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.

The trend in the last few decades is that current unmanned aerial vehicles are completely made of composite materials rather than metallic, such as carbon-fiber or fiberglass composites. From the electromagnetic point of view, this fact forces engineers and scientists to assess how these materials may affect their radar response or their electronics in terms of electromagnetic compatibility. In order to evaluate this, electromagnetic characterization of different composite materials has become a need. Several techniques exist to perform this characterization, all of them based on the utilization of different sensors for measuring different parameters. In this paper, an implementation of the metal-backed free-space technique, based on the employment of antenna probes, is utilized for the characterization of composite materials that belong to an actual drone. Their extracted properties are compared with those given by a commercial solution, an open-ended coaxial probe (OECP). The discrepancies found between both techniques along with a further evaluation of the methodologies, including measurements with a split-cavity resonator, conclude that the implemented free-space technique provides more reliable results for this kind of composites than the OECP technique.

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.

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.

Current small unmanned aerial vehicles (UAV) or target drones used for artillery training are being made of materials other than metallic, such as carbon-fiber or fiberglass composites. From the electromagnetic point of view this fact forces engineers and scientists to assess how these changes may affect the structure in terms of, for instance, electromagnetic compatibility (EMC) or radar response. In order to do so, estimations of the constitutive parameters of these new materials has become a need. Several techniques exist to perform this kind of estimations, all of them based on the utilization of different sensors. For this paper, an own implementation of the so-called metal-backed free-space technique, based on the employment of antenna probes, is utilized. Apart from the technique itself, different extraction algorithms can be chosen. In this regard, this paper examines the behavior of several algorithms when applied to extract the complex permittivity from the reflection coefficient of a set of materials. The algorithms studied include Pattern Search, Genetic Algorithms, Particle Swarm Optimization, Newton Search and Müller Search. First, the algorithms are analyzed using simulated reflection coefficient data. Then, those that perform better are applied to actual measured reflection coefficients of fiberglass materials that form part of a target drone employed by INTA. The results are compared with the estimations given by a commercial solution based on another sensor and technique: the open ended coaxial probe. Proper conclusions are extracted regarding the performance of the adopted approach.

Since High Resolution Radar provides the ability to recognize targets at long distance and under any weather condition, Non-Cooperative Target Identification based on High Resolution Range Profiles has become a key research domain in the Defense industry. According to that, to face this problem, an approach to Non-Cooperative Target Identification based on the exploitation of Singular Value Decomposition applied to a matrix of range profiles is presented. The method compares a collection of profiles of a given target, namely test set, with the profiles included in a database, namely training set. The classification is improved by using the decomposition, since it allows to model each aircraft as a subspace and to accomplish recognition in a transformed domain where the main features are easier to extract hence, reducing unwanted information. In order to evaluate the performance, range profiles are obtained through numerical simulation of seven civil aircraft at defined trajectories taken from an actual measurement campaign. Taking into account the nature of the datasets, the main drawback of using simulated profiles instead of actual measured profiles is that the former implies an ideal identification scenario, while measured profiles suffer from unsought information. So as to confirm the feasibility of the approach the addition of noise has been considered before the creation of the test set. Identification experiments of profiles are conducted for demonstrating which methodology provides the best robustness against noise in an actual possible scenario. Future experiments with larger sets are expected to be conducted with the aim of finally using actual profiles as test sets in a real hostile situation.