Monocular cameras are prominently used for estimating motion of Unmanned Aerial Vehicles. With growing interest in autonomous vehicle technology, the use of monocular cameras in ground vehicles is on the rise. This is especially favorable for localization in situations where Global Navigation Satellite System (GNSS) is unreliable, such as open-pit mining environments. However, most monocular camera based approaches suffer due to obscure scale information. Ground vehicles impose a greater difficulty due to high speeds and fast movements. This thesis aims to estimate the scale of monocular vision data by using an inertial sensor in addition to the camera. It is shown that the simultaneous estimation of pose and scale in autonomous ground vehicles is possible by the fusion of visual and inertial sensors in an Extended Kalman Filter (EKF) framework. However, the convergence of scale is sensitive to several factors including the initialization error. An accurate estimation of scale allows the accurate estimation of pose. This facilitates the localization of ground vehicles in the absence of GNSS, providing a reliable fall-back option. / Monokulära kameror används ofta vid rörelseestimering av obemannade flygande farkoster. Med det ökade intresset för autonoma fordon har även användningen av monokulära kameror i fordon ökat. Detta är fram för allt fördelaktigt i situationer där satellitnavigering (Global Navigation Satellite System (GNSS)) äropålitlig, exempelvis i dagbrott. De flesta system som använder sig av monokulära kameror har problem med att estimera skalan. Denna estimering blir ännu svårare på grund av ett fordons större hastigheter och snabbare rörelser. Syftet med detta exjobb är att försöka estimera skalan baserat på bild data från en monokulär kamera, genom att komplettera med data från tröghetssensorer. Det visas att simultan estimering av position och skala för ett fordon är möjligt genom fusion av bild- och tröghetsdata från sensorer med hjälp av ett utökat Kalmanfilter (EKF). Estimeringens konvergens beror på flera faktorer, inklusive initialiseringsfel. En noggrann estimering av skalan möjliggör också en noggrann estimering av positionen. Detta möjliggör lokalisering av fordon vid avsaknad av GNSS och erbjuder därmed en ökad redundans.

The most common method for securing components to each other during manufacturing of products is by joining these using screws, nuts and bolts. The benefit of using this method is that it is cheap and makes it easy to join and separate components quickly. The clamping force in the threaded joint is critical to the quality and in some respect the life length of the product, which makes it important to have good control of the clamping force. There are two main tightening strategies used when tightening a threaded joint – torque controlled tightening and angle controlled tightening. The first method monitors the applied torque during the entire tightening and halts when the target torque is reached. The second method, angle controlled tightening, measures the rotation of the threaded fastener in the joint. This method generally produces more accurate results with less scatter in the final clamping force. In order to apply angle controlled tightening using a hand-held tool it is required to not only control the output angle of the tool, but also how the tool moves in relation to the joint. This is to ensure that the control signal from the motor actually translates to clamping force in the joint and not to rotation of the tool itself. This thesis project aims to analyze data from an IMU (Inertial Measurement Unit) built into a hand-held tightening tool in order to estimate tool movement and thereby react to undesired tool movement. An analysis has been performed to evaluate how the two sensor fusion methods – Kalman filter and Particle filter – perform in terms of estimating the orientation of the tool by combining measurements from the IMU’s accelerometers and gyroscopes. Data was collected from the tool IMU during a number of angle tightening sequences with varying setups. Test were performed both for when the tool was kept still during the entire tightening and for when the tools was allowed to move freely. Tests were also carried out for a couple of different tool orientations to better understand the behavior of the two sensor fusion models. The results from the tests showed that the Kalman Filter was able to better estimate the tool orientation. Especially in terms of accuracy, repeatability and reliability. / Den vanligaste metoden för att fästa komponenter till varandra vid tillverkning av produkter är genom att sammanfoga komponenterna med hjälp av skruvar, bultar och muttrar. Fördelen med denna metod är att den är billig och gör det enkelt att sammanfoga och lossa komponenter snabbt. Klämkraften i skruvförbandet är avgörande för hur väl en produkt är ihopsatt och påverkar därmed dess kvalitet, samt i viss mån livslängd. Det finns i huvudsak två olika strategier vid åtdragning i ett skruvförband – momentåtdragning och vinkelåtdragning. Den första metoden bygger på att man kontinuerligt mäter momentet under åtdragning och avbryter åtdragningen när rätt moment uppnåtts. Den andra metoden, vinkelåtdragning, mäter hur många grader fästelementet roterat i förbandet. Metoden producerar i regel högre precision med mindre spridning av den slutgiltiga klämraften. För att kunna tillämpa vinkelåtdragning med ett handhållet verktyg räcker det inte att kontroll över rotationen av verktygets utgående axel, utan även hur verktyget rör sig i förhållande till förbandet under åtdragning. Detta för att säkerställa att verktygets motorstyrning resulterar i önskad klämkraft i förbandet och inte rotation av själva verktyget. Detta examensarbete ämnar analysera data från en IMU (Inertial Measurement Unit) integrerad i ett handhållet åtdragningsverktyg för att estimera verktygets rörelse under vinkelåtdragning och därmed kompensera för oönskade rörelser. En analys har gjorts för hur väl de två olika sensorfusions-modellerna - Kalmanfilter och Partikelfilter – presterar när det kommer till att uppskatta orientering för verktyget genom att kombinera data från IMU-enhetens accelerometrar och gyroskop. Data samlades in från verktygets IMU från ett antal dragningar med varierande uppställning. Tester genomfördes dels då verktyget hölls stilla under hela åtdragningen och dels då det tilläts röra sig fritt. Tester genomfördes även för flera olika orienteringar av verktyget för att i större utsträckning kunna säga hur de olika sensorfusions-modellerna presterade. Resultatet av testerna visade att Kalmanfiltret kunde producera bättre estimeringar av verktygets orientering, speciellt i avseende precision, repeterbarhet och tillförlitlighet.

