Target Tracking in UWB Multistatic Radars

Detection, localization and tracking of non-collaborative objects moving inside an area is of great interest to many surveillance applications. An ultra- wideband (UWB) multistatic radar is considered as a good infrastructure for such anti-intruder systems, due to the high range resolution provided by the UWB impulse-radio and the spatial diversity achieved with a multistatic configuration. Detection of targets, which are typically human beings, is a challenging task due to reflections from unwanted objects in the area, shadowing, antenna cross-talks, low transmit power, and the blind zones arised from intrinsic peculiarities of UWB multistatic radars. Hence, we propose more effective detection, localization, as well as clutter removal techniques for these systems. However, the majority of the thesis effort is devoted to the tracking phase, which is an essential part for improving the localization accuracy, predicting the target position and filling out the missed detections. Since UWB radars are not linear Gaussian systems, the widely used tracking filters, such as the Kalman filter, are not expected to provide a satisfactory performance. Thus, we propose the Bayesian filter as an appropriate candidate for UWB radars. In particular, we develop tracking algorithms based on particle filtering, which is the most common approximation of Bayesian filtering, for both single and multiple target scenarios. Also, we propose some effective detection and tracking algorithms based on image processing tools. We evaluate the performance of our proposed approaches by numerical simulations. Moreover, we provide experimental results by channel measurements for tracking a person walking in an indoor area, with the presence of a significant clutter. We discuss the existing practical issues and address them by proposing more robust algorithms

A Target Detection and Tracking Method for Multiple Radar Systems

Multiple radar systems represent an attractive option for target tracking because they can significantly enlarge the area coverage and improve both the probability of trajectory detection and the localization accuracy. The presence of multiple extended targets or weak targets is a challenge for multiple radar systems. Moreover, their performance may be severely deteriorated by regions characterized by a high clutter density. In this article, an algorithm for detection and tracking of multiple targets, extended or weak, based on measurements provided by multiple radars in an environment with heavily cluttered regions, is proposed. The proposed method features three stages. In the first stage, past measurements are exploited to build a spatiotemporal clutter map in each radar; a weight is then assigned to each measurement to assess its significance. In the second stage, a track-before-detect algorithm, based on a weighted 3-D Hough transform, is applied to obtain target tracklets. In the third stage, a low-complexity tracklet association method, exploiting a lion reproduction model, is applied to associate tracklets of the same target. Three experiments are presented to illustrate the effectiveness of the proposed approach. The first experiment is based on synthetic data, the second one is based on actual data from a radar network with two homogeneous air surveillance radars, and the third one is based on actual data from a radar network with four different marine surveillance radars. The results reveal that the proposed method can outperform competing approaches

