With the recent advances in sensor technology and resulting sensor resolution, conven- tional point-based target tracking algorithms are becoming insufficient, particularly in application domains such as autonomous vehicles, visual tracking and surveillance using high resolution sensors. This has renewed the interest in extended target (ET) tracking, which aims to track not only the centroid of a target, but also its shape and size over time. This thesis addresses three of the most challenging problems in the domain of ET tracking applications. The first investigated challenge is the need for an accu- rate shape and centre estimate for the ET object with an arbitrary unknown star- convex shape in presence of non-Gaussian noise. The proposed method is based on a Student’s-t process regression algorithm which is defined in a recursive framework to be applicable for on-line tracking problems. The second problem tries to relax any constraints, including the star-convex con- straint, that is imposed on the shape of the ET object during the course of estimation by defining a novel Random Polytopes shape descriptor. Also, the proposed solution introduces a method to mitigate the troubles caused as a result of self-occlusion in ET tracking applications which its ignorance may cause catastrophic divergence in the ET state estimate.Finally, a framework for tracking multiple ET objects in the presence of clutter and occlusion is studied and a solution is proposed. The proposed method can estimate the centre and shape of the ET objects in a realistically scenario with the self- and mutual-occlusion challenges being considered. The proposed approach defines a time varying state-dependent probability of detection for each ET that enables the track to prolong even under adverse conditions caused due to mutual-occlusion. Plus, the proposed algorithm uses set-membership uncertainty models to bound the association and target shape uncertainties of occluded ET, to obtain more accurate state and shape estimates of an ET object. The performance of the proposed methods are quantified on realistically simulated scenarios with self- and mutual-occlusions and their results are compared against existing state-of-the-art methods for ET tracking applications. / Thesis / Doctor of Philosophy (PhD)

<p>Basic radar systems use electromagnetic wave reflections from targets to determine the motion characteristics of these targets. Synthetic Aperture Radar (SAR) systems use the reflections to produce target images as well. SAR is an imaging radar system that produces high resolution images of a scene or target by using radar motion to synthesize the antenna aperture. A SAR model to handle extended targets and point targets in faster time is presented, as are some simulated results. This thesis explains synthetic aperture concepts, the model used and a simulation of a SAR system. It runs through modelling point targets as well as extended targets by using the resolution cells of the radar, creating the raw signal data from the target information and then the signal processing that converts the raw data to a SAR image. The simulation was done for better understanding of synthetic aperture parameters and it was done in C++ programming language for improved processing speed. In comparison to previous simulations obtained from literature review, there is an increase in speed of more than 2.5 times as the number of targets increases, producing higher resolution images in less time. A model to handle extended targets was presented while also showing the imperfections due to the model assumptions. These assumptions are then explained as the best option in the absence of extra geographic information on the target scene.</p> / Master of Applied Science (MASc)

The level of automation in our society is ever increasing. Technologies like self-driving cars, virtual reality, and fully autonomous robots, which all were unimaginable a few decades ago, are realizable today, and will become standard consumer products in the future. These technologies depend upon autonomous localization and situation awareness where careful processing of sensory data is required. To increase efficiency, robustness and reliability, appropriate models for these data are needed.In this thesis, such models are analyzed within three different application areas, namely (1) magnetic localization, (2) extended target tracking, and (3) autonomous learning from raw pixel information. Magnetic localization is based on one or more magnetometers measuring the induced magnetic field from magnetic objects. In this thesis we present a model for determining the position and the orientation of small magnets with an accuracy of a few millimeters. This enables three-dimensional interaction with computer programs that cannot be handled with other localization techniques. Further, an additional model is proposed for detecting wrong-way drivers on highways based on sensor data from magnetometers deployed in the vicinity of traffic lanes. Models for mapping complex magnetic environments