Radar target classification by micro-Doppler contributions

This thesis studies non-cooperative automatic radar target classification. Recent developments in silicon-germanium and monolithic microwave integrated circuit technologies allows to build cheap and powerful continuous wave radars. Availability of radars opens new applications in different areas. One of these applications is security. Radars could be used for surveillance of huge areas and detect unwanted moving objects. Determination of the type of the target is essential for such systems. Microwave radars use high frequencies that reflect from objects of millimetre size. The micro-Doppler signature of a target is a time-varying frequency modulated contribution that arose in radar backscattering and caused by the relative movement of separate parts of the target. The micro-Doppler phenomenon allows to classify non-rigid moving objects by analysing their signatures. This thesis is focused on designing of automatic target classification systems based on analysis of micro-Doppler signatures. Analysis of micro-Doppler radar signatures is usually performed by second-order statistics, i.e. common energy-based power spectra and spectrogram. However, the information about phase coupling content in backscattering is totally lost in these energy-based statistics. This useful phase coupling content can be extracted by higher-order spectral techniques. We show that this content is useful for radar target classification in terms of improved robustness to various corruption factors. A problem of unmanned aerial vehicle (UAV) classification using continuous wave radar is covered in the thesis. All steps of processing required to make a decision out of the raw radar data are considered. A novel feature extraction method is introduced. It is based on eigenpairs extracted from the correlation matrix of the signature. Different classes of UAVs are successfully separated in feature space by support vector machine. Within experiments or real radar data, achieved high classification accuracy proves the efficiency of the proposed solutions. Thesis also covers several applications of the automotive radar due to very high growth in technologies for intelligent vehicle radar systems. Such radars are already build-in in the vehicle and ready for new applications. We consider two novel applications. First application is a multi-sensor fusion of video camera and radar for more efficient vehicle-to-vehicle video transmission. Second application is a frequency band invariant pedestrian classification by an automotive radar. This system allows us to use the same signal processing hardware/software for different countries where regulations vary and radars with different operating frequency are required. We consider different radar applications: ground moving target classification, aerial target classification, unmanned aerial vehicles classification, pedestrian classification. The highest priority is given to verification of proposed methods on real radar data collected with frequencies equal to 9.5, 10, 16.8, 24 and 33 GHz

BISSIAM: Bispectrum Siamese Network Based Contrastive Learning for UAV Anomaly Detection

In recent years, a surging number of unmanned aerial vehicles (UAVs) are pervasively utilized in many areas. However, the increasing number of UAVs may cause privacy and security issues such as voyeurism and espionage. It is critical for individuals or organizations to manage their behaviors and proactively prevent the misbehaved invasion of unauthorized UAVs through effective anomaly detection. The UAV anomaly detection framework needs to cope with complex signals in noisy-prone environments and to function with very limited labeled samples. This paper proposes BISSIAM, a novel framework that is capable of identifying UAV presence, types, and operation modes. BISSIAM converts UAV signals to bispectrum as the input and exploits a siamese network-based contrastive learning model to learn the vector encoding. A sampling mechanism is proposed for optimizing the sample size involved in the model training whilst ensuring the model accuracy without compromising the training efficiency. Finally, we present a similarity-based fingerprint matching mechanism for detecting unseen UAVs without the need of retraining the whole model. Experimental results show that our approach outperforms other baselines and can reach 92.85% accuracy of UAV type detection in unsupervised learning scenarios, and 91.4% accuracy for detecting the UAV type of the out-of-sample UAVs

Online Radar Target Recognition with Decoys

RÉSUMÉ : La classification automatique de plusieurs observations en mouvement, utilisant des radars de surveillance au sol, a fait l’objet de beaucoup d’attention en recherche. Le problème de la classification automatique sans assistance humaine reste un défi pour les systèmes radar modernes. En particulier, la distinction entre les petits drones et les oiseaux est un nouveau défi auquel les systèmes précédents n’avaient pas à faire face. La classification est souvent effectuée hors ligne, à l’aide de techniques d’apprentissage supervisées, mais la reconnaissance de classes invisibles reste difficile. En règle générale, les méthodes de classification en ligne nécessitent un niveau élevé d’informations et prennent beaucoup de temps. Dans ce travail, nous décrivons et analysons un modèle de classification simple pour une formalisation du problème, que nous appelons la reconnaissance de cibles de radar avec leurres. De plus, nous évaluons les performances du modèle proposé à la fois sur des données simulées et sur un jeu de données radar réel. Les résultats des expériences montrent que le modèle proposé offre une meilleure performance que les méthodes actuelles.----------ABSTRACT : Automatic classification of multiple moving targets, using ground surveillance radars has received a lot of attention in radar technology research. The problem of automatic classification without human assistance remains a challenge for modern radar systems. In particular, distinguishing between small drones and birds is a novel challenging problem that older systems did not have to face. The classification is often performed offline, using supervised learning techniques, but recognition of unseen classes remains difficult. Typically, online classification methods requires fine grained information and are very time consuming. This study aims to develop a new method for recognizing the observations from unknown classes. In this work, we describe and analyze a new model for a formalization of the problem, which we call online target recognition with decoys. The proposed model is an extension of Bayesian quadratic discriminant analysis for classification with additional abilities to recognize objects from unknown classes, called decoys. We aim to study how effectively the new model can predict the class of the new observations and predict the noise objects as decoys. Furthermore, we evaluate the performance of the proposed model on simulated data and on a real radar dataset, and compare it to quadratic discriminant analysis and support vector machine methods. Experiment results show that the proposed model improve over these baselines

