Advances in the Theory and Implementation of GNSS Antenna Array Receivers

Arribas, Javier; Closas, Pau; Fernández‐Prades, Carles; Cuntz, Manuel; Meurer, Michael; Konovaltsev, Andriy

doi:10.1002/9781118405864.ch9

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…Several of these effects have been reported in the state-of-the-art [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. Moreover, several interference countermeasures have been already proposed at different stages of the receiver including the antenna design [ 13 , 14 , 15 , 16 , 17 , 18 ], radio-frequency front-end [ 19 , 20 ], and signal processing [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. However, a key part of GNSS receivers is the data demodulation stage, which allows recovering essential information.…”

Section: Introductionmentioning

confidence: 99%

Low Complexity Robust Data Demodulation for GNSS

Ortega¹,

Poulliat

Boucheret

et al. 2021

Sensors

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In this article, we provide closed-form approximations of log-likelihood ratio (LLR) values for direct sequence spread spectrum (DS-SS) systems over three particular scenarios, which are commonly found in the Global Navigation Satellite System (GNSS) environment. Those scenarios are the open sky with smooth variation of the signal-to-noise ratio (SNR), the additive Gaussian interference, and pulsed jamming. In most of the current communications systems, block-wise estimators are considered. However, for some applications such as GNSSs, symbol-wise estimators are available due to the low data rate. Usually, the noise variance is considered either perfectly known or available through symbol-wise estimators, leading to possible mismatched demodulation, which could induce errors in the decoding process. In this contribution, we first derive two closed-form expressions for LLRs in additive white Gaussian and Laplacian noise channels, under noise uncertainty, based on conjugate priors. Then, assuming those cases where the statistical knowledge about the estimation error is characterized by a noise variance following an inverse log-normal distribution, we derive the corresponding closed-form LLR approximations. The relevance of the proposed expressions is investigated in the context of the GPS L1C signal where the clock and ephemeris data (CED) are encoded with low-density parity-check (LDPC) codes. Then, the CED is iteratively decoded based on the belief propagation (BP) algorithm. Simulation results show significant frame error rate (FER) improvement compared to classical approaches not accounting for such uncertainty.

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Section: Introductionmentioning

confidence: 99%

Low Complexity Robust Data Demodulation for GNSS

Ortega¹,

Poulliat

Boucheret

et al. 2021

Sensors

Self Cite

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“…Therefore, application of antenna arrays to GNSS receivers should be carefully designed considering that it is not a mere adaptation from wireless communication systems' concepts. Examples of works specifically devoted to GNSS particularities can be found in [26]- [38]. The specific problem of precise GNSS positioning using antenna arrays has been addressed in [39], [40].…”

Section: Introductionmentioning

confidence: 99%

Robust GNSS Receivers by Array Signal Processing: Theory and Implementation

Fernández‐Prades¹,

Arribas²,

Closas³

2016

Proc. IEEE

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107

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One of the main vulnerabilities of GNSS receivers is their exposure to intentional or unintentional jamming signals, which could even cause service unavailability. Several alternatives to counteract these effects were proposed in the literature, being the most promising those based on multiple antenna architectures. This is specially the case for high-grade receivers used in applications requiring reliability and robustness. This article provides an overview of the possible receiver architectures encompassing antenna arrays and the associated signal processing techniques. Emphasis is also put on the most typical implementation issues found when dealing with such technology. A thorough survey is complemented with a set of experiments, including real data processing by a working prototype, which exemplifies the above ideas.

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“…The focus is on short-range multipath signals with specular reflections. As GNSS signals are below the noise level before the correlation process, spatial processing to mitigate multipath signals is mostly performed after the de-spreading process (i.e., correlation and Doppler removal) (Arribas et al 2012;Chen et al 2012). The inherent capability of DAS and MPDR beamformers to mitigate multipath are studied first without any preprocessing to decorrelate the LOS and multipath signals.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of GNSS multipath mitigation using antenna arrays

Vagle

Broumandan

Jafarnia-Jahromi

2016

J. Glob. Position. Syst.

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Multipath affects the shape of the correlation function and results in biased pseudorange measurements and erroneous navigation solutions. Antenna array processing, which uses signal spatial characteristics, is an effective method to mitigate various types of interference signals. However, the performance of most of the distortionless beamforming techniques degrades in multipath conditions due to the correlation between the desired Line of Sight (LOS) signal and multipath signals. This paper characterizes the performance of different beamforming techniques to mitigate multipath signals through the processing and analysis of simulated and actual data. The main novelty is the investigation of multipath mitigation performance of practically realizable antenna array-based GNSS receivers when the beamforming process is completely integrated into the tracking module after de-spreading. Beamforming techniques such as Delay And Sum (DAS) beamforming, Minimum Power Distortionless Response (MPDR) with and without spatial smoothing are considered. A novel multi-antenna simulator test-bed is developed to generate multipath signals for a multi-antenna platform. A software multi-antenna GPS receiver incorporating different beamforming techniques is then developed to generate pseudorange measurements and position solutions. Carrier-to-Noise ratio (C/N 0 ), pseudorange errors and position solutions before and after beamforming are compared to show the effectiveness of different beamforming techniques to mitigate multipath. Results with simulated and actual GPS signals show improved performance using the MPDR beamformer with spatial smoothing. The utilization of spatial processing results in a pseudorange error reduction of up to 60 % and a position error reduction of up to 30 %.

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Advances in the Theory and Implementation of GNSS Antenna Array Receivers

Cited by 10 publications

References 13 publications

Low Complexity Robust Data Demodulation for GNSS

Low Complexity Robust Data Demodulation for GNSS

Robust GNSS Receivers by Array Signal Processing: Theory and Implementation

Performance analysis of GNSS multipath mitigation using antenna arrays

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