Radar Adaptive Detection Architectures for Heterogeneous Environments

Abstract

In this paper, four adaptive radar architectures for target detection in heterogeneous Gaussian environments are devised. The first architecture relies on a cyclic optimization exploiting the Maximum Likelihood Approach in the original data domain, whereas the second detector is a function of transformed data which are normalized with respect to their energy and with the unknown parameters estimated through an Expectation-Maximization-based alternate procedure. The remaining two architectures are obtained by suitably combining the estimation procedures and the detector structures previously devised. Performance analysis, conducted on both simulated and measured data, highlights that the architecture working in the transformed domain guarantees the constant false alarm rate property with respect to the interference power variations and a limited detection loss with respect to the other detectors, whose detection thresholds nevertheless are very sensitive to the interference power.

References in corpus (3)

• Robust Shrinkage Estimation of High-dimensional Covariance Matrices
• A Unifying Framework for Adaptive Radar Detection in Homogeneous plus Structured Interference-Part II: Detectors Design
• Regularized $M$-estimators of scatter matrix

Cited by in corpus (6)

• Adaptive radar detection of subspace-based distributed target in power heterogeneous clutter
• LDA-MIG Detectors for Maritime Targets in Nonhomogeneous Sea Clutter
• Adaptive Radar Detection in Heterogeneous Clutter-dominated Environments
• Innovative Cognitive Approaches for Joint Radar Clutter Classification and Multiple Target Detection in Heterogeneous Environments
• Classification Schemes for the Radar Reference Window: Design and Comparisons
• Impact of covariance mismatched training samples on constant false alarm rate detectors