Study On Multirate Sensor Based State Fusion Estimation And Multiresolution Image Fusion Algorithms

Information fusion has been widely applied in many military and non-military fields because it has the advantages of improving the reliability and the stability, expanding the space and the time covering scope, and improving scale etc. State fusion estimation and image fusion are two important topics of information fusion, which are discussed in this dissertation.State fusion estimation is studied first. Aimed at multirate linear time-varying dynamic systems with synchronous sampling, multirate linear time-invariant dynamic systems with asynchronous sampling, and multirate linear time-varying dynamic systems with asynchronous sampling, the related data fusion state estimation algorithms are proposed, respectively. For the synchronous sampling system, the blockwised, the distributed fusion structure with feedback and without feedback are used to obtain the effective state fusion estimation. For the asynchronous sampling time-invariant system, through multiscale modeling and scale recursion, the optimal state fusion estimation is generated in the sense of minimizing the traces of the estimation error covariances. For the asynchronous sampling time-varying system, by augmentation of the state and measurements and by dividing them into proper blocks, the multirate asynchronous sampling system is formalized into a synchronous sampling system with single sampling rate, therefore, by use of Kalman filter and distributed data fusion structure, the optimal state fusion estimation in the sense of linear minimum variance is achieved. The effectiveness of the proposed algorithms is shown through computer simulations.On image fusion, multisensor multiresolutional image fusion algorithms, and the performance evaluation of image fusion results are studied, respectively. For the fusion of multisensor multiresolution images, the system equations are formulated first, and then by use of 2D Kalman filter, the images that have different resolutions taken by the same sensor are fused. Later, by use of entropy to construct the weight, the weighted average image fusion method is used to fuse the former fused images. The final fused image is obtained by modifying its edges. Multiple experiments aimed at multiple groups of images that have different performances show the effectiveness of the proposed image fusion algorithm. Performance evaluation is still an open problem in the field of image fusion. In view of information theory and human perception system, respectively and collaborately, three performance evaluation indices, including collective entropy, normalized information entropy and normalized human perception entropy, are proposed successively. Experiments show that the normalized human perception entropy is predominant among the existing performance evaluation indices, because it pays attention to both the completeness of information transmission and the characteristics of human perception.