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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.01445 |
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| _version_ | 1866912516593942528 |
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| author | Snow, Luke Krishnamurthy, Vikram |
| author_facet | Snow, Luke Krishnamurthy, Vikram |
| contents | Suppose there is an adversarial UAV network being tracked by a radar. How can the radar determine whether the UAVs are coordinating, in some well-defined sense? How can the radar infer the objectives of the individual UAVs and the network as a whole? We present an abstract interpretation of such a strategic interaction, allowing us to conceptualize coordination as a linearly constrained multi-objective optimization problem. Then, we present some tools from microeconomic theory that allow us to detect coordination and reconstruct individual UAV objective functions, from radar tracking signals. This corresponds to performing inverse multi-objective optimization. We present details for how the abstract microeconomic interpretation corresponds to, and naturally arises from, physical-layer radar waveform modulation and multi-target filtering. This article serves as a tutorial, bringing together concepts from several established research contributions in an expository style. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01445 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Agent Inverse Learning for Sensor Networks: Identifying Coordination in UAV Networks Snow, Luke Krishnamurthy, Vikram Systems and Control Suppose there is an adversarial UAV network being tracked by a radar. How can the radar determine whether the UAVs are coordinating, in some well-defined sense? How can the radar infer the objectives of the individual UAVs and the network as a whole? We present an abstract interpretation of such a strategic interaction, allowing us to conceptualize coordination as a linearly constrained multi-objective optimization problem. Then, we present some tools from microeconomic theory that allow us to detect coordination and reconstruct individual UAV objective functions, from radar tracking signals. This corresponds to performing inverse multi-objective optimization. We present details for how the abstract microeconomic interpretation corresponds to, and naturally arises from, physical-layer radar waveform modulation and multi-target filtering. This article serves as a tutorial, bringing together concepts from several established research contributions in an expository style. |
| title | Multi-Agent Inverse Learning for Sensor Networks: Identifying Coordination in UAV Networks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2508.01445 |