In this study, novel methods for high-resolution radar target detection in non-Gaussian clutter environment are proposed. In solution of the problem, two approaches are used: Non-coherent detection that operates on the envelope-detected signal for thresholding and coherent detection that performs clutter suppression, Doppler processing and thresholding at the same time. The proposed non-coherent detectors, which are designed to operate in non-Gaussian and range-heterogeneous clutter, yield higher performance than the conventional methods that were designed either for Gaussian clutter or heterogeneous clutter. The proposed coherent detector exploits the information in all the range cells and pulses and performs the clutter reduction and thresholding simultaneously. The design is performed for uncorrelated, partially correlated and fully correlated clutter among range cells. The performance analysis indicates the superiority of the designed methods over the classical ones, in fully correlated and partially correlated situations. In addition, by design of detectors for multiple targets and making corrections to the conventional methods, the target-masking problem of the classical detectors is alleviated.

The main aim of using radars in industrial safety system is to detect the presence of target accurately. The conventional methods of radar target detection algorithm such as the Cell averaging constant false alarm rate method (CA-CFAR), Greatest of constant false alarm method (GO-CFAR) and the Smallest of constant false alarm rate method (SO-CFAR) has their own disadvantage when it comes to precise target detection which is a key factor for a safety system. This thesis investigates the above mentioned conventional CFAR algorithms for its pros and cons in target detection and proposes a new and improved method called Cell Evaluation target detection method. The proposed method is shown to mitigate the limitations present and the assumptions made in the conventional target detection method. Further more angular estimation is performed to determine the precise location of the target and the artifacts due to the angular estimation is eliminated by aggregating the detected points from multiple radar modules by linear translation. This gives a better visualization of the target. / Radarteknik kan användas inom maskinsäkerhet (MS) för att detektera skyddsvärda objekt, typiskt människor i arbete nära maskiner. Konventionella metoder för detektering med given frekvens falsk alarm (eng. Constant False Alarm Rate (CFAR)) som baseras på medelvärden, har dock betydande brister. Främst beträffande precision och tillförlitlighet, vilket är centralt för MS. Exempel som studerats i detta examensarbete är “Cell-averaging CFAR” (CA-CFAR), “Greatest of CFAR” (GO-CFAR) samt “Smallest of CFAR” (SO-CFAR). Med målet att förbättra detektionen föreslås även en ny CFAR-metod, vilken benämns ”Cell Evaluation target detection”. I detta arbete visas denna metod undertrycka begränsningar med konventionella tekniker. Den undviker även en del antaganden som inte alltid stämmer i praktiken. Studien inkluderar även skattning av riktning. Det visas hur visualisering av skyddsobjekt kan förbättras, genom att felaktigheter elimineras efter sammanläggning av detektioner från flera radarmoduler efter koordinattransformation.

