Determining the Dimension of the Improper Signal Subspace in Complex-Valued Data

TL;DR

This paper introduces two novel methods, based on information theory and hypothesis testing, to estimate the dimension of the improper signal subspace in complex-valued data, effective even with limited observations and colored noise.

Contribution

It presents new approaches for estimating the improper signal subspace dimension, including reduced-rank versions that handle small sample sizes and colored noise scenarios.

Findings

Effective in scenarios with limited data

Work in the presence of colored noise

Applicable to complex-valued measurements

Abstract

A complex-valued signal is improper if it is correlated with its complex conjugate. The dimension of the improper signal subspace, i.e., the number of improper components in a complex-valued measurement, is an important parameter and is unknown in most applications. In this letter, we introduce two approaches to estimate this dimension, one based on an information- theoretic criterion and one based on hypothesis testing. We also present reduced-rank versions of these approaches that work for scenarios where the number of observations is comparable to or even smaller than the dimension of the data. Unlike other techniques for determining model orders, our techniques also work in the presence of additive colored noise.