A modified PUMA/EPUMA for direction-of-arrival estimation of coherent sources - Équipe SIGNAL (SIgnal processinG aNd ALgorithm)
Article Dans Une Revue Digital Signal Processing Année : 2025

A modified PUMA/EPUMA for direction-of-arrival estimation of coherent sources

Résumé

The principal-singular-vector utilization modal analysis (PUMA) related algorithms have been proposed to address the problem of insufficient robustness of the method of direction estimation (MODE) related algorithms, which are sensitive to the parity of the number of sources due to the additional assumption and constraints on the symmetry of the root polynomial coefficients. Moreover, the MODE-related algorithms do not have severe performance degradation when the source covariance matrix is rank deficient, however, the initial PUMA-related algorithms will have a degraded performance under such circumstances. The initial PUMA is developed using a full rank source covariance matrix hypothesis, which is not valid for coherent sources. In this paper, a rigorous extension of the PUMA and enhanced-PUMA (EPUMA) is proposed to handle the case where the source covariance matrix may be rank deficient. The modified PUMA/EPUMA (Mod-PUMA/EPUMA) can be applied rigorously in the case of multiple coherent sources. In addition, it has lower computational complexity and faster convergence than the initial PUMA/EPUMA. The effectiveness of the Mod-PUMA/EPUMA is shown by experimental comparison with the initial PUMA-related algorithms and MODE-related algorithms.
Fichier principal
Vignette du fichier
DSP-D-24-01034_author.pdf (946.42 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04876991 , version 1 (09-01-2025)

Licence

Identifiants

Citer

Diyuan Xu, Yide Wang, Biyun Ma, Qingqing Zhu, Julien Sarrazin. A modified PUMA/EPUMA for direction-of-arrival estimation of coherent sources. Digital Signal Processing, 2025, 158, pp.104967. ⟨10.1016/j.dsp.2024.104967⟩. ⟨hal-04876991⟩
0 Consultations
0 Téléchargements

Altmetric

Partager

More