The Volume-Correlation Subspace Detector

TL;DR

This paper introduces a novel volume-correlation subspace detector that identifies target signals in cluttered environments without prior knowledge of clutter subspace, leveraging geometric volume calculations to achieve simultaneous detection and clutter elimination.

Contribution

The paper presents a new detector that detects signals without needing clutter subspace knowledge, enabling effective detection in complex environments.

Findings

Detector performs 'detecting while learning' of clutter.

Theoretical analysis confirms perfect detection in clutter environments.

Numerical simulations validate the detector's effectiveness.

Abstract

Detecting the presence of subspace signals with unknown clutter (or interference) is a widely known difficult problem encountered in various signal processing applications. Traditional methods fails to solve this problem because they require knowledge of clutter subspace, which has to be learned or estimated beforehand. In this paper, we propose a novel detector, named volume-correlation subspace detector, that can detect signal from clutter without any knowledge of clutter subspace. This detector effectively makes use of the hidden geometrical connection between the known target signal subspace to be detected and the subspace constructed from sampled data to ascertain the existence of target signal. It is derived based upon a mathematical tool, which basically calculates volume of parallelotope in high-dimensional linear space. Theoretical analysis show that while the proposed detector is detecting the known target signal, the unknown clutter signal can be explored and eliminated simultaneously. This advantage is called "detecting while learning", and implies perfect performance of this detector in the clutter environment. Numerical simulation validated our conclusion.