RF signal sensing and source localisation systems using Software Defined Radios

Radio frequency (RF) source localisation is a critical technology in numerous location-based military and civilian applications. In this thesis, the problem of RF source localisation has been studied from the perspective of the system implementation for real-world applications. Commercial off-the-shelf Software Defined Radio (SDR) devices are used to demonstrate the practical RF source localisation systems. Compared to the conventional localisation systems, which rely on dedicated hardware, the SDR-based system is developed using general-purpose hardware and software-defined components, offering great flexibility and cost efficiency in system design and implementation. In this thesis, the theoretical results of source localisation are evaluated and put into practice. To be specific, the practical localisation systems using different measurement techniques, including received-signal-strength-indication (RSSI) measurements, time-difference-of-arrival (TDOA) measurements and joint TDOA and frequency-difference-of-arrival (FDOA) measurements, are demonstrated to localise the stationary RF signal sources using the SDRs. The RSSI-based localisation system is demonstrated in small indoor and outdoor areas with a range of several metres using the SDR-based transceivers. Furthermore, interests from the defence area motivated us to implement the time-based localisation systems. The TDOA-based source localisation system is implemented using multiple spatially distributed SDRs in a large outdoor area with the sensor-target range of several kilometres. Moreover, they are implemented in a fully passive way without prior knowledge of the signal emitter, so the solutions can be applied in the localisation of non-cooperative signal sources provided that emitters are distant. To further reduce the system cost, and more importantly, to deal with the situation when the deployment of multiple SDRs, due to geographical restrictions, is not feasible, a joint TDOA and FDOA-based localisation system is also demonstrated using only one stationary SDR and one mobile SDR. To improve the localisation accuracy, the methods that can reduce measurement error and obtain accurate location estimates are studied. Firstly, to obtain a better understanding of the measurement error, the error sources that affect the measurement accuracy are systematically analysed from three aspects: the hardware precision, the accuracy of signal processing methods, and the environmental impact. Furthermore, the approaches to reduce the measurement error are proposed and verified in the experiments. Secondly, during the process of the location estimation, the theoretical results on the pre-existing localisation algorithms which can achieve a good trade-off between the accuracy of location estimation and the computational cost are evaluated, including the weight least-squares (WLS)-based solution and the Extended Kalman Filter (EKF)-based solution. In order to use the pre-existing algorithms in the practical source localisation, the proper adjustments are implemented. Overall, the SDR-based platforms are able to achieve low-cost and universal localisation solutions in the real-world environment. The RSSI-based localisation system shows tens of centimetres of accuracy in a range of several metres, which provides a useful tool for the verification of the range-based localisation algorithms. The localisation accuracy of the TDOA-based localisation system and the joint TDOA and FDOA-based localisation system is several tens of metres in a range of several kilometres, which offers potential in the low-cost localisation solutions in the defence area

Diffusing wave spectroscopy applied to material analysis and process control

Diffusing Wave Spectroscopy (DWS) was studied as a method of laboratory analysis of submicron particles, and developed as a prospective in-line, industrial, process control sensor, capable of near real-time feedback. No sample pre-treatment was required and measurement was via a noninvasive, flexible, dip in probe. DWS relies on the concept of the diffusive migration of light, as opposed to the ballistic scatter model used in conventional dynamic light scattering. The specific requirements of the optoelectronic hardware, data analysis methods and light scattering model were studied experimentally and, where practical, theoretically resulting in a novel technique of analysis of particle suspensions and emulsions of volume fractions between 0.01 and 0.4. Operation at high concentrations made the technique oblivious to dust and contamination. A pure homodyne (autodyne) experimental arrangement described was resilient to environmental disturbances, unlike many other systems which utilise optical fibres or heterodyne operation. Pilot and subsequent prototype development led to a highly accurate method of size ranking, suitable for analysis of a wide range of suspensions and emulsions. The technique was shown to operate on real industrial samples with statistical variance as low as 0.3% with minimal software processing. Whilst the application studied was the analysis of TiO2 suspensions, a diverse range of materials including polystyrene beads, cell pastes and industrial cutting fluid emulsions were tested. Results suggest that, whilst all sizing should be comparative to suitable standards, concentration effects may be minimised and even completely modelled-out in many applications. Adhesion to the optical probe was initially a significant problem but was minimised after the evaluation and use of suitable non stick coating materials. Unexpected behaviour in the correlation in the region of short decay times led to consideration of the effects of rotational diffusion coefficient. The inherent instability of high density suspensions instigated high speed analysis techniques capable of monitoring suspensions that were undergoing rapid change as well as suggesting novel methods for the evaluation of the state of sample dispersion

