A Unifying Framework for Adaptive Radar Detection in Homogeneous plus Structured Interference-Part II: Detectors Design

TL;DR

This paper develops and analyzes new adaptive radar detectors for multidimensional signals in environments with structured interference, extending existing models and ensuring constant false alarm rate (CFAR) properties, supported by theoretical proofs and simulations.

Contribution

It introduces several new detectors based on the Maximal Invariant Statistic for complex interference scenarios, establishing their CFAR property and connections to simpler models.

Findings

Detectors are CFAR due to their dependence on the MIS.

Some detectors are statistically equivalent in the general model.

Simulation results compare the performance of proposed detectors.

Abstract

This paper deals with the problem of adaptive multidimensional/multichannel signal detection in homogeneous Gaussian disturbance with unknown covariance matrix and structured (unknown) deterministic interference. The aforementioned problem extends the well-known Generalized Multivariate Analysis of Variance (GMANOVA) tackled in the open literature. In a companion paper, we have obtained the Maximal Invariant Statistic (MIS) for the problem under consideration, as an enabling tool for the design of suitable detectors which possess the Constant False-Alarm Rate (CFAR) property. Herein, we focus on the development of several theoretically-founded detectors for the problem under consideration. First, all the considered detectors are shown to be function of the MIS, thus proving their CFARness property. Secondly, coincidence or statistical equivalence among some of them in such a general signal model is proved. Thirdly, strong connections to well-known simpler scenarios found in adaptive detection literature are established. Finally, simulation results are provided for a comparison of the proposed receivers.