are also analyzed. Such magnetic maps can be used for indoor localization where other systems, such as GPS, do not work. In the second application area, models for tracking objects from laser range sensor data are analyzed. The target shape is modeled with a Gaussian process and is estimated jointly with target position and orientation. The resulting algorithm is capable of tracking various objects with different shapes within the same surveillance region. In the third application area, autonomous learning based on high-dimensional sensor data is considered. In this thesis, we consider one instance of this challenge, the so-called pixels to torques problem, where an agent must learn a closed-loop control policy from pixel information only. To solve this problem, high-dimensional time series are described using a low-dimensional dynamical model. Techniques from machine learning together with standard tools from control theory are used to autonomously design a controller for the system without any prior knowledge. System models used in the applications above are often provided in continuous time. However, a major part of the applied theory is developed for discrete-time systems. Discretization of continuous-time models is hence fundamental. Therefore, this thesis ends with a method for performing such discretization using Lyapunov equations together with analytical solutions, enabling efficient implementation in software. / Hur kan man få en dator att följa pucken i bordshockey för att sammanställa match-statistik, en pensel att måla virtuella vattenfärger, en skalpell för att digitalisera patologi, eller ett multi-verktyg för att skulptera i 3D?  Detta är fyra applikationer som bygger på den patentsökta algoritm som utvecklats i avhandlingen. Metoden bygger på att man gömmer en liten magnet i verktyget, och placerar ut ett antal tre-axliga magnetometrar - av samma slag som vi har i våra smarta telefoner - i ett nätverk kring vår arbetsyta. Magnetens magnetfält ger upphov till en unik signatur i sensorerna som gör att man kan beräkna magnetens position i tre frihetsgrader, samt två av dess vinklar. Avhandlingen tar fram ett komplett ramverk för dessa beräkningar och tillhörande analys. En annan tillämpning som studerats baserat på denna princip är detektion och klassificering av fordon. I ett samarbete med Luleå tekniska högskola med projektpartners har en algoritm tagits fram för att klassificera i vilken riktning fordonen passerar enbart med hjälp av mätningar från en två-axlig magnetometer. Tester utanför Luleå visar på i princip 100% korrekt klassificering. Att se ett fordon som en struktur av magnetiska dipoler i stället för en enda stor, är ett exempel på ett så kallat utsträckt mål. I klassisk teori för att följa flygplan, båtar mm, beskrivs målen som en punkt, men många av dagens allt noggrannare sensorer genererar flera mätningar från samma mål. Genom att ge målen en geometrisk utsträckning eller andra attribut (som dipols-strukturer) kan man inte enbart förbättra målföljnings-algoritmerna och använda sensordata effektivare, utan också klassificera målen effektivare. I avhandlingen föreslås en modell som beskriver den geometriska formen på ett mer flexibelt sätt och med en högre detaljnivå än tidigare modeller i litteraturen. En helt annan tillämpning som studerats är att använda maskininlärning för att lära en dator att styra en plan pendel till önskad position enbart genom att analysera pixlarna i video-bilder. Metodiken går ut på att låta datorn få studera mängder av bilder på en pendel, i det här fallet 1000-tals, för att förstå dynamiken av hur en känd styrsignal påverkar pendeln, för att sedan kunna agera autonomt när inlärningsfasen är klar. Tekniken skulle i förlängningen kunna användas för att utveckla autonoma robotar. / <p>In the electronic version figure 2.2a is corrected.</p> / COOPLOC

There is growing interest in the application of high-resolution radars in autonomous vehicles due to their affordability and high angular resolution. However, the azimuth ambiguity caused by the large physical distance between radar antennas relative to the signal wavelength is a challenge for its application. The problem of multiple extended target tracking using high-resolution radar measurements with azimuth ambiguity is considered. A novel pseudo-3D assignment (P3DA) method based on the pseudo measurement set (PMS) is proposed to resolve the azimuth ambiguity. This method can resolve mono (single) and split (duplicated) azimuth ambiguities common in extended target tracking. The Lagrangian relaxation based on a flexible search (LR-FS) algorithm is proposed to solve the P3DA-PMS problem efficiently. Simulation and experiment results show that the proposed algorithm outperforms conventional methods that do not address the azimuth ambiguity of extended target tracking. Since data association with only one data frame will lose information about target evolution and cannot change an association later based on subsequent measurements, a novel two-step multiframe assignment method is proposed to