[pt] Nesta Tese, eu me concentro em desenvolvimentos sobre o filtro de Kalman sujeito a restrições lineares gerais. Há essencialmente três tipos de contribuições: (i) provas alternativas para resultados previamente estabelecidos na literatura sobre o filtro de Kalman com restrições; (ii) resultados que presumidamente destacam aspectos teóricos e metodológicos para modelagens em espaço de estado sob restrições; e (iii) aplicações que parecem ser inéditas até então em finanças (análise de investimentos) e em macroeconomia, nas quais os métodos propostos são ilustrados e avaliados. No final, eu sugiro algumas extensões adicionais sobre o tema, as quais, novamente, dividem-se em teoria, métodos e aplicações. / [en] In this Thesis, I bring the attention to developments on Kalman filtering subject to general linear constraints. There are essentially three kinds of contributions: (i) new proofs for already established results within the restricted Kalman filtering literature; (ii) new results which are supposed to shed light on theoretical and methodological frameworks for linear state space modeling under linear restrictions; and (iii) applications that seem to be new in investment analysis and in macroeconomics, where the proposed methods are illustrated and evaluated. At the end, I suggest some further extensions in the subject, which, again, step into theory, methods and applications.

Recent advances in the telecommunications industry and the resulting applicationssuch as autonomous vehicles, vehicle surveillance and traffic safetyhas increased the demand for accurate and robust vehicle positioning systems.Existing Global Navigation Satellite System (GNSS) based positioning techniquesface significant performance loss in the tunnels and urban canyons.Recent researches have shown that radio-based positioning techniques are theoreticallypromising to make an accurate navigation system to fill the GNSSgaps. Fifth generation of mobile communication (5G) will utilize wide bandwidthstogether with beamforming enabled by antenna arrays to provide higherdata rates to mobile users. These features make 5G a favorable candidate forhigh accuracy positioning. On the other hand, sensor fusion is commonly employedto develop more robust and accurate navigation systems for vehicles. Inthis work, the range and angle measurements from 5G base stations are fusedwith the acceleration measurements by the means of the extended Kalman filterto generate position estimates for a moving car. The accuracy of this positioningsystem is studied with centimeter wave (cmWave) and millimeter wave(mmWave) 5G cellular networks which are set up by practical parameters. Towardsthat, the positioning system is tested in a simulation-based experimentwhere a car is moving on a highway and the 5G base stations are deployedalongside of it. Based on that, a detailed analysis of the Kalman filter’s rootmean squared error (RMSE) and the 5G’s different parameters and limitingfactors such as the line of sight (LOS) blockage is carried out. Our numericalresults show that vehicles connected to 5G can benefit from this system to enhancethe robustness and accuracy of their navigation system. / De senaste framstegen inom telekommunikationsindustrin och de resulterandeapplikationerna som autonoma fordon, fordonsövervakning och trafiksäkerhethar ökat efterfrågan på exakta fordonspositioneringssystem. ExisterandeGlobal Navigation Satellite System (GNSS) baserade positioneringsteknikerhar en betydande prestandaförlust i tunnlar och urbana kanjoner. Forskninghar visat att radiobaserade positioneringstekniker har mindre distributionskostnaderoch kan vara mer exakta än satellitbaserade navigationssystem.I den femte generation av mobilkommunikation (5G) används tekniker sommillimeterWave (mmWave) och multiple-input multiple-output (MIMO) därradio-terminaler består av stora matrisantenner och arbetar med stora bandbredder.Dessa funktioner gör 5G-system gynnsamma för positionering medhög noggrannhet. Å andra sidan har informationsfusion av Inertial NavigationSystems (INS) och andra positioneringstekniker vanligen använts för attutveckla mer robusta och exakta spårningssystem. I denna studie föreslår viett INS/5G-positioneringssystem för att spåra landfordon baserat på Kalmanfiltret. Vi adresserar systempositioneringsgränserna i termer av 5G nya radio(NR) subsystem och en detaljerad analys av beroendet av rotmedelfelteradkvadratfel (RMSE) för olika systemparametrar som utförs. Systemet testas iett enkelt simuleringsbaserat experiment som består av en rak motorväg medbasstationerna placerade bredvid det. Slutligen visar våra numeriska resultatatt det föreslagna systemet är i stånd att lokalisera ett UE-monterat fordon medsub-meter lägesfel även i närvaro av hård siktlinje blockering.

