Radar sensors hold excellent capabilities to estimate distance and motion accu- rately, penetrate nonmetallic objects, and remain unaffected by weather conditions. These capabilities make these devices extremely flexible in their applications. Elec- tromagnetic waves centered at frequencies around 24 GHz offer high precision target measurements, compact antenna and circuitry design, and lower atmospheric absorp- tion than higher frequency-based systems. This thesis presents a case study for a 24 GHz frequency modulated continuous wave radar module. We start by addressing the theoretical background necessary for this work and describing the architecture of the module used. We present three classes’ classification accuracy, namely pedes- trians, cyclists, and cars. A set of features for the classification is designed based on theoretical models, and their effectiveness is validated through experiments. The features are extracted from the available geometrical and motion-related information and used to train different classification models to compare the results. Finally, a trade-off between feature number and accuracy is presented.

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Micro-Doppler effects are phenomena that occur because of micro-motion. A micro-motion is either a vibration, rotation, or acceleration which is small relative to the motion of the target. These effects can be used in order to characterize a target through their signature movement. These effects were captured using a Frequency Modulated Continous Wave (FMCW) radar on several targets with a distinct signature. The targets were a four-armed drone, a cyclist, and a pedestrian. Using conventional- and super-resolution algorithms allows the user to process the captured data. To best be able to determine these signatures, different algorithms were used, Short-Time Fourier Transform (STFT), Smoothed Pseudo-Wigner-Ville Distribution (SPWVD), Pade Fourier approximation (PFA), and MUltiple SIgnal Classification (MUSIC). The comparison of the algorithms on the measured data was done in MATLAB where the best possible scenario was taken. From the comparison, it was noticed that in order to capture the most details, the MUSIC, PFA, STFT, and SPWVD performed the best with a decreasing order. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

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Radar (RAdio Detection And Ranging) uses radio waves to detect the presence of a target and measure its position and other properties. This sensor has found many civilian and military applications due to advantages such as possible large surveillance areas and operation day and night and in all weather. The contributions of this thesis are within applied signal processing for radar in two somewhat separate research areas: 1) radar with array antennas and 2) radar with micro-Doppler measurements. Radar with array antennas: An array antenna consists of several small antennas in the same space as a single large antenna. Compared to a traditional single-antenna radar, an array antenna radar gives higher flexibility, higher capacity, several radar functions simultaneously and increased reliability, and makes new types of signal processing possible which give new functions and higher performance. The contributions on array antenna radar in this thesis are in three different problem areas. The first is High Resolution DOA (Direction Of Arrival) Estimation (HRDE) as applied to radar and using real measurement data. HRDE is useful in several applications, including radar applications, to give new functions and improve the performance. The second problem area is suppression of interference (clutter, direct path jamming and scattered jamming) which often is necessary in order to detect and localize the target. The thesis presents various results on interference signal properties, antenna geometry and subarray design, and on interference suppression methods. The third problem area is measurement techniques for which the thesis suggests two measurement designs, one for radar-like measurements and one for scattered signal measurements. Radar with micro-Doppler measurements: There is an increasing interest and need for safety, security and military surveillance at short distances. Tasks include detecting targets, such as humans, animals, cars, boats, small aircraft and consumer drones; classifying the target type and target activity; distinguishing between target individuals; and also predicting target intention. An approach is to employ micro-Doppler radar to perform these tasks. Micro-Doppler is created by the movement of internal parts of the target, like arms and legs of humans and animals, wheels of cars and rotors of drones. Using micro-Doppler, this thesis presents results on feature extraction for classification; on classification of targets types (humans, animals and man-made objects) and human gaits; and on information in micro-Doppler signatures for re-identification of the same human individual. It also demonstrates the ability to use different kinds of radars for micro-Doppler measurements. The main conclusion about micro-Doppler radar is that it should be possible to use for safety, security and military surveillance applications.

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Les méthodes traditionnelles de formation d'images ISAR supposent que la cible est rigide et ne tiennent pas compte de ses déformations géométriques. Ces mouvements, qui s'ajoutent au déplacement global de la cible, créent une modulation en fréquence sur le signal réfléchi. Ce phénomène, aussi appelé effet micro-Doppler, se traduit d'un point de vue spectral par un étalement des fréquences autour de la fréquence Doppler centrale. Comme les caractéristiques de ces modulations sont directement reliées aux propriétés géométriques et dynamiques de la cible, l'analyse de l'effet micro-Doppler peut apporter des informations complémentaires aux méthodes existantes de reconnaissance de cibles mobiles. Les travaux précédents ont principalement été consacrés à l'analyse temporelle de l'effet micro-Doppler sans tenir compte de la dimension spatiale. En outre, mis à part les cas d'étude théoriques, il existe très peu de modélisations et de données réelles de cibles déformables. A travers les exemples de la roue et du piéton, cette thèse consiste à caractériser finement les effets des déformations géométriques en imagerie radar, en combinant l'analyse en distance et en Doppler. En outre, un accent est mis sur l'influence de la géométrie relative entre le radar et la cible.\\ Ces travaux s'appuient sur un large volet expérimental où sont exploitées les données issues du radar HYCAM, un système d'acquisition large bande développé par l'ONERA. En complément des mesures, le développement d'un outil de simulation permet de faire le lien entre les données réelles et le modèle de l'objet afin d'extraire des grandeurs physiques du phénomène étudié.