resolve split and azimuth ambiguity separately. In the first step, the split ambiguity is resolved by the PMS-to-PMS association, resulting in a merged PMS (MPMS). In the second step, the azimuth ambiguity is resolved by the Track-to-MPMS association. Numerical results show that the proposed method performs better than the P3DA-PMS-based method. The vehicles tracking with high-resolution radars need to provide information about their orientation and shape to achieve lidar-like performance. Due to self-occlusion, the L-shape model is frequently utilized to depict the structure of a typical vehicle. Since the measurement accuracy of high-resolution radars is not as high as that of lidars, radar measurement noise cannot be ignored. Moreover, as a side effect of using large wavelengths, multiple measurements may be produced per time step due to multipath effects. As a result, more outliers and inliers can be generated in high-resolution radar measurements. A novel lognormal likelihood-aided L-shape model is proposed to approximate the distribution of high-resolution radar measurements of vehicles. Numerical results evaluated on simulation data and the KITTI dataset show that the proposed algorithm achieves smaller orientation and position errors and larger generalized intersection over union (GIoU) compared to existing L-shape fitting algorithms for lidar measurements. / Dissertation / Doctor of Philosophy (PhD)

High resolution sensors such as automotive radar and LiDAR have become prevalent in target tracking applications in recent times. Data from such sensors demands extended target tracking in which, the shape of the target is to be estimated along with the kinematics. Several applications benefit from extended target tracking, for example, autonomous vehicles and robotics. This thesis proposes a different approach to extended target tracking compared to existing literature. Instead of a single shape descriptor to describe the entire target shape, different parts of the extended target are assumed to be distinct targets constrained by the target rigid body shape. This formulation is able to handle issues such as self-occlusion and clutter which, are not addressed sufficiently in literature. Firstly, a framework for extended target tracking is developed based on the formulation proposed. Using 2D convex hull as a shape descriptor, an algorithm to track 2D convex polytope shaped targets is developed. Further, the point target Probabilistic Multiple Hypotheses Tracker (PMHT) is modified to derive an extended target PMHT (ET-PMHT) equations to track 3D convex polytope shapes, using a Delaunay triangulation to describe the shape. Finally, the approach is extended to handle target maneuvers, as well as, clutter and measurements from the interior of the target. In all three cases, the issue of self-occlusion is considered and the algorithms are still able to effectively capture the target shape. Since the true target center may not be observable, the shape descriptor abandons the use of target center in the state, and the shape is described by its boundary alone. The shape descriptors also support addition and deletion of faces, which is useful for handling newly visible parts of the target and clutter, respectively. The algorithms proposed have been compared with the existing literature for various scenarios, and it is seen that the proposed algorithms outperform, especially in the presence of self-occlusion. / Thesis / Candidate in Philosophy

In high range-resolution (HRR) radar systems, the returns from a single target may fall in multiple adjacent range bins which individually vary in amplitude. A target following this representation is commonly referred to as an extended target and results in more information about the target. However, extracting this information from the radar returns is challenging due to several complexities. These complexities include the single dimensional nature of the radar measurements, complexities associated with the scattering of electromagnetic waves, and complex environments in which radar systems are required to operate. There are several applications of HRR radar systems which extract target information with varying levels of success. A commonly used application is that of imaging referred to as synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging. These techniques combine multiple single dimension measurements in order to obtain a single two dimensional image. These techniques rely on rotational motion between the target and the radar occurring during the collection of the single dimension measurements. In the case of ISAR, the radar is stationary while motion is induced by the target. There are several difficulties associated with the unknown motion of the target when standard Doppler processing techniques are used to synthesise ISAR images. In this dissertation, a non-standard Dop-pler approach, based on Bayesian inference techniques, was considered to address the difficulties. The target and observations were modelled with a non-linear state space model. Several different Bayesian techniques were implemented to infer the hidden states of the model, which coincide with the unknown characteristics of the target. A simulation platform was designed in order to analyse the performance of the implemented techniques. The implemented techniques were capable of successfully tracking a randomly generated target in a controlled environment. The influence of varying several parameters, related to the characteristics of the target and the implemented techniques, was explored. Finally, a comparison was made between standard Doppler processing and the Bayesian methods proposed. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Electrical, Electronic and Computer Engineering / unrestricted

