Kalman filtering: with real-time applications (2nd ed.)
Kalman filtering: with real-time applications (2nd ed.)
Multisensor Decision and Estimation Fusion
Multisensor Decision and Estimation Fusion
Optimal dimensionality reduction of sensor data in multisensor estimation fusion
IEEE Transactions on Signal Processing
Optimal linear estimation fusion .I. Unified fusion rules
IEEE Transactions on Information Theory
Data fusion with minimal communication
IEEE Transactions on Information Theory
Power constrained distributed estimation with cluster-based sensor collaboration
IEEE Transactions on Wireless Communications
IEEE Transactions on Signal Processing
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When there exists the limitation of communication bandwidth between sensors and a fusion center, one needs to optimally pre-compress sensor outputs-sensor observations or estimates before sensors' transmission to obtain a constrained optimal estimation at the fusion center in terms of the linear minimum error variance criterion. This paper will give an analytic solution of the optimal linear dimensionality compression matrix for the single sensor case and analyze the existence of the optimal linear dimensionality compression matrix for the multisensor case, as well as how to implement a Gauss-Seidel algorithm to search for an optimal solution to linear dimensionality compression matrix.