Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing
By Chen Feng, Wain Sy Anthea Au, Shahrokh Valaee, Z. Tan. IEEE Transactions on Mobile Computing, 2012
569 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/6995ad2d8f733162bed9648434ce73b8feeab6b8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Byonghyo Shim
- Dong In Kim
- Jun Won Choi
- Yacong Ding
- Bhaskar D. Rao
- Nikolaos Gatsis
- David Akopian
- S. Chan
In Computer Networks and Communications
- Byonghyo Shim
- Bhaskar D. Rao
- Nikolaos Gatsis
- David Akopian
- Jun Won Choi
- S. Chan
- Yacong Ding
- Robert Piché
In Electrical and Electronic Engineering
- Pavel Davidson
- S. Chan
- Suining He
- Bhaskar D. Rao
- Byonghyo Shim
- Nikolaos Gatsis
- David Akopian
- Jun Won Choi
Citations in the corpus
- Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, methodology): “. The set of edges consists of all pairs (j;j0); j;j0= 1;:::;N. This method is based on an iterative message exchange between the nodes to find clusters and an exemplar (cluster head) for each cluster [116]. The messages are simply the negative of the Euclidean distance between the fingerprints i j an”
- Deep Neural Networks for wireless localization in indoor and outdoor environments (2016, methodology): “Machine (SVM) [4] and Compressive Sensing (CS) [9] [10] was incorporated to correlate the RSSIs with the samples of the fingerprint database.”
- Compressed Sensing for Wireless Communications: Useful Tips and Tricks (2015, methodology): “r of target nodes is much fewer than the number of elements in the grid, the location information of the targets is sparse, and thus the CS technique can be used to perform efficient localization [61]–[63]. Let the average RSS from the jth grid element to the ith AP be pi,j. The average RSS 9There ar”
- A Survey of Selected Indoor Positioning Methods for Smartphones (2017, methodology): “It is possible that a fingerprint contains more information that might be useful than what is presented here, see examples [17, 18, 19].”
- Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons (2016, methodology): “Euclidean distance [54]–[56], cosine similarity [57] and Tanimato similarity [58] have been implemented for signal comparison [31].”