Kernel-Based Positioning in Wireless Local Area Networks

By Azadeh Kushki, K. Plataniotis, Anastasios N. Venetsanopoulos. IEEE Transactions on Mobile Computing, 2007

443 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/f811850a8235f3dac4ae2118cd0f1e269f0dfb6d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Xiaodai Dong
2. Z. Tan
3. Chen Feng
4. Nikolaos Gatsis
5. Shahrokh Valaee
6. David Akopian
7. Paul A. Zandbergen
8. Wain Sy Anthea Au

In Electrical and Electronic Engineering

1. Xiaodai Dong
2. Z. Tan
3. Chen Feng
4. Nikolaos Gatsis
5. Shahrokh Valaee
6. David Akopian
7. Paul A. Zandbergen
8. Wain Sy Anthea Au

In Ocean Engineering

1. Z. Tan
2. Chen Feng
3. Nikolaos Gatsis
4. Shahrokh Valaee
5. David Akopian
6. Paul A. Zandbergen
7. Wain Sy Anthea Au
8. Eric A. Wan

Citations in the corpus

• Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, methodology): “AND REPRESENTATIVE REFERENCES Methods Related Works Deterministic KNN [14], [68], [74], [77]–[80], Median filtering [68], [74], [81] Probabilistic MAP [62], [82], [83], ML [68], Weighted probabilistic [84]–[86] Pattern recognition CCA [87], SVM [88], [89], Neural Networks [78], Linear Discriminant An”
• Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing (2012, methodology): “Received-Signal-Strength-based (RSS-based) localization algorithms have been extensively studied as an inexpensive solution for indoor positioning in recent years [3], [4], [5], [6].”
• Recurrent Neural Networks for Accurate RSSI Indoor Localization (2019, methodology): “2) Training Time Requirement: Compared with other conventional methods, e.g., KNN-RADAR [12], probabilistic Kernel method [9] and Kalman filter [23], the proposed RNN method outperforms.”
• Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning (2009, background): “…in recent years to refine WiFi positioning for controlled indoor locations, including the development of novel approaches to fingerprint-ing (e.g. Kushki et al. 2007), development of time-of-arrival techniques (e.g. Golden and Bateman 2007), and modeling the performance of WiFi positioning…”
• RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers (2009, methodology): “The fingerprinting based RSSI approach is one of the most widely used technology seen in the literature [2], [8], [12], [13].”