This study compares independent use of two known algorithms (Kalmar filter with background subtraction and Particle Filter) that are commonly deployed in object tracking applications. Object tracking in general is very challenging; it presents numerous problems that need to be addressed by the application in order to facilitate its successful deployment. Such problems range from abrupt object motion, during tracking, to a change in appearance of the scene and the object, as well as object to scene occlusions, and camera motion among others. It is important to take into consideration some issues, such as, accounting for noise associated with the image in question, ability to predict to an acceptable statistical accuracy, the position of the object at a particular time given its current position. This study tackles some of the issues raised above prior to addressing how the use of either of the aforementioned algorithm, minimize or in some cases eliminate the negative effects

This thesis evaluates different solutions to the target tracking problem with the use of airborne radar measurements. The purpose of this report is to present and compare options that can improve the tracking performance when the target is performing various manoeuvres while the radar measurements are noisy. A simulation study is done to evaluate and compare the presented solutions, where the evaluating criteria are the estimation errors and the computational complexity. The algorithms investigated are the general pseudo Bayesian of order one (GPB(1)) filter and the interacting multiple model (IMM) filter, each using three motion models, along with several single model Kalman filters. Additionally, the impact on the tracking performance by different choices of radar parameters is also examined. The results show that filters using multiple models are best suited for tracking targets performing different manoeuvres. The tracking performance is improved with both the GPB(1) and IMM algorithms compared to the filters using a single model. Looking at the estimation errors, IMM outperforms the other algorithms and achieves a better general performance for different kinds of manoeuvres. However, IMM have a much higher computational complexity than the filters with a single model. GPB(1) could therefore be more suited for applications where computational power poses a problem, since it is less computationally demanding than IMM. Furthermore, it is shown that different radar parameters have an impact on the tracking performance. The choice of pulse repetition frequency (PRF) and duty cycle used by the radar affects the accuracy of the measurements. The estimation errors of the tracking filters become larger with poor measurements, which also makes it more difficult for the multiple model algorithms to make good use of the different motion models. In most cases, IMM is however less sensitive to the choice of PRF, in relation to how the models are used in the algorithm, compared to GPB(1). Nevertheless, the study shows that there are cases where some combinations of radar parameters drastically reduces the tracking performance and no clear improvement can be seen, not even for IMM.

In the last decades research and development in the field of robotics has grown rapidly. This growth has resulted in the emergence of service robots that need to be able to physically interact with humans for different applications. One of these applications involves robots and humans cooperating in handling an object together. In such cases, there is usually an initial arrangement of how the robot and the humans hold the object and the arrangement stays the same throughout the manipulation task. Real-world scenarios often require that the initial arrangement changes throughout the task, therefore, it is important that the robot is able to recognize these changes and act accordingly. We consider a setting where a robot holds a large flat object with one or two humans. The aim of this research project is to detect the change in the number of agents grasping the object using only force and torque information measured at the robot's wrist. The proposed solution involves defining a transition sequence of four steps that the humans should perform to go from the initial scenario to the final one. The force and torque information is used to estimate the grasping point of the agents with a Kalman filter. While the humans are going from one scenario to the other, the estimated point changes according to the step of the transition the humans are in. These changes are used to track the steps in the sequence using a hidden Markov model (HMM). Tracking the steps in the sequence means knowing how many agents are grasping the object. To evaluate the method, humans that were not involved in the training of the HMM were asked to perform two tasks: a) perform the previously defined sequence as is, and b) perform a deviation of the sequence. The results of the method show that it is possible to detect the change between one human and two humans holding the object using only force and torque information.

