A Volume Correlation Subspace Detector for signals buried in unknown clutter

TL;DR

This paper introduces a novel volume correlation subspace detector capable of identifying target signals in unknown clutter environments without prior clutter knowledge, utilizing geometric relations and volume calculations.

Contribution

It proposes a new detector that operates without clutter subspace knowledge and can learn clutter while detecting targets, advancing signal detection methods.

Findings

The detector effectively identifies signals in clutter without prior clutter information.

The method is validated through theoretical analysis and numerical simulations.

It achieves simultaneous detection and learning of clutter.

Abstract

Detecting the presence of target subspace signals with unknown clutters is a well-known hard problem encountered in various signal processing applications. Traditional methods fails to solve this problem because prior knowledge of clutter subspace is required, which can not be obtained when target and clutter are intimately mixed. In this paper, we propose a novel subspace detector that can detect target signal buried in clutter without knowledge of clutter subspace. This detector makes use of the geometrical relation between target and clutter subspaces and is derived based upon the calculation of volume of high dimensional geometrical objects. Moreover, the proposed detector can accomplish the detection simultaneously with the learning processes of clutter, a property called "detecting while learning". The performance of detector was showed by theoretical analysis and numerical simulation.