Bayesian Approaches to Tracking, Sensor Fusion and Intent Prediction

This thesis presents work on the development of model-based Bayesian approaches to object tracking and intent prediction. Successful navigation/positioning applications rely fundamentally on the choice of appropriate dynamic model and the design of effective tracking algorithms capable of maximising the use of the structure of the dynamic system and the information from sensors. While the tracking problem with frequent and accurate position data has been well studied, we push back the frontiers of current technology where an object can undergo fast manoeuvres and position fixes are limited. On the other hand, intent prediction techniques which extract higher level information such as the intended destination of a moving object can be designed, given the ability to perform successful tracking. Such techniques can play important roles in various application areas, including traffic monitoring, intelligent human computer interaction systems and autonomous route planning. In the first part of this thesis Bayesian tracking methods are designed based on a standard fix-rate setting in which the dynamic system is formulated into a Markovian state space form. We show that the combination of an intrinsic coordinate dynamic model and sensors in the object's body frame leads to novel state space models according to which efficient proposal kernels can be designed and implemented by the sequential Monte Carlo (SMC) methods. Also, sequential Markov chain Monte Carlo schemes are considered for the first time to tackle the sequential batch inference problems due to the presence of infrequent position data. Performance evaluation on both synthetic and real-world data shows that the proposed algorithms are superior to simpler particle filters, implying that they can be favourable alternatives to tracking problems with inertial sensors. The modelling assumption that leads to Markovian state space models can be restrictive for real-world systems as it stipulates that the state sequence has to be synchronised with the observations. In the second major part of this thesis we relax this assumption and work with a more natural class of models, termed variable rate models. We generalise the existing variable rate intrinsic model to incorporate acceleration, speed, distance and position data and introduce new variable rate particle filtering methods tailored to the derived model to accommodate multi-sensor multi-rate tracking scenarios. The proposed algorithms can achieve substantial improvements in terms of tracking accuracy and robustness over a bootstrap variable rate particle filter. Moreover, full Bayesian inference schemes for the learning of both the hidden state and system parameters are presented, with numerical results illustrating their effectiveness. The last part of the thesis is about designing efficient intent prediction algorithms within a Bayesian framework. A pseudo-observation based approach to the incorporation of destination knowledge is introduced, making the mathematics of the dynamical model and the observation process consistent with the Markov state process. Based on the new interpretation, two algorithms are proposed to sequentially estimate the probability of all possible endpoints. Whilst the synthetic maritime surveillance data demonstrate that the proposed methods can achieve comparable prediction performance with reduced computational cost in comparison to the existing bridging distribution based methods, the results on an extensive freehand pointing database, which contains 95 three-dimensional pointing trajectories, show that the new algorithms can outperform other state-of-the-art techniques. Some sensitivity tests are also performed, confirming the good robustness of the introduced methods against model mismatches

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

An Asynchronous Simulation Framework for Multi-User Interactive Collaboration: Application to Robot-Assisted Surgery

The field of surgery is continually evolving as there is always room for improvement in the post-operative health of the patient as well as the comfort of the Operating Room (OR) team. While the success of surgery is contingent upon the skills of the surgeon and the OR team, the use of specialized robots has shown to improve surgery-related outcomes in some cases. These outcomes are currently measured using a wide variety of metrics that include patient pain and recovery, surgeon’s comfort, duration of the operation and the cost of the procedure. There is a need for additional research to better understand the optimal criteria for benchmarking surgical performance. Presently, surgeons are trained to perform robot-assisted surgeries using interactive simulators. However, in the absence of well-defined performance standards, these simulators focus primarily on the simulation of the operative scene and not the complexities associated with multiple inputs to a real-world surgical procedure. Because interactive simulators are typically designed for specific robots that perform a small number of tasks controlled by a single user, they are inflexible in terms of their portability to different robots and the inclusion of multiple operators (e.g., nurses, medical assistants). Additionally, while most simulators provide high-quality visuals, simplification techniques are often employed to avoid stability issues for physics computation, contact dynamics and multi-manual interaction. This study addresses the limitations of existing simulators by outlining various specifications required to develop techniques that mimic real-world interactions and collaboration. Moreover, this study focuses on the inclusion of distributed control, shared task allocation and assistive feedback -- through machine learning, secondary and tertiary operators -- alongside the primary human operator

Wireless indoor localisation within the 5G internet of radio light

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonNumerous applications can be enhanced by accurate and efficient indoor localisation using wireless sensor networks, however trade-offs often exist between these two parameters. In this thesis, realworld and simulation data is used to examine the hybrid millimeter wave and Visible Light Communications (VLC) architecture of the 5G Internet of Radio Light (IoRL) Horizon 2020 project. Consequently, relevant localisation challenges within Visible Light Positioning (VLP) and asynchronous sampling networks are identified, and more accurate and efficient solutions are developed. Currently, VLP relies strongly on the assumed Lambertian properties of light sources. However, in practice, not all lights are Lambertian. To support the widespread deployment of VLC technology in numerous environments, measurements from non-Lambertian sources are analysed to provide new insights into the limitations of existing VLP techniques. Subsequently, a novel VLP calibration technique is proposed, and results indicate a 59% accuracy improvement against existing methods. This solution enables high accuracy centimetre level VLP to be achieved with non- Lambertian sources. Asynchronous sampling of range-based measurements is known to impact localisation performance negatively. Various Asynchronous Sampling Localisation Techniques (ASLT) exist to mitigate these effects. While effective at improving positioning performance, the exact suitability of such solutions is not evident due to their additional processes, subsequent complexity, and increased costs. As such, extensive simulations are conducted to study the effectiveness of ASLT under variable sampling latencies, sensor measurement noise, and target trajectories. Findings highlight the computational demand of existing ASLT and motivate the development of a novel solution. The proposed Kalman Extrapolated Least Squares (KELS) method achieves optimal localisation performance with a significant energy reduction of over 50% when compared to current leading ASLT. The work in this thesis demonstrates both the capability for high performance VLP from non- Lambertian sources as well as the potential for energy efficient localisation for sequentially sampled range measurements.Horizon 202

Optimising subdomain aspect ratios for parallel load balancing

In parallel adaptive Finite Element simulations the work load 011the individual processors can change frequently. To (re)distribute the load evenly over the processors a load balancing heuristic is needed. Common strategies try to minimise subdomain dependencies by minimising the number of cut edges in the partition. For many solvers this is the most influential factor. However for example, for certain preconditioned Conjugate Gradient solvers this cutsize can play only a minor role, but their convergence can be highly dependent on the subdomain shapes. Degenerated subdomain shapes can cause them to need significantly more iterations to converge. Common heuristics often fail to address these requirements. In this thesis a new strategy is introduced which directly addresses the problem of generating and conserving reasonably good subdomain shapes while balancing the load in a dynamically changing Finite Element Simulation. A new definition of Aspect Ratio is presented which assesses subdomain shapes. The common methodology of using adjacency information to select the best elements to be migrated is not considered since it is not necessarily related to the subdomain shapes. Instead, geometric data is used to formulate several cost functions to rate elements in terms of their suitability to be migrated. The well known diffusive and Generalised Dimension Exchange methods which calculate the necessary load flow are enhanced by weighting the subdomain edges in order to influence their impact on the resulting partition positively. The results of comprehensive tests are presented and demonstrate that the proposed methods are competitive with state-of-the-art load balancing tools

Gaussian belief propagation for real-time decentralised inference

For embodied agents to interact intelligently with their surroundings, they require perception systems that construct persistent 3D representations of their environments. These representations must be rich; capturing 3D geometry, semantics, physical properties, affordances and much more. Constructing the environment representation from sensory observations is done via Bayesian probabilistic inference and in practical systems, inference must take place within the power, compactness and simplicity constraints of real products. Efficient inference within these constraints however remains computationally challenging and current systems often require heavy computational resources while delivering a fraction of the desired capabilities. Decentralised algorithms based on local message passing with in-place processing and storage offer a promising solution to current inference bottlenecks. They are well suited to take advantage of recent rapid developments in distributed asynchronous processing hardware to achieve efficient, scalable and low-power performance. In this thesis, we argue for Gaussian belief propagation (GBP) as a strong algorithmic framework for distributed, generic and incremental probabilistic estimation. GBP operates by passing messages between the nodes on a factor graph and can converge with arbitrary asynchronous message schedules. We envisage the factor graph being the fundamental master environment representation, and GBP the flexible inference tool to compute local in-place probabilistic estimates. In large real-time systems, GBP will act as the `glue' between specialised modules, with attention based processing bringing about local convergence in the graph in a just-in-time manner. This thesis contains several technical and theoretical contributions in the application of GBP to practical real-time inference problems in vision and robotics. Additionally, we implement GBP on novel graph processor hardware and demonstrate breakthrough speeds for bundle adjustment problems. Lastly, we present a prototype system for incrementally creating hierarchical abstract scene graphs by combining neural networks and probabilistic inference via GBP.Open Acces

Parallel Lagrangian particle transport : application to respiratory system airways

This thesis is focused on particle transport in the context of high computing performance (HPC) in its widest range, from the numerical modeling to the physics involved, including its parallelization and post-process. The main goal is to obtain a general framework that enables understanding all the requirements and characteristics of particle transport using the Lagrangian frame of reference. Although the idea is to provide a suitable model for any engineering application that involves particle transport simulation, this thesis uses the respiratory system framework. This means that all the simulations are focused on this topic, including the benchmarks for testing, verifying and optimizing the results. Other applications, such as combustion, ocean residuals, or automotive, have also been simulated by other researchers using the same numerical model proposed here. However, they have not been included here in the interest of allowing the project to advance in a specific direction, and facilitate the structure and comprehension of this work. Human airways and respiratory system simulations are of special interest for medical purposes. Indeed, human airways can be significantly different in every individual. This complicates the study of drug delivery efficiency, deposition of polluted particles, etc., using classic in-vivo or in-vitro techniques. In other words, flow and deposition results may vary depending on the geometry of the patient and simulations allow customized studies using specific geometries. With the help of the new computational techniques, in the near future it may be possible to optimize nasal drugs delivery, surgery or other medical studies for each individual patient though a more personalized medicine. In summary, this thesis prioritizes numerical modeling, wide usability, performance, parallelization, and the study of the physics that affects particle transport. In addition, the simulation of the respiratory system should carry out interesting biological and medical results. However, the interpretation of these results will be only done from a pure numerical point of view.Aquesta tesi se centra en el transport de partícules dins el context de la computació d'alt rendiment (HPC), en el seu ventall més ampli; des del model numèric fins a la física involucrada, incloent-hi la part de paral·lelització del codi i de post-procés. L'objectiu principal és obtenir un esquema general que permeti entendre tant els requeriments com les característiques del transport de partícules fent servir el marc de referència Lagrangià. Encara que la idea sigui definir un model capaç¸ de simular qualsevol aplicació en el camp de l'enginyeria que involucri el transport de partícules, aquesta tesi utilitza el sistema respiratori com a temàtica de referència. Això significa que totes les simulacions estan emmarcades en aquest camp d'estudi, incloent-hi els tests de referència, verificacions i optimitzacions de resultats. L'estudi d'altres aplicacions, com ara la combustió, els residus oceànics, l'automoció o l'aeronàutica també han estat dutes a terme per altres investigadors utilitzant el mateix model numèric proposat aquí. Tot i així, aquests resultats no han estat inclosos en aquesta tesi per simplificar-la i avançar en una sola direcció; facilitant així l'estructura i millor comprensió d'aquest treball. Pel que fa al sistema respiratori humà i les seves simulacions, tenen especial interès per a propòsits mèdics. Particularment, la geometria dels conductes respiratoris pot variar de manera considerable en cada persona. Això complica l'estudi en aspectes com el subministrament de medicaments o la deposició de partícules contaminants, per exemple, utilitzant les tècniques clàssiques de laboratori (in-vivo o in-vitro). En altres paraules, tant el flux com la deposició poden canviar en funció de la geometria del pacient i aquí és on les simulacions permeten estudis adaptats a geometries concretes. Gràcies a les noves tècniques de computació, en un futur proper és probable que puguem optimitzar el subministrament de medicaments per via nasal, la cirurgia o altres estudis mèdics per a cada pacient mitjançant una medicina més personalitzada. En resum, aquesta tesi prioritza el model numèric, l'amplitud d'usos, el rendiment, la paral·lelització i l'estudi de la física que afecta directament a les partícules. A més, el fet de basar les nostres simulacions en el sistema respiratori dota aquesta tesi d'un interès biològic i mèdic pel que fa als resultats

LOCATE-US: Indoor Positioning for Mobile Devices Using Encoded Ultrasonic Signals, Inertial Sensors and Graph- Matching

Indoor positioning remains a challenge and, despite much research and development carried out in the last decade, there is still no standard as with the Global Navigation Satellite Systems (GNSS) outdoors. This paper presents an indoor positioning system called LOCATE-US with adjustable granularity for use with commercial mobile devices, such as smartphones or tablets. LOCATE-US is privacy-oriented and allows every device to compute its own position by fusing ultrasonic, inertial sensor measurements and map information. Ultrasonic Local Positioning Systems (ULPS) based on encoded signals are placed in critical zones that require an accuracy below a few decimeters to correct the accumulated drift errors of the inertial measurements. These systems are well suited to work at room level as walls confine acoustic waves inside. To avoid audible artifacts, the U-LPS emission is set at 41.67 kHz, and an ultrasonic acquisition module with reduced dimensions is attached to the mobile device through the USB port to capture signals. Processing in the mobile device involves an improved Time Differences of Arrival (TDOA) estimation that is fused with the measurements from an external inertial sensor to obtain real-time location and trajectory display at a 10 Hz rate. Graph-matching has also been included, considering available prior knowledge about the navigation scenario. This kind of device is an adequate platform for Location-Based Services (LBS), enabling applications such as augmented reality, guiding applications, or people monitoring and assistance. The system architecture can easily incorporate new sensors in the future, such as UWB, RFiD or others.Universidad de AlcaláJunta de Comunidades de Castilla-La ManchaAgencia Estatal de Investigació