Bayesian Signal Subspace Estimation with Compound Gaussian Sources

Autor: R. Ben Abdallah, D. Lautru, M. N. El Korso, Arnaud Breloy
Přispěvatelé: Laboratoire Energétique Mécanique Electromagnétisme (LEME), Université Paris Nanterre (UPN)
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Computer science
Minimum mean square distance
Gaussian
Bingham distribution
Array processing
02 engineering and technology
symbols.namesake
Signal-to-noise ratio
0202 electrical engineering
electronic engineering
information engineering
Maximum a posteriori estimation
Orthonormal basis
Majorization-minimization
Electrical and Electronic Engineering
Bayes estimator
Estimator
020206 networking & telecommunications
Langevin distribution
Maximum a posteriori
Bayesian estimation
Subspace estimation
[SPI.ELEC]Engineering Sciences [physics]/Electromagnetism
Additive white Gaussian noise
Control and Systems Engineering
Signal Processing
symbols
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Compound Gaussian distribution
Algorithm
Software
Subspace topology
Signal subspace
Zdroj: Signal Processing
Signal Processing, Elsevier, 2019, 167, pp.107310. ⟨10.1016/j.sigpro.2019.107310⟩
Signal Processing, 2019, 167, pp.107310. ⟨10.1016/j.sigpro.2019.107310⟩
ISSN: 0165-1684
1872-7557
Popis: International audience; In this paper, we consider the problem of low dimensional signal subspace estimation in a Bayesian con- text. We focus on compound Gaussian signals embedded in white Gaussian noise, which is a realistic modeling for various array processing applications. Following the Bayesian framework, we derive two algorithms to compute the maximum a posteriori (MAP) estimator and the so-called minimum mean square distance (MMSD) estimator, which minimizes the average natural distance between the true range space of interest and its estimate. Such approaches have shown their interests for signal subspace esti- mation in the small sample support and/or low signal to noise ratio contexts. As a byproduct, we also introduce a generalized version of the complex Bingham Langevin distribution in order to model the prior on the subspace orthonormal basis. Finally, numerical simulations illustrate the performance of the proposed algorithms.
Databáze: OpenAIRE
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