Modeling of indoor positioning systems based on location fingerprinting

By Kamol Kaemarungsi, Prashant V. Krishnamurthy. IEEE INFOCOM 2004, 2004

754 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/b8987edc2d1cf1e43ef81269ff7107808594859a

How researchers see this paper

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

In Aerospace Engineering

1. Paul A. Zandbergen
2. Z. Tan
3. Chen Feng
4. Shahrokh Valaee
5. Wain Sy Anthea Au
6. Prashant V. Krishnamurthy
7. K. Plataniotis
8. Anastasios N. Venetsanopoulos

In Electrical and Electronic Engineering

1. Paul A. Zandbergen
2. K. Plataniotis
3. Anastasios N. Venetsanopoulos
4. Azadeh Kushki
5. Z. Tan
6. Chen Feng
7. Shahrokh Valaee
8. Wain Sy Anthea Au

In Ocean Engineering

1. Paul A. Zandbergen
2. Z. Tan
3. Chen Feng
4. Shahrokh Valaee
5. Wain Sy Anthea Au
6. Kamol Kaemarungsi
7. Prashant V. Krishnamurthy
8. K. Plataniotis

Citations in the corpus

• Kernel-Based Positioning in Wireless Local Area Networks (2007, background): “In cases where the measured RSS from an AP exhibits a high degree of variance, the survey values may be very different than the online measurement, degrading the accuracy of estimation [29].”
• Properties of indoor received signal strength for WLAN location fingerprinting (2004, background): “It can provide insights into how many access points are needed to uniquely identify a location [13] with a given accuracy and precision, whether preprocessing of the RSS measurements can improve the accuracy and so on.”
• Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning (2009, methodology): “…for this, including k-nearest neighbor estimation, support vector regression, smallest M-vertex polygon, Bayesian modeling, neural networks and kernel-ized distance estimation (see Roos et al. 2002, Youssef et al. 2003, Kaemarungsi and Krishnamurthy 2004, Kolodziej and Hjelm 2006, Yim 2008).”
• A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time (2017, background): “Table 2 lists specific criteria and brief descriptions for elderly care systems [33,37,41].”
• Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing (2012, methodology): “Thus, instead of using a propagation model to describe the relationship between RSS and position [10], a prebuilt radio map is used in fingerprinting methods to localize a Wi-Fi device [11], [12].”