Paper Info
Title
Aligning Training Models With Smartphone Properties In Wifi Fingerprinting Based Indoor Localization
Abstract
We are concerned with the so-called fingerprinting method for WiFi-based indoor positioning, where the measured received signal strength index (RSSI) is compared with training data to come up with an estimate of the user's location. We introduce a method for adapting the trained models to the statistics of the RSSI values of the target (testing) WiFi device, which is derived from the Maximum Likelihood Linear Regression (MLLR) framework. By introducing regression classes the assumption of a linear relationship between the RSSI readings of the testing device and the training data is relaxed, leading to superior adaptation performance. Parameter adaptation formulas are derived for the general case of censored and dropped data. While censoring occurs due to the limited sensitivity of WiFi chips, dropping is probably caused by limitations of the operating system of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms.
Year
DOI
Venue
2015
10.1109/ICASSP.2015.7178317
2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP)
Keywords
Field
DocType
Indoor positioning, signal strength, censored data, expectation maximization, maximum likelihood linear regression
Training set,Regression,Pattern recognition,Computer science,Real-time computing,Maximum likelihood linear regression,Signal strength,Artificial intelligence,Censoring (statistics),Embedded system
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
9
3
Name
Order
Citations
PageRank
Manh Kha Hoang171.40
Joerg Schmalenstroeer26511.46
Reinhold Haeb-Umbach31487211.71