Enhannced sampling algorithm in bluetooth low energy for indoor localization
Wireless network is a network set up by using radio signal frequency to communicate among computers and other network devices that are not connected by cables of any kind. These network mainly used for communication and data exchange. However, it is also being used to acquired one’s or device’s posi...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/69025/1/FSKTM%202018%2053%20-%20IR.pdf |
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| Summary: | Wireless network is a network set up by using radio signal frequency to communicate among computers and other network devices that are not connected by cables of any kind. These network mainly used for communication and data exchange. However, it is also being used to acquired one’s or device’s position with certain accuracy. For outdoor, Global Positioning System (GPS) is being used by peoples to navigate to any places in the world and also used in rocket or guided missile system as well.
In case of indoor positioning or localization, GPS system is not possible due to the satellite signal is being block and therefore weak. Bluetooth Low Energy (BLE) is one of many solutions to cover this problem. BLE indoor localizationtechnology able to obtain the position byutilizing the Receive Signal Strength Index (RSSI) which is then used in positioning algorithm such as fingerprinting or trilateration. But there is limitation with theaccuracy of this systemmainly due to unstable RSSI.
There are algorithms to stabilized the RSSI such as Kalman Filter, Moving Average, Delta Sampling and many more. And by doing so, it will help to improve the accuracy of the indoor localization. Each of the algorithms has their own strength and weakness. Delta Sampling algorithm is simple to be implemented, yet it is good in removing the flier point and doing so will help to stabilize the RSSI. However, it come with problems that is undecided sample size and number of invalid samples which may lead to wrong result. Therefore, this project enhanced the Delta Sampling algorithm in order to mitigate this issues and through the experiments, it is proven that the proposed algorithm able to stabilize the signal while solving the sample size and number of invalid samples issue. |
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