Raúl Fernández-Recio

NEW BENCHMARK RADAR TARGETS FOR SCATTERING ANALYSIS AND ELECTROMAGNETIC SOFTWARE VALIDATION

A validation process, in which simulations and measurements are compared, is necessary to have confidence in the results obtained by numerical methods that solve scattering problems. This paper presents Radar Cross Section (RCS) measurements of new targets suitable for electromagnetic software comparison and validation. These measurements can be used as an RCS reference data for testing existing and future codes, as well as for the analysis of the scattering mechanisms.

Computational Burden Resulting from Image Recognition of High Resolution Radar Sensors

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.

Non-Cooperative Target Recognition by Means of Singular Value Decomposition Applied to Radar High Resolution Range Profiles

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.

Performance Analysis of Metaheuristic Approaches for Single-Snapshot DOA Estimation

This letter addresses the application of metaheuristics to the single-snapshot direction-of-arrival estimation. The performances of five of the most popular optimization algorithms-Particle Swarm Optimization, Ant Colony Optimization for Continuous Domains, Simulated Annealing, Genetic Algorithms, and Differential Evolution-are compared in terms of convergence, accuracy, resolution, and computational cost in order to find the most suitable one. Also, running parameters are suggested for all of them. The featured numerical simulations include both uniform and nonuniform arrays and different noise scenarios. Results show that all of the presented optimization algorithms are capable of solving the problem, but Ant Colony Optimization for Continuous Domains is the most recommendable .

Class Identification of Aircrafts by Means of Artificial Neural Networks Trained with Simulated Radar Signatures.

Non-Cooperative Target Identification (NCTI) of aircraft from radar measurements is a formidable problem that has drawn the attention of engineers and scientists over the last years. NCTI techniques typically involve a database with a huge amount of information from different known targets and a reliable identification algorithm able to highlight the likeness between measured and stored data. This paper uses High Resolution Range Profiles produced with a high-frequency software tool to train Artificial Neural Networks for distinguishing between different classes of aircraft. Actual data from the ORFEO measurement campaign are used to assess the performance of the trained networks.

Methodology to achieve accurate non cooperative target identification using high resolution radar and a synthetic database

In the last few years, there is a great interest in developing an identification system capable to make a reliable classification of aircrafts into different groups (friendly, hostile or neutral). Depending on the context in which these systems are deployed, incorrect identification may lead to serious problems, such as fratricide or engagement of civilian aircrafts. Different techniques have been researched to face this problem, but non-cooperative ones have awakened more interest because they do not require aircraft collaboration. Non Cooperative Target Identification (NCTI) using radar is a complex task, mainly due to the fact that a database of possible targets is needed. To populate this database, Radar Cross Section (RCS) predictions produced by computer simulation seem to be the most feasible way to perform this task, since measurements alone cannot cover the vast range of targets, configurations and required aspect angles. These predictions are typically performed in the frequency domain and a specific processing must be done to obtain both High Resolution Range Profiles (HRRPs) and 2D Inverse Synthetic Aperture Radar (2D-ISAR) images. This paper shows a methodology to face the NCTI task, which use both synthetic HRRPs and 2D-ISAR to achieve an accurate identification.

Dual band and dual polarization short-circuited ring patch antenna

This paper presents a modification of the short-circuited ring patch antenna that operates in two bands: the lower band with linear polarization and the upper band with circular polarization. The new single-feed antenna has been manufactured and a comparison between measurements and numerical simulations is shown. Different characteristics such as radiation patterns, gain, and resonant frequencies have been analyzed. Moreover, a parametric study has been performed in both bands.

Non-Cooperative Target Identification based on Singular Value Decomposition

Non-Cooperative Target Identification based on High Resolution Range Profiles is a key research domain in the Defense industry. In this paper a method based on the application of Singular Value Decomposition to a matrix of range profiles is defined. The decomposition is applied to reduce dimensionality and to accomplish recognition in the transformed domain. So as to confirm the feasibility of the methodology, identification experiments of profiles coming from electromagnetic simulations are conducted, revealing promising results.