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Statistical Inference for Safe and Continuous Navigation in the Presence of Gnss Spoofing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Inference for Safe and Continuous Navigation in the Presence of Gnss Spoofing./
作者:	Rothmaier, Fabian Pascal.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	191 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-07, Section: B.
Contained By:	Dissertations Abstracts International83-07B.
標題:	Receivers & amplifiers. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28927417
ISBN:	9798762124928

Statistical Inference for Safe and Continuous Navigation in the Presence of Gnss Spoofing.
Rothmaier, Fabian Pascal.

Statistical Inference for Safe and Continuous Navigation in the Presence of Gnss Spoofing. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 191 p.

Source: Dissertations Abstracts International, Volume: 83-07, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

The past decades have been a success story of Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS). Around the world, they have become a fundamental source of position, velocity and time in countless applications such as aircraft, ships, smartphones, power grids and the financial markets. The current rapid advances in automation will likely increase this reliance on satellite navigation, with increased numbers of autonomous systems requiring precise navigation information. However, the accuracy, availability and integrity that has made GNSS a trusted cornerstone of navigation is nowadays challenged by increasing levels of interference. The most dangerous type of interference is spoofing, an event during which the receiver's navigation solution is compromised and provides erroneous information without warning. This can have dire consequences from mission failure to the loss of a vehicle. The purpose of this thesis is to develop statistical algorithms and methods for a GNSS receiver to detect and mitigate spoofing. Specifically, in this thesis I make three main contributions.1. I develop a highly sensitive, broadly applicable implementation of Direction of Arrival (DoA) based spoofing detection. The method shows two to ten times fewer missed detections than other results published to date. The algorithm and considerations are validated against flight data and live spoofing data.2. Many more detection metrics apart from DoA exist, each with strengths and weaknesses. I present a general framework for an optimal detection based on an arbitrary number of metrics. The framework results in more than two times fewer missed detections under the most challenging conditions with a guaranteed low number of false alerts. Leveraging the framework, I further demonstrate that the ideal defense design depends on the expected attack mode.3. Detection of a spoofing event generally results in discontinued use of GNSS for a given time or until having left a particular area. In absence of a comparable alternative to GNSS, the navigation error and uncertainty grows over time. I present an approach enabling the continued use of authentic GNSS signals despite an ongoing attack, while breaking with major assumptions in the literature. I further provide for the first time in the open literature integrity error bounds similar to those provided by Receiver Autonomous Integrity Monitoring (RAIM) throughout the duration of the attack. The results are validated using driving data and simulated spoofing attacks.

ISBN: 9798762124928Subjects--Topical Terms:

3559205
Receivers & amplifiers.

Statistical Inference for Safe and Continuous Navigation in the Presence of Gnss Spoofing.
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520 $a The past decades have been a success story of Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS). Around the world, they have become a fundamental source of position, velocity and time in countless applications such as aircraft, ships, smartphones, power grids and the financial markets. The current rapid advances in automation will likely increase this reliance on satellite navigation, with increased numbers of autonomous systems requiring precise navigation information. However, the accuracy, availability and integrity that has made GNSS a trusted cornerstone of navigation is nowadays challenged by increasing levels of interference. The most dangerous type of interference is spoofing, an event during which the receiver's navigation solution is compromised and provides erroneous information without warning. This can have dire consequences from mission failure to the loss of a vehicle. The purpose of this thesis is to develop statistical algorithms and methods for a GNSS receiver to detect and mitigate spoofing. Specifically, in this thesis I make three main contributions.1. I develop a highly sensitive, broadly applicable implementation of Direction of Arrival (DoA) based spoofing detection. The method shows two to ten times fewer missed detections than other results published to date. The algorithm and considerations are validated against flight data and live spoofing data.2. Many more detection metrics apart from DoA exist, each with strengths and weaknesses. I present a general framework for an optimal detection based on an arbitrary number of metrics. The framework results in more than two times fewer missed detections under the most challenging conditions with a guaranteed low number of false alerts. Leveraging the framework, I further demonstrate that the ideal defense design depends on the expected attack mode.3. Detection of a spoofing event generally results in discontinued use of GNSS for a given time or until having left a particular area. In absence of a comparable alternative to GNSS, the navigation error and uncertainty grows over time. I present an approach enabling the continued use of authentic GNSS signals despite an ongoing attack, while breaking with major assumptions in the literature. I further provide for the first time in the open literature integrity error bounds similar to those provided by Receiver Autonomous Integrity Monitoring (RAIM) throughout the duration of the attack. The results are validated using driving data and simulated spoofing attacks.
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