Deep Learning Techniques in Radar Emitter Identification

In the field of electronic warfare (EW), one of the crucial roles of electronic intelligence is the identification of radar signals. In an operational environment, it is very essential to identify radar emitters whether friend or foe so that appropriate radar countermeasures can be taken against them. With the electromagnetic environment becoming increasingly complex and the diversity of signal features, radar emitter identification with high recognition accuracy has become a significantly challenging task. Traditional radar identification methods have shown some limitations in this complex electromagnetic scenario. Several radar classification and identification methods based on artificial neural networks have emerged with the emergence of artificial neural networks, notably deep learning approaches. Machine learning and deep learning algorithms are now frequently utilized to extract various types of information from radar signals more accurately and robustly. This paper illustrates the use of Deep Neural Networks (DNN) in radar applications for emitter classification and identification. Since deep learning approaches are capable of accurately classifying complicated patterns in radar signals, they have demonstrated significant promise for identifying radar emitters. By offering a thorough literature analysis of deep learning-based methodologies, the study intends to assist researchers and practitioners in better understanding the application of deep learning techniques to challenges related to the classification and identification of radar emitters. The study demonstrates that DNN can be used successfully in applications for radar classification and identification.   &nbsp

Joint 1D and 2D Neural Networks for Automatic Modulation Recognition

The digital communication and radar community has recently manifested more interest in using data-driven approaches for tasks such as modulation recognition, channel estimation and distortion correction. In this research we seek to apply an object detector for parameter estimation to perform waveform separation in the time and frequency domain prior to classification. This enables the full automation of detecting and classifying simultaneously occurring waveforms. We leverage a lD ResNet implemented by O\u27Shea et al. in [1] and the YOLO v3 object detector designed by Redmon et al. in [2]. We conducted an in depth study of the performance of these architectures and integrated the models to perform joint detection and classification. To our knowledge, the present research is the first to study and successfully combine a lD ResNet classifier and Yolo v3 object detector to fully automate the process of AMR for parameter estimation, pulse extraction and waveform classification for non-cooperative scenarios. The overall performance of the joint detector/ classifier is 90 at 10 dB signal to noise ratio for 24 digital and analog modulations

Radar intra–pulse signal modulation classification with contrastive learning

The existing research on deep learning for radar signal intra–pulse modulation classification is mainly based on supervised leaning techniques, which performance mainly relies on a large number of labeled samples. To overcome this limitation, a self–supervised leaning framework, contrastive learning (CL), combined with the convolutional neural network (CNN) and focal loss function is proposed, called CL––CNN. A two–stage training strategy is adopted by CL–CNN. In the first stage, the model is pretrained using abundant unlabeled time–frequency images, and data augmentation is used to introduce positive–pair and negative–pair samples for self–supervised learning. In the second stage, the pretrained model is fine–tuned for classification, which only uses a small number of labeled time–frequency images. The simulation results demonstrate that CL–CNN outperforms the other deep models and traditional methods in scenarios with Gaussian noise and impulsive noise–affected signals, respectively. In addition, the proposed CL–CNN also shows good generalization ability, i.e., the model pretrained with Gaussian noise–affected samples also performs well on impulsive noise–affected samples

Automatic tissue characterization from optical coherence tomography images for smart laser osteotomy

Fascinating experiments have proved that in the very near future, laser will completely replace mechanical tools in bone surgery or osteotomy. Laser osteotomy overcomes mechanical tools’ shortcomings, with less damage to surrounding tissue, lower risk of viral and bacterial infections, and faster wound healing. Furthermore, the current development of artificial intelligence has pushed the direction of research toward smart laser osteotomy. This thesis project aimed to advance smart laser osteotomy by introducing an image-based automatic tissue characterization or feedback system. The Optical Coherence Tomography (OCT) imaging system was selected because it could provide a high-resolution subsurface image slice over the laser ablation site. Experiments were conducted and published to show the feasibility of the feedback system. In the first part of this thesis project, a deep-learning-based OCT image denoising method was demonstrated and yielded a faster processing time than classical denoising methods, while maintaining image quality comparable to a frame-averaged image. Next part, it was necessary to find the best deep-learning model for tissue type identification in the absence of laser ablation. The results showed that the DenseNet model is sufficient for detecting tissue types based on the OCT image patch. The model could differentiate five different tissue types (bone, bone marrow, fat, muscle, and skin tissues) with an accuracy of 94.85 %. The last part of this thesis project presents the result of applying the deep-learning-based OCT-guided laser osteotomy in real-time. The first trial experiment took place at the time of the writing of this thesis. The feedback system was evaluated based on its ability to stop bone cutting when bone marrow was detected. The results show that the deep-learning-based setup successfully stopped the ablation laser when bone marrow was detected. The average maximum depth of bone marrow perforation was only 216 μm. This thesis project provides the basic framework for OCT-based smart laser osteotomy. It also shows that deep learning is a robust approach to achieving real-time application of OCT-guided laser osteotomy. Nevertheless, future research directions, such as a combination of depth control and tissue classification setup, and optimization of the ablation strategy, would make the use of OCT in laser osteotomy even more feasible

Electronic Warfare and Artificial Intelligence

Electronic warfare is a critical component of modern military operations and has undergone significant advances in recent years. This book provides an overview of electronic warfare, its historical development, key components, and its role in contemporary conflict scenarios. It also discusses emerging trends and challenges in electronic warfare and its contemporary relevance in an era of advanced technology and cyber threats, emphasizing the need for continued research and development in this area. The book explores the burgeoning intersection of artificial intelligence and electronic warfare, highlighting the evolving landscape of modern conflicts and the implications of integrating advanced technologies. The multifaceted roles of artificial intelligence in electronic warfare are highlighted, examining its potential advantages, ethical considerations, and challenges associated with its integration

La guerre électronique et l'intelligence artificielle

La guerre électronique est un élément essentiel des opérations militaires modernes et a connu des progrès significatifs ces dernières années. Ce livre donne un aperçu de la guerre électronique, de son évolution historique, de ses composants clés et de son rôle dans les scénarios de conflit contemporains. Il aborde également les tendances et les défis émergents en matière de guerre électronique et sa pertinence contemporaine à l'ère des technologies avancées et des cybermenaces, en soulignant la nécessité de poursuivre la recherche et le développement dans ce domaine. Le livre explore l’intersection naissante de l’intelligence artificielle et de la guerre électronique, mettant en lumière l’évolution du paysage des conflits modernes et les implications de l’intégration des technologies avancées. Les rôles multiformes de l'intelligence artificielle dans la guerre électronique sont mis en évidence, en examinant ses avantages potentiels, les considérations éthiques et les défis associés à son intégration

Războiul electronic și inteligența artificială

Războiul electronic este o componentă critică a operațiunilor militare moderne și a suferit progrese semnificative în ultimii ani. Această carte oferă o privire de ansamblu asupra războiului electronic, a dezvoltării sale istorice, a componentelor cheie și a rolului său în scenariile de conflict contemporane. De asemenea, se discută tendințele și provocările emergente în războiul electronic și și relevanța sa contemporană într-o eră a tehnologiei avansate și a amenințărilor cibernetice, subliniind necesitatea cercetării și dezvoltării continue în acest domeniu. Cartea explorează intersecția în plină dezvoltare dintre inteligența artificială și războiul electronic, evidențiind peisajul evolutiv al conflictelor moderne și implicațiile integrării tehnologiilor avansate. Se evidențiază rolurile cu mai multe fațete ale inteligenței artificiale în războiul electronic, examinând avantajele sale potențiale, considerentele etice și provocările asociate cu integrarea acesteia