Relative global positioning system (GPS) navigation is currently used for autonomous rendezvous and docking of two spacecraft as well as formation flying applications. GPS receivers deliver measurements to flight software that use this information to determine estimates of the current states of the spacecraft. The success of autonomous proximity operations in the presence of an uncertain environment and noisy measurements depends primarily on the navigation accuracy. This thesis presents the implementation and calibration of a spaceborne GPS receiver model, a visibility analysis for multiple GPS antenna cone angles, the implementation of four different extended Kalman filter architectures and a comparison of the advantages and disadvantages of each filter used for relative GPS navigation. A spaceborne GPS model is developed to generate simulated GPS measurements for a spacecraft located on any orbit around the Earth below the GPS constellation. Position and velocity estimation algorithms for GPS receivers are developed and implemented. A visibility analysis is performed to determine the number of visible satellites throughout the duration of the rendezvous. Multiple constant fields of view are analyzed and results compared to develop an understanding of how the GPS constellation evolves during the proximity operations. The comparison is used to choose a field of view with adequate satellite coverage. The advantages and disadvantages of the relative navigation architectures are evaluated based on a trade study involving several parameters. It is determined in this thesis that a reduced pseudorange filter provides the best overall performance in both relative and absolute navigation with less computational cost than the slightly more accurate pseudorange lter. A relative pseudorange architecture experiences complications due to multipath rich environments and performs well in only relative navigation. A position velocity architecture performs well in absolute state estimation but the worst of the four filters studied in relative state estimation.

In this thesis, the problem of intercepting highly manoeuvrable threats using seeker-less interceptors that operate in the command guidance mode, is addressed. These systems are more prone to estimation errors than standard seeker-based systems. Several non-linear and optimal estimation and guidance concepts are presented in this thesis for interception of randomly maneuvering targets by seeker-less interceptors. The key contributions of the thesis can be broadly categorized into six groups, namely (i) an optimal selection of bank of lters in interactive multiple model (IMM) scheme to cater to various maneuvers that are expected during the end-game, (ii) an innovative algorithm to reduce chattering phenomenon and formulate effective guidance algorithm based on 'differential game guidance law' (modi ed DGL), (iii) IMM/DGL and IMM/modified DGL based integrated estimation/guidance (IEG) strategy, (iv) sensitivity and robustness analysis of Kalman lters and ne tuning of lters in filter bank using innovation covariance, (v) Performance of tuned IMM/PN, tuned IMM/DGL and tuned IMM/modi ed DGL against various target maneuvers, (vi) Performance comparison with realistic missile model. An innovative generalized state estimation formulation has been proposed in this the-sis for accurately estimating the states of incoming high speed randomly maneuvering targets. The IMM scheme and an optimal selection of lters, to cater to various maneu-vers that are expected during the end-game, is described in detail. The key advantage of this formulation is that it is generic and can capture evasive target maneuver as well as straight moving targets in a uni ed framework without any change of target model and tuning parameters. In this thesis, a game optimal guidance law is described in detail for 2D and 3D engagements. The performance of the differential game based guidance law (DGL) is compared with conventional Proportional Navigation (PN) guidance law, especially for 3D interception scenarios. An innovative chatter removal algorithm is introduced by modifying the differential game based guidance law (modified DGL). In this algorithm, chattering is reduced to the maximum extent possible by introducing a boundary layer around the switching surface and using a continuous control within the boundary layer. The thesis presents performance of the modified DGL algorithm against PN and DGL, through a comparison of miss distances and achieved accelerations. Simulation results are also presented for varying fiight path angle errors. Apart from the guidance logic, two novel ideas have been presented following the evolving "integrated estimation and guidance" philosophy. In the rst approach, an in-tegrated estimation/guidance (IEG) algorithm that integrates IMM estimator with DGL law (IMM/DGL), is proposed for seeker-less interception. In this interception scenario, the target performs an evasive bang-bang maneuver, while the sensor has noisy measure-ments and the interceptor is subject to an acceleration bound. The guidance parameters (i.e., the lateral acceleration commands) are computed with the help of zero e ort miss distance. The thesis presents the performance of the IEG algorithm against combined IMM with PN (IMM/PN), through a comparison of miss distances. In the second ap-proach, a novel modi ed IEG algorithm composed of IMM estimator and modi ed DGL guidance law is introduced to eliminate the chattering phenomenon. Results from both of these integrated approaches are quite promising. Monte Carlo simulation results re-veal that modi ed IEG algorithm achieves better homing performance, even if the target maneuver model is unknown to the estimator. These results and their analysis o er an insight to the interception process and the proposed algorithms. The selection of lter tuning parameters puts forward a major challenge for scien-tists and engineers. Two recently developed metrics, based on innovation covariance, are incorporated for determining the filter tuning parameters. For predicting the proper combination of the lter tuning parameters, the metrics are evaluated for a 3D interception problem. A detailed sensitivity and robustness analysis is carried out for each type of Kalman lters. Optimal and tuned Kalman lters are selected in the IMM con guration to cater to various maneuvers that are expected during the end-game. In the interception scenario examined in this thesis, the target performs various types of maneuvers, while the sensor has noisy measurements and the interceptor is subject to acceleration bound. The tuned IMM serves as a basis for synthesis of e cient lters for tracking maneuvering targets and reducing estimation errors. A numerical study is provided which demonstrates the performance and viability of tuned IMM/modi ed DGL based modi ed IEG strategy. In this thesis, comparison is also performed between tuned IMM/PN, tuned IMM/DGL and tuned IMM/modi ed DGL in integrated estimation/guidance scheme. The results are illustrated by an extensive Monte Carlo simulation study in the presence of estimation errors. Simulation results are also presented for end game maneuvers and varying light path angle errors . Numerical simulations to study the aerodynamic e ects on integrated estimation/ guidance structure and its e ect on performance of guidance laws are presented. A detailed comparison is also performed between tuned IMM/PN, tuned IMM/DGL and tuned IMM/modi ed DGL in integrated estimation/guidance scheme with realistically modelled missile against various target maneuvers. Though the time taken to intercept is higher when a realistic model is considered, the integrated estimation/guidance law still performs better. The miss distance is observed to be similar to the one obtained by considering simpli ed kinematic models.

No description available.

With the expected growth of Unmanned Aerial Vehicle (UAV) traffic in the coming years, the demand for UAV tracking solutions in the Air Traffic Control (ATC) industry has been incentivized. To ensure the safe integration of UAVs into airspace, Air Traffic Management (ATM) systems will need to provide a number of services such as UAV tracking. The Interacting Multiple Model Extended Kalman Filter (IMM-EKF) is an industry standard for aircraft tracking, but no such algorithm has been tried and tested for UAV tracking. This thesis aims to determine a suitable tracking algorithm for the specific case of non-cooperative tracking of UAVs from radar data. In non-cooperative tracking scenarios, we do not have any information regarding the UAV other than radar measurements indicating the target’s position. We investigate an Adaptive Interacting Multiple Model Unscented Kalman Filter (IMM-UKF) method with three different motion model combinations in addition to comparing a Cartesian vs. Spherical measurement model. A comparison of motion models shows that using a Constant Jerk (CJ) model to model target maneuvers in the IMM structure reduces the risk of filter divergence as compared to using a turn model, such as Constant Turn (CT) or Constant Angular Velocity (CAV). The CJ model is thus a suitable choice to have as one of the motion models in an IMM structure and works well in conjunction with two Constant Velocity (CV) models. We were not able to determine if the Spherical measurement model is better than the Cartesian measurement model in general. However, the Spherical measurement model improves the accuracy of the state estimate in some cases. Adaptive tuning of the system noise covariance Q and measurement noise covariance R does not improve the accuracy of the state estimate but it improves the filter robustness and consistency when the filter is incorrectly tuned. Based on our results, we believe that the adaptive IMM-UKF shows promise but that there is still room for improvement with regards to both the accuracy and consistency. However, we will need to perform extensive tests with real UAV radar data to draw concrete conclusions. / Med den förväntade tillväxten av trafik med obemannade flygfordon (UAV) under de kommande åren kommer efterfrågan för spårningslösningar för UAV inom flygövervakning. För att säkerställa en säker integration av UAV:er i luftrummet, kommer Air Traffic Management (ATM)-system att behöva tillhandahålla tjänster för UAV-spårning. Det så kallade Interacting Multiple Model Extended Kalman Filter (IMM-EKF) filtret är en industristandard spårning av flygplan, men ingen sådan algoritm har prövats och testats för UAV-spårning. Denna avhandling syftar till att fastställa en lämplig spårningsalgoritm för det specifika fallet med icke samarbetande spårning av UAV från radardata. I icke samarbetande spårningsscenarier har vi ingen information om UAV:n utöver radarmätningar. Vi presenterar en adaptiv metod baserad på IMM-UKF, där vi ersätter EKF i industristandarden IMM-EKF med ett filter av typen UKF. Vi undersöker tre olika kombinationer av rörelsemodeller och jämför också en kartesisk med en sfärisk mätmodell. Vår jämförelse av rörelsemodeller visar om man använder en Constant Jerk (CJ) modell för manövrar i IMM-strukturen minskar risken för divergens jämfört med att använda en svängmodell, såsom Constant Turn (CT) eller Constant Angular Velocity (CAV). CJ-modellen är alltså ett lämpligt val att ha som en av rörelsemodellerna i en IMM-struktur och fungerar bra i kombination med två Constant Velocity (CV) modeller. Vi kunde inte avgöra om den sfäriska modellen var bättre än den kartesiska modellen. Adaptiv inställning av systembrusets kovarians Q och mätbrus kovarians R förbättrar inte tillståndsuppskattningens noggrannhet men den förbättrar filtrets robusthet och konsistens när filtret är felaktigt inställt. Baserat på våra resultat tror vi att den adaptiva IMM-UKF metoden är lovande men att det fortfarande finns utrymme för förbättringar när det gäller både noggrannhet och konsistens i spårningen. Vi kommer dock att behöva utföra omfattande tester med riktiga UAV-radardata för att dra konkreta slutsatser.

Autonomous vehicles perceive their environment based on several sensors that are onboard the vehicle. These sensors constantly monitor the vehicle’s movement as well as the environment. There is a wide variety of sensors that can be utilized based on the type of data it provides, accuracy and cost. While not all of them are required, some combination of sensors is required to have a functional and reliable autonomous vehicle. For a robust autonomous vehicle, typically, the sensor quality and accuracy need to be high. Having high-quality sensors drives up the procurement costs and computational requirements, which in turn increases the vehicle cost for manufacturers and customers alike. One way to reduce costs is to limit the number of sensors. However, this also limits the vehicle’s sensing capability and range. A vehicle’s sensing capability and range can be improved with the use of off-board sensors, such as an external camera, placed strategically at crucial points on the road, such as in intersections. These off-board sensors can be connected to an autonomous vehicle over the internet using low-latency communication technologies such as 5G. The problem that this work tried to tackle was how to improve the reliability of an autonomous vehicle while limiting the need for many expensive sensors. It aims to show how a camera placed off-board can be used to complement one or more vehicles’ onboard sensors and achieve an extension of the vehicle’s operational design domain, while relaxing constraints on the onboard sensors. This was investigated by building a physical prototype using a 1/5th scaled car with a Lidar and an Inertial Measurement Unit and extending its sensing capability and range with the use of a camera based off-board sensor. The car was robust enough to navigate and make driving decisions. This also meant that the costs of procuring the hardware needed can be reduced. The minimum distance for a lane merging scenario was first derived mathematically and then compared to experimental data. The experimental findings were consistent with the mathematical model within an 11 percent margin of error. / Autonoma fordon uppfattar dess omgivning baserat på flera sensorer som är ombord fordonet. Dessa sensorer mäter konstant fordonets rörelse och omgivning. Ther finns en stor variation av sensorer som kan användas baserat på vad för typ av data som mäts, dess precision och kostnad. Alla sensorer är inte nödvändiga men någon kombination av sensorer krävs för att ha ett funktionellt och tillförlitligt autonomt fordon. För ett robust autonomt fordon brukar sensorers kvalite och precision vara hög. Att använda sig av hög kvalité på sensorer driver upp anskaffningsvärde samt höjer mängden datorberäkningar. Detta i sin tur höjer kostnaden för biltillverkare och kunder. Ett sätt att minska kostnaden är att minska antalet sensorer. Dock så minskar detta även fordonets möjlighet till att uppfatta omgivningen samt sensorers utsträckning. Ett fordons uppfattnings kapabilitet och utsträckning kan förbättras genom att använda sig av externa sensorer, såsom en extern kamera placerad vid en strategisk position i trafiken, såsom i en korsning. Dessa externa sensorer kan vara uppkopplade till ett autonomt fordon över internet genom att använda sig av kommunikations teknologier med låg latens såsom 5G. Det problem som adresseras i detta arbete är hur man kan förbättra pålitligheten för ett autonomt fordon när antalet dyra sensorer är begränsat. Målet är att påvisa hur en extern sensor, i form av en kamera, kan användas som ett komplement till en eller flera sensorer ombord fordonet och därmed förlänga fordonets användningsområde medans kraven på fordonets sensorer blir mindre. Detta undersöktes genom att bygga en fysisk prototyp med en skala på 1 till 5 för en bil. Denna bil hade Lidar och en tröghetssensor och den kamerabaserade externa sensorn förlänger fordonets uppfattning av omgivningen. Bilen var robust nog för att kunna navigera och göra körningsbeslut. Detta betydde att anskaffningsvärdet för nödvändig hårdvara var lägre. Det minsta avståndet för ett experiment av sammanfogning av två körfält räknades först ut matematiskt och jämfördes sedan med experimentell data. Resultatet från experimentet visade sig vara överens med den matematiska modellen med en felmarginal på 11 procent.

Design trade-offs between estimation performance, processing delay and communication cost for a sensor scheduling problem is discussed. We consider a heterogeneous sensor network with two types of sensors: the first type has low-quality measurements, small processing delay and a light communication cost, while the second type is of high quality, but imposes a large processing delay and a high communication cost. Such a heterogeneous sensor network is common in applications, where for instance in a localization system the poor sensor can be an ultrasound sensor while the more powerful sensor can be a camera. Using a time-periodic Kalman filter, we show how one can find an optimal schedule of the sensor communication. One can significantly improve estimation quality by only using the expensive sensor rarely. We also demonstrate how simple sensor switching rules based on the Riccati equation drives the filter into a stable time-periodic Kalman filter. ￜ 2008 IEEE. / <p>QC 20110224</p>

Series data, sequences of measured values, are ubiquitous. Whenever observations are made along a path in space or time, a data sequence results. To comprehend nature and shape it to our will, or to make informed decisions based on what we know, we need methods to make sense of such data. Of particular interest are probabilistic descriptions, which enable us to represent uncertainty and random variation inherent to the world around us. This thesis presents and expands upon some tools for creating probabilistic models of sequences, with an eye towards applications involving speech and language. Modelling speech and language is not only of use for creating listening, reading, talking, and writing machines---for instance allowing human-friendly interfaces to future computational intelligences and smart devices of today---but probabilistic models may also ultimately tell us something about ourselves and the world we occupy. The central theme of the thesis is the creation of new or improved models more appropriate for our intended applications, by weakening limiting and questionable assumptions made by standard modelling techniques. One contribution of this thesis examines causal-state splitting reconstruction (CSSR), an algorithm for learning discrete-valued sequence models whose states are minimal sufficient statistics for prediction. Unlike many traditional techniques, CSSR does not require the number of process states to be specified a priori, but builds a pattern vocabulary from data alone, making it applicable for language acquisition and the identification of stochastic grammars. A paper in the thesis shows that CSSR handles noise and errors expected in natural data poorly, but that the learner can be extended in a simple manner to yield more robust and stable results also in the presence of corruptions. Even when the complexities of language are put aside, challenges remain. The seemingly simple task of accurately describing human speech signals, so that natural synthetic speech can be generated, has proved difficult, as humans are highly attuned to what speech should sound like. Two papers in the thesis therefore study nonparametric techniques suitable for improved acoustic modelling of speech for synthesis applications. Each of the two papers targets a known-incorrect assumption of established methods, based on the hypothesis that nonparametric techniques can better represent and recreate essential characteristics of natural speech. In the first paper of the pair, Gaussian process dynamical models (GPDMs), nonlinear, continuous state-space dynamical models based on Gaussian processes, are shown to better replicate voiced speech, without traditional dynamical features or assumptions that cepstral parameters follow linear autoregressive processes. Additional dimensions of the state-space are able to represent other salient signal aspects such as prosodic variation. The second paper, meanwhile, introduces KDE-HMMs, asymptotically-consistent Markov models for continuous-valued data based on kernel density estimation, that additionally have been extended with a fixed-cardinality discrete hidden state. This construction is shown to provide improved probabilistic descriptions of nonlinear time series, compared to reference models from different paradigms. The hidden state can be used to control process output, making KDE-HMMs compelling as a probabilistic alternative to hybrid speech-synthesis approaches. A final paper of the thesis discusses how models can be improved even when one is restricted to a fundamentally imperfect model class. Minimum entropy rate simplification (MERS), an information-theoretic scheme for postprocessing models for generative applications involving both speech and text, is introduced. MERS reduces the entropy rate of a model while remaining as close as possible to the starting model. This is shown to produce simplified models that concentrate on the most common and characteristic behaviours, and provides a continuum of simplifications between the original model and zero-entropy, completely predictable output. As the tails of fitted distributions may be inflated by noise or empirical variability that a model has failed to capture, MERS's ability to concentrate on high-probability output is also demonstrated to be useful for denoising models trained on disturbed data. / <p>QC 20131128</p> / ACORNS: Acquisition of Communication and Recognition Skills / LISTA – The Listening Talker

Bayesian approaches for sparse signal recovery have enjoyed a long-standing history in signal processing and machine learning literature. Among the Bayesian techniques, the expectation maximization based Sparse Bayesian Learning(SBL) approach is an iterative procedure with global convergence guarantee to a local optimum, which uses a parameterized prior that encourages sparsity under an evidence maximization frame¬work. SBL has been successfully employed in a wide range of applications ranging from image processing to communications. In this thesis, we propose novel, efficient and low-complexity SBL-based algorithms that exploit structured sparsity in the presence of fully/partially known measurement matrices. We apply the proposed algorithms to the problem of channel estimation and data detection in Orthogonal Frequency Division Multiplexing(OFDM) systems. Further, we derive Cram´er Rao type lower Bounds(CRB) for the single and multiple measurement vector SBL problem of estimating compressible vectors and their prior distribution parameters. The main contributions of the thesis are as follows: We derive Hybrid, Bayesian and Marginalized Cram´er Rao lower bounds for the problem of estimating compressible vectors drawn from a Student-t prior distribution. We derive CRBs that encompass the deterministic or random nature of the unknown parameters of the prior distribution and the regression noise variance. We use the derived bounds to uncover the relationship between the compressibility and Mean Square Error(MSE) in the estimates. Through simulations, we demonstrate the dependence of the MSE performance of SBL based estimators on the compressibility of the vector. OFDM is a well-known multi-carrier modulation technique that provides high spectral efficiency and resilience to multi-path distortion of the wireless channel It is well-known that the impulse response of a wideband wireless channel is approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread. In this thesis, we consider the estimation of the unknown channel coefficients and its support in SISO-OFDM systems using a SBL framework. We propose novel pilot-only and joint channel estimation and data detection algorithms in block-fading and time-varying scenarios. In the latter case, we use a first order auto-regressive model for the time-variations, and propose recursive, low-complexity Kalman filtering based algorithms for channel estimation. Monte Carlo simulations illustrate the efficacy of the proposed techniques in terms of the MSE and coded bit error rate performance. • Multiple Input Multiple Output(MIMO) combined with OFDM harnesses the inherent advantages of OFDM along with the diversity and multiplexing advantages of a MIMO system. The impulse response of wireless channels between the Nt transmit and Nr receive antennas of a MIMO-OFDM system are group approximately sparse(ga-sparse),i.e. ,the Nt Nr channels have a small number of significant paths relative to the channel delay spread, and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wire¬less channels are also group approximately-cluster sparse(ga-csparse),i.e.,every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this thesis, we cast the problem of estimating the ga-sparse and ga-csparse block-fading and time-varying channels using a multiple measurement SBL framework. We propose a bouquet of novel algorithms for MIMO-OFDM systems that generalize the algorithms proposed in the context of SISO-OFDM systems. The efficacy of the proposed techniques are demonstrated in terms of MSE and coded bit error rate performance.

<p>An emerging technology in the field of transient thermal management is thermal energy storage, or TES, which enables temporary, on-demand heat rejection via storage as latent heat in a phase-change material.  Latent TES devices have enabled advances in many thermal management applications, including peak load shifting for reducing energy demand and cost of HVAC systems and providing supplemental heat rejection in transient thermal management systems.  However, the design of a transient thermal management system with integrated storage comprises many challenges which are yet to be solved.  For example, design approaches and performance metrics for determining the optimal dimensions of the TES device have only recently been studied.  Another area of active research is estimation of the internal temperature state of the device, which can be difficult to directly measure given the transient nature of the thermal storage process.  Furthermore, in contrast to the three main functions of a thermal-fluid system--heat addition, thermal transport, and heat rejection--thermal storage introduces the need for active, real-time control and automated decision making for managing the operation of the thermal storage device. </p> <p>In this thesis, I present the design process for integrating thermal energy storage into a single-phase thermal management system for rejecting transient heat loads, including design of the TES device, state estimation and control algorithm design, and validation in both simulation and experimental environments. Leveraging a reduced-order finite volume simulation model of a plate-fin TES device, I develop a design approach which involves a transient simulation-based design optimization to determine the required geometric dimensions of the device to meet transient performance objectives while maximizing power density.  The optimized TES device is integrated into a single-phase thermal-fluid testbed for experimental testing.  Using the finite volume model and feedback from thermocouples embedded in the device, I design and experimentally validate a state estimator based on the state-dependent Riccati equation approach for determining the internal temperature distribution to a high degree of accuracy.  Real-time knowledge of the internal temperature state is critical for making control decisions; to manage the operation of the TES device in the context of a transient thermal management system, I design and test, both in simulation and experimentally, a logic-based control strategy that uses fluid temperature measurements and estimates of the TES state to make real-time control decisions to meet critical thermal management objectives. Together, these advances demonstrate the potential of thermal energy storage technology as a component of thermal management systems and the feasibility of logic-based control strategies for real-time control of thermal management objectives.</p>