Name
Abstract | ||
|---|---|---|
Exploiting prior knowledge, we use Bayesian estimation to localize a source heard by a fixed sensor network. The method has two main aspects: Firstly, the probability density function (PDF) of a function of the source location is approximated by a Gaussian mixture model (GMM). This approximation can theoretically be made arbitrarily accurate, and allows a closed form minimum mean square error (MMSE) estimator for that function. Secondly, the source location is retrieved by minimizing the Euclidean distance between the function and its MMSE estimate using a gradient method. Our method avoids the issues of a numerical MMSE estimator but shows comparable accuracy. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICASSP.2011.5947018 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
Closed form MMSE estimators,Gaussian mixture models,Localization,Sensor Networks | Gradient method,Mathematical optimization,Pattern recognition,Euclidean distance,Minimum mean square error,Gaussian,Artificial intelligence,Bayes estimator,Probability density function,Mixture model,Mathematics,Estimator | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4577-0537-3 | 978-1-4577-0537-3 | 4 |
PageRank | References | Authors |
0.46 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| John T. Flåm | 1 | 35 | 4.68 |
| Joakim Jalden | 2 | 243 | 21.59 |
| Saikat Chatterjee | 3 | 320 | 40.34 |