Adaptivne tehnike u sistemima za praćenje pokretnih ciljeva

The most critical and challenging task in the algorithms of multiple target tracking in the presence of false observations is the correct assignment of measurements to tracks the so-called data association task. That is the core component of all target tracking systems. Regardless of the particular method used, the efficiency of any target tracking system depends on the understanding of the background or clutters “certain parameters that describe the environment”, and the parameters that describe the detection properties of the objects. The character of these parameters is statistical, and not only they are usually unknown in practice, and they are also time-invariant. Moreover, the statistics that describe the environment are spatially dependent. The most important among these are the probability of target detection and the density of false alarm. These parameters are usually unknown as well as variable, and even though there are many algorithms for estimation of these parameters, the usefulness of these estimates is quite limited. Successful implementation of any target tracking system depends on the precise knowledge of the statistical quantities such as the probability of target detection and density of false alarm. This thesis proposes one approach for estimating the time-varying probability of detection of each tracked object individually and the density of false alarm in the immediate vicinity of the current position of an object. The proposed approach is based on the generalized maximum likelihood (GML) approach, assuming the tracking of a single target. To reduce the numerical complexity, the proposed technique reduces the number of the formulated hypotheses based on the calculation of their likelihood. The obtained estimators have a very simple form, but as shown, this simplicity comes with a significant bias, which is present in most similar techniques, and relatively large variance of the estimators. The research presented in the thesis coped with these two problems and resulted in an algorithm with significantly reduced bias and error variances. This thesis also analyses the influences of the unknown measurement noise covariance on an estimation of the probability of target detection and density of false alarm and proposes an improvement in the case of noise covariance matrix uncertainty. The thesis presents the applicability and constraints of the proposed solution. The results are illustrated by simulations and present a fair analysis of the proposed algorithm. Finally, the ideas for further improvement of the method are given.Vrlo izazovan i kritičan zadatak u algoritmima praćenja pokretnih ciljeva uz prisustvo lažnih alarma jeste pravilna asocijacija pristiglih opservacija takozvanim tragovima. To je osnovni i verovatno najvažniji deo svakog sistema za praćenje više pokretnih ciljeva. Bez obzira na to koja se metoda pridruživanja podataka koristi, efikasnost bilo kog takvog sistema itekako zavisi od poznavanja statističkih parametara koji karakterišu okruženje i parametara koji karakterišu ponašanje praćenih objekata, u smislu njihove detektibilnosti. Nažalost, u praksi, ovi podaci nikada nisu poznati, i gore od toga, vremenski su promenljivi, a parametri prisustva takozvanih lažnih alarma sui prostorno zavisni. Najvažniji od tih parametara su verovatnoća detekcije cilja i gustina lažnog alarma. Sama činjenica da postoje različiti pristupi za estimaciju ovih parametara govori, kako o njihovom značaju, tako i o kompleksnosti procedura za njihovu estimaciju. Lako se pokazuje da uspešna primena bilo kog algoritma za praćenje itekako zavisi od kvaliteta i nivoa neodređenosti u poznavanju ovih statističkih parametara kakvi su verovatnoća detekcije cilja i gustina lažnih alarma. U ovoj doktorskoj disertaciji je predložen novi pristup za procenu vremenski promenljive verovatnoće detekcije ciljeva kao i gustine lažnog alarma ali u naposrednom okruženju objekta koji se prati. Predloženi pristup je zasnovan na dobro poznatom metodu maksimalne verodostojnosti, pri čemu je pretpostavljeno da se u prostoru od interesa nalazi samo jedan pokretni objekat. Kako bi se minimizovala numerička složenost predloženog algoritma, minimizovan je i broj hipoteza za koje se računaju odgovarajuće verodostojnosti. Dobijeni estimatori imaju vrlo jednostavnu formu. Međutim, kao što se i očekivalo, statističke osobine dobijenih estimatora su vrlo slične onim estimatorima koji su dostupni u literature. Naime, pokazalo se da izvedeni estimatori imaju značajan pomeraj u proceni kao i nedopustivo veliku varijansu. Zato je posebna pažnja u disertaciji posvećena postupcima za eliminaciju pomeraja i smenjenje varijanse. Pokazano je da se uz minimalno povećanje numeričke složenosti algoritma značajno popravljaju njegove statističke performanse. U ovoj doktorskoj disertaciji je takođe razmatran uticaj nepoznavanja statistika mernog šuma na kvalitet estimatora verovatnoće detekcije ciljeva i gustine lažnih alarma. Pokazano je da ova neodređenost može značajno da degradira kvalitet celokupnog postupka, tako da je predložena dodatna adaptacija koja u kontekstu primenjenog Kalmanovog filtra estimira kovarijacionu matricu mernog šuma. Konačno, u tezi su ilustrovani primenjivost kao i ograničenja predloženog rešenja. Svi zaključci i pretpostavke su potkrepljeni iscrpnim simulacijama koje su kroz Monte Carlo simulacije sa više od 20.000 ponavljanja uspevale da potisnu uticaj nesavršenosti generatora slučajnih brojeva. Na kraju teze su date i ideje za dalje unapređenje predložene metode

