Advanced sparsity-driven models and methods for radar applications /

The book has 9 chapters. The following topics are dealt with: Introduction; Hybrid greedy pursuit algorithms for enhancing radar imaging quality; Two-level block sparsity model for multichannel radar signals; Parametric sparse representation for radar imaging with model uncertainty; Poisson disk sam...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Li, Gang (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London, United Kingdom : SciTech Publishing, 2020.
Temas:
Doppler radar.
Imaging.
Radar.
Signal processing.
Synthetic aperture radar.
Target acquisition.
Testing.
Radar Doppler.
Traitement du signal.
Radar à synthèse d'ouverture.
Acquisition d'objectif.
Essais (Technologie)
radar.
testing.
Doppler radar
Radar
Signal processing
Synthetic aperture radar
Target acquisition
Testing
compressed sensing.
greedy algorithms.
image classification.
image coding.
image enhancement.
image representation.
image resolution.
image sampling.
iterative methods.
radar detection.
radar imaging.
radar resolution.
radar target recognition.
synthetic aperture radar.
locally most powerful test
radar networks
sparse signals distributed detection
radar target classification
sparsity aware microDoppler analysis
coarsely quantized radar data
advanced sparse signal models
wide-swath SAR imaging
high-resolution SAR imaging
Poisson disk sampling
model uncertainty
parametric sparse representation
multichannel radar signals
two-level block sparsity model
radar imaging quality enhancement
hybrid greedy pursuit algorithms
radar applications
advanced sparsity-driven models
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Contents
  • About the author
  • Preface
  • Acknowledgments
  • Notation
  • 1. Introduction
  • 1.1 Sparsity of radar signals
  • 1.2 Fundamentals of sparse signal recovery
  • References
  • 2. Hybrid greedy pursuit algorithms for enhancing radar imaging quality
  • 2.1 Introduction
  • 2.2 Radar imaging with multiple measurement vectors
  • 2.3 Hybrid matching pursuit algorithm
  • 2.4 Look-ahead hybrid matching pursuit algorithm
  • 2.5 Conclusion
  • References
  • 3. Two-level block sparsity model for multichannel radar signals
  • 3.1 Introduction
  • 3.2 Formulation of the two-level block sparsity model
  • 3.3 TWRI based on two-level block sparsity
  • 3.4 STAP based on two-level block sparsity
  • 3.5 Conclusion
  • References
  • 4. Parametric sparse representation for radar imaging with model uncertainty
  • 4.1 Introduction
  • 4.2 Parametric dictionary
  • 4.3 Application to SAR refocusing of moving targets
  • 4.4 Application to SAR motion compensation
  • 4.5 Application to ISAR imaging of aircrafts
  • 4.6 Conclusion
  • References
  • 5. Poisson disk sampling for high-resolution and wide-swath SAR imaging
  • 5.1 Introduction
  • 5.2 Tradeoff between high-resolution and wide-swath in SAR imaging
  • 5.3 Poisson disk sampling scheme
  • 5.4 SAR imaging algorithm with Poisson disk sampled data
  • 5.5 Experimental results
  • 5.6 Conclusion
  • References
  • 6. When advanced sparse signal models meet coarsely quantized radar data
  • 6.1 Introduction
  • 6.2 Parametric quantized iterative hard thresholding for SAR refocusing of moving targets with coarsely quantized data
  • 6.3 Enhanced 1-bit radar imaging by exploiting two-level block sparsity
  • 6.4 Conclusion
  • References
  • 7. Sparsity aware micro-Doppler analysis for radar target classification
  • 7.1 Introduction
  • 7.2 Micro-Doppler parameter estimation via PSR
  • 7.3 Dynamic hand gesture recognition via Gabor-Hausdorff algorithm
  • 7.4 Conclusion
  • References
  • 8. Distributed detection of sparse signals in radar networks via locally most powerful test
  • 8.1 Introduction
  • 8.2 The original LMPT detector
  • 8.3 The quantized LMPT detector
  • 8.4 Conclusion
  • References
  • 9. Summary and perspectives
  • 9.1 Summary
  • 9.2 Perspectives
  • References
  • Index

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