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We present the design, development, and test of three novel, distinct automatic target recognition (ATR) systems for the recognition of airplanes and, more specifically, non-cooperative airplanes, i.e. airplanes that do not provide information when interrogated, in the framework of passive bistatic radar systems. Passive bistatic radar systems use one or more illuminators of opportunity (already present in the field), with frequencies up to 1 GHz for the transmitter part of the systems considered here, and one or more receivers, deployed by the persons managing the system, and not co-located with the transmitters. The sole source of information are the signal scattered on the airplane and the direct-path signal that are collected by the receiver, some basic knowledge about the transmitter, and the geometrical bistatic radar configuration. The three distinct ATR systems that we built respectively use the radar images, the bistatic complex radar cross-section (BS-RCS), and the bistatic radar cross-section (BS-RCS) of the targets. We use data acquired either on scale models of airplanes placed in an anechoic, electromagnetic chamber or on real-size airplanes using a bistatic testbed consisting of a VOR transmitter and a software-defined radio (SDR) receiver, located near Orly airport, France. We describe the radar phenomenology pertinent for the problem at hand, as well as the mathematical underpinnings of the derivation of the bistatic RCS values and of the construction of the radar images.For the classification of the observed targets into pre-defined classes, we use either extremely randomized trees or subspace methods. A key feature of our approach is that we break the recognition problem into a set of sub-problems by decomposing the parameter space, which consists of the frequency, the polarization, the aspect angle, and the bistatic angle, into regions. We build one recognizer for each region. We first validate the extra-trees method on the radar images of the MSTAR dataset, featuring ground vehicles. We then test the method on the images of the airplanes constructed from data acquired in the anechoic chamber, achieving a probability of correct recognition up to 0.99.We test the subspace methods on the BS-CRCS and on the BS-RCS of the airplanes extracted from the data acquired in the anechoic chamber, achieving a probability of correct recognition up to 0.98, with variations according to the frequency band, the polarization, the sector of aspect angle, the sector of bistatic angle, and the number of (Tx,Rx) pairs used. The ATR system deployed in the field gives a probability of correct recognition of $0.82$, with variations according to the sector of aspect angle and the sector of bistatic angle.

This thesis focuses on detection algorithms of radar targets, namely on group of techniques for removing of disturbing reflections from static objects - clutter and for suppression of distortion products caused by the phase noise of the transmitter and receiver. Methods for distortion suppression in received signal are designed for implementation in the developed active multistatic radar, which operates in the code division multiplex of several transmitters on single frequency. The aim of the doctoral thesis is to design, implement in tool for technical computing MATLAB and analyze the effectiveness and computational complexity of these techniques on simulated and real data.

Luftvärnsradarns lokalisering av flygföretag genom emittering av energi gör den sårbar för fientliga motmedel och verkan. Passiva radarsystem, som inte sänder egen energi utan parasiterar på icke-kooperativa sändarantenner undersöks i detta arbete som potentiellt komplement tillluftvärnsförbandets befintliga aktiva radarsystem. Syftet med arbetet är att beskriva hur användandet av passiva sensorer påverkar luftvärnets förmågor inom ramen för luftförsvaret över Sverige. Arbetets frågeställningar är:- Hur skulle en implementering av passiva radarsystem påverka luftvärnets grundläggande förmågor? - Vilken militär nytta har passiva radarer inom ramen för luftvärnsförbandet För att besvara frågeställningarna har MUAFT-metoden använts som en teoretisk ramverksmodell för att analysera och bedöma passiva radarers militära nytta genom att analysera deras påverkan på luftvärnsförbandets grundläggande förmågor. Inledningsvis konstruerades två spelkort utifrån nuvarande luftvärnsorganisation med och utan tillförd passiv radar, vilket presenterades i en SWOT-analys. Därefter bedömdes det passiva systemet påverkan utifrån de militära grundläggande förmågorna, samt faktorerna DOTPMFLI för att slutligen kunna bedöma systemets militära nytta. Resultaten från studien pekar mot att passiva radarer påverkar samtligagrundläggande förmågor, dock främst förbandets uthållighet och överlevnad. Vidare slutsatser tyder på att implementering av passiva radarsystem skulle ge militär nytta inom ramen för luftvärnsbataljon när de nyttjas i synergi med befintligt aktivt radarsystem / In the search of hostile aircraft air defence radars emit electromagnetic waves making them vulnerable to enemy countermeasures and effects. Passive radars, lacking dedicated transmitters utilize the electromagnetic radiation of non-cooperative transmitters. The possible introduction of passive radars is examined in this report as a potential complement to the currently used active radars of the Swedish Air Defence. The purpose of this study is to explore how the utilization of passive radars within the Swedish Air Defence effects the military capabilities regarding Air Defence over Sweden. The research questions of this report are:- How would an implementation of passive radars affect the military capabilities of the Swedish air defence?- Would passive radars within the Swedish Air Defence provide military utility? To answer the questions, the MUAFT-method has been used as a theoretical framework model to analyze and assess the military utility of passive radars and their impact on the Air Defence’s military capabilities. Initially, two conceptual technical systems were presented based on the current organization of the Swedish Air Defence. One system with and one without added passive radar. Both systems were then subjected to a SWOT-analysis. Thereafter, the passive radar system was assessed on the basis of its impact on the military capabilities, as well as its footprint according to DOTPMFLI. Finally, the military utility of passive radars in Air Defence was assessed. The results indicate that an implementation of passive radar would impact all of the military capability factors, although most prominently regarding endurance and survivability. Conclusions regarding the implementation of passive radars indicate that the system does have military utility within the Air Defence unit, provided they are used as intended, in synergy with existing active radars.

Dans cette thèse, nous présentons la conception, le développement et le test de trois systèmes de reconnaissance automatique de cibles (ATR) visant à reconnaître des avions non-coopératifs, c’est-à-dire des avions ne fournissant par leur identité, en utilisant des signaux de radars passifs bistatiques. Les radars passifs bistatiques utilisent un ou plusieurs émetteurs d’opportunité (déjà présents sur le terrain), avec des fréquences allant jusqu’à 1 GHz pour les émetteurs considérés ici, et un ou plusieurs récepteurs déployés par le gestionnaire du système et non-colocalisés avec les émetteurs. Les seules informations utilisées sont les signaux réfléchis sur les avions et les signaux directement reçus qui sont tous les deux collectés par le récepteur, quelques informations concernant l’émetteur, et la configuration géométrique du radar bistatique.Les trois systèmes ATR que nous avons construits utilisent respectivement les images radar, les surfaces équivalentes radar (SER) complexes bistatiques et les SER réelles bistatiques. Nous utilisons des données acquises soit sur des modèles d’avions placés en chambre anéchoique à l’ONERA, soit sur des avions réels en utilisant un banc d’essai bistatique consistant en un émetteur VOR et un récepteur basé sur la radio-logicielle (SDR), et que nous avons déployé aux alentours de l’aéroport d’Orly. Nous décrivons d’abord la phénoménologie radar pertinente pour notre problème ainsi que les fondements mathématiques pour la dérivation de la SER bistatique d’un objet, et pour la construction d’images radar d’un objet.Nous utilisons deux méthodes pour la classification de cibles en classes prédéfinies : les arbres extrêmement aléatoires (extra-trees) et les méthodes de sous-espaces. Une caractéristique-clé de notre approche est que nous divisons le problème de reconnaissance global en un ensemble de sous-problèmes par décomposition de l’espace des paramètres (fréquence, polarisation, angle d’aspect et angle bistatique) en régions. Nous construisons un classificateur par région.Nous validons en premier lieu la méthode des extra-trees sur la base de données MSTAR, composée d’images radar de véhicules terrestres. Ensuite, nous testons cette méthode sur des images radar d’avions que nous avons construites à partir des données acquises en chambre anéchoique. Nous obtenons un pourcentage de classification allant jusqu’à 99%. Nous testons ensuite la méthode de sous-espaces sur les SER bistatiques (complexes et réelles) des avions que nous avons extraits des données de chambre anéchoique. Nous obtenons un pourcentage de classification allant jusqu’à 98%, avec des variations suivant la fréquence, la polarisation, l’angle d’aspect, l’angle bistatique et le nombre de paires émetteur-récepteur utilisées. Nous testons enfin la méthode de sous-espaces sur les SER bistatiques (réelles) extraites des signaux acquis par le banc d’essai déployé à Orly. Nous obtenons une probabilité de classification de 82%, avec des variations suivant l’angle d’aspect et l’angle bistatique. On a donc démontré dans cette thèse que l’on peut reconnaitre des cibles aériennes à partir de leur SER acquise en utilisant des signaux de radars passifs bistatiques. / We present the design, development, and test of three novel, distinct automatic target recognition (ATR) systems for the recognition of airplanes and, more specifically, non-cooperative airplanes, i.e. airplanes that do not provide information when interrogated, in the framework of passive bistatic radar systems. Passive bistatic radar systems use one or more illuminators of opportunity (already present in the field), with frequencies up to 1 GHz for the transmitter part of the systems considered here, and one or more receivers, deployed by the persons managing the system, and not co-located with the transmitters. The sole source of information are the signal scattered on the airplane and the direct-path signal that are collected by the receiver, some basic knowledge about the transmitter, and the geometrical bistatic radar configuration. The three distinct ATR systems that we built respectively use the radar images, the bistatic complex radar cross-section (BS-RCS), and the bistatic radar cross-section (BS-RCS) of the targets. We use data acquired either on scale models of airplanes placed in an anechoic, electromagnetic chamber or on real-size airplanes using a bistatic testbed consisting of a VOR transmitter and a software-defined radio (SDR) receiver, located near Orly airport, France. We describe the radar phenomenology pertinent for the problem at hand, as well as the mathematical underpinnings of the derivation of the bistatic RCS values and of the construction of the radar images.For the classification of the observed targets into pre-defined classes, we use either extremely randomized trees or subspace methods. A key feature of our approach is that we break the recognition problem into a set of sub-problems by decomposing the parameter space, which consists of the frequency, the polarization, the aspect angle, and the bistatic angle, into regions. We build one recognizer for each region. We first validate the extra-trees method on the radar images of the MSTAR dataset, featuring ground vehicles. We then test the method on the images of the airplanes constructed from data acquired in the anechoic chamber, achieving a probability of correct recognition up to 0.99.We test the subspace methods on the BS-CRCS and on the BS-RCS of the airplanes extracted from the data acquired in the anechoic chamber, achieving a probability of correct recognition up to 0.98, with variations according to the frequency band, the polarization, the sector of aspect angle, the sector of bistatic angle, and the number of (Tx,Rx) pairs used. The ATR system deployed in the field gives a probability of correct recognition of $0.82$, with variations according to the sector of aspect angle and the sector of bistatic angle.