Estimating the Number of Sources: An Efficient Maximization Approach

TL;DR

This paper introduces a new, computationally efficient algorithm for estimating the number of sources in array signal processing, outperforming traditional methods like AIC and MDL at low SNR and limited samples.

Contribution

The paper presents a novel eigenvalue-based estimation algorithm using moving increment and standard deviation metrics, improving accuracy and efficiency.

Findings

Better performance at low SNR

More computationally efficient

Effective with fewer samples

Abstract

Estimating the number of sources received by an antenna array have been well known and investigated since the starting of array signal processing. Accurate estimation of such parameter is critical in many applications that involve prior knowledge of the number of received signals. Information theo- retic approaches such as Akaikes information criterion (AIC) and minimum description length (MDL) have been used extensively even though they are complex and show bad performance at some stages. In this paper, a new algorithm for estimating the number of sources is presented. This algorithm exploits the estimated eigenvalues of the auto correlation coefficient matrix rather than the auto covariance matrix, which is conventionally used, to estimate the number of sources. We propose to use either of a two simply estimated decision statistics, which are the moving increment and moving standard deviation as metric to estimate the number of sources. Then process a simple calculation of the increment or standard deviation of eigenvalues to find the number of sources at the location of the maximum value. Results showed that our proposed algorithms have a better performance in comparison to the popular and more computationally expensive AIC and MDL at low SNR values and low number of collected samples.