Acoustic Source Localisation in constrained environments

Acoustic Source Localisation (ASL) is a problem with real-world applications across multiple domains, from smart assistants to acoustic detection and tracking. And yet, despite the level of attention in recent years, a technique for rapid and robust ASL remains elusive – not least in the constrained environments in which such techniques are most likely to be deployed. In this work, we seek to address some of these current limitations by presenting improvements to the ASL method for three commonly encountered constraints: the number and configuration of sensors; the limited signal sampling potentially available; and the nature and volume of training data required to accurately estimate Direction of Arrival (DOA) when deploying a particular supervised machine learning technique. In regard to the number and configuration of sensors, we find that accuracy can be maintained at state-of-the-art levels, Steered Response Power (SRP), while reducing computation sixfold, based on direct optimisation of well known ASL formulations. Moreover, we find that the circular microphone configuration is the least desirable as it yields the highest localisation error. In regard to signal sampling, we demonstrate that the computer vision inspired algorithm presented in this work, which extracts selected keypoints from the signal spectrogram, and uses them to select signal samples, outperforms an audio fingerprinting baseline while maintaining a compression ratio of 40:1. In regard to the training data employed in machine learning ASL techniques, we show that the use of music training data yields an improvement of 19% against a noise data baseline while maintaining accuracy using only 25% of the training data, while training with speech as opposed to noise improves DOA estimation by an average of 17%, outperforming the Generalised Cross-Correlation technique by 125% in scenarios in which the test and training acoustic environments are matched.Heriot-Watt University James Watt Scholarship (JSW) in the School of Engineering & Physical Sciences

The acoustics of concentric sources and receivers – human voice and hearing applications

One of the most common ways in which we experience environments acoustically is by listening to the reflections of our own voice in a space. By listening to our own voice we adjust its characteristics to suit the task and audience. This is of particular importance in critical voice tasks such as actors or singers on a stage with no additional electroacoustic or other amplification (e.g. in ear monitors, loudspeakers, etc.). Despite the usualness of this situation, there are very few acoustic measurements aimed to quantify it and even fewer that address the problem of having a source and receiver that are very closely located. The aim of this thesis is to introduce new measurement transducers and methods that quantify correctly this situation. This is achieved by analysing the characteristics of the human as a source, a receiver and their interaction in close proximity when placed in acoustical environments. The characteristics of the human voice and human ear are analysed in this thesis in a similar manner as a loudspeaker or microphone would be analysed. This provides the basis for further analysis by making them analogous to measurement transducers. These results are then used to explore the consequences of having a source and receiver very closely located using acoustic room simulation. Different techniques for processing data using directional transducers in real rooms are introduced. The majority of the data used in this thesis was obtained in rooms used for performance. The final chapters of this thesis include details of the design and construction of a concentric directional transducer, where an array of microphones and loudspeakers occupy the same structure. Finally, sample measurements with this transducer are presented

Large-Scale Textured 3D Scene Reconstruction

Die Erstellung dreidimensionaler Umgebungsmodelle ist eine fundamentale Aufgabe im Bereich des maschinellen Sehens. Rekonstruktionen sind für eine Reihe von Anwendungen von Nutzen, wie bei der Vermessung, dem Erhalt von Kulturgütern oder der Erstellung virtueller Welten in der Unterhaltungsindustrie. Im Bereich des automatischen Fahrens helfen sie bei der Bewältigung einer Vielzahl an Herausforderungen. Dazu gehören Lokalisierung, das Annotieren großer Datensätze oder die vollautomatische Erstellung von Simulationsszenarien. Die Herausforderung bei der 3D Rekonstruktion ist die gemeinsame Schätzung von Sensorposen und einem Umgebunsmodell. Redundante und potenziell fehlerbehaftete Messungen verschiedener Sensoren müssen in eine gemeinsame Repräsentation der Welt integriert werden, um ein metrisch und photometrisch korrektes Modell zu erhalten. Gleichzeitig muss die Methode effizient Ressourcen nutzen, um Laufzeiten zu erreichen, welche die praktische Nutzung ermöglichen. In dieser Arbeit stellen wir ein Verfahren zur Rekonstruktion vor, das fähig ist, photorealistische 3D Rekonstruktionen großer Areale zu erstellen, die sich über mehrere Kilometer erstrecken. Entfernungsmessungen aus Laserscannern und Stereokamerasystemen werden zusammen mit Hilfe eines volumetrischen Rekonstruktionsverfahrens fusioniert. Ringschlüsse werden erkannt und als zusätzliche Bedingungen eingebracht, um eine global konsistente Karte zu erhalten. Das resultierende Gitternetz wird aus Kamerabildern texturiert, wobei die einzelnen Beobachtungen mit ihrer Güte gewichtet werden. Für eine nahtlose Erscheinung werden die unbekannten Belichtungszeiten und Parameter des optischen Systems mitgeschätzt und die Bilder entsprechend korrigiert. Wir evaluieren unsere Methode auf synthetischen Daten, realen Sensordaten unseres Versuchsfahrzeugs und öffentlich verfügbaren Datensätzen. Wir zeigen qualitative Ergebnisse großer innerstädtischer Bereiche, sowie quantitative Auswertungen der Fahrzeugtrajektorie und der Rekonstruktionsqualität. Zuletzt präsentieren wir mehrere Anwendungen und zeigen somit den Nutzen unserer Methode für Anwendungen im Bereich des automatischen Fahrens

Advanced Wireless Localisation Methods Dealing with Incomplete Measurements

Positioning techniques have become an essential part of modern engineering, and the improvement in computing devices brings great potential for more advanced and complicated algorithms. This thesis first studies the existing radio signal based positioning techniques and then presents three developed methods in the sense of dealing with incomplete data. Firstly, on the basis of received signal strength (RSS) location fingerprinting techniques, the Kriging interpolation methods are applied to generate complete fingerprint databases of denser reference locations from sparse or incomplete data sets, as a solution of reducing the workload and cost of offline data collection. Secondly, with incomplete knowledge of shadowing correlation, a new approach of Bayesian inference on RSS based multiple target localisation is proposed taking advantage of the inverse Wishart conjugate prior. The MCMC method (Metropolis-within-Gibbs) and the maximum a posterior (MAP) / maximum likelihood (ML) method are then considered to produce target location estimates. Thirdly, a new information fusion approach is developed for the time difference of arrival (TDOF) and frequency difference of arrival (FDOA) based dual-satellite geolocation system, as a solution to the unknown time and frequency offsets. All proposed methods are studied and validated through simulations. Result analyses and future work directions are discussed

Applying single-molecule localisation microscopy to achieve virtual optical sectioning and study T-cell activation

Single-molecule localisation microscopy (SMLM) allows imaging of fluorescently-tagged proteins in live cells with a precision well below that of the diffraction limit. As a single-molecule technique, it has also introduced a new quantitative approach to fluorescence microscopy. In the Part A of this thesis, the design and building of three SMLM instruments, the implementation of a custom-developed image analysis package and the characterisation of the photo-physical properties of the photo-activable fluorescent protein used in this thesis (mEos), are discussed. Then, a new post-processing method for SMLM analysis is characterised: axial optical sectioning of SMLM images is demonstrated by thresholding fitted localisations using their fitted width and amplitude to reject fluorophores that emit from above or below a virtual ‘light-sheet’, a thin volume centred on the focal plane of the microscope. This method provides qualitative and quantitative improvements to SMLM. In the Part B of this thesis, SMLM is applied to study T cell activation. Although the T cell receptor plays a key role in immunity, its stoichiometry in the membrane of resting T cells is still a matter of debate. Here, single-molecule counting methods are implemented to compare the stoichiometry of TCRs fused with mEos2 in resting T cells to monomeric and dimeric controls. However, because of the stochasticity of mEos2 photo-physics, results are inconclusive and new counting techniques based on structural imaging are discussed. In addition to TCR triggering, T cells require the co-stimulatory triggering of the CD28 transmembrane receptor to become fully activated. However, some immobilised anti-CD28 antibodies, referred to as super-agonists (SA), can directly activate T cells without triggering the TCR. In this thesis, single-molecule tracking techniques are used to investigate the molecular mechanism of CD28 super-agonism in live T cells. The results indicate that the diffusion of CD28 is slowed by SA binding. This effect is further discussed in light of the kinetic-segregation model proposed for TCR triggering. Quantitative SMLM as implemented and further developed in this work offers new tools to investigate the molecular mechanisms initiating T cell activation, ultimately facilitating the discovery of novel approaches to target these pathways for therapeutic purposes.This work was supported by the Wellcome Trust [studentship number 093756/B/10/Z]

Optical imaging methods for the study of disease models from the nano to the mesoscale

The visualisation of disease phenotypes allows scientists to study fundamental mechanisms of disease. Optical imaging methods are useful not only to observe anatomical features of biological samples, but also to infer interactions between molecular species using fluorescence labelling. This thesis presents the development of imaging and analysis tools to study biological questions in three models of disease, with samples ranging from the sub-cellular to the organ scale. First, the role of the alpha-synuclein (a-syn) protein, whose dysfunction is a hallmark of Parkinson’s Disease, was studied with respect to vesicle trafficking at the synapse. Synaptic vesicles are ∼40 nm in diameter; imaging vesicles therefore requires methods with resolution below the diffraction limit. Single-molecule localisation microscopy (SMLM), which circumvents the diffraction limit by separating fluorophore emission in time to localise individual molecules in space with ∼20 nm precision, was thus implemented to study a-syn in purified synaptic boutons. A software package was developed to analyse the colocalisation of a-syn with internalised vesicles, and the clustering of a-syn under differing synaptic calcium levels. The colocalisation of a-syn and internalised vesicles was found to be temperature independent, suggesting that a-syn is involved in non-canonical trafficking mechanisms. Ground truth simulations from a synaptosome model were used to benchmark two cluster analysis methods. Both methods applied on the experimental data showed that a-syn becomes less clustered at low synaptic calcium levels. Second, the spatiotemporal association of ESCRT-II, a protein complex whose role in the budding of the human immunodeficiency virus (HIV) was previously considered dispensable, and the HIV polyprotein Gag was studied during viral egress using novel image analysis tools. A nearest-neighbour analysis showed the ESCRT-II protein EAP45 colocalises with Gag similarly to ALIX, a protein well known to be involved in HIV budding. However, upon deletion of EAP45’s N-terminus, its colocalisation with Gag was significantly impaired, highlighting the importance of this EAP45 domain in linking to Gag. Single particle tracking was used to trace the trajectories of EAP45 and Gag in live cells, and an algorithm was developed to visualise the simultaneous motion of two particles; these analyses revealed three types of potential dynamic interaction between EAP45 and Gag. Finally, an open-source instrument to visualise phenotypes from large organs in 3D was developed for the study of chronic obstructive pulmonary disease (COPD) models. The instrument implements Optical Projection Tomography, a technique which can reconstruct cross-sectional slices of a transparent object from its orthographic projections, using off-the- shelf components and novel ImageJ plugins for artefact correction and volume reconstructions. Excised and cleared mouse lungs were imaged in which high order airways can be discerned with 50 μm resolution. The raw lung data, instructions for building the instrument, the free ImageJ plugins, and a detailed software manual are available in an online repository to encourage the widespread use of OPT for imaging large samples.Gates Cambridg

Three-Dimensional Geometry Inference of Convex and Non-Convex Rooms using Spatial Room Impulse Responses

This thesis presents research focused on the problem of geometry inference for both convex- and non-convex-shaped rooms, through the analysis of spatial room impulse responses. Current geometry inference methods are only applicable to convex-shaped rooms, requiring between 6--78 discretely spaced measurement positions, and are only accurate under certain conditions, such as a first-order reflection for each boundary being identifiable across all, or some subset of, these measurements. This thesis proposes that by using compact microphone arrays capable of capturing spatiotemporal information, boundary locations, and hence room shape for both convex and non-convex cases, can be inferred, using only a sufficient number of measurement positions to ensure each boundary has a first-order reflection attributable to, and identifiable in, at least one measurement. To support this, three research areas are explored. Firstly, the accuracy of direction-of-arrival estimation for reflections in binaural room impulse responses is explored, using a state-of-the-art methodology based on binaural model fronted neural networks. This establishes whether a two-microphone array can produce accurate enough direction-of-arrival estimates for geometry inference. Secondly, a spherical microphone array based spatiotemporal decomposition workflow for analysing reflections in room impulse responses is explored. This establishes that simultaneously arriving reflections can be individually detected, relaxing constraints on measurement positions. Finally, a geometry inference method applicable to both convex and more complex non-convex shaped rooms is proposed. Therefore, this research expands the possible scenarios in which geometry inference can be successfully applied at a level of accuracy comparable to existing work, through the use of commonly used compact microphone arrays. Based on these results, future improvements to this approach are presented and discussed in detail