This thesis assesses UK banking liquidity regulation and supervision and the Basel liquidity requirements, and models banks' liquidity risk. The study reveals that the FSA's risk-assessment framework before 2008 was too general without specifically considering banks' liquidity risk (as well as its failures on Northern Rock). The study also lists the limitations of the FSA's banking liquidity regimes before 2008. The thesis reviews whether the FSA's new liquidity regimes after 2008 would have coped with UK banks' liquidity risks if they have been applied properly. The fundamental changes in the FSA's liquidity supervision reflect three considerations. First, it introduces a systemic control requirement by measuring individual fifirm's liquidity risk with a market-wide stress or combination of idiosyncratic and market-wide stresses. Second, it emphasizes the monitoring of business model risks and the capability of senior managers. Third, it allows both internal and external managers to access more information by increasing the liquidity reporting frequencies. The thesis also comments on the Basel Liquidity Principles of 2008 and the two Liquidity Standards. The Principles of 2008 represents a substantial revision of the Principles of 2000 and reflect the lessons of the fifinancial market turmoil since 2007. The study argues that the implementation of the sound principles by banks and supervisors should be fexible, but also need to be consistent to make sure they understand banks' liquidity positions quite well. The study also explains the composition of the Basel liquidity ratios as well as the side effect of Basel liquidity standards; for example, it will reshape interbank deposit markets and bond markets as a result of the increase in demand for `liquid assets' and `stable funding'. This thesis uses quantitative balance sheet liquidity analysis, based upon modified versions of the BCBS (2010b) and Moody's (2001) models, to estimate eight UK banks' short and long-term liquidity positions from 2005 to 2010 respectively. The study shows that only Barclays Bank remained liquid on a short-term basis throughout the sample period (2005-2010); while the HSBC Bank also proved liquid on a short-term basis, although not in 2008 and 2010. On a long-term basis, RBS has remained liquid since 2008 after receiving government support; while Santander UK also proved liquid, except in 2009. The other banks,especially Natwest, are shown to have faced challenging conditions, on both a short-term and long-term basis, over the sample period. This thesis also uses the Exposure-Based Cash-Flow-at-Risk (CFaR) model to forecast UK banks' liquidity risk. Based on annual data over the period 1997 to 2010, the study predicts that by the end of 2011, the (102) UK banks' average CFaR at the 95% confidence level will be -£5.76 billion, Barclays Bank's (Barclays') CFaR will be -£0.34 billion, the Royal Bank of Scotland's (RBS's) CFaR will be -£40.29 billion, HSBC Bank's (HSBC's) CFaR will be £0.67 billion, Lloyds TSB Bank's (Lloyds TSB's) CFaR will be -£4.90 billion, National Westminister Bank's (Natwest's) CFaR will be -£10.38 billion, and Nationwide Building Society's (Nationwide's) CFaR will be -£0.72 billion. Moreover, it is clear that Lloyds TSB and Natwest are associated with the largest risk, according to the biggest percentage difference between downside cash flow and expected cash flow (3600% and 816% respectively). Since I summarize a bank's liquidity risk exposure in a single number (CFaR), which is the maximum shortfall given the targeted probability level, it can be directly compared to the bank's risk tolerance and used to guide corporate risk management decisions. Finally, this thesis estimates the long-term United Kingdom economic impact of the Basel III capital and liquidity requirements. Using quarterly data over the period 1997:q1 to 2010:q2, the study employs a non-linear-in-factor probit model to show increases in bank capital and liquidity would reduce the probability of a bank crisis significantly. The study estimates the long-run cost of the Basel III requirements with a Vector Error Correction Model (VECM), which shows holding higher capital and liquidity would reduce output by a small amount but increase bank profitability in the long run. The maximum temporary net benefit and permanent net benefit is shown to be 1.284% and 35.484% of pre-crisis GDP respectively when the tangible common equity ratio stays at 10%. Assuming all UK banks also meet the Basel III long-term liquidity requirements, the temporary net benefit and permanent net benefit will be 0.347% and 14.318% of pre-crisis GDP respectively. Therefore, the results suggest that, in terms of the impact on output, there is considerable room to further tighten capital and liquidity requirements, while still providing positive effects for the United Kingdom economy.

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.

In this work, the interference sensitivity of an FMCW radar has been evaluated by studying the impact on a simulated detection chain. A commercially available FMCW radar was first characterized and its properties then laid the foundation for a simulation model implemented in Matlab. Different interference methods have been studied and a selection was made based on the results of previous research. One method aims to inject a sufficiently large amount of energy in the form of pulsed noise into the receiver. The second method aims to deceive the radar into seeing targets that do not actually exist by repeating the transmitted signal and thus giving the radar a false picture of its surroundings. The results show that if it is possible to synchronize with the transmitted signal then repeater jamming can be effective in misleading the radar. In one scenario the false target even succeeded in hiding the real target by exploiting the Cell-Averaging CFAR detection algorithm. The results suggests that without some smart countermeasures the radar has no way of distinguishing a coherent repeater signal, but just how successful the repeater is in creating a deceptive environment is highly dependent on the detection algorithm used. Pulsed noise also managed to disrupt the radar and with a sufficiently high pulse repetition frequency the detector could not find any targets despite a simulated object in front of the radar. On the other hand, a rather significant effective radiated power level was required for the pulse train to achieve any meaningful effect on the radar, which may be due to an undersampled signal in the simulation. It is therefore difficult based on this work to draw any conclusions about how suitable pulsed noise is in a non-simulated interference context and what parameter values to use.

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.