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Detection with Distributed Sensors
Abstract
The extension of classical detection theory to the case of distributed sensors is discussed, based on the theory of statistical hypothesis testing. The development is based on the formulation of a decentralized or team hypothesis testing problem. Theoretical results concerning the form of the optimal decision rule, examples, application to data fusion, and open problems are presented.
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Cited by
Information fusion for wireless sensor networks: Methods, models, and classifications
Nakamura, E. F., Loureiro, A. A. F., & Frery, A. C. (2007) · ACM Computing Surveys · doi:10.1145/1267070.1267073 · Cited by 537
Distributed Fusion Architectures and Algorithms for Target Tracking
Liggins, M. E., Chong, C.-Y., Kadar, I., Alford, M. G., Vannicola, V., & Thomopoulos, S. (1997) · Proceedings of the IEEE · doi:10.1109/jproc.1997.554211 · Cited by 353
Multi modal decision fusion for continuous authentication
Fridman, L., Stolerman, A., Acharya, S., Brennan, P., Juola, P., Greenstadt, R., & Kam, M. (2015) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2014.10.018 · Cited by 64
Application-specific compression for time delay estimation in sensor networks
Vasudevan, L., Ortega, A., & Mitra, U. (2003) · Proceedings of the 1st international conference on Embedded networked sensor systems · doi:10.1145/958491.958520 · Cited by 33
Metadata
- Authors
- Tenney, R., & Sandell, N.
- Venue
- IEEE Transactions on Aerospace and Electronic Systems
- Published
- 1981-07
- Volume
- AES-17
- Issue
- 4
- Pages
- 501-510
- ISSN
- 0018-9251 (ISSN-L)
- OpenAlex
- W2144052787
- Cited by
- 846
- References
- 0
- FWCI
- 4.53
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Detection with Distributed Sensors. https://app.cochat.ai/papers/10.1109/taes.1981.309178
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- Tenney, R., & Sandell, N. (1981). Detection with Distributed Sensors. IEEE Transactions on Aerospace and Electronic Systems. https://doi.org/10.1109/taes.1981.309178
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