En environnements hostiles, les signaux GNSS (Global Navigation Satellite System)peuvent être soumis à des risques de brouillages intentionnels. Basées sur un réseau d'antennes adaptatif, les solutions spatio-temporelles (STAP) ont déjà montré de bonnes performances de réjection des interférences. Toutefois, lorsque le module GNSS est placé sous les pales d'un hélicoptère, des effets non-stationnaires, appelés Rotor Blade Modulation (RBM), créés par les multiples réflexions du signal sur les pales du rotor, peuvent dégrader les techniques usuelles d’antibrouillage. Le signal utile GNSS n’est alors plus accessible. Le travail de la thèse consiste donc à élaborer un système de protection des signaux GNSS adapté à la RBM. Pour cela, un modèle innovant de multitrajets, adapté à ce type de phénomène, a été développé. La comparaison de simulations électromagnétiques représentatives et de mesures expérimentales sur hélicoptère EC-120 a permis de valider ce modèle. Celui-ci permet d'estimer, par maximum de vraisemblance, les paramètres de la contribution non-stationnaire du signal reçu. Enfin, l'association d'un algorithme de filtrage des multitrajets par projection oblique et d'un traitement STAP permet d'éliminer la contribution dynamique puis statique de l'interférence. Les simulations montrent que le signal utile GNSS est alors de nouveau exploitable. / In hostile environments, Global Navigation Satellite System (GNSS) can be disturbed by intentional jamming. Using antenna arrays, space-time adaptive algorithm (STAP) isone of the most efficient methods to deal with these threats. However, when a GNSS receiver is placed near rotating bodies, non-stationary effects called Rotor Blade Modulation (RBM) are created by the multipaths on the blades of the helicopter. They can degrade significantly the anti-jamming system and the signal of interest could belost. The work of the thesis is, consequently, to develop a GNSS protection system adapted to the RBM. In this way, an innovative multipath model, adapted to this phenomenon, has been developed. The model is then confirmed by comparison with a symptotic electromagnetic simulations and experiments conducted on an EC-120helicopter. Using a Maximum Likelihood algorithm, the parameters of the non-stationary part of the received signal have been estimated. And finally, the RBM anti-jamming solution, combining oblique projection algorithm and academic STAP, can mitigate dynamic and static contributions of interferences. In the end, the navigation information is available again.

Focusing on the problem of Drone vs. Unknown classification based on radar frequency-amplitude spectra using Deep Learning (DL), especially 1-Dimensional Convolutional Neural Networks (1D-CNNs), this thesis aims at reducing the current gap in the research related to adequate pre-processing techniques for hardware deployment. The primary challenge tackled in this work is determining a pipeline that facilitates industrial deployment while maintaining high classification metrics. After presenting a comprehensive review of existing research on radar signal classification and the application of DL techniques in this domain, the technical background of signal processing is described to provide a practical scenario where the solutions could be implemented. A thorough description of technical constraints, such as Field Programmable Gate Array (FPGA) data type requirements, follows the entire project justifying the necessity of a learning-based pre-processing technique for highly skewed distributions. The results demonstrate that data-adaptive preprocessing eases hardware deployment and maintains high classification metrics, while other techniques contribute to noise and information loss. In conclusion, this thesis contributes to the field of radar frequency-amplitude spectra classification by identifying effective methods to support efficient hardware deployment of 1D-CNNs, without sacrificing performance. This work lays the foundation for future studies in the field of DL for real-world signal processing applications. / Med fokus på problemet med klassificering av drönare kontra okänt baserat på radarfrekvens-amplitudspektra med Deep Learning (DL), särskilt 1-Dimensional Convolutional Neural Networks (1D-CNNs), syftar denna avhandling till att minska det nuvarande gapet i forskningen relaterad till adekvata förbehandlingstekniker för hårdvarudistribution. Den främsta utmaningen i detta arbete är att fastställa en pipeline som underlättar industriell driftsättning samtidigt som höga klassificeringsmått bibehålls. Efter en omfattande genomgång av befintlig forskning om klassificering av radarsignaler och tillämpningen av DL-tekniker inom detta område, beskrivs den tekniska bakgrunden för signalbehandling för att ge ett praktiskt scenario där lösningarna kan implementeras. En grundlig beskrivning av tekniska begränsningar, såsom krav på datatyper för FPGA (Field Programmable Gate Array), följer hela projektet och motiverar nödvändigheten av en inlärningsbaserad förbehandlingsteknik för mycket skeva fördelningar. Resultaten visar att dataanpassad förbehandling underlättar hårdvaruimplementering och bibehåller höga klassificeringsmått, medan andra tekniker bidrar till brus och informationsförlust. Sammanfattningsvis bidrar denna avhandling till området klassificering av radarfrekvens-amplitudspektra genom att identifiera effektiva metoder för att stödja effektiv hårdvarudistribution av 1D-CNN, utan att offra prestanda. Detta arbete lägger grunden för framtida studier inom området DL för verkliga signalbehandlingstillämpningar.