Name
Abstract | ||
|---|---|---|
Knowing channel sight condition is important as it has a great impact on localization performance. In this paper, a RSS-based localization algorithm, which jointly takes into consideration the effect of channel sight conditions, is investigated. In our approach, the channel sight conditions experience by a moving target to all sensors is modeled as a hidden Markov model (HMM), with the quantized measured RSSs as its observation. The parameters of HMM are obtained by an off-line training assuming that the LOS/NLOS can be identified during the training phase. With the HMM matrices, a forward-only algorithm can be utilized for real time sight conditions identification. The target is localized by extended Kalman Filter (EKF) by suitably combining with the sight conditions. Simulation results show that our proposed localization strategy can provide good identification to channel sight conditions, hence results in a better localization estimation. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICC.2014.6883312 | ICC |
Keywords | Field | DocType |
extended kalman filter,kalman filters,rss ekf localization method,wireless sensor networks,hmm based los nlos channel identification,hidden markov models,channel sight condition,channel estimation,hidden markov model,estimation,real time systems,vectors,markov processes,time measurement | Non-line-of-sight propagation,Extended Kalman filter,Computer science,Matrix (mathematics),Algorithm,Communication channel,Real-time computing,Speech recognition,Sight,Hidden Markov model,RSS | Conference |
ISSN | Citations | PageRank |
1550-3607 | 6 | 0.47 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiufang Shi | 1 | 7 | 0.82 |
| Yong Huat Chew | 2 | 9 | 1.24 |
| Chau Yuen | 3 | 4493 | 263.28 |
| Zaiyue Yang | 4 | 9 | 1.23 |