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© 2018

Non-Cooperative Target Tracking, Fusion and Control

Algorithms and Advances

Book

Part of the Information Fusion and Data Science book series (IFDS)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Background and Fundamentals

    1. Front Matter
      Pages 1-1
    2. Zhongliang Jing, Han Pan, Yuankai Li, Peng Dong
      Pages 3-6
    3. Zhongliang Jing, Han Pan, Yuankai Li, Peng Dong
      Pages 7-22
  3. Target Tracking and Fusion

    1. Front Matter
      Pages 23-23
    2. Zhongliang Jing, Han Pan, Yuankai Li, Peng Dong
      Pages 25-40
    3. Zhongliang Jing, Han Pan, Yuankai Li, Peng Dong
      Pages 41-70
    4. Zhongliang Jing, Han Pan, Yuankai Li, Peng Dong
      Pages 71-86
    5. Zhongliang Jing, Han Pan, Yuankai Li, Peng Dong
      Pages 87-103
    6. Zhongliang Jing, Han Pan, Yuankai Li, Peng Dong
      Pages 127-149
    7. Zhongliang Jing, Han Pan, Yuankai Li, Peng Dong
      Pages 151-160
  4. Visual Tracking and Fusion

    1. Front Matter
      Pages 161-161
    2. Zhongliang Jing, Han Pan, Yuankai Li, Peng Dong
      Pages 163-185
    3. Zhongliang Jing, Han Pan, Yuankai Li, Peng Dong
      Pages 187-207
    4. Zhongliang Jing, Han Pan, Yuankai Li, Peng Dong
      Pages 209-228
    5. Zhongliang Jing, Han Pan, Yuankai Li, Peng Dong
      Pages 229-249
    6. Zhongliang Jing, Han Pan, Yuankai Li, Peng Dong
      Pages 251-268
    7. Zhongliang Jing, Han Pan, Yuankai Li, Peng Dong
      Pages 269-281
  5. Spacecraft Control for Tracking

    1. Front Matter
      Pages 283-283

About this book

Introduction

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.

Keywords

multi-target tracking visual target tracking multi-target Bayesian filter Multi-focus image fusion oversampled filter banks probabilistic visual tracking pulse coupled neural network control of spacecraft maneuvers

Authors and affiliations

  1. 1.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina
  3. 3.University of Electronic Science and Technology of ChinaChengduChina
  4. 4.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina

About the authors

Professor Zhongliang Jing is the Cheung Kong professor and the Executive Dean working in the School of Aeronautics and Astronautics at Shanghai Jiao Tong University.  He is the recipient of the Science and Technology Award for Chinese Youth and a member of the National High-Tech R&D Program Space and Aviation Expert Group. His research interests include adaptive filtering, target tracking, information fusion, avionics integration, and aerospace control.

Dr. Han Pan works at Shanghai Jiao Tong University's School of Aeronautics and Astronautics with skills and expertise in information fusion, optimization algorithms and space robotics. 

Dr. Yuankai Li is currently at the School of Aeronautics and Astronautics, University of Electronic Science and Technology of China. His research interests include nonlinear estimation and control, autonomous target tracking and GNC techniques for aerospace vehicles.

 Dr. Peng Dong works at Shanghai Jiao Tong University's School of Aeronautics and Astronautics with skills and expertise in information fusion, target tracking and nonlinear filtering.


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