Maritime target tracking from an airborne radar faces many issues due to the features of theenvironment, the targets to be tracked and the movement of the radar platform. Therefore, aunique tracking algorithm is not always able to reach the best possible performance for everyencountered situation. It needs to self-adapt to the environment and to the targets which areobserved in order to always be as ecient as possible. Adaptability is thus a key issue of radartracking.Several implementations of the mathematical Bayesian estimation theory, commonly called lters,have been used in the literature in order to estimate as precisely as possible targets trajectory.Depending on the situations and the assumptions that are considered, some of themare expected to perform better. This thesis suggests to look deeper into the tracking techniquesthat can be found in the literature and compare them in order to dene more precisely the advantagesof each of them over the others. This should enable to wisely choose the method thatis most likely to provide the best performance for a given situation. In particular, the nonlinearconversion between the Cartesian coordinates with which the state vector is dened and thespherical coordinates used for the measurements is investigated. A measure of nonlinearity isintroduced, studied and used to compare the extended Kalman lter and the particle lter.The size of the detected maritime targets is a special feature that makes it possible to draw amaneuverability-based classication which enables to adapt the tracking technique to be used.Joint tracking and classication (JTC) has already been described in the literature with a specicmeasurement model. This thesis makes this model more realistic using a random distribution ofthe reection point on the target's shape. The tracking method is modied to take into accountthis new measurement model and some simulations are run.This modied JTC algorithm proves to be more ecient than the JTC structure presented inthe literature. Eventually, this thesis shows that nonlinearity is a paramount issue that needsto be considered to implement an ecient self-adapatable radar tracking algorithm, this beingespecially true for extended targets. / Maritim malfoljning fran en luftburen radar star infor manga problem pa grund av miljons karaktar, de mal som ska sparas och radarplattformens rorelse. Darfor kan en unik sparningsalgoritminte na basta mojliga prestanda for varje situation som uppstar. Den maste anpassa sig sjalvtill miljon och till de mal som overvakas for att bli sa eektiv som mojligt. Anpassningsformagaar alltsa en viktig fraga inom radarsparning.Flera implementeringar av den matematiska Bayesianska berakningsteorin, vanligtvis kalladelter, har anvants i litteraturen for att forutsaga malbanor sa exakt som mojligt. Beroendepa situationer och antaganden som beaktas forvantas vissa av dem bli battre. Denna avhandlingforeslar att noggrant undersoka sparningsteknikerna som kan hittas i litteraturen ochjamfora dem for att mer precist deniera fordelarna av var och en framfor de andra. Det skulleunderlatta ett klokt val av metoden som mest sannolikt ger basta prestanda for varje given situation.Sarskilt undersoks den icke-linjara omvandlingen mellan kartesiska koordinatsystemet,som denierar tillstandsvektorn, och sfariska koordinater som anvands for matningarna. Ettmatt pa icke-linjaritet presenteras, studeras och anvands for att jamfora ett utokat Kalmanltermed partikelltret.Storleken pa de detekterade maritima malen ar en speciell egenskap som gor det mojligt attgora en klassicering baserad pa manovrerbarhet som hjalper till att anpassa sparningsteknikensom ska anvandas. Simultan foljning och klassiering, "joint tracking and classication" (JTC)pa engelska, har redan beskrivits i litteraturen med en specik matmodell. Denna avhandlinggor modellen mer realistisk med hjalp av en slumpmassig fordelning av reektionspunkten pamalets form. Sparningsmetoden ar modierad for att beakta denna nya matmodell och nagrasimuleringar utfors.Denna modierade JTC-struktur visar sig mer eektiv an JTC-strukturen som presenteras ilitteraturen. Slutligen visar denna avhandling att icke-linjaritet ar en viktig fraga som mastebeaktas for att erhalla en eektiv radarsparningsalgoritm som kan anpassa sig sjalv. Dettagaller sarskilt for utstrackta mal.