Adaptive Radar Detection in Gaussian Interference Using Clutter-Free Training Data

This paper addresses adaptive detection of range spread targets in the presence of thermal noise, jammer, and clutter. After motivating the study, a set of clutter-free training (CFT) data is considered to assist radar detection in absence of conventional secondary data sharing the same spectral properties as the interference of the cells under test. To this end, a maximum likelihood (ML) estimate of the unknown parameters is derived under the alternative hypothesis by leveraging the primary data and the CFT data simultaneously. Subsequently, the ML estimate is used to design decision rules based on generalized likelihood ratio, complex parameter Wald, and complex parameter Gradient test criteria. Furthermore, conditions guaranteeing the constant false alarm rate (CFAR) property of the proposed detectors are discussed. At the analysis stage, numerical examples are presented to evaluate the effectiveness of the proposed detectors in comparison with other detection schemes available in the literature.

Automated summary

This paper discusses the adaptive detection of range spread targets in the presence of thermal noise, jammer, and clutter. It proposes a method that uses clutter-free training data to estimate unknown parameters and design decision rules based on maximum likelihood estimation. The paper also discusses conditions for ensuring a constant false alarm rate property. Numerical examples are presented to evaluate the effectiveness of the proposed method compared to other detection schemes.