Adaptive Radar Detection for Sample-Starved Gaussian Training Conditions

Adaptive Radar Detection for Sample-Starved Gaussian Training Conditions

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The problem of radar target detection in the background interference (plus noise) environment is the central problem in statistical radar theory. As a result, there are a number of well-established optimal (in the Neyman-Pearson sense) solutions for Gaussian signals and interference models with known interference covariance matrices, as presented in the first two chapters of this book.

Chapter Contents:

  • 6.1 Introduction
  • 6.2 Improving Adaptive Detection Using EL-Selected Loading
  • 6.2.1 Single Adaptive Filter Formed with Secondary Data, Followed by Adaptive Thresholding Using Primary Data
  • 6.2.2 Different Adaptive Process per Test Cell with Combined Adaptive Filtering and Detection Using Secondary Data
  • 6.2.3 Observations
  • 6.3 Improving Adaptive Detection Using Covariance Matrix Structure
  • 6.3.1 Background: TVAR(m) Approximation of a Hermitian Covariance Matrix, ML Model Identification and Order Estimation
  • 6.3.2 Performance Analysis of TVAR(m)-Based Adaptive Filters and Adaptive Detectors for TVAR(m) or AR(m) Interferences
  • 6.3.3 Simulation Results of TVAR(m)-Based Adaptive Detectors for TVAR(m) or AR(m) Interferences
  • 6.3.4 Observations
  • 6.4 Improving Adaptive Detection Using Data Partitioning
  • 6.4.1 Analysis Performance of "One-Stage" Adaptive CFAR Detectors versus "Two-Stage" Adaptive Processing
  • 6.4.2 Comparative Detection Performance Analysis
  • 6.4.3 Observations
  • References

Inspec keywords: radar detection; radar interference; covariance matrices; object detection; adaptive signal detection; radar signal processing; Gaussian noise

Other keywords: background interference environment; statistical radar theory; radar target detection problem; adaptive radar detection; sample-starved Gaussian training condition; interference covariance matrices; plus noise environment; Gaussian signal interference model; Neyman-Pearson sense

Subjects: Algebra; Electromagnetic compatibility and interference; Radar theory; Other topics in statistics; Signal detection

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