Sample eigenvalue based detection of high dimensional signals in white noise using relatively few samples

TL;DR

This paper introduces a simple, robust eigenvalue-based method for estimating the number of high-dimensional signals in white noise with limited samples, highlighting fundamental detection limits and demonstrating consistent estimation in large-sample regimes.

Contribution

It proposes a mathematically justified, computationally simple estimator for signal detection that accounts for asymptotic detection limits and defines the effective number of identifiable signals.

Findings

The estimator consistently estimates the true number of signals in large sample regimes.

There is a fundamental asymptotic limit for detecting weak or closely spaced signals with limited samples.

Adding more sensors can worsen detection performance under sample size constraints.

Abstract

We present a mathematically justifiable, computationally simple, sample eigenvalue based procedure for estimating the number of high-dimensional signals in white noise using relatively few samples. The main motivation for considering a sample eigenvalue based scheme is the computational simplicity and the robustness to eigenvector modelling errors which are can adversely impact the performance of estimators that exploit information in the sample eigenvectors. There is, however, a price we pay by discarding the information in the sample eigenvectors; we highlight a fundamental asymptotic limit of sample eigenvalue based detection of weak/closely spaced high-dimensional signals from a limited sample size. This motivates our heuristic definition of the effective number of identifiable signals which is equal to the number of "signal" eigenvalues of the population covariance matrix which exceed the noise variance by a factor strictly greater than 1+sqrt(Dimensionality of the system/Sample size). The fundamental asymptotic limit brings into sharp focus why, when there are too few samples available so that the effective number of signals is less than the actual number of signals, underestimation of the model order is unavoidable (in an asymptotic sense) when using any sample eigenvalue based detection scheme, including the one proposed herein. The analysis reveals why adding more sensors can only exacerbate the situation. Numerical simulations are used to demonstrate that the proposed estimator consistently estimates the true number of signals in the dimension fixed, large sample size limit and the effective number of identifiable signals in the large dimension, large sample size limit.