Feasibility study of probabilistic Location Inference for Indoor Environment
Date Issued
2010
Date
2010
Author(s)
Chen, Ying-Chih
Abstract
In fingerprint based localization methods, it is generally believed that compared with deterministic one, there is more robust for probabilistic one against noise result from many possible factors like multipath fading channel, hardware, obstacles shadowing…etc, and more accuracy and stable localization results are also expected.
In this work, deterministic and probabilistic methods are implemented as Euclidean Distance and Multivariate Gaussian Inference (Mahalanobis distance) respectively with received signal strength data sets. Not as expected in last paragraph, higher location error happened in MGI than in Euclidean distance. From advance study of MGI, or its dominate item, Mahanobis distance, we learned that it provides not only average RSSI vector used in Euclidean distance but also covariance matrix which provides information about variance and covariance of all signal sources. Distance distribution is proposed here to project high dimension distance distribution to 2-d diagram and thorough these observations, the reason why worse location result in Mahalanobis distance is confirmed by experimenting different training and tracking data combination. Dissimilar RSSI vectors distribution of the same position tells us the instability of covariance matrix varying with time severer than average RSSI vectors. Extreme case of dissimilarity is even happening 100% packet loss in training phase but partially received in tracking phase, resulting zero divided in the calculation of Mahalanobis distance, and MTGI (Multivariate Truncated Gaussian Inference) is proposed to mitigate this situation by filtering packet receive ratio before distance calculation. In the end, two data sets from two testbeds with different environmental characteristic are implied by ED, MGI and MTGI for comparison and verify whole discussion of this work.
Subjects
Indoor Localization
Fingerprint
Probabilistic method
Deterministic method
Multivariate Gaussian
Type
thesis
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