Relative location estimation in wireless sensor networks

By Neal Patwari, Alfred O. Hero, Matt Perkins, Neiyer S. Correal, R. J. O'Dea. IEEE Transactions on Signal Processing, 2003

1,841 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/231358f151026ec57d8a4725bfb4446be4e6abc9

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Citations in the corpus

Weighted Centroid Localization in Zigbee-based Sensor Networks (2007, background): “The beacons Bi (i∈{1;2;3;4}) are configured as router and their tasks is to broadcast their own positions to the field with an optimal transmission range tr=39 adjusted by setting appropriate transmission power [9][11].”
RSS-Based Location Estimation with Unknown Pathloss Model (2006, methodology): “where GDOPxy,u and GDOPα are geometric dilution of precision (GDOP) defined following the convention in the literature [14], [23] as shown at the top of the page.”
Localization via Ultra- Wideband Radios (2005, methodology): “is given in the case of σT = 6n sand σsh/n = 2. These values are determined from wideband field measurements reported in [ 68 ].”
Correlated link shadow fading in multi-hop wireless networks (2008, methodology): “These measurements will enable the statistical analysis and model development in subsequentsections.”
Distributed Sensor Localization in Random Environments Using Minimal Number of Anchor Nodes (2008, background): “Illustrative centralized localization algorithms include: maximum likelihood estimators that are formulated when the data is known to be described by a statistical model [4], [5]; multidimensional scaling (MDS) algorithms that formulate the localization problem as a least squares problem at a centra”
Robust distributed network localization with noisy range measurements (2004, methodology): “use one-hop multilateration from reference nodes in a physical experiment using both received signal strength (RSS) and time of arrival (ToA) [14].”
Information fusion for wireless sensor networks: Methods, models, and classifications (2007, methodology): “In this context, the method is often used to obtain accurate distance (or direction, angle) estimations that are used to compute the location of nodes [Patwari et al. 2003; Fang et al. 2005] or sources (targets) [Sheng and Hu 2005; Niu and Varshney 2006; Li et al. 2006; Chen et al.”
Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking (2009, methodology): “For comparison purposes, we also used the same testbed and the exactly same tests with the stochastic trilateration method of (Patwari et al. 2003).”
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing (2020, methodology): “(cid:2) The above mentioned algorithms adopt empirical models, such as the log-distance path-loss model for RSS measurements [30], and Gaussian mixture model for non-line-of-sight propagation [28].”
Indoor Localization System using RSSI Measurement of Wireless Sensor Network based on ZigBee Standard (2006, neutral): “Keywords performance evaluation, localization, RSSI, ZigBee”
RF Sensor Networks for Device-Free Localization: Measurements, Models, and Algorithms (2010, background): “velocity and sequence of positions over a duration of time) have been reported, with accuracy of less than one meter of average error [8], [14], [13] or less than two meters median error over a 1500 m(2) area [17]; these results are at least as good as reported location error when locating radio tag”
A practical evaluation of radio signal strength for ranging-based localization (2007, methodology): “8m in another study, but only when the nodes had a 2-3 meter spacing and RSSI was measured using the Berkeley Varitronix Fox receiver, a high-fidelity Wi-Fi propagation analyzer [21].”
Survey of Wireless Indoor Positioning Techniques and Systems (2007, methodology): “Some systems also use TOA measurement [68], [70].”