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- W67496651 abstract "To increase the accuracy of Location-aware personal computing application, multi-observers of IEEE802.11 (Wi-Fi) signals can be used to track indoor user location. Even-though Wi-Fi is more and more widelyavailable on most mobile devices, unfortunately, because of the reflection, refraction, temperature, humidityand the dynamic changing in the environment, the reading of Wi-Fi’s signal fluctuates greatly; the deviationcan reach up to 33% from single Wi-Fi’s access point. This creates problem in tracking user location indoor.Moreover, the use of light estimation algorithms such as fingerprinting, ranking algorithm, Weighted Centroidmethod, k-Nearest Neighbour, did not give a good tracking result. This paper proposes the use of Particle Filterin improving user location estimation which involves the modeling of non-linear and non-Gaussian systems.The aim is to increase the accuracy of tracking user location indoor. In our experiments, the real time data ofmulti-observer Wi-Fi signals have been used and the loss of diversity and parameter chosen in order to reducethe ambiguity has also been observed. We improve the algorithm in reducing the computational complexity bygiving target/reference points. The paper discussed the comparison between the true location and the estimatedlocation based on two types of signals data: normal data and noise data. The location estimation is predictedbased on real-time signal and then compare it to the training data set. This approach shows a promising resultin tracking user location indoor using particle filter algorithm." @default.
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- W67496651 date "2011-01-01" @default.
- W67496651 modified "2023-09-27" @default.
- W67496651 title "Particle filter approach for estimating indoor mobile user location using IEEE 802.11 signals (F1090)" @default.
- W67496651 hasPublicationYear "2011" @default.
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