Accurate wi-fi signal strength recovery method using chebyshev wavelet- based approximation for indoor positioning
In many scenarios of everyday life and especially in warehousing, manufacturing and logistics, it is highly desirable to locate objects or persons quickly and accurately. Nowadays, fingerprinting based Wi-Fi positioning system provides enterprises the ability to track their various resources more...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/69350/1/FSKTM%202016%2016%20IR.pdf |
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| Summary: | In many scenarios of everyday life and especially in warehousing, manufacturing and
logistics, it is highly desirable to locate objects or persons quickly and accurately.
Nowadays, fingerprinting based Wi-Fi positioning system provides enterprises the
ability to track their various resources more efficiently and effectively. The main idea
behind fingerprinting is to build signal strength database of target area prior to location
estimation. This process is called calibration and the positioning accuracy highly
depends on calibration intensity. Unfortunately, calibration procedure requires a huge
amount of time and effort and makes large-scale deployments of Wi-Fi based indoor
positioning systems non-trivial.
In this research, we present a new method of recovering Wi-Fi Radio Map (WRM)
database based on few sample received signal strength indicators –RSSI and this
recovered data is used as radio map – constructed Wi-Fi RSS based fingerprint
database for indoor positing. In contrary to conventional calibration method, our
method requires only a few signal samples to be collected and rest of the data are
approximated using Chebyshev wavelets. The main goal of our research is to minimize
the calibration workload while maintaining recovered data accuracy and achieve
acceptable results on positioning accuracy.
Compared to the conventional way, proposed a new method to construct accurate Wi-
Fi signal strength indicators using Chebyshev wavelet based approximation requires
only a few reference RSSI samples, and this significantly will reduce the calibration
effort. Also, field test results showed that proposed method achieves better
approximation accuracy than existing interpolation methods, such as VORO and
MOSM.
Also recovered RSSI data - fingerprint database was used with positioning software to
evaluate results of positioning accuracy. Results show that positioning accuracy is significantly improved compared with conventional, as well as, other two related
methods. |
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