Titlebook: Non-Cooperative Target Tracking, Fusion and Control; Algorithms and Advan Zhongliang Jing,Han Pan,Peng Dong Book 2018 Springer Internationa

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书目名称Non-Cooperative Target Tracking, Fusion and Control影响因子(影响力)




书目名称Non-Cooperative Target Tracking, Fusion and Control影响因子(影响力)学科排名




书目名称Non-Cooperative Target Tracking, Fusion and Control网络公开度




书目名称Non-Cooperative Target Tracking, Fusion and Control网络公开度学科排名




书目名称Non-Cooperative Target Tracking, Fusion and Control被引频次




书目名称Non-Cooperative Target Tracking, Fusion and Control被引频次学科排名




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书目名称Non-Cooperative Target Tracking, Fusion and Control读者反馈




书目名称Non-Cooperative Target Tracking, Fusion and Control读者反馈学科排名

全能

Introductionradar surveillance systems. Master’s thesis, University of Waterloo, 2014). Much of these objects can be traced to a few fundamental properties, such as unknown geometric appearance, uncooperative and non-communicative.

fulcrum

Evaluation of Focus Measures in Multi-Focus Image Fusions definition in the spatial domain. An efficient scheme to assess focus measures according to the capability of distinguishing focused image blocks from defocused image blocks is proposed. Experiments and numerical results demonstrated that sum-modified-Laplacian can achieve better performance than other focus measures.

depreciate

Dynamic Bias Estimation with Gaussian Mean Shift Registrationle target, which is an iterative optimization method. Besides, the proposed algorithm is close to the theoretical lower bound. Simulations show that the proposed method has significant improvement and is more efficient in estimating the dynamic bias than other methods.

GILD

badinage

Joint Detection, Tracking, and Classification Using Finite Set Statistics and Generalized Bayesian Rtion is obtained according to the generalized Bayesian risk. The Gaussian mixture implementation of the proposed recursive JDTC algorithm is developed. The parameter selection for the new Bayesian risk is discussed, too. Simulation results show that the proposed method outperforms the traditional methods.

FRONT

Constrained Image Deblurring with Sparse Proximal Newton Splitting Methods derived. Some theoretical aspects related to the proposed method are also discussed. Numerical experiments on various blurring conditions demonstrate the advantage of the proposed method in comparison to other iterative shrinkage-thresholding algorithms.

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伪造者

invade