Data-Driven Vector-Measurement-Sensor Selection Based on Greedy Algorithm
Yuji Saito, Taku Nonomura, Koki Nankai, Keigo Yamada, Keisuke Asai, Yasuo Sasaki, Daisuke Tsubakino
Abstract
A vector-measurement-sensor problem for the least squares estimation is considered, by extending a previous novel approach in this letter. An extension of the vector-measurement-sensor selection of the greedy algorithm is proposed and is applied to particle-image-velocimetry data to reconstruct the full state based on the information given by sparse vector-measurement sensors.
Topics & Concepts
Greedy algorithmGreedy randomized adaptive search procedureSelection (genetic algorithm)Computer scienceAlgorithmExtension (predicate logic)Feature selectionMathematical optimizationMathematicsMinificationLeast-squares function approximationArtificial intelligenceState (computer science)Selection algorithmData miningPattern recognition (psychology)Key (lock)Sparse approximationSparse matrixTarget Tracking and Data Fusion in Sensor NetworksControl Systems and IdentificationSparse and Compressive Sensing Techniques