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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2510.05343 |
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| _version_ | 1866918229490794496 |
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| author | Kim, Mingyu Sarker, Pronoy Kim, Seungmo Stilwell, Daniel J. Jimenez, Jorge |
| author_facet | Kim, Mingyu Sarker, Pronoy Kim, Seungmo Stilwell, Daniel J. Jimenez, Jorge |
| contents | This paper studies sensor placement when detection performance varies stochastically due to environmental factors over space and time and false alarms are present, but a filter is used to attenuate the effect. We introduce a unified model that couples detection and false alarms through an availability function, which captures how false alarms reduce effective sensing and filtering responses to the disturbance. Building on this model, we give a sufficient condition under which filtering improves detection. In addition, we derive a coverage-based lower bound on the void probability. Furthermore, we prove robustness guarantees showing that performance remains stable when detection probabilities are learned from limited data. We validate the approach with numerical studies using AIS vessel-traffic data and synthetic maritime scenarios. Together, these results provide theory and practical guidance for deploying sensors in dynamic, uncertain environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05343 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Sensor Placement for Poisson Arrivals with False Alarm Aware Spatiotemporal Sensing Kim, Mingyu Sarker, Pronoy Kim, Seungmo Stilwell, Daniel J. Jimenez, Jorge Systems and Control This paper studies sensor placement when detection performance varies stochastically due to environmental factors over space and time and false alarms are present, but a filter is used to attenuate the effect. We introduce a unified model that couples detection and false alarms through an availability function, which captures how false alarms reduce effective sensing and filtering responses to the disturbance. Building on this model, we give a sufficient condition under which filtering improves detection. In addition, we derive a coverage-based lower bound on the void probability. Furthermore, we prove robustness guarantees showing that performance remains stable when detection probabilities are learned from limited data. We validate the approach with numerical studies using AIS vessel-traffic data and synthetic maritime scenarios. Together, these results provide theory and practical guidance for deploying sensors in dynamic, uncertain environments. |
| title | Robust Sensor Placement for Poisson Arrivals with False Alarm Aware Spatiotemporal Sensing |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.05343 |