Auxiliary Vehicle Positioning Based on Robust DOA Estimation With Unknown Mutual Coupling
As an important branch of the Internet of Vehicles (IoV), vehicle positioning has drawn extensive attention. Traditional positioning systems based on a global positioning system incur long delays, and may fail due to obstructions. In this article, we propose an auxiliary positioning architecture, wh...
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Veröffentlicht in: | IEEE internet of things journal 2020-06, Vol.7 (6), p.5521-5532 |
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description | As an important branch of the Internet of Vehicles (IoV), vehicle positioning has drawn extensive attention. Traditional positioning systems based on a global positioning system incur long delays, and may fail due to obstructions. In this article, we propose an auxiliary positioning architecture, whose core is to estimate the direction of arrival (DOA) of signals from landmarks, such as wireless access points, utilizing a sensor array in the vehicle. Due to space limitations, the array may be placed in an arbitrary geometry and may suffer from unknown mutual coupling. Most algorithms are only effective for sensor arrays with special geometries, e.g., a uniform linear array or rectangular array. To tackle this problem, an improved multiple signal classification algorithm is derived, which is superior to the state-of-the-art iterative method from the perspective of computational complexity. Detailed analysis concerning identifiability, computational complexity, and Cramér-Rao bounds are given. The simulation results verify the improvement of the proposed DOA estimation algorithm. The proposed architecture can obtain robust self-localization with existing vehicular ad hoc networks, and it can collaborate with other positioning systems to provide a safe driving environment. |
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Traditional positioning systems based on a global positioning system incur long delays, and may fail due to obstructions. In this article, we propose an auxiliary positioning architecture, whose core is to estimate the direction of arrival (DOA) of signals from landmarks, such as wireless access points, utilizing a sensor array in the vehicle. Due to space limitations, the array may be placed in an arbitrary geometry and may suffer from unknown mutual coupling. Most algorithms are only effective for sensor arrays with special geometries, e.g., a uniform linear array or rectangular array. To tackle this problem, an improved multiple signal classification algorithm is derived, which is superior to the state-of-the-art iterative method from the perspective of computational complexity. Detailed analysis concerning identifiability, computational complexity, and Cramér-Rao bounds are given. The simulation results verify the improvement of the proposed DOA estimation algorithm. 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Traditional positioning systems based on a global positioning system incur long delays, and may fail due to obstructions. In this article, we propose an auxiliary positioning architecture, whose core is to estimate the direction of arrival (DOA) of signals from landmarks, such as wireless access points, utilizing a sensor array in the vehicle. Due to space limitations, the array may be placed in an arbitrary geometry and may suffer from unknown mutual coupling. Most algorithms are only effective for sensor arrays with special geometries, e.g., a uniform linear array or rectangular array. To tackle this problem, an improved multiple signal classification algorithm is derived, which is superior to the state-of-the-art iterative method from the perspective of computational complexity. Detailed analysis concerning identifiability, computational complexity, and Cramér-Rao bounds are given. The simulation results verify the improvement of the proposed DOA estimation algorithm. The proposed architecture can obtain robust self-localization with existing vehicular ad hoc networks, and it can collaborate with other positioning systems to provide a safe driving environment.</description><subject>Algorithms</subject><subject>Antenna arrays</subject><subject>Arbitrary geometry</subject><subject>Complexity</subject><subject>Computer architecture</subject><subject>Computer Science</subject><subject>Computer Science, Information Systems</subject><subject>Computer simulation</subject><subject>Direction of arrival</subject><subject>direction-of-arrival (DOA) estimation</subject><subject>Direction-of-arrival estimation</subject><subject>Engineering</subject><subject>Engineering, Electrical & Electronic</subject><subject>Estimation</subject><subject>Geometry</subject><subject>Global positioning systems</subject><subject>GPS</subject><subject>Internet of Things</subject><subject>Internet of Vehicles</subject><subject>Internet of Vehicles (IoV)</subject><subject>Iterative methods</subject><subject>Linear arrays</subject><subject>Mobile ad hoc networks</subject><subject>Mutual coupling</subject><subject>Obstructions</subject><subject>Robustness</subject><subject>Science & Technology</subject><subject>sensor array</subject><subject>Sensor arrays</subject><subject>Signal classification</subject><subject>Technology</subject><subject>Telecommunications</subject><subject>vehicle positioning</subject><subject>Wireless communication</subject><subject>Wireless communication systems</subject><issn>2327-4662</issn><issn>2327-4662</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>AOWDO</sourceid><recordid>eNqNkMtOwzAQRSMEEhXwAYiNJZaoxY8kjpelFCgqKkI8xCpKnDF1CXaJHZX-PQ6tEEtWcxf3zGhOFB0TPCAEi_PbyexxQDHFAyq44JzsRD3KKO_HaUp3_-T96Mi5BcY4YAkRaS96HbZfutZFs0bPMNeyBnRvnfbaGm3e0EXhoELWoAdbts6jy9kQjZ3XH0XXQC_az9GTeTd2ZdBd69uiRiPbLuvAHkZ7qqgdHG3nQfR0NX4c3fSns-vJaDjtS8ZS3wfIKpyyImMkVlmVxBWIuICYlbIsFRG4UkRhCamIgRNVyKSslAAAmUjJUskOotPN3mVjP1twPl_YtjHhZE5jwijhJEtCi2xasrHONaDyZRO-aNY5wXknMe8k5p3EfCsxMGcbZgWlVU5qMBJ-uWAxiRlJOA8p7S5k_2-PtP8xGGQZH9CTDarDX7-IwIxwStk3thWPog</recordid><startdate>20200601</startdate><enddate>20200601</enddate><creator>Wen, Fangqing</creator><creator>Wang, Juan</creator><creator>Shi, Junpeng</creator><creator>Gui, Guan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Traditional positioning systems based on a global positioning system incur long delays, and may fail due to obstructions. In this article, we propose an auxiliary positioning architecture, whose core is to estimate the direction of arrival (DOA) of signals from landmarks, such as wireless access points, utilizing a sensor array in the vehicle. Due to space limitations, the array may be placed in an arbitrary geometry and may suffer from unknown mutual coupling. Most algorithms are only effective for sensor arrays with special geometries, e.g., a uniform linear array or rectangular array. To tackle this problem, an improved multiple signal classification algorithm is derived, which is superior to the state-of-the-art iterative method from the perspective of computational complexity. Detailed analysis concerning identifiability, computational complexity, and Cramér-Rao bounds are given. The simulation results verify the improvement of the proposed DOA estimation algorithm. 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subjects | Algorithms Antenna arrays Arbitrary geometry Complexity Computer architecture Computer Science Computer Science, Information Systems Computer simulation Direction of arrival direction-of-arrival (DOA) estimation Direction-of-arrival estimation Engineering Engineering, Electrical & Electronic Estimation Geometry Global positioning systems GPS Internet of Things Internet of Vehicles Internet of Vehicles (IoV) Iterative methods Linear arrays Mobile ad hoc networks Mutual coupling Obstructions Robustness Science & Technology sensor array Sensor arrays Signal classification Technology Telecommunications vehicle positioning Wireless communication Wireless communication systems |
title | Auxiliary Vehicle Positioning Based on Robust DOA Estimation With Unknown Mutual Coupling |
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