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Muestra
Ficha Técnica
Descripción

Collaborative Scheduling for Electromagnetic Detection Satellites: Model and Reinforcement Learning-based Evolutionary Algorithms focuses on the models and algorithms related to electromagnetic detection satellite scheduling problems. Sections provide an overview, including background and current state of development and then move on to the basic model of cooperative scheduling and the framework of learning evolutionary algorithms. Next, the book examines cooperative scheduling of homogeneous electromagnetic detection satellites for stationary target detection and reviews research on cooperative planning of heterogeneous electromagnetic detection satellites for low-speed moving target detection.

Final sections examine cooperative scheduling of heterogeneous electromagnetic detection satellites for high-speed moving target detection. Readers will find this to be an in-depth introduction to electromagnetic detection satellite problems.

  • Highlights Collaborative Scheduling for Electromagnetic Detection Satellites
  • Provides the first book to address the problem of scheduling for electromagnetic detection satellites
  • Presents a new model of construction and algorithms
  • Contains a large volume of pseudo-code for readers to use
  • Includes simulation experiments that can be used by researchers for comparative reference

1. Chapter 1: Introduction - p. 1 2. Chapter 2: Basic model for electromagnetic detection satellites collaborative scheduling and framework of the learning-based evolutionary algorithm - p. 31 3. Chapter 3: Homogeneous electromagnetic detection satellites collaborative scheduling model and algorithm for stationary targets - p. 79 4. Chapter 4: Heterogeneous electromagnetic detection satellites collaborative scheduling model and algorithm for low-speed moving targets - p. 115 5. Chapter 5: Heterogeneous electromagnetic detection satellites collaborative scheduling model and algorithm for high-speed moving targets - p. 165 6. Chapter 6: Conclusion and future work - p. 207 7. Bibliography - p. 213 8. Index - p. 223

Descripcion
Descripción

Collaborative Scheduling for Electromagnetic Detection Satellites: Model and Reinforcement Learning-based Evolutionary Algorithms focuses on the models and algorithms related to electromagnetic detection satellite scheduling problems. Sections provide an overview, including background and current state of development and then move on to the basic model of cooperative scheduling and the framework of learning evolutionary algorithms. Next, the book examines cooperative scheduling of homogeneous electromagnetic detection satellites for stationary target detection and reviews research on cooperative planning of heterogeneous electromagnetic detection satellites for low-speed moving target detection.

Final sections examine cooperative scheduling of heterogeneous electromagnetic detection satellites for high-speed moving target detection. Readers will find this to be an in-depth introduction to electromagnetic detection satellite problems.

  • Highlights Collaborative Scheduling for Electromagnetic Detection Satellites
  • Provides the first book to address the problem of scheduling for electromagnetic detection satellites
  • Presents a new model of construction and algorithms
  • Contains a large volume of pseudo-code for readers to use
  • Includes simulation experiments that can be used by researchers for comparative reference

1. Chapter 1: Introduction - p. 1 2. Chapter 2: Basic model for electromagnetic detection satellites collaborative scheduling and framework of the learning-based evolutionary algorithm - p. 31 3. Chapter 3: Homogeneous electromagnetic detection satellites collaborative scheduling model and algorithm for stationary targets - p. 79 4. Chapter 4: Heterogeneous electromagnetic detection satellites collaborative scheduling model and algorithm for low-speed moving targets - p. 115 5. Chapter 5: Heterogeneous electromagnetic detection satellites collaborative scheduling model and algorithm for high-speed moving targets - p. 165 6. Chapter 6: Conclusion and future work - p. 207 7. Bibliography - p. 213 8. Index - p. 223

Indice
Autores
Muestra
Ficha Técnica