Examinando por Autor "Bravo, I."

Mostrando 1 - 6 de 6

Acceso Abierto
Computational Burden Resulting from Image Recognition of High Resolution Radar Sensors
(Multidisciplinary Digital Publishing Institute (MDPI), 2013-04-22) López Rodríguez, P.; Fernández Recio, R.; Bravo, I.; Gardel, Alfredo; Lázaro, José L.; Rufo, Elena; Instituto Nacional de Técnica Aeroespacial (INTA)
This paper presents a methodology for high resolution radar image generation and automatic target recognition emphasizing the computational cost involved in the process. In order to obtain focused inverse synthetic aperture radar (ISAR) images certain signal processing algorithms must be applied to the information sensed by the radar. From actual data collected by radar the stages and algorithms needed to obtain ISAR images are revised, including high resolution range profile generation, motion compensation and ISAR formation. Target recognition is achieved by comparing the generated set of actual ISAR images with a database of ISAR images generated by electromagnetic software. High resolution radar image generation and target recognition processes are burdensome and time consuming, so to determine the most suitable implementation platform the analysis of the computational complexity is of great interest. To this end and since target identification must be completed in real time, computational burden of both processes the generation and comparison with a database is explained separately. Conclusions are drawn about implementation platforms and calculation efficiency in order to reduce time consumption in a possible future implementation.
Acceso Abierto
Evaluación de algoritmos de identificación no cooperativa de blancos aéreos mediante perfiles de alta resolución
(Ministerio de Defensa: Secretaria General Técnica, 2018-11-20) Hernán Vega, O.; López Rodríguez, P.; Escot Bocanegra, D.; Poyatos Martínez, D.; Fernández Recio, R.; Bravo, I.
El principal desafío tecnológico relacionado con la Seguridad y Defensa es la identificación de forma rápida y fiable de diferentes aeronaves en vuelo. La identificación no cooperativa de blancos aéreos basada en perfiles en distancia de alta resolución, permite conocer si un blanco es amigo o enemigo sin necesidad de establecer un enlace de comunicación con él. Este artículo presenta la simulación de un sistema de reconocimiento basado en la aplicación de la descomposición de valores singulares a un conjunto de perfiles sintéticos, para extraer sus características principales. Mediante esta técnica, cada blanco se modela como un subespacio, y de esta manera es posible trabajar con la información más significativa en el dominio transformado, reduciendo efectos no deseados como el ruido u otras posibles interferencias.
Restringido
Identificación no cooperativa de blancos reales aéreos mediante radar utilizando una base de datos sintética y descomposición en valores singulares
(Ministerio de Defensa: Secretaria General Técnica, 2013-11-07) López Rodríguez, P.; Fernández Recio, R.; Bravo, I.; De Diego Custodio, E.; Poyatos Martínez, D.; Escot Bocanegra, D.; Instituto Nacional de Técnica Aeroespacial (INTA)
El reconocimiento de blancos aéreos no cooperativos mediante perfiles de alta resolución es un procedimiento de gran interés en el ámbito de la defensa. La creación de la base de datos para el reconocimiento a partir de simulaciones electromagnéticas es un tema recursivo en el mundo de la identificación no cooperativa. Se trata de una solución de bajo coste que permite tener información de todos los posibles blancos, rangos de frecuencia y diferentes ángulos de aspecto. Sin embargo, el uso de simulaciones electromagnéticas como base de datos en el reconocimiento de aviones reales hace del proceso una tarea complicada. Mediante la descomposición en valores singulares de un conjunto de perfiles se consigue la reducción de dimensionalidad añadiendo la capacidad de trabajar en el dominio transformado para realizar el proceso de identificación. La principal dificultad de este estudio estriba en el reconocimiento de medidas reales con la utilización de una base de datos construida sintéticamente mediante simulaciones electromagnéticas de modelos CAD de aeronaves.
Acceso Abierto
Non-cooperative identification of civil aircraft using a generalised mutual subspace method
(The Institution of Engeering and Technology, 2016-01-01) López Rodríguez, P.; Escot Bocanegra, D.; Fernández Recio, R.; Bravo, I.; Instituto Nacional de Técnica Aeroespacial (INTA)
The subspace-based methods are effectively applied to classify sets of feature vectors by modelling them as subspaces. However, their application to the field of non-cooperative target identification of flying aircraft is barely seen in the literature. In these methods, setting the subspace dimensionality is always an issue. Here, it is demonstrated that a modified mutual subspace method, which uses softweights to set the importance of each subspace basis, is a promising classifier for identifying sets of range profiles coming from real in-flight targets with no need to set the subspace dimensionality in advance. The assembly of a recognition database is also a challenging task. In this study, this database comprises predicted range profiles coming from electromagnetic simulations. Even though the predicted and actual profiles differ, the high recognition rates achieved reveal that the algorithm might be a good candidate for its application in an operational target recognition system.
Acceso Abierto
Non-Cooperative Target Recognition by Means of Singular Value Decomposition Applied to Radar High Resolution Range Profiles
(Multidisciplinary Digital Publishing Institute (MDPI), 2015-01-05) López Rodríguez, P.; Escot Bocanegra, D.; Fernández Recio, R.; Bravo, I.; Instituto Nacional de Técnica Aeroespacial (INTA); Agencia Estatal de Investigación (AEI)
Radar high resolution range profiles are widely used among the target recognition community for the detection and identification of flying targets. In this paper, singular value decomposition is applied to extract the relevant information and to model each aircraft as a subspace. The identification algorithm is based on angle between subspaces and takes place in a transformed domain. In order to have a wide database of radar signatures and evaluate the performance, simulated range profiles are used as the recognition database while the test samples comprise data of actual range profiles collected in a measurement campaign. Thanks to the modeling of aircraft as subspaces only the valuable information of each target is used in the recognition process. Thus, one of the main advantages of using singular value decomposition, is that it helps to overcome the notable dissimilarities found in the shape and signal-to-noise ratio between actual and simulated profiles due to their difference in nature. Despite these differences, the recognition rates obtained with the algorithm are quite promising.
Acceso Abierto
Singular Value Decomposition Applied to Automatic Target Recognition with High Resolution Range Profiles
(Multidisciplinary Digital Publishing Institute (MDPI), 2014-01-02) López Rodríguez, P.; Escot Bocanegra, D.; Fernández Recio, R.; Bravo, I.; Instituto Nacional de Técnica Aeroespacial (INTA)
Abstract: With the development of wideband radars new applications have emerged related to this kind of sensor. That is the case of automatic target recognition based on radar imagery. In this paper a target recognition methodology based on one dimensional high resolution radar imagery is presented. 1D radar images, namely high resolution range profiles (HRRP) are comprised of range bins and contain the distribution of the scattering centers of a target providing information about target structure. In this manuscript, identification of HRRP coming from measurements of in-flight aircraft is carried out by comparison with a database of simulated HRRPs. Simulated HRRPs have a very clean signature while actual HRRPs suffer from noise and other unwanted effects making the recognition process an arduous task. In order to overcome the differences between profiles, Singular Value Decomposition (SVD) is applied to matrices of HRRP. SVD is a robust tool for the decomposition of any matrix into orthogonal basis spaces, thus, by applying SVD to the HRRP matrices and selecting the most significant singular vectors, the matrices can be split into a signal and a noise subspace. The identification algorithm proposed in this paper is based on finding the aircraft which minimizes the angle between signal subspaces. Confusion matrices for the classification of the whole test set and error rates obtained will be provided in the paper full-version. As will be shown, the use of SVD provides good recognition rates even the lack of similarity between actual and simulated profiles.