Trusted Data Fusion, Multi-Agent Autonomy, Autonomous Vehicles
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911073983004672 |
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| author | Hallyburton, R. Spencer Pajic, Miroslav |
| author_facet | Hallyburton, R. Spencer Pajic, Miroslav |
| contents | Multi-agent collaboration enhances situational awareness in intelligence, surveillance, and reconnaissance (ISR) missions. Ad hoc networks of unmanned aerial vehicles (UAVs) allow for real-time data sharing, but they face security challenges due to their decentralized nature, making them vulnerable to cyber-physical attacks. This paper introduces a trust-based framework for assured sensor fusion in distributed multi-agent networks, utilizing a hidden Markov model (HMM)-based approach to estimate the trustworthiness of agents and their provided information in a decentralized fashion. Trust-informed data fusion prioritizes fusing data from reliable sources, enhancing resilience and accuracy in contested environments. To evaluate the assured sensor fusion under attacks on system/mission sensing, we present a novel multi-agent aerial dataset built from the Unreal Engine simulator. We demonstrate through case studies improved ISR performance and an ability to detect malicious actors in adversarial settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17875 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trusted Data Fusion, Multi-Agent Autonomy, Autonomous Vehicles Hallyburton, R. Spencer Pajic, Miroslav Systems and Control Multi-agent collaboration enhances situational awareness in intelligence, surveillance, and reconnaissance (ISR) missions. Ad hoc networks of unmanned aerial vehicles (UAVs) allow for real-time data sharing, but they face security challenges due to their decentralized nature, making them vulnerable to cyber-physical attacks. This paper introduces a trust-based framework for assured sensor fusion in distributed multi-agent networks, utilizing a hidden Markov model (HMM)-based approach to estimate the trustworthiness of agents and their provided information in a decentralized fashion. Trust-informed data fusion prioritizes fusing data from reliable sources, enhancing resilience and accuracy in contested environments. To evaluate the assured sensor fusion under attacks on system/mission sensing, we present a novel multi-agent aerial dataset built from the Unreal Engine simulator. We demonstrate through case studies improved ISR performance and an ability to detect malicious actors in adversarial settings. |
| title | Trusted Data Fusion, Multi-Agent Autonomy, Autonomous Vehicles |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2507.17875 |