Enhanced space-time covariance estimation based on a system identification approach

Khattak, Faizan A. and Proudler, Ian K. and Weiss, Stephan; (2022) Enhanced space-time covariance estimation based on a system identification approach. In: 2022 Sensor Signal Processing for Defence Conference, SSPD 2022 - Proceedings. 2022 Sensor Signal Processing for Defence Conference, SSPD 2022 - Proceedings . IEEE, GBR. ISBN 9781665483483 (https://doi.org/10.1109/SSPD54131.2022.9896183)

[thumbnail of Khattak-etal-SSPD-2022-Enhanced-space-time-covariance-estimation-based-on-a-system-identification-approach]
Preview
Text. Filename: Khattak_etal_SSPD_2022_Enhanced_space_time_covariance_estimation_based_on_a_system_identification_approach.pdf
Accepted Author Manuscript
License: Strathprints license 1.0

Download (723kB)| Preview

Abstract

The error inflicted on a space-time covariance estimate due to the availability of only finite data is known to perturb the eigenvalues and eigenspaces of its z-domain equivalent, i.e., the cross-spectral density matrix. In this paper, we show that a significantly more accurate estimate can be obtained if the source signals driving the signal model are also accessible, such that a system identication approach for the source model becomes viable. We demonstrate this improved accuracy in simulations, and discuss its dependencies on the sample size and the signal to noise ratio of the data.

ORCID iDs

Khattak, Faizan A., Proudler, Ian K. and Weiss, Stephan ORCID logoORCID: https://orcid.org/0000-0002-3486-7206;