An electrocardiogram (ECG) is used to monitor the activity of the heart. The human heart beats seventy times on an average per minute. The rate at which a human heart beats can exhibit a periodic variation. This is known as heart rate variability (HRV). Heart rate variability is an important measurement that can predict the survival after a heart attack. Studies have shown that reduced HRV predicts sudden death in patients with Myocardial Infarction (MI). The time interval between each beat is called an RR interval, where the heart rate is given by the reciprocal of the RR interval expressed in beats per minute. For a deeper insight into the dynamics underlying the beat to beat RR variations and for understanding the overall variance in HRV, an accurate method of estimating the RR interval must be obtained. Before an HRV computation can be obtained the quality of the RR interval data obtained must be good and reliable. Most QRS detection algorithms can easily miss a QRS pulse producing unreliable RR interval values. Therefore it is necessary to estimate the RR interval in the presence of missing QRS beats. The approach in this thesis is to apply KALMAN estimation algorithm to the RR interval data calculated from the ECG. The goal is to improve the RR interval values obtained from missed beats of ECG data.

The field of robotics has always been one of fascination right from the day of Terminator. Even though we still do not have robots that can actually replicate human action and intelligence, progress is being made in the right direction. Robotic applications range from defense to civilian, in public safety and fire fighting. With the increase in urban-warfare robot tracking inside buildings and in cities form a very important application. The numerous applications range from munitions tracking to replacing soldiers for reconnaissance information. Fire fighters use robots for survey of the affected area. Tracking robots has been limited to the local area under consideration. Decision making is inhibited due to limited local knowledge and approximations have to be made. An effective decision making would involve tracking the robot in earth co-ordinates such as latitude and longitude. GPS signal provides us sufficient and reliable data for such decision making. The main drawback of using GPS is that it is unavailable indoors and also there is signal attenuation outdoors. Indoor geolocation forms the basis of tracking robots inside buildings and other places where GPS signals are unavailable. Indoor geolocation has traditionally been the field of wireless networks using techniques such as low frequency RF signals and ultra-wideband antennas. In this thesis we propose a novel method for achieving geolocation and enable tracking. Geolocation and tracking are achieved by a combination of Gyroscope and encoders together referred to as the Inertial Navigation System (INS). Gyroscopes have been widely used in aerospace applications for stabilizing aircrafts. In our case we use gyroscope as means of determining the heading of the robot. Further, commands can be sent to the robot when it is off balance or off-track. Sensors are inherently error prone; hence the process of geolocation is complicated and limited by the imperfect mathematical modeling of input noise. We make use of Kalman Filter for processing erroneous sensor data, as it provides us a robust and stable algorithm. The error characteristics of the sensors are input to the Kalman Filter and filtered data is obtained. We have performed a large set of experiments, both indoors and outdoors to test the reliability of the system. In outdoors we have used the GPS signal to aid the INS measurements. When indoors we utilize the last known position and extrapolate to obtain the GPS co-ordinates.

