracking error in meters as the performance metrics. The signal model & assumptions and canonical form of DLL are first introduced. Then, analytical model is derived for DLL tracking accuracy and the performance metrics, tracking loss is defined. Finally, simulation results of two next-generation modulations for GPS and Galileo, namely CBOC and TMBOC, are presented. Keywords: BSQ, bandlimiting, quantization, sampling, DLL Classification: Electronic instrumentation and control References

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Front-end filtering and quantisation effects on GNSS signal processing

Cited by 15 publications

References 8 publications

Analytical Model for GNSS Receiver Implementation Losses

Analytical Model for GNSS Receiver Implementation Losses

Joint Determination of Precorrelation Bandwidth, Sampling Frequency and Quantization in Wideband Compass Receivers

An analytical model of code-tracking performance for next-generation GNSS modulations

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