Teleoperation system is an important tool to control a device or model in an isolated area remotely where the operator cannot perform the task locally. The vast majority of teleoperation systems provides the operator with visual and haptic control to accomplish the assignment as naturally as possible. However, on a teleoperation system with considerable distance, the time delay could cause a drop in performance. This thesis aims to minimize delay problem by implementing a prediction approach using Kalman Filter. Kalman Filter algorithm has been widely used to estimate user movement for tracking systems. Kalman filter provides an efficient mechanism to predict future state based on Bayesian estimation to sequentially predict future states and measure an actual system to update system parameters. The primary objective of this work is to extract information generated by our prototyping model and visualizing the data to reflect the performance of the system. We use Phantom Omni devices and 3D arm as a model. Different type of Kalman filter algorithms is used to test the accuracy and performance of predicted state generated by the filter. The result shows that the implementation of Extended Kalman Filter (EKF) and smoothing function could overcome the networking delay on certain degrees. The comparison shows that the EKF has better accuracy and performance compared to Unscented Kalman Filter (UKF) when estimating the future state. Additionally, the implementation of smoothing function could improve the stability of teleoperation system. / Teleoperation systemet är ett viktigt verktyg för att styra en enhet eller modell i ett isolerat område på distans där operatören inte kan utföra uppgiften lokalt. De allra flesta av teleoperation system ger föraren visuell och haptisk kontroll för att utföra uppdraget så naturligt som möjligt. Men på en teleoperation system med stort avstånd, kan tidsfördröjningen medföra en nedgång i prestanda. Denna avhandling syftar till att minimera förseningar problem genom att implementera en förutsägelse tillvägagångssätt med Kalman Filter. Kalman filteralgoritm har i stor utsträckning används för att uppskatta användarens rörlighet för spårning. Kalman filter ger en effektiv mekanism för att förutsäga framtida stat grundad på Bayesian uppskattningen att sekventiellt förutsäga framtida tillstånd och mäta ett verkligt system för att uppdatera systemparametrar. Det primära syftet med detta arbete är att extrahera information som genereras av vår prototypmodell och visualisera data för att återspegla systemets prestanda. Vi använder Phantom Omni enheter och 3D-arm som en modell. Olika typer av Kalman filter algoritmer används för att testa riktigheten och prestandan hos förutsagda tillståndet genereras av filtret. Resultatet visar att genomförandet av Extended Kalman filter (EKF) och utjämningsfunktionen kan övervinna nätverk dröjsmålsvissa grader. Jämförelsen visar att EKF har bättre noggrannhet och prestanda jämfört med Unscented Kalman Filter (UKF) vid bedömningen av framtida tillstånd. Dessutom, genomförandet av utjämningsfunktionen skulle kunna förbättra stabiliteten hos teleoperation systemet.

Latency has always been one of the essential problems within Virtual Reality (VR) domain since VR is inherently an interactive paradigm which performs the real-time estimation of human motions. From the user's point of view, the latency extremely reduces the presence experience of VR systems, especially when user won’t able to perform interactions accurately. To compensate the excessive latency, different prediction methods on human motion were studied in recent years. Among them, Kalman Filter was the most popular choice. However, the effectiveness of using Kalman Filter to eliminate the latency for VR systems is not always satisfactory in practice since the accuracy of the estimation of the users’ motion depends on several factors: the linearity of the motion, the prediction time, the computational time, and the algorithm’s limitation.Therefore, this thesis presents a VR-based haptic teleoperation system to study how to effectively eliminate the latency effectively using Kalman Filter. For investigating the performances of different prediction methods for VR systems with several factors considered, two types of Kalman Filter: Linear Kalman Filter (LKF) and Unscented Kalman Filter (UKF) have been used to predict the haptic motion dataset, under different amount of simulated latencies.The result shows, both LKF and UKF provide a good performance at compensating the latency. For 200ms latency, both filters satisfactorily eliminate the latency and improve the interaction effectiveness. The comparative study shows, LKF provides better performance since the linear rotational motion dataset captured by haptic device was used; both filters show a reduced performance when the prediction time is increased. Besides, UKF requires more computational time than LKF.

It is well known that the inflation linked breakeven inflation, defined as the difference between a nominal yield and an inflation linked yield, sometimes is used as an approximation of the market’s inflation expectation. D’Amico et al. (2009, [5]) show that this is a poor approximation for the US market. Based on their work, this thesis shows that the approximation also is poor for the Swedish bond market. This is done by modelling the Swedish bond market using a five-factor latent variable model, where an inflation linked bond specific premium is introduced. Latent variables and parameters are estimated using a Kalman filter and a maximum likelihood estimation. The conclusion is drawn that the modelling was successful and that the model implied outputs gave plausible results.