Gait Analysis and Recognition for Automated Visual Surveillance

Human motion analysis has received a great attention from researchers in the last decade due to its potential use in different applications such as automated visual surveillance. This field of research focuses on the perception and recognition of human activities, including people identification. We explore a new approach for walking pedestrian detection in an unconstrained outdoor environment. The proposed algorithm is based on gait motion as the rhythm of the footprint pattern of walking people is considered the stable and characteristic feature for the classification of moving objects. The novelty of our approach is motivated by the latest research for people identification using gait. The experimental results confirmed the robustness of our method to discriminate between single walking subject, groups of people and vehicles with a successful detection rate of 100%. Furthermore, the results revealed the potential of our method to extend visual surveillance systems to recognize walking people. Furthermore, we propose a new approach to extract human joints (vertex positions) using a model-based method. The spatial templates describing the human gait motion are produced via gait analysis performed on data collected from manual labeling. The Elliptic Fourier Descriptors are used to represent the motion models in a parametric form. The heel strike data is exploited to reduce the dimensionality of the parametric models. People walk normal to the viewing plane, as major gait information is available in a sagittal view. The ankle, knee and hip joints are successfully extracted with high accuracy for indoor and outdoor data. In this way, we have established a baseline analysis which can be deployed in recognition, marker-less analysis and other areas. The experimental results confirmed the robustness of the model-based approach to recognise walking subjects with a correct classification rate of 95% using purely the dynamic features derived from the joint motion. Therefore, this confirms the early psychological theories claiming that the discriminative features for motion perception and people recognition are embedded in gait kinematics. Furthermore, to quantify the intrusive nature of gait recognition we explore the effects of the different covariate factors on the performance of gait recognition. The covariate factors include footwear, clothing, carrying conditions and walking speed. As far as the author can determine, this is the first major study of its kind in this field to analyse the covariate factors using a model-based method

Exploring space situational awareness using neuromorphic event-based cameras

The orbits around earth are a limited natural resource and one that hosts a vast range of vital space-based systems that support international systems use by both commercial industries, civil organisations, and national defence. The availability of this space resource is rapidly depleting due to the ever-growing presence of space debris and rampant overcrowding, especially in the limited and highly desirable slots in geosynchronous orbit. The field of Space Situational Awareness encompasses tasks aimed at mitigating these hazards to on-orbit systems through the monitoring of satellite traffic. Essential to this task is the collection of accurate and timely observation data. This thesis explores the use of a novel sensor paradigm to optically collect and process sensor data to enhance and improve space situational awareness tasks. Solving this issue is critical to ensure that we can continue to utilise the space environment in a sustainable way. However, these tasks pose significant engineering challenges that involve the detection and characterisation of faint, highly distant, and high-speed targets. Recent advances in neuromorphic engineering have led to the availability of high-quality neuromorphic event-based cameras that provide a promising alternative to the conventional cameras used in space imaging. These cameras offer the potential to improve the capabilities of existing space tracking systems and have been shown to detect and track satellites or ‘Resident Space Objects’ at low data rates, high temporal resolutions, and in conditions typically unsuitable for conventional optical cameras. This thesis presents a thorough exploration of neuromorphic event-based cameras for space situational awareness tasks and establishes a rigorous foundation for event-based space imaging. The work conducted in this project demonstrates how to enable event-based space imaging systems that serve the goals of space situational awareness by providing accurate and timely information on the space domain. By developing and implementing event-based processing techniques, the asynchronous operation, high temporal resolution, and dynamic range of these novel sensors are leveraged to provide low latency target acquisition and rapid reaction to challenging satellite tracking scenarios. The algorithms and experiments developed in this thesis successfully study the properties and trade-offs of event-based space imaging and provide comparisons with traditional observing methods and conventional frame-based sensors. The outcomes of this thesis demonstrate the viability of event-based cameras for use in tracking and space imaging tasks and therefore contribute to the growing efforts of the international space situational awareness community and the development of the event-based technology in astronomy and space science applications

