Robustness of subspace-based algorithms with respect to the distribution of the noise: Application to DOA estimation
| العنوان: | Robustness of subspace-based algorithms with respect to the distribution of the noise: Application to DOA estimation |
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| المؤلفون: | Jean-Pierre Delmas, Habti Abeida |
| المساهمون: | Department of Electrical Engineering (University of Taif), Communications, Images et Traitement de l'Information (CITI), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Traitement de l'Information Pour Images et Communications (TIPIC-SAMOVAR), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Institut Polytechnique de Paris (IP Paris), Centre National de la Recherche Scientifique (CNRS) |
| المصدر: | Signal Processing Signal Processing, Elsevier, 2019, 164, pp.313-319. ⟨10.1016/j.sigpro.2019.06.017⟩ |
| بيانات النشر: | Elsevier BV, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Covariance matrix, Perturbation (astronomy), Asymptotic distribution, 020206 networking & telecommunications, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], 02 engineering and technology, Covariance, Sample mean and sample covariance, law.invention, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Projector, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Control and Systems Engineering, law, Robustness (computer science), Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Algorithm, Software, Subspace topology, Mathematics |
| الوصف: | International audience; This paper addresses the theoretical analysis of the robustness of subspace-based algorithms with respect to non-Gaussian noise distributions using perturbation expansions. Its purpose is twofold. It aims, first, to derive the asymptotic distribution of the estimated projector matrix obtained from the sample covariance matrix (SCM) for arbitrary distributions of the useful signal and the noise. It proves that this distribution depends only of the second-order statistics of the useful signal, but also on the second and fourth-order statistics of the noise. Second, it derives the asymptotic distribution of the estimated projector matrix obtained from any M-estimate of the covariance matrix for both real (RES) and complex elliptical symmetric (CES) distributed observations. Applied to the MUSIC algorithm for direction-of-arrival (DOA) estimation, these theoretical results allow us to theoretically evaluate the performance loss of this algorithm for heavy-tailed noise distributions when it is based on the SCM, which is significant for weak signal-to-noise ratio (SNR) or closely spaced sources. These results also make it possible to prove that this performance loss can be alleviated by replacing the SCM by an M-estimate of the covariance for CES distributed observations, which has been observed until now only by numerical experiments. |
| تدمد: | 0165-1684 1872-7557 |
| DOI: | 10.1016/j.sigpro.2019.06.017 |
| DOI: | 10.1016/j.sigpro.2019.06.017⟩ |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a1de072192531b8b65a8ef30cfdd004 https://doi.org/10.1016/j.sigpro.2019.06.017 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....1a1de072192531b8b65a8ef30cfdd004 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 01651684 18727557 |
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| DOI: | 10.1016/j.sigpro.2019.06.017 |