Adaptive Detection of Point-like Targets in Spectrally Symmetric Interference

TL;DR

This paper introduces adaptive radar detection methods that exploit spectral symmetry in clutter to improve target detection, using statistical tests and iterative algorithms validated on real data.

Contribution

It develops novel detection schemes leveraging spectral symmetry and provides theoretical and empirical validation of their effectiveness.

Findings

Superiority of symmetry-based detectors over conventional methods.

Effective iterative algorithms with proven convergence.

Validated performance improvements on real radar data.

Abstract

We address adaptive radar detection of targets embedded in ground clutter dominated environments characterized by a symmetrically structured power spectral density. At the design stage, we leverage on the spectrum symmetry for the interference to come up with decision schemes capable of capitalizing the a-priori information on the covariance structure. To this end, we prove that the detection problem at hand can be formulated in terms of real variables and, then, we apply design procedures relying on the GLRT, the Rao test, and the Wald test. Specifically, the estimates of the unknown parameters under the target presence hypothesis are obtained through an iterative optimization algorithm whose convergence and quality guarantee is thoroughly proved. The performance analysis, both on simulated and on real radar data, confirms the superiority of the considered architectures over their conventional counterparts which do not take advantage of the clutter spectral symmetry.