The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Cooperative Localization in Wireless Networks
Open accessAbstract
Location-aware technologies will revolutionize many aspects of commercial, public service, and military sectors, and are expected to spawn numerous unforeseen applications. A new era of highly accurate ubiquitous location-awareness is on the horizon, enabled by a paradigm of cooperation between nodes. In this paper, we give an overview of cooperative localization approaches and apply them to ultrawide bandwidth (UWB) wireless networks. UWB transmission technology is particularly attractive for short- to medium-range localization, especially in GPS-denied environments: wide transmission bandwidths enable robust communication in dense multipath scenarios, and the ability to resolve subnanosecond delays results in centimeter-level distance resolution. We will describe several cooperative localization algorithms and quantify their performance, based on realistic UWB ranging models developed through an extensive measurement campaign using FCC-compliant UWB radios. We will also present a powerful localization algorithm by mapping a graphical model for statistical inference onto the network topology, which results in a net-factor graph, and by developing a suitable net-message passing schedule. The resulting algorithm (SPAWN) is fully distributed, can cope with a wide variety of scenarios, and requires little communication overhead to achieve accurate and robust localization.
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Cites
RADAR: an in-building RF-based user location and tracking system
Bahl, P., & Padmanabhan, V. N. (2002) · Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064) · doi:10.1109/infcom.2000.832252 · Cited by 8,380
Factor graphs and the sum-product algorithm
Kschischang, F. R., Frey, B. J., & Loeliger, H.-A. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.910572 · Cited by 6,486
Robust Monte Carlo localization for mobile robots
Thrun, S., Fox, D., Burgard, W., & Dellaert, F. (2001) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00069-8 · Cited by 1,809
Cited by
Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications
Yassin, A., Nasser, Y., Awad, M., Al-Dubai, A., Liu, R., Yuen, C., Raulefs, R., & Aboutanios, E. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2632427 · Cited by 916
Survey of Cellular Mobile Radio Localization Methods: From 1G to 5G
del Peral-Rosado, J. A., Raulefs, R., Lopez-Salcedo, J. A., & Seco-Granados, G. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2785181 · Cited by 664
Message Passing Algorithms for Scalable Multitarget Tracking
Meyer, F., Kropfreiter, T., Williams, J. L., Lau, R., Hlawatsch, F., Braca, P., & Win, M. Z. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2789427 · Cited by 355
Self-citationA Survey of Localization in Wireless Sensor Network
Cheng, L., Wu, C., Zhang, Y., Wu, H., Li, M., & Maple, C. (2012) · International Journal of Distributed Sensor Networks · doi:10.1155/2012/962523 · Cited by 261
A Scalable Algorithm for Tracking an Unknown Number of Targets Using Multiple Sensors
Meyer, F., Braca, P., Willett, P., & Hlawatsch, F. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2017.2688966 · Cited by 230
Metadata
- Authors
- Wymeersch, H., Lien, J., & Win, M. Z.
- Venue
- Proceedings of the IEEE
- Published
- 2009-02
- Volume
- 97
- Issue
- 2
- Pages
- 427-450
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2012197287
- Cited by
- 1,182
- References
- 3
- FWCI
- 52.27
- Type
- journal article
- Language
- en
- Open access
- green
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