WFRF:(Liu Wenjie)
Sökning: WFRF:(Liu Wenjie) > Extending RAIM with...
- 11 av 21
- Föregående post
- Nästa post
- Till träfflistan
Extending RAIM with a Gaussian Mixture of Opportunistic Information
-
- Liu, Wenjie (författare)
- KTH,Programvaruteknik och datorsystem, SCS
-
- Papadimitratos, Panos (författare)
- KTH,Programvaruteknik och datorsystem, SCS
- (creator_code:org_t)
- 2024
- 2024
- Engelska.
- Ingår i: Proceedings of the 2024 International Technical Meeting of The Institute of Navigation. ; , s. 454-466
- Konferensbidrag (refereegranskat)
Abstract
Ämnesord
Stäng
- Global navigation satellite systems (GNSS) are indispensable for various applications, but they are vulnerable to spoofing attacks. The original receiver autonomous integrity monitoring (RAIM) was not designed for securing GNSS. In this context, RAIM was extended with wireless signals, termed signals of opportunity (SOPs), or onboard sensors, typically assumed benign. However, attackers might also manipulate wireless networks, raising the need for a solution that considers untrustworthy SOPs. To address this, we extend RAIM by incorporating all opportunistic information, i.e., measurements from terrestrial infrastructures and onboard sensors, culminating in one function for robust GNSS spoofing detection. The objective is to assess the likelihood of GNSS spoofing by analyzing locations derived from extended RAIM solutions, which include location solutions from GNSS pseudorange subsets and wireless signal subsets of untrusted networks. Our method comprises two pivotal components: subset generation and location fusion. Subsets of ranging information are created and processed through positioning algorithms, producing temporary locations. Onboard sensors provide speed, acceleration, and attitude data, aiding in location filtering based on motion constraints. The filtered locations, modeled with uncertainty, are fused into a composite likelihood function normalized for GNSS spoofing detection. Theoretical assessments of GNSS-only and multi-infrastructure scenarios under uncoordinated and coordinated attacks are conducted. The detection of these attacks is feasible when the number of benign subsets exceeds a specific threshold. A real-world dataset from the Kista Science City area is used for experimental validation. Comparative analysis against baseline methods shows a significant improvement in detection accuracy achieved by our Gaussian Mixture RAIM approach. Moreover, we discuss leveraging RAIM results for plausible location recovery. The theoretical analysis and experimental validation underscore the efficacy of our spoofing detection approach.
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
Till lärosätets databas
- 11 av 21
- Föregående post
- Nästa post
- Till träfflistan
Hitta mer i SwePub
- Av författaren/redakt...
- Liu, Wenjie
- Papadimitratos, ...
- Artiklar i publikationen
- Proceedings of t ...
- Av lärosätet
- Kungliga Tekniska Högskolan
Sök utanför SwePub
- Sök vidare i:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - Nationella bibliotekssystem
- LIBRIS.kb.se
