A novel fast adaptive tracking scheme based on stochastic neural network for multi-maneuvering targets is presented. The properties of the joint probabilistic da-ta association (JPDA) are analysed, the data association is reduced to be a sort of con-straint combinatorial optimization problem, and the computational burden of multi-maneuvering target tracking (MMTT) can be decreased drastically by stochastic neural network. Computer simulations show that the scheme proposed has high convergence performance, a good tracking accuracy and robustness to the uncertainty of maneuvering targets and clutter environments.

A novel image fusion method based on an expectation maximization (EM) algorithm and the discrete wavelet frame (DWF) transform is proposed. The registered images are first decomposed using the DWF transform, which is both aliasing-free and translation-invariant. The DWF decomposes the image signal into a multiresolution representation with both low-frequency coarse information and high-frequency detail information. The EM algorithm is used to fuse the low-frequency coarse information of the registered images. The informative importance measure is applied to fuse the high-frequency detail information of the registered images. The final fused image is obtained by taking the inverse transform of the composite multiresolution representations. Simulation results show that the proposed method outperforms the conventional image fusion methods.

In this paper a fusion method is proposed for merging a high-resolution panchromatic image and a low- resolution multispectral image. The algorithm is based on discrete wavelet transform (DWT). It uses correlation moment rule to the low frequency bands and local deviation rule to the high frequency bands separately Enperimental results indicate that the proposed approach outperforms the traditional methods.

In this paper, a novel image fusion method based on the expectation maximization (EM) algorithm and steerable pyramid is proposed. The registered images are first decomposed by using steerable pyramid. The EM algorithm is used to fuse the image components in the low frequency band. The selection method involving the informative importance measure is applied to those in the high frequency band. The final fused image is then computed by taking the inverse transform on the composite coefficient representations. Experimental results show that the proposed method outperforms conventional image fusion methods.