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.

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.

Abstract. The tracking of multiple maneuvering targets in a dense clutter environment is investigated. An effective parallel processing algorithm based on state fusion and fast joint probabilistic data association (FJPDA) is proposed. State fusion and feedback of all state information are used to fit different movements of targets. The FJPDA, combining cluster matrix decomposition with a fast data association algorithm, is used for tracking multiple targets. The advantages of this algorithm are not only keeping the accurate estimation and fast response for target maneuvering, but also reducing the computational burden of data association from N! to N/4* (4!). Three examples are simulated to prove the validity and reliability of the proposed new algorithm.

Due to the limited depth-of-field of optical lenses, it is difficult to get an image with all objects in focus. One way to overcome this problem is to take several images with different focus points and combine them into a single composite which contains all the regions full focused. This paper describes a pixel-clarity-based multifocus image fusion algorithm. The characteristic of this approach is that the pixels of the fused image are selected from the clearest pixels in the input images according to pixel clarity criteria. For each pixel in the source images, the pixel clarity is calculated. The fusion procedure is performed by a selection mode according to the magnitude of pixel clarity. Consistency verification is performed on the selected pixels. Experiments show that the proposed algorithm works well in multifocus image fusion.

An adaptive algorithm for tracking maneuvering targets is proposed. This algorithm is implemented with two filters and a multilayer feedforward neural network using state fusion, together with the current statistic model and adaptive filtering. The neural network fuses automatically all the state information of the two filters and tunes adaptively the system variance for one of the two filters to adapt to different target maneuvers when the two filters track the same maneuvering target in parallel. Simulation results show that the adaptive algorithm tracks very well maneuvering targets over a wide range of maneuvers with high precision, in both one and three-dimensional cases.