Enhanced Sparse Bayesian Learning via Statistical Thresholding for Signals in Structured Noise

• Autores: Hurtado, Martin; Muravchik, Carlos Horacio; Nehorai, Arye
• Año de publicación: 2013
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Bayesian Estimation; Constant False Alarm Rate (Cfar); Probabilistic Framework; Radar; Radar Detection; Sparse Model; Sparse Signal Reconstruction; Statistical Thresholding
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

In this paper we address the problem of sparse signal reconstruction. We propose a new algorithm that determines the signal support applying statistical thresholding to accept the active components of the model. This adaptive decision test is integrated into the sparse Bayesian learning method, improving its accuracy and reducing convergence time. Moreover, we extend the formulation to accept multiple measurement sequences of signal contaminated by structured noise in addition to white noise. We also develop analytical expressions to evaluate the algorithm estimation error as a function of the problem sparsity and indeterminacy. By simulations, we compare the performance of the proposed algorithm with respect to other existing methods. We show a practical application processing real data of a polarimetric radar to separate the target signal from the clutter.
Fil: Hurtado, Martin. Universidad Nacional de La Plata. Facultad de Ingenieria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Muravchik, Carlos Horacio. Universidad Nacional de La Plata. Facultad de Ingenieria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Nehorai, Arye. Washington University in St. Louis; Estados Unidos