Deep Learning of GNSS Acquisition
Signal acquisition is a crucial step in Global Navigation Satellite System (GNSS) receivers, which is typically solved by maximizing the so-called Cross-Ambiguity Function (CAF) as a hypothesis testing problem. This article proposes to use deep learning models to perform such acquisition, whereby th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/3/1566 |
| _version_ | 1797623225984745472 |
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| author | Parisa Borhani-Darian Haoqing Li Peng Wu Pau Closas |
| author_facet | Parisa Borhani-Darian Haoqing Li Peng Wu Pau Closas |
| author_sort | Parisa Borhani-Darian |
| collection | DOAJ |
| description | Signal acquisition is a crucial step in Global Navigation Satellite System (GNSS) receivers, which is typically solved by maximizing the so-called Cross-Ambiguity Function (CAF) as a hypothesis testing problem. This article proposes to use deep learning models to perform such acquisition, whereby the CAF is fed to a data-driven classifier that outputs binary class posteriors. The class posteriors are used to compute a Bayesian hypothesis test to statistically decide the presence or absence of a GNSS signal. The versatility and computational affordability of the proposed method are addressed by splitting the CAF into smaller overlapping sections, which are fed to a bank of parallel classifiers whose probabilistic results are optimally fused to provide a so-called probability ratio map from which acquisition is decided. Additionally, the article shows how noncoherent integration schemes are enabled through optimal data fusion, with the goal of increasing the resulting classifier accuracy. The article provides simulation results showing that the proposed data-driven method outperforms current CAF maximization strategies, enabling enhanced acquisition at medium-to-high carrier-to-noise density ratios. |
| first_indexed | 2024-03-11T09:25:39Z |
| format | Article |
| id | doaj.art-d10bfc112b8a4c7cbd2fb37cd1e152d2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T09:25:39Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d10bfc112b8a4c7cbd2fb37cd1e152d22023-11-16T18:02:56ZengMDPI AGSensors1424-82202023-02-01233156610.3390/s23031566Deep Learning of GNSS AcquisitionParisa Borhani-Darian0Haoqing Li1Peng Wu2Pau Closas3Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USADepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USADepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USADepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USASignal acquisition is a crucial step in Global Navigation Satellite System (GNSS) receivers, which is typically solved by maximizing the so-called Cross-Ambiguity Function (CAF) as a hypothesis testing problem. This article proposes to use deep learning models to perform such acquisition, whereby the CAF is fed to a data-driven classifier that outputs binary class posteriors. The class posteriors are used to compute a Bayesian hypothesis test to statistically decide the presence or absence of a GNSS signal. The versatility and computational affordability of the proposed method are addressed by splitting the CAF into smaller overlapping sections, which are fed to a bank of parallel classifiers whose probabilistic results are optimally fused to provide a so-called probability ratio map from which acquisition is decided. Additionally, the article shows how noncoherent integration schemes are enabled through optimal data fusion, with the goal of increasing the resulting classifier accuracy. The article provides simulation results showing that the proposed data-driven method outperforms current CAF maximization strategies, enabling enhanced acquisition at medium-to-high carrier-to-noise density ratios.https://www.mdpi.com/1424-8220/23/3/1566GNSS acquisitionmachine learningdeep learningdata fusion |
| spellingShingle | Parisa Borhani-Darian Haoqing Li Peng Wu Pau Closas Deep Learning of GNSS Acquisition Sensors GNSS acquisition machine learning deep learning data fusion |
| title | Deep Learning of GNSS Acquisition |
| title_full | Deep Learning of GNSS Acquisition |
| title_fullStr | Deep Learning of GNSS Acquisition |
| title_full_unstemmed | Deep Learning of GNSS Acquisition |
| title_short | Deep Learning of GNSS Acquisition |
| title_sort | deep learning of gnss acquisition |
| topic | GNSS acquisition machine learning deep learning data fusion |
| url | https://www.mdpi.com/1424-8220/23/3/1566 |
| work_keys_str_mv | AT parisaborhanidarian deeplearningofgnssacquisition AT haoqingli deeplearningofgnssacquisition AT pengwu deeplearningofgnssacquisition AT pauclosas deeplearningofgnssacquisition |