Multi-observers of particle filter approach for estimating indoor mobile user location
Location-aware personal computing application can work accurately by estimating user location using IEEE 802.11 (Wi-Fi) signals in indoor environment. Nowadays, Wi-Fi is more and more widely available and installed on most mobile devices. Unfortunately, the Wi-Fi’s signal fluctuates greatly up to 33...
| Main Authors: | , , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
2011
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/6971/ http://irep.iium.edu.my/6971/ http://irep.iium.edu.my/6971/1/PID2043681_ParticleFilter_Mantoro.pdf http://irep.iium.edu.my/6971/4/Multi-observers_of_Particle_Filter_Approach.pdf |
| Summary: | Location-aware personal computing application can work accurately by estimating user location using IEEE 802.11 (Wi-Fi) signals in indoor environment. Nowadays, Wi-Fi is more and more widely available and installed on most mobile devices. Unfortunately, the Wi-Fi’s signal fluctuates greatly up to 33%, some of the causes are reflection, refraction, temperature, humidity, the dynamic environment. These make user location not in good accuracy to be estimated. In this paper we propose the use of Particle Filter to improve user location estimation which involves the modeling of non-linear and non-Gaussian systems. To make the estimation accurate, the real time data of multi-observer Wi-Fi signals is used. The loss of diversity and parameter chosen in order to reduce the ambiguity is observed in tracking user location. We improve the Particle Filter Algorithm by giving target/reference points and by reducing the computational complexity. This paper shows that the estimation of the location from real-time data is close to the real tracking object. |
|---|