Name
Abstract | ||
|---|---|---|
When there exists the limitation of communication bandwidth between sensors and a fusion center, one needs to optimally precompress sensor outputs-sensor observations or estimates before the sensors' transmission in order to obtain a constrained optimal estimation at the fusion center in terms of the linear minimum error variance criterion, or when an allowed performance loss constraint exists, one needs to design the minimum dimension of sensor data. This paper will answer the above questions by using the matrix decomposition, pseudo-inverse, and eigenvalue techniques. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TSP.2005.845429 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
performance loss constraint,eigenvalue technique,fusion center,multisensor estimation fusion,pseudo-inverse technique,minimum variance estimation,communication bandwidth,sensor data,optimal estimation,minimum dimension,linear minimum error variance criterion,linear minimum error variance,matrix decomposition,optimal dimensionality reduction,linear compression,precompress sensor outputs-sensor observation,eigenvalues and eigenfunctions,optimally precompress sensor outputs-sensor observation,sensor fusion,minimum variance,computer science,eigenvalues,bandwidth,constrained optimization,system performance,mathematics | Mathematical optimization,Dimensionality reduction,Control theory,Matrix decomposition,Optimal estimation,Sensor fusion,Fusion center,Eigenvalues and eigenvectors,Mathematics,Data reduction,Constrained optimization | Journal |
Volume | Issue | ISSN |
53 | 5 | 1053-587X |
Citations | PageRank | References |
60 | 5.09 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| yunmin zhu | 1 | 557 | 67.35 |
| enbin song | 2 | 406 | 30.20 |
| Jie Zhou | 3 | 214 | 20.41 |
| Zhisheng You | 4 | 417 | 52.22 |