Detectability and Electronic Warfare Laboratory

Torrejón de Ardoz, Spain

Detectability and Electronic Warfare Laboratory

Torrejón de Ardoz, Spain

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Lopez-Rodriguez P.,Detectability and Electronic Warfare Laboratory | Escot-Bocanegra D.,Detectability and Electronic Warfare Laboratory | Fernandez-Recio R.,Technical University of Madrid | Bravo I.,University of Alcalá
Sensors (Switzerland) | Year: 2015

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. © 2014 by the authors; licensee MDPI, Basel, Switzerland.


Lopez-Rodriguez P.,Detectability and Electronic Warfare Laboratory | Escot-Bocanegra D.,Detectability and Electronic Warfare Laboratory | Fernandez-Recio R.,Technical University of Madrid | Bravo I.,University of Alcalá
IET Radar, Sonar and Navigation | Year: 2016

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. © The Institution of Engineering and Technology.

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