Relative Location Error Prediction for KNN-based Fingerprinting Localization System
Date Issued
2011
Date
2011
Author(s)
Tsai, Wen-Cheng
Abstract
For a RSSI fingerprinting localization system, due to the multipath transmission, environment or other impacts, measured signals are different each time at the same place. When two location fingerprints are similar in signal, selecting wrong location fingerprints happened easily. The longer distances with the wrong selected location fingerprints the higher location errors. To reduce the impact of location error coming from survey data, reexamining survey data and properly dealing with the problem parts are needed.
In order to examine survey data for finding location fingerprints which may cause high location error, we propose a relative error prediction method in this thesis. It considers probability of selecting wrong fingerprints and error distance to finds a correlation of location error between grids based on KNN localization system. In our deployed indoor localization system, the correlation coefficients of estimations and location error can achieve 0.6 to 0.8 for self-test data. However, the correlation coefficients are only 0 to 0.2 in cross-test data. The reason is the measured location fingerprints are not identical in the same location at different time. After we analyze a 30-day RSSI measurement, using more samples to make location fingerprints can decrease the difference of two location fingerprints in the same location at different time. Furthermore, the result shows that the number of samples needed to from the location fingerprints depends on different environments. For picking problematic location fingerprints from relative error and deal with them, we use a cluster method which is called agglomerative hierarchical clustering to pick the outliers of relative error. In our experiment result, location error is not improved by removing the selected location fingerprints. In detail analysis, to improve location error can not only remove the problematic location fingerprints, but also need to figure out why there are some distant location fingerprints with high similarity in signal.
Subjects
Indoor Localization
Location Fingerprint
KNN
Error Estimation
Type
thesis
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