This thesis presents methods for robust precision landing of unmanned air vehicles (UAVs) on platforms at sea. Localization methods are proposed for UAV-to-boat state estimation for systems that employ real- time kinematic (RTK) global navigation satellite system (GNSS) and vision sensors. Solutions for GNSS-only are first presented, followed by the fusion of GNSS and vision. The important problem of sensor intrinsic calibration is solved with a novel offline batch estimation approach. Hardware results are presented for all methods. Our calibration of GNSS-to-camera is shown to estimate sensor offsets with millimeter level accuracy. Localization systems are combined with custom state machines that manage the landing attempt via a novel descent cone. This conical threshold enforces a safe and accurate landing. Our landing methods are demonstrated in real-world experiments and achieve consistent accurate landings with error below 10 cm. The fusion of camera and RTK is shown to produce a robust landing system with redundant localization sources.

This text provides an overview of problems in the field of data assimilation. We explore the possibility of recreating unknown data by continuously inserting known data into certain dynamical systems, under certain regularity assumptions. Additionally, we discuss an alternative statistical approach to data assimilation and investigate the utilization of the Ensemble Kalman Filter for assimilating data into dynamical models. A key challenge in numerical weather prediction is incorporating convective precipitation into an idealized setting for numerical computations. To answer this question we examine the modified rotating shallow water equations, a nonlinear coupled system of partial differential equations and further assess if this primitive model accurately mimics phenomena observed in operational numerical weather prediction models. Numerical experiments conducted using a Deterministic Ensemble Kalman Filter algorithm support its applicability for convective-scale data assimilation. Furthermore, we analyze the frequency spectrum of numerical forecasts using the Wavelet transform. Our frequency analysis suggests that, under certain experimental settings, there are similarities in the initialization of operational models, which can aid in understanding the problem of intialization of numerical weather prediction models.

In recent years, the particle filter has gained prominence in the area of target tracking because it is robust to non-linear target motion and non-Gaussian additive noise. Traditional track filters, such as the Kalman filter, have been well studied for linear tracking applications, but perform poorly for non-linear applications. The particle filter has been shown to perform well in non-linear applications. The particle filter method is computationally intensive and advances in processor speed and computational power have allowed this method to be implemented in real-time tracking applications. This thesis explores the use of particle filters to detect and track stealthy targets in noisy imagery. Simulated point targets are applied to noisy image data to create an image sequence. A particle filter method known as Track-Before-Detect is developed and used to provide detection and position tracking estimates of a single target as it moves in the image sequence. This method is then extended to track multiple moving targets. The method is analyzed to determine its performance for targets of varying signal-to-noise ratio and for varying particle set sizes. The simulation results show that the Track-Before-Detect method offers a reliable solution for tracking stealthy targets in noisy imagery. The analysis shows that the proper selection of particle set size and algorithm improvements will yield a filter that can track targets in low signal-to-noise environments. The multi-target simulation results show that the method can be extended successfully to multi-target tracking applications. This thesis is a continuation of automatic target recognition and target tracking research at Cal Poly under Dr. John Saghri and is sponsored by Raytheon Space and Airborne Systems.

Attitude determination and estimation algorithms are developed and implemented in simulation for the Exocube satellite currently under development by PolySat at Cal Poly. A mission requirement of ±5˚ of attitude knowledge has been flowed down from the NASA Goddard developed payload, and this requirement is to be met with a basic sensor suite and the appropriate algorithms. The algorithms selected in this work are TRIAD and an Extended Kalman Filter, both of which are placed in a simulation structure along with models for orbit propagation, spacecraft kinematics and dynamics, and sensor and reference vector models. Errors inherent from sensors, orbit position knowledge, and reference vector generation are modeled as well. Simulations are then run for anticipated dynamic states of Exocube while varying parameters for the spacecraft, attitude algorithms, and level of error. The nominal case shows steady state convergence to within 1˚ of attitude knowledge, with sensor errors set to 3.5˚ and reference vector errors set to 2˚. The algorithms employed have their functionality confirmed with the use of STK, and the simulations have been structured to be used as tools to help evaluate attitude knowledge capabilities for the Exocube mission and future PolySat missions.