Compilation of thesis abstracts, June 2007

NPS Class of June 2007This quarter’s Compilation of Abstracts summarizes cutting-edge, security-related research conducted by NPS students and presented as theses, dissertations, and capstone reports. Each expands knowledge in its field.http://archive.org/details/compilationofsis109452750

Novel Models and Algorithms Paving the Road towards RF Convergence

After decades of rapid evolution in electronics and signal processing, the technologies in communications, positioning, and sensing have achieved considerable progress. Our daily lives are fundamentally changed and substantially defined by the advancement in these technologies. However, the trend is challenged by a well-established fact that the spectrum resources, like other natural resources, are gradually becoming scarce. This thesis carries out research in the field of RF convergence, which is regarded as a mean to intelligently exploit spectrum resources, e.g., by finding novel methods of optimising and sharing tasks between communication, positioning, and sensing. The work has been done to closely explore opportunities for supporting the RF convergence. As a supplement for the electromagnetic waves propagation near the ground, ground-to-air channel models are first proposed and analysed, by incorporating the atmospheric effects when the altitude of aerial users is higher than 300 m. The status quos of techniques in communications, positioning, and sensing are separately reviewed, and our newly developments in each field are briefly introduced. For instance, we study the MIMO techniques for interference mitigation on aerial users; we construct the reflected echoes, i.e., the radar receiving, for the joint sensing and communications system. The availability of GNSS signals is of vital importance to the GNSS-enabled services, particularly the life-critical applications. To enhance the resilience of GNSS receivers, the RF fingerprinting based anti-spoofing techniques are also proposed and discussed. Such a guarantee on GNSS and ubiquitous GNSS services drive the utilisation of location information, also needed for communications, hence the proposal of a location-based beamforming algorithm. The superposition coding scheme, as an attempt of the waveform design, is also brought up for the joint sensing and communications. The RF convergence will come with many facets: the joint sensing and communications promotes an efficient use of frequency spectrum; the positioning-aided communications encourage the cooperation between systems; the availability of robust global positioning systems benefits the applications relying on the GNSS service

The Cosmic 21-cm Revolution Charting the first billion years of our universe

The redshifted 21-cm signal is set to transform astrophysical cosmology, bringing a historically data-starved field into the era of Big Data. Corresponding to the spin-flip transition of neutral hydrogen, the 21-cm line is sensitive to the temperature and ionization state of the cosmic gas, as well as to cosmological parameters. Crucially, with the development of new interferometers it will allow us to map out the first billion years of our universe, enabling us to learn about the properties of the unseen first generations of galaxies. Rapid progress is being made on both the observational and theoretical fronts, and important decisions on techniques and future direction are being made. The Cosmic 21-cm Revolution gathers contributions from current leaders in this fast-moving field, providing both an overview for graduate students and a reference point for current researchers

Summary of Research 1994

The views expressed in this report are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government.This report contains 359 summaries of research projects which were carried out under funding of the Naval Postgraduate School Research Program. A list of recent publications is also included which consists of conference presentations and publications, books, contributions to books, published journal papers, and technical reports. The research was conducted in the areas of Aeronautics and Astronautics, Computer Science, Electrical and Computer Engineering, Mathematics, Mechanical Engineering, Meteorology, National Security Affairs, Oceanography, Operations Research, Physics, and Systems Management. This also includes research by the Command, Control and Communications (C3) Academic Group, Electronic Warfare Academic Group, Space Systems Academic Group, and the Undersea Warfare Academic Group

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

1995 BRAC Commission

Data Call - Naval Command, Control and Ocean Surveillance Center, Research Development Test Evaluation Division - San Diego, CA. Data Call #5. Box 186, L-118