Enabling High-Integrity Vehicular Satellite Navigation Operations via Automatic Gain Control

Levigne, Nathan; Puskar, Charles; Akos, Dennis

doi:10.1109/vtcfall.2019.8891311

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Civil GNSS signals are unencrypted and unauthenticated, leaving vulnerabilities that can be exploited with counterfeit (spoofed) signals. Just less than ten years ago, GNSS spoofing required specialized expertise and equipment costs of upwards of $50,000 [39]. Now, with open source software and more accessible hardware, spoofing attacks can be accomplished for as little as $100 [39].…”

Section: Localization In Autonomymentioning

confidence: 99%

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Satellite Navigation for the Age of Autonomy

Reid¹,

Chan²,

Goel³

et al. 2020

2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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Global Navigation Satellite Systems (GNSS) brought navigation to the masses. Coupled with smartphones, the blue dot in the palm of our hands has forever changed the way we interact with the world. Looking forward, cyber-physical systems such as self-driving cars and aerial mobility are pushing the limits of what localization technologies including GNSS can provide. This autonomous revolution requires a solution that supports safety-critical operation, centimeter positioning, and cybersecurity for millions of users. To meet these demands, we propose a navigation service from Low Earth Orbiting (LEO) satellites which deliver precision in-part through faster motion, higher power signals for added robustness to interference, constellation autonomous integrity monitoring for integrity, and encryption / authentication for resistance to spoofing attacks. This paradigm is enabled by the 'New Space' movement, where highly capable satellites and components are now built on assembly lines and launch costs have decreased by more than tenfold. Such a ubiquitous positioning service enables a consistent and secure standard where trustworthy information can be validated and shared, extending the electronic horizon from sensor line of sight to an entire city. This enables the situational awareness needed for true safe operation to support autonomy at scale.

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Section: Localization In Autonomymentioning

confidence: 99%

“…Just less than ten years ago, GNSS spoofing required specialized expertise and equipment costs of upwards of $50,000 [39]. Now, with open source software and more accessible hardware, spoofing attacks can be accomplished for as little as $100 [39].…”

Section: Localization In Autonomymentioning

confidence: 99%

Satellite Navigation for the Age of Autonomy

Reid¹,

Chan²,

Goel³

et al. 2020

2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)

View full text Add to dashboard Cite

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“…According to the position of exposure in the GNSS receiver processing chain, interference detection can be divided into RF front-end detection, pre correlation detection, and post correlation detection. In the radio frequency front-end, antenna array technologies [7] and automatic gain control (AGC) [8] could be adopted. Pre correlation interference detection algorithms can be mainly divided into time domain [9], frequency domain [10], statistical domain [11], and the combination of several domains [12], [13].…”

Section: Introductionmentioning

confidence: 99%

A Double Threshold Cooperative GNSS Interference Detection Algorithm Based on Fuzzy Logic

et al. 2020

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In this paper, an interference detection algorithm based on fuzzy logic fusion is proposed for the interference monitoring system (IMS) of the global satellite navigation system (GNSS). In particular, for the problem of reduced detection performance caused by noise fluctuations in a complex electromagnetic environment, the proposed algorithm introduces the idea of fuzzy logic. According to the credibility of local detection results in different regions determined by noise uncertainty, we adopt a two-step cooperative detection mechanism in the fusion center (FC). When a definite detection result is obtained in the first step, the decision is finished. Otherwise, we will execute the decision algorithm based on the fuzzy logic fusion of the second step. The experimental results show that the proposed algorithm has the best detection performance, compared with the traditional energy detection algorithm and the energy detection algorithm based on soft data. Furthermore, compared with soft data, the system overhead of the proposed algorithm is also reduced without increasing the amount of calculation.

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