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Book Description

The adaptive configuration of nodes in a sensor network has the potential to improve sequential estimation performance by intelligently allocating limited sensor network resources.

In addition, the use of heterogeneous sensing nodes provides a diversity of information that also enhances estimation performance. This work reviews cognitive systems and presents a cognitive fusion framework for sequential state estimation using adaptive configuration of heterogeneous sensing nodes and heterogeneous data fusion. This work also provides an application of cognitive fusion to the sequential estimation problem of target tracking using foveal and radar sensors.

Table of Contents

  1. Introduction (1/2)
  2. Introduction (2/2)
  3. Cognitive Fusion
    1. State Space Formulation
      1. State Evolution Model
      2. Measurement Model
      3. Measurement Processing
    2. Cognitive Fusion Framework
      1. Prediction
      2. Node Configuration
      3. Measurement Update
  4. Cognitive Fusion for Target Tracking with Foveal and Radar Nodes
    1. State Space Formulation
      1. Target Motion Model
      2. Foveal Measurement Model
      3. Compressive Cognitive Radar Measurement Model
    2. Target Tracking Method
      1. Prediction
      2. Foveal Node Configuration
      3. Radar Node Configuration
      4. Measurement Update
    3. Simulation Scenario
  5. Conclusions
  6. Sensing Node Configuration
  7. Adaptive Compressive Sensing Matrix
    1. Sensing Matrix Construction
    2. Sensing Matrix Configurations
      1. Adaptive Sampling CSP (ASCSP)
      2. Non-Adaptive CSP (NACSP)
      3. Nyquist Sensing and Processing (NSP)
  8. Bibliography (1/2)
  9. Bibliography (2/2)
  10. Author's Biography
  11. Blank Page (1/2)
  12. Blank Page (2/2)
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