Name
Title | ||
|---|---|---|
A hidden semi-Markov model for indoor radio source localization using received signal strength. |
Abstract | ||
|---|---|---|
•Hidden Markov models (HMMs) can be applied for incorporation of environmental constraints for localization.•Hidden semi-Markov models (HsMMs) are more flexible for modelling radio source dynamic property.•The flexibility of modelling state sojourn time in the HsMM makes it a better option to cope with missing data problem.•The HsMM-based algorithms are tested to perform better than the corresponding HMM-based algorithms, while require more computation power. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.sigpro.2019.07.023 | Signal Processing |
Keywords | Field | DocType |
Localization,Hidden Markov model (HMM),Hidden semi-Markov Model (HsMM),Missing measurement | Multipath propagation,Transmitter,Mathematical optimization,Software-defined radio,Algorithm,Missing data,Hidden Markov model,RSS,Mathematics,Radio propagation,Hidden semi-Markov model | Journal |
Volume | ISSN | Citations |
166 | 0165-1684 | 1 |
PageRank | References | Authors |
0.37 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuai Sun | 1 | 1 | 2.06 |
| xuezhi wang | 2 | 5 | 2.19 |
| Bill Moran | 3 | 141 | 23.49 |
| Wayne S T Rowe | 4 | 1 | 2.40 |