Skip to Main content Skip to Navigation
Journal articles

A bias-compensated MUSIC for small number of samples

Abstract : The multiple signal classification (MUSIC) method is known to be asymptotically efficient, yet with a small number of snapshots its performance degrades due to bias in MUSIC localization function. In this communication, starting from G-MUSIC which improves over MUSIC in low sample support, a high signal to noise ratio approximation of the G-MUSIC localization function is derived, which can be interpreted as a bias correction of the conventional MUSIC localization function. The resulting method, referred to as sG-MUSIC, is somewhat simpler than G-MUSIC as the weights applied to each eigenvector of the sample covariance matrix are obtained in closed-form, similarly to MUSIC. Numerical simulations indicate that sG-MUSIC incur only a marginal loss in terms of mean square error of the direction of arrival estimates, as compared to G-MUSIC, and performs better than MUSIC.
Complete list of metadatas

Cited literature [17 references]  Display  Hide  Download

https://hal.inria.fr/hal-02486066
Contributor : Frédéric Pascal <>
Submitted on : Wednesday, February 26, 2020 - 10:40:04 AM
Last modification on : Wednesday, September 16, 2020 - 4:50:17 PM
Long-term archiving on: : Wednesday, May 27, 2020 - 2:37:35 PM

File

sGMUSIC_revised.pdf
Files produced by the author(s)

Identifiers

Citation

François Vincent, Frédéric Pascal, Olivier Besson. A bias-compensated MUSIC for small number of samples. Signal Processing, Elsevier, 2017, 138, pp.117-120. ⟨10.1016/j.sigpro.2017.03.015⟩. ⟨hal-02486066⟩

Share

Metrics

Record views

102

Files downloads

200