In vehicle control practice, there are some variables, such as lateral tire force, body slip angle and yaw rate, that cannot or is hard to be measured directly and accurately. Vehicle model, like the bicycle model, offers an alternative way to get them indirectly, however due to the widely existent simplification and inaccuracy of vehicle models, there are always biases and errors in prediction from them. When developing advanced vehicle control functions, it is necessary and significant to know these variables in relatively high precision. Kalman filter offers a choice to estimate these variables accurately with measurable variables and with vehicle model together. In this thesis, estimation models based on Extended Kalman Filter (EKF) and Uncented Kalman Filter (UKF) are built separately to evaluate the lateral tire force, body slip angel and yaw rate of two typical passenger vehicles. Matlab toolbox EKF/UKF developed by Simo Särkkä, et al. is used to implement the estimation models. By comparing their principle, algorithm and results, the better one for vehicle state estimation will be chosen and justified. The thesis is organized in the following 4 parts: First, EKF and UKF are studied from their theory and features. Second, vehicle model used for prediction in Kalman filter is build and justified. Third, algorithms of EKF and UKF for this specific case are analysed. EKF and UKF are then implemented based on the algorithms with the help of Matlab toolbox EKF/UKF. Finally, comparisons between EKF and UKF are presented and discussed.

<div><div><div><p>Structural identification is a useful tool for detecting damage and damage evolution in a structure. The initiation of damage in a structure and its subsequent growth are mainly associated with nonlinear behaviors. While linear dynamics of a structure are easy to simulate, nonlinear structural dynamics have more complex dynamics and amplitude dependence that do require more sophisticated simulation tools and identification methods compared to linear systems. Additionally, there are generally many more parameters in nonlinear models and the responses may not be sensitive to all of them for all inputs. To develop model selection methods, an experiment is conducted that uses an existing device with repeatable behavior and having an expected model from the literature. In this case, an MR damper is selected as the experimental device. The objective of this research is to develop and demonstrate a method to select the most appropriate model from a set of identified stochastic models of a nonlinear device. The method is developed using numerical example of a common nonlinear system, and is then implemented on an experimental structural system with unknown nonlinear properties. Bayesian methods are used because they provide a distinct advantage over many other existing methods due to their ability to provide confidence on answers given the observed data and initial uncertainty. These methods generate a description of the parameters of the system given a set of observations. First, the selected model of the MR damper is simulated and used for demonstrating the results on a numerical example. Second, the model selection process is demonstrated on an experimental structure based on experimental data. This study explores the use of the Bayesian approach for nonlinear structural identification and identifies a number of lessons for others aiming to employ Bayesian inference.</p></div></div></div>

The main objective of this paper is to equip the trade policy analyst with an appropriate method of seasonally adjusting trade data with weekly observations. To that end, a structural time series model containing a trend, seasonal and irregular component is specified. The seasonal component is represented by a time-varying periodic spline. Casting the model in state-space form enables time-varying parameters as well as use of the powerful Kalman filter for trend estimation. The resulting trend can then be interpreted as a seasonally adjusted series. A simulation exercise shows that the correct trend is identified with an average absolute error of 0.4 percent. An application to Swedish imports during 2017-2021 shows that the model produces a reasonable trend estimate when applied in 'real-time' and that its application is preferred to smoothing the series using a simple moving average.

Vibration assisted drilling (VAD) is a type of machining process in which high-frequency vibrations with a small amplitude are induced in the cutting tool to improve the cutting process of hard and brittle materials. These vibrations create an unsteady repetitive processing effect which eventually reduce the cutting forces. It is also crucial to measure these forces in some way because their knowledge directly aids in determining the best machining parameters. Direct and indirect methods can be used to measure these forces, but due to serious limitations of direct measurement methods, an indirect measurement method is required which is capable of online monitoring of high-frequency cutting forces. In this thesis, an indirect method is proposed to estimate thrust force and torque from the voltage signal generated by piezoelectric sensor and torsional deflection signal measured through piezoelectric accelerometer. The estimation of two input signals requires a multi-input multi-output (MIMO) model of VAD system which is developed using Receptance Coupling and Substructure Analysis (RCSA) method. Experimental and numerical methods are used to validate the constituent single-input single-output (SISO) transfer functions of the MIMO model. As the estimated forces are distorted by the dynamics of VAD structure, a Kalman Filter is employed to compensate the dynamics. The accuracy and similarity of results is determined by comparing the estimated cutting force values with the force measured from a load cell in time and frