Non-Cooperative Target Tracking, Fusion and Control Algorithms and Advances için kapak resmi
Non-Cooperative Target Tracking, Fusion and Control Algorithms and Advances
Başlık:
Non-Cooperative Target Tracking, Fusion and Control Algorithms and Advances
Yazar:
Jing, Zhongliang. author.
ISBN:
9783319907161
Edisyon:
1st ed. 2018.
Fiziksel Niteleme:
XIX, 340 p. 142 illus., 80 illus. in color. online resource.
Seri:
Information Fusion and Data Science,
İçindekiler:
Introduction -- Part I Multi-Target Tracking -- Gaussian mixture CPHD filter with gating technique -- Detection-guided multi-target Bayesian filter -- On the sensor order in sequential integrated probability data association filter -- New method for dynamic bias estimation: Gaussian mean shift registration -- Part II Visual Target Tracking -- Learning-based appearance model for probabilistic visual tracking -- Incremental visual tracking with L1 norm approximation and Grassmann update -- A dual-kernel-based tracking approach for visual target -- Kernel joint visual tracking and recognition based on structured sparse representation -- Part III Image Fusion and Deblurring -- A simple method to build oversampled filter banks and tight frames -- Multi-focus image fusion using pulse coupled neural network -- Evaluation of focus measures in multi-focus image fusion -- Multi-modality image fusion via generalized Riesz-wavelet transformation -- A sparse proximal Newton splitting method for constrained image deblurring -- Part IV Control of Spacecraft Maneuvers -- Maneuver-aided active satellite tracking using six-DOF optimal dynamic inversion control -- Dynamic optimal sliding-mode control for six-DOF follow-up robust tracking of active satellite -- Redundant adaptive robust tracking of active satellite and error evaluation.
Özet:
This book gives a concise and comprehensive overview of non-cooperative target tracking, fusion and control. Focusing on algorithms rather than theories for non-cooperative targets including air and space-borne targets, this work explores a number of advanced techniques, including Gaussian mixture cardinalized probability hypothesis density (CPHD) filter, optimization on manifold, construction of filter banks and tight frames, structured sparse representation, and others. Containing a variety of illustrative and computational examples, Non-cooperative Target Tracking, Fusion and Control will be useful for students as well as engineers with an interest in information fusion, aerospace applications, radar data processing and remote sensing.