Myriad non‐linearity for GNSS robust signal processing

Borio, Daniele

doi:10.1049/iet-rsn.2016.0610

Cited by 22 publications

(59 citation statements)

References 18 publications

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“…An approach followed in IC is to explicitly introduce a parametric waveform for the interference, whose parameters are estimated to generate the interference replica. An alternate approach is to assume different probability density functions (pdfs) for η BB [ n ] . It was shown that a non‐Gaussian pdf can be adopted to effectively describe the joint distribution of noise and interference.…”

Section: Gnss Signals and Processingmentioning

confidence: 99%

“…By expanding the squares in , it is possible to restate the minimization process as

\begin{matrix} right \{\hat{τ}, {\hat{f}}_{d}\} & left = \arg \max_{τ, f_{d}} | C (τ, f_{d}) | & right \\ right \hat{φ} & left = ∠ C (\hat{τ}, {\hat{f}}_{d}) \end{matrix},

where

C false(τ, f_{d} false) = true \sum_{n = 0}^{N - 1} y false[n false] c ((), n T_{s} - τ) e^{- j 2 π f_{d} n T_{s}}

is the CAF . When moving from to , the signal amplitude, A , is factored out and removed from the estimation process.…”

Section: Gnss Signals and Processingmentioning

confidence: 99%

“…If f ( z ) is heavy‐tailed, then robust estimators for the signal parameters are obtained and model uncertainties can be effectively tolerated. The M‐estimator approach has been recently applied to GNSS signal processing() showing significant performance improvement in the presence of pulsed jamming. The complex signum and the myriad non‐linearity, derived from Laplace and Cauchy pdfs, were analyzed in previous studies() showing the effectiveness of robust signal processing for GNSS.…”

Section: Gnss Signals and Processingmentioning

confidence: 99%

“…For this reason, alternative solutions based on the usage of robust statistical approaches and robust signal processing schemes have been recently considered. () A technique is considered statistically robust if it is able to effectively deal with model uncertainties and model mismatches. ()…”

Section: Introductionmentioning

confidence: 99%

“…In order to overcome this limitation, we extend the work described in previous studies() to transform domains (TDs) where interference admits a sparse representation and thus appears as a set of outliers.…”

Section: Introductionmentioning

confidence: 99%

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Robust transform domain signal processing for GNSS

Borio

Closas

2019

NAVIGATION

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Robust signal processing techniques are effective mitigation approaches that can enable Global Navigation Satellite System (GNSS) receiver operations even in the presence of strong interference. We extend robust GNSS signal processing to the case where interference admits a sparse representation in a transform domain (TD). More specifically, an orthonormal transformation is used to project the received GNSS samples into an appropriate TD. Robust signal processing strategies are then applied. The robust transform domain (RTD) cross‐ambiguity function (CAF) is obtained by applying a zero‐memory nonlinearity (ZMNL) to the TD samples, which, in turn, are used for the evaluation of the CAF. Different nonlinearities are analyzed and different implementations for the evaluation of the RTD CAF are proposed. The RTD approach is characterized from a theoretical point of view, with the usage of Monte Carlo simulations and through the processing of GNSS signals generated with a hardware simulator.

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Section: Gnss Signals and Processingmentioning

confidence: 99%

“…By expanding the squares in , it is possible to restate the minimization process as

\begin{matrix} right \{\hat{τ}, {\hat{f}}_{d}\} & left = \arg \max_{τ, f_{d}} | C (τ, f_{d}) | & right \\ right \hat{φ} & left = ∠ C (\hat{τ}, {\hat{f}}_{d}) \end{matrix},

where

C false(τ, f_{d} false) = true \sum_{n = 0}^{N - 1} y false[n false] c ((), n T_{s} - τ) e^{- j 2 π f_{d} n T_{s}}

is the CAF . When moving from to , the signal amplitude, A , is factored out and removed from the estimation process.…”

Section: Gnss Signals and Processingmentioning

confidence: 99%

Section: Gnss Signals and Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust transform domain signal processing for GNSS

Borio

Closas

2019

NAVIGATION

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GNSS interference mitigation: A measurement and position domain assessment

Borio

Gioia

2020

Navigation

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Modern Global Navigation Satellite System (GNSS) receivers have to withstand significant levels of interference in order to operate under harsh conditions, such as in the presence of jamming and of other Radio Frequency (RF) threats. A possibility is to implement pre‐correlation interference mitigation techniques that operate directly on the samples provided by the receiver front‐end. This paper provides an assessment of five interference mitigation techniques at the measurement and position level. The analysis focuses on the Adaptive Notch Filter (ANF) and on four Robust Interference Mitigation (RIM) techniques. Several data collections were performed in the presence of jamming, and the data collected were used for the analysis that shows that RIM techniques do not introduce biases at both the measurement and position level. While the ANF delays pseudorange measurements, the biases introduced are predominantly common to all the observations with a negligible impact on a Single Point Positioning (SPP) solution.

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