Power constrained distributed estimation with correlated sensor data
IEEE Transactions on Signal Processing
Power constrained distributed estimation with cluster-based sensor collaboration
IEEE Transactions on Wireless Communications
Hyperplane-based vector quantization for distributed estimation in wireless sensor networks
IEEE Transactions on Information Theory
A collaborative sensor-fault detection scheme for robust distributed estimation in sensor networks
IEEE Transactions on Communications
Decomposable principal component analysis
IEEE Transactions on Signal Processing
Audio-visual group recognition using diffusion maps
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Sensors' optimal dimensionality compression matrix in estimation fusion
Automatica (Journal of IFAC)
Multisensor data fusion: A review of the state-of-the-art
Information Fusion
| Hi-index | 35.77 |
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.