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【期刊论文】Practical system for tracking multiple maneuvering targets
敬忠良, Jian-xun Li Zhong-liang Jing
Opt. Eng. 42(8)2439-2451 (August 2003),-0001,():
-1年11月30日
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.
state fusion, fast joint probabilistic data association, multiple maneuvering target tracking.,
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敬忠良, Shijun Zhang, Zhongliang Jin, and Jianxun Li
CHINESE OPTICS LETTERS Vol. 3, No.1 January 10, 2005,-0001,():
-1年11月30日
The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-orgamzing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate. robust adaptability, quick training, and better generalization of the proposed method are achieved.
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【期刊论文】STOCHASTIC NEURAL NETWORK AND ITS APPLICATION TO MULTI-MANEUVERING TARGET TRACKING
敬忠良, Jing Zhongliang, Dai Guanzhong, Tong Mingan, Zhou Hongren
Chinese Journal of Acronautics Feb. 1995 Vol. 8 No.1,-0001,():
-1年11月30日
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.
multiple target tracking,, data correlation,, neural nets,, networks
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【期刊论文】Multi-sensor image fusion using discrete wavelet frame transform
敬忠良, Zhenhua Li, Zhongliang Jing, and Shaoyuan Sun
CHINESE OPTICS LETTERS Vol. 2, No.10 October 10, 2004,-0001,():
-1年11月30日
An algorithm is presented for multi-sensor image fusion using discrete wavelet frame transform(DWFT). The source images to be fused are firstly decomposed by DWFT. The fusion process is the combining of the source coefficients. Before the image fusion process, image segmentation is performed on each source image in order to obtain the region representation of each source image. For each source image, the salience of each region in its region representation is calculated. By overlapping all these region representations of all the source images, we produce a shared region representation to label all the input images. The fusion process is guided by these region representations. Region match measure of the source images is calculated for each region in the shared region representation. When fusing the similar regions, weighted averaging mode is performed; otherwise selection mode is performed. Experimental results using real data show that the proposed algorithm outperforms the traditional pyramid transform based or discrete wavelet transform (DWT) based algorithms in multi-sensor image fusion.
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【期刊论文】Fusion of visible and infrared imagery for face recognition
敬忠良, Xuerong Chen, Zhongliang Jin, Shaoyuan Sun, and Gang Xiao
CHINESE OPTICS LETTERS December 10, 2004 Vol. 2, No.12,-0001,():
-1年11月30日
In recent years face recognition has received substantial attention, but still remained very challenging in real applications. Despite the variety of approaches and tools studied, face recognition is not accurate or robust enough to be used in uncontrolled environments. Infrared (IR) imagery of human faces offers a promising alternative to visible imagery, however, IR has its own limitations. In this paper, a scheme to fuse information from the two modalities is proposed. The scheme is based on eigenfaces and probabilistic neural network (PNN), using fuzzy integral to fuse the objective evidence supplied by each modality. Recognition rate is used to evaluate the fusion scheme. Experimental results show that the scheme improves recognition performance substantially.
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