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Record W2756684097 · doi:10.1109/access.2017.2754414

Improve the Security of GNSS Receivers Through Spoofing Mitigation

2017· article· en· W2756684097 on OpenAlex
Shuai Han, Lei Chen, Weixiao Meng, Cheng Li

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Access · 2017
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsMemorial University of Newfoundland
FundersNational Natural Science Foundation of China
KeywordsSpoofing attackGNSS applicationsComputer scienceInterference (communication)Satellite systemSubspace topologySatellite navigationProjection (relational algebra)Real-time computingGlobal Positioning SystemComputer securityTelecommunicationsArtificial intelligenceAlgorithm

Abstract

fetched live from OpenAlex

Spoofing attacks are one of the most dangerous threats for the application of the global navigation satellite system (GNSS), especially for autonomous driving and unmanned aerial vehicles. In this paper, we present a more robust spoofing mitigation algorithm based on subspace projection that is independent of the number of antennas and that can be utilized in single-antenna GNSS receivers. During a spoofing attack, authentic signals are contaminated by spoofing signals. We demonstrate that all spoofing signals can be eliminated by projecting the received signal onto the orthogonal null space of the spoofing signals. Moreover, two types of receiver structures are designed: a centralized structure that has the ability to suppress cross-correlation interference and a distributed structure with lower computational complexity and lower projection power losses. The proposed algorithm is verified by the Beidou B1I signals for improving the security of the receiver.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.291
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it