Mathematical Models and Monte-Carlo Algorithms for Improved Detection of Targets in the Commercial Maritime Domain

Commercial Vessel Traffic Monitoring Services (VTMSs) are widely used by port authorities and the military to improve the safety and efficiency of navigation, as well as to ensure the security of ports and marine life as a whole. Technology based on the Kalman Filtering framework is in widespread use in modern operational VTMS systems. At a research level, there has also been a significant interest in Particle Filters, which are widely researched but far less widely applied to deliver an operational advantage. The Monte-Carlo nature of Particle Filters places them as the ideal candidate for solving the highly non-linear, non-Gaussian problems encountered by modern VTMS systems. However, somewhat counter-intuitively, while Particle Filters are best suited to exploit such non-linear, non-Gaussian problems, they are most frequently used within a context that is mostly linear and Gaussian. The engineering challenge tackled by the PhD project reported in this thesis was to study and experiment with models that are well placed to capitalise on the abilities of Particle Filters and to develop solutions that make use of such models to deliver a direct operational advantage in real applications within the commercial maritime domain

Gait analysis and recognition for automated visual surveillance

Human motion analysis has received a great attention from researchers in the last decade due to its potential use in different applications such as automated visual surveillance. This field of research focuses on the perception and recognition of human activities, including people identification. We explore a new approach for walking pedestrian detection in an unconstrained outdoor environment. The proposed algorithm is based on gait motion as the rhythm of the footprint pattern of walking people is considered the stable and characteristic feature for the classification of moving objects. The novelty of our approach is motivated by the latest research for people identification using gait. The experimental results confirmed the robustness of our method to discriminate between single walking subject, groups of people and vehicles with a successful detection rate of 100%. Furthermore, the results revealed the potential of our method to extend visual surveillance systems to recognize walking people. Furthermore, we propose a new approach to extract human joints (vertex positions) using a model-based method. The spatial templates describing the human gait motion are produced via gait analysis performed on data collected from manual labeling. The Elliptic Fourier Descriptors are used to represent the motion models in a parametric form. The heel strike data is exploited to reduce the dimensionality of the parametric models. People walk normal to the viewing plane, as major gait information is available in a sagittal view. The ankle, knee and hip joints are successfully extracted with high accuracy for indoor and outdoor data. In this way, we have established a baseline analysis which can be deployed in recognition, marker-less analysis and other areas. The experimental results confirmed the robustness of the model-based approach to recognise walking subjects with a correct classification rate of 95% using purely the dynamic features derived from the joint motion. Therefore, this confirms the early psychological theories claiming that the discriminative features for motion perception and people recognition are embedded in gait kinematics. Furthermore, to quantify the intrusive nature of gait recognition we explore the effects of the different covariate factors on the performance of gait recognition. The covariate factors include footwear, clothing, carrying conditions and walking speed. As far as the author can determine, this is the first major study of its kind in this field to analyse the covariate factors using a model-based method.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Vision-Aided Autonomous Precision Weapon Terminal Guidance Using a Tightly-Coupled INS and Predictive Rendering Techniques

This thesis documents the development of the Vision-Aided Navigation using Statistical Predictive Rendering (VANSPR) algorithm which seeks to enhance the endgame navigation solution possible by inertial measurements alone. The eventual goal is a precision weapon that does not rely on GPS, functions autonomously, thrives in complex 3-D environments, and is impervious to jamming. The predictive rendering is performed by viewpoint manipulation of computer-generated of target objects. A navigation solution is determined by an Unscented Kalman Filter (UKF) which corrects positional errors by comparing camera images with a collection of statistically significant virtual images. Results indicate that the test algorithm is a viable method of aiding an inertial-only navigation system to achieve the precision necessary for most tactical strikes. On 14 flight test runs, the average positional error was 166 feet at endgame, compared with an inertial-only error of 411 feet