Projection matrix optimization for sparse signals in structured noise

Authors
Pazos, Sebastian; Hurtado, Martin; Muravchik, Carlos H.; Nehorai, Arye
Publication Year
2015
Language
English
Format
article
Status
Published version
Description
We consider the problem of estimating a signal which has been corrupted with structured noise. When the signal of interest accepts a sparse representation, only a small number of measurements are required to retain all the information. The measurements are mapped to a lower dimensional space through a projection matrix. We propose a method to optimize the design of this matrix where the objective is not only to reduce the amount of data to be processed but also to reject the undesired signal components. As a result, we reduce the computation time and the error on the estimation of the unknown parameters of the sparse model, with respect to the uncompressed data. The proposed method has tunable parameters that can affect its performance. Optimal tuning would require a comprehensive study of parameter variations and options. To avoid this learning burden, we also introduce a variant of the algorithm that is free from tuning, without significant loss of performance. Using synthetic data, we analyze the performance of the proposed algorithms and their robustness against errors in the model parameters. Additionally, we illustrate the performance of the method through a radar application using real clutter data with a still target and with a synthetic moving target.
Fil: Pazos, Sebastian. Universidad Nacional de la Plata. Facultad de Ingenieria. Departamento de Electrotecnia. Laboratorio de Electronica Ind., Control E Instrumentac.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Hurtado, Martin. Universidad Nacional de la Plata. Facultad de Ingenieria. Departamento de Electrotecnia. Laboratorio de Electronica Ind., Control E Instrumentac.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Muravchik, Carlos H.. Universidad Nacional de la Plata. Facultad de Ingenieria. Departamento de Electrotecnia. Laboratorio de Electronica Ind., Control E Instrumentac.; Argentina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina
Fil: Nehorai, Arye. Washington University in St. Louis; Estados Unidos
Subject
Projection matrix optimization
Sparse models
Compressive sensing
Radar
Telecomunicaciones
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
INGENIERÍAS Y TECNOLOGÍAS
Access level
Restricted access
License
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repository
CONICET Digital (CONICET)
Institution
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identifier
oai:ri.conicet.gov.ar:11336/13715