In recent years, ultra-wideband (UWB) radars are gaining popularity in the radar field mainly inindustrial and commercial areas. The UWB radar has the potential of dramatically improving thecontrol and surveillance of industrial processes in confined areas.The report provides an introduction to radar systems and detail working principle of M-sequenceUWB radar and methodology of how detection of targets is carried out. First two chapters of thereport describes the working of radar systems and M-sequence radar whereas in the later part ofthe report, different detection algorithms are discussed which has been implemented in thepresent radar simulations. In conventional radar the main detection algorithm is matched filteringwhere the transmitted signal is correlated with the received signal. Whereas UWB signal is nonsinusoidalthat is vulnerable to change in its shape during entire radar operation. This is thereason, the traditional signal processing methods like matched filtering or correlation process arenot advisable for UWB signals. Therefore, a different detection scheme known as Inter-periodcorrelation process (IPCP) has been studied.IPCP technique had been implemented and a comparison was made with the conventional targetdetection algorithm. On the basis of comparison made in this project, it has been observed thatthe conventional target detection methods are not effective in case of M-sequence UWB radar.The simulation results shows that by implementing IPCP method, performance close to 8-bitADC can be achievable with 1-bit comparator, also with IPCP implementation system resolutioncan be enhance effectively.Main focus was to analyze how close the system can detect two targets, therefore in all themeasurements i.e. practical and simulated measurements, only two targets were used.

"This thesis presents a real-time noncontact system that can monitor an infant's respiration and detect apnea when it occurs. For infants, bedside monitoring of respiratory signals using non-contact sensors is desirable at the hospital and for in-home care. Traditional approach employs acoustic sensors which can hardly detect infant breathing due to low SNR. In this thesis, a novel method is introduced by using a ultra-wideband (UWB) radar that obtains breathing signal from an infant's weak chest vibration. Furthermore, advanced signal processing techniques are proposed to monitor the breathing signal and to detect apnea. Since an infant may move in the crib, a location algorithm is applied periodically to track the current location of the infant's chest. An apnea warning is issued when the respiration is absent for a pre-defined period of time."

Non-contact life detection is a significant component of both civilian and military rescue applications. As a consequence, this interest has resulted in a very active area of research. The primary goal of this research is reliable detection of a human breathing signal. Additional goals of this research are to carry out detection under realistic conditions, to distinguish between two targets, to determine human breathing rate and estimate the posture. Range gating and Singular Value Decomposition (SVD) have been used to remove clutter in order to detect human breathing under realistic conditions. However, the information of the target range or what principal component contains target information may be unknown. DFT and Short Time Fourier Transform (STFT) algorithms have been used to detect the human breathing and discriminate between two targets. However, the algorithms result in many false alarms because they detect breathing when no target exists. The unsatisfactory performance of the DFT-based estimators in human breathing rate estimation is due to the fact that the second harmonic of the breathing signal has higher magnitude than the first harmonic. Human posture estimation has been performed by measuring the distance of the chest displacements from the ground. This requires multiple UWB receivers and a more complex system. In this thesis, monostatic UWB radar is used. Initially, the SVD method was combined with the skewness test to detect targets, discriminate between two targets, and reduce false alarms. Then, a novel human breathing rate estimation algorithm was proposed using zero-crossing method. Subsequently, a novel method was proposed to distinguish between human postures based on the ratios between different human breathing frequency harmonics magnitudes. It was noted that the ratios depend on the abdomen displacements and higher harmonic ratios were observed when the human target was sitting or standing. The theoretical analysis shows that the distribution of the skewness values of the correlator output of the target and the clutter signals in a single range-bin do not overlap. The experimental results on human breathing detection, breathing rate, and human posture estimation show that the proposed methods improve performance in human breathing detection and rate estimation.

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The feasibility of Ultra Wide Band (UWB) through the wall surveillance system is studied in this thesis. The transmitter and receiver architectures are discussed and an experimental set-up is constructed to verify the theory of UWB sensing. The constructed system has 80 mW peak, 6 uW average transmit power and 500 kHz PRF and a range resolution better than 1 cm. Using the experimental set-up, two problems are examined. The first problem is the respiration rate detection problem. It has been shown that the respiration rate can be accurately estimated and the signs of vital activity can be determined behind the wall. The second problem studied in this thesis is the through the wall imaging problem. The imaging system is based on the construction of a synthetic aperture by sliding the transmit-receive antenna pair along the cross range direction. The cross range resolution is improved by applying a migration algorithm to the collected data. It has been shown that imaging of a scene 8 meters in range, behind a wall of 20 cm thickness is possible with the available power.

Stålindustrierna Sandvik, Sandviken och SSAB, Borlänge tillverkar billets (kvadratiska ochlångsmala stålämnen). Vid tillverkningen av billets förflyttas stålämnena stegvis i ugnensamtidigt som de hettas upp. När stålämnena når slutet av ugnen lyfts de ur av en externmaskin. För att tids- och energieffektivisera den sista etappen krävs positionsbestämning avämnena då de når slutet av ugnen. Dessa aspekter är viktiga ur en ekonomisk såväl sommiljövänlig synpunkt då induktiva ugnar använder en stor mängd energi. Tillämpningen ärtänkt att användas av stålindustrin. UWB-radarns bredbandiga karakteristisk gör den till en lämplig ersättare till dagens sensorersom kräver håltagning i ugnens valv. Det breda frekvensspektrat hos en UWB-radarmöjliggör ytmonterade enheter i kontrast till de konventionella sensorer som används idag.Underhållsstopp för rengöring av sensorhålen kan då undvikas. Arbetet började med teoretisk studie rörande UWB-teknik och radar i allmänhet. Därefterutformades testscenarion för att studera radarvågen under varierande förhållanden. Denradaruppställning som användes under testscenariorna var framåtspridande radar. Deresulterande mätningarna signalbehandlades i Matlab. Resultatet visar att det är möjligt att detektera objekt av olika dimensioner och former på olikaavstånd, med hjälp av UWB-radar. Denna metod fungerar även som ett passagelarm, vilkenkan användas inom fler områden. / The steel industries Sandvik, Sandviken and SSAB, Borlänge, produces billets (quadratic,long steel units). Billets travelling through the furnace will heat up. At the end of the furnace,the billets will require precision measurements regarding its position, due to the extractingdevice. Sensors that are used today require an unobstructed view, which is achieved by holesin the furnace walls. Maintenance is needed in order to ensure that no impurities are cloggingthe holes. The goal with this thesis is to investigate whether it is possible to detect rectangular billets byusing an UWB-radar system. The broadband characteristics of an UWB-unit makes it asuitable successor, as free sight is not a requirement. This will decrease downtown due tomaintenance and optimize the time required for billets extraction.This involves economic and environmental aspects as well, due to lower energy consumption. This will be tested by collecting radar measurements for further signal processing. The usedradar system is forward scattering radar. The work started with a theoretical study aboutUWB-technique and basics about radar. Thereafter test scenarios were designed to study howthe radar wave is affected by changing environments. The resulting measurements were latersignal processed in Matlab. This work shows that it is possible to detect billets with various dimensions, using UWBradar.The algorithm can also be used as a passage alarm, which can be used in other areasthan furnaces.

Nous nous intéressons dans cette thèse à la vision à travers les murs (VTM) par radar ULB, avec comme objectif la mise au point d’une chaîne de traitement de l’information (CTI) complète pouvant être utilisée par différents types de radar imageur VTM. Pour ce faire, nous souhaitons prendre en compte le moins possible d’information a priori, ni sur les cibles, ni sur leur contexte environnemental. De plus, la CTI doit répondre à des critères d’adaptabilité et de modularité pour pouvoir traiter les informations issues de deux types de radar, notamment, le pulsé et le FMCW, développés dans deux projets dans lesquels s’inscrivent les travaux de cette thèse. L’imagerie radar est un point important dans ce contexte, nous l’abordons par la combinaison des algorithmes de rétroprojection et trilatération, et montrons l’amélioration apportée avec l’utilisation d’un détecteur TFAC prenant en compte la forme des signatures des cibles. La mise au point de la CTI est notre principale contribution. Le flux d’images radar obtenu est scindé en deux parties. La première séquence dynamique contient les cibles mobiles qui sont ensuite suivies par une approche multihypothèse. La seconde séquence statique contient les cibles stationnaires ainsi que les murs intérieurs qui sont détectés par une méthode s’appuyant sur la transformée de Radon. Nous avons produit un simulateur VTM fonctionnant dans le domaine temporel et fréquentiel pour mettre au point les algorithmes de la CTI et tester leur robustesse. Plusieurs scénarios de simulation ainsi que de mesures expérimentales, montrent que la CTI construite est pertinente et robuste. Elle est ainsi validée pour les deux systèmes radar. / This report is focused on Through-the-wall surveillance (TTS) using UWB radar, with the objective of developing a complete information processing pipeline (IPP) which can be used by different types of imaging radar. To do this, we want to take into account any a priori information, nor on the target, or their environmental context. In addition, the IPP must meet criteria of adaptability and modularity to process information from two types of radar, including pulsed and FMCW developed in two projects that are part of the work of this thesis. Radar imaging is an important point in this context ; we approach it by combining backprojection and trilateration algorithms and show the improvement with the use of a CFAR detector taking into account the shape of the targets signatures.The development of the IPP is our main contribution. The flow of radar images obtained is divided into two parts. The first dynamic sequence contains moving targets are tracked by a multiple hypothesis approach. The second static sequence contains stationary targets and interior walls that are highlighted by Radon transformbases approach. We developed a simulator operating in time and frequency domain to design the algorithms of the IPP and test their robustness. Several simulated scenarios and experimental measurements show that our IPP is relevant and robust. It is thus validated for both radar systems.

Dans ce travail de thèse, nous présentons nos travaux qui portent sur l’identification des cibles en général par un radar Ultra-Large Bande (ULB) et en particulier l’identification des cibles dont la surface équivalente radar est faible telles que les piétons et les cyclistes. Ce travail se décompose en deux parties principales, la détection et la reconnaissance. Dans la première approche du processus de détection, nous avons proposé et étudié un détecteur de radar ULB robuste qui fonctionne avec des données radar 1-D (A-scan) à une dimension. Il exploite la combinaison des statistiques d’ordres supérieurs et du détecteur de seuil automatique connu sous le nom de CA-CFAR pour Cell-Averaging Constant False Alarm Rate. Cette combinaison est effectuée en appliquant d’abord le HOS sur le signal reçu afin de supprimer une grande partie du bruit. Puis, après avoir éliminé le bruit du signal radar reçu, nous implémentons le détecteur de seuil automatique CA-CFAR. Ainsi, cette combinaison permet de disposer d’un détecteur de radar ULB à seuil automatique robuste. Afin d’améliorer le taux de détection et aller plus loin dans le traitement, nous avons évalué l’approche des données radar 2-D (B-Scan) à deux dimensions. Dans un premier temps, nous avons proposé une nouvelle méthode de suppression du bruit, qui fonctionne sur des données B-Scan. Il s’agit d’une combinaison de WSD et de HOS. Pour évaluer les performances de cette méthode, nous avons fait une étude comparative avec d’autres techniques de suppression du bruit telles que l’analyse en composantes principales, la décomposition en valeurs singulières, la WSD, et la HOS. Les rapports signal à bruit -SNR- des résultats finaux montrent que les performances de la combinaison WSD et HOS sont meilleures que celles des autres méthodes rencontrées dans la littérature. A la phase de reconnaissance, nous avons exploité les données des deux approches à 1-D et à 2-D obtenues à partir du procédé de détection. Dans la première approche à 1-D, les techniques SVM et le DBN sont utilisées et évaluées pour identifier la cible en se basant sur la signature radar. Les résultats obtenus montrent que la technique SVM donne de bonnes performances pour le système proposé où le taux de reconnaissance global moyen atteint 96,24%, soit respectivement 96,23%, 95,25% et 97,23% pour le cycliste, le piéton et la voiture. Dans la seconde approche à 1-D, les performances de différents types d’architectures DBN composées de différentes couches ont été évaluées et comparées. Nous avons constaté que l’architecture du réseau DBN avec quatre couches cachées est meilleure et la précision totale moyenne peut atteindre 97,80%. Ce résultat montre que les performances obtenues avec le DBN sont meilleures que celles obtenues avec le SVM (96,24%) pour ce système de reconnaissance de cible utilisant un radar ULB. Dans l’approche bidimensionnelle, le réseau de neurones convolutifs a été utilisé et évalué. Nous avons proposé trois architectures de CNN. La première est le modèle modifié d’Alexnet, la seconde est une architecture avec les couches de convolution arborescentes et une couche entièrement connectée, et la troisième est une architecture avec les cinq couches de convolution et deux couches entièrement connectées. Après comparaison et évaluation des performances de ces trois architectures proposées nous avons constaté que la troisième architecture offre de bonnes performances par rapport aux autres propositions avec une précision totale moyenne qui peut atteindre 99,59%. Enfin, nous avons effectué une étude comparative des performances obtenues avec le CNN, DBN et SVM. Les résultats montrent que CNN a les meilleures performances en termes de précision par rapport à DBN et SVM. Cela signifie que l’utilisation de CNN dans les données radar bidimensionnels permet de classer correctement les cibles radar ULB notamment pour les cibles à faible SER et SNR telles que les cyclistes ou les piétons. / In this thesis work, we focused on the study and development of a system identification using UWB-Ultra-Wide-Band short range radar to detect the objects and particularly the vulnerable road users (VRUs) that have low RCS-Radar Cross Section- such as cyclist and pedestrian. This work is composed of two stages i.e. detection and recognition. In the first approach of detection stage, we have proposed and studied a robust UWB radar detector that works on one dimension 1-D radar data ( A-scan). It relies on a combination of Higher Order Statistics (HOS) and the well-known CA-CFAR (Cell-Averaging Constant False Alarm Rate) detector. This combination is performed by firstly applying the HOS to the received radar signal in order to suppress the noise. After eliminating the noise of the received radar signal, we apply the CA-CFAR detector. By doing this combination, we finally have an UWB radar detector which is robust against the noise and works with the adaptive threshold. In order to enhance the detection performance, we have evaluated the approach of using two dimensions 2-D (B-Scan) radar data. In this 2-D radar approach, we proposed a new method of noise suppression, which works on this B-Scan data. The proposed method is a combination of WSD (Wavelet Shrinkage Denoising) and HOS. To evaluate the performance of this method, we performed a comparative study with the other noise removal methods in literature including Principal Component Analysis (PCA), Singular Value Decomposition (SVD), WSD and HOS. The Signal-to-Noise Ratio (SNR) of the final result has been computed to compare the effectiveness of individual noise removal techniques. It is observed that a combination of WSD and HOS has better capability to remove the noise compared to that of the other applied techniques in the literature; especially it is found that it allows to distinguish efficiency the pedestrian and cyclist over the noise and clutters whereas other techniques are not showing significant result. In the recognition phase, we have exploited the data from the two approaches 1-D and 2-D, obtained from the detection method. In the first 1-D approach, Support Vector Machines (SVM) and Deep Belief Networks (DBN) have been used and evaluated to identify the target based on the radar signature. The results show that the SVM gives good performances for the proposed system where the total recognition accuracy rate could achieve up to 96,24%. In the second approach of this 1-D radar data, the performance of several DBN architectures compose of different layers have been evaluated and compared. We realised that the DBN architecture with four hidden layers performs better than those of with two or three hidden layers. The results show also that this architecture achieves up to 97.80% of accuracy. This result also proves that the performance of DBN is better than that of SVM (96.24%) in the case of UWB radar target recognition system using 1-D radar signature. In the 2-D approach, the Convolutional Neural Network (CNN) has been exploited and evaluated. In this work, we have proposed and investigated three CNN architectures. The first architecture is the modified of Alexnet model, the second is an architecture with three convolutional layers and one fully connected layer, and the third is an architecture with five convolutional layers and two fully connected layers. The performance of these proposed architectures have been evaluated and compared. We found that the third architecture has a good performance where it achieves up to 99.59% of accuracy. Finally, we compared the performances obtained using CNN, DBN and SVM. The results show that CNN gives a better result in terms of accuracy compared to that of DBN and SVM. It allows to classify correctly the UWB radar targets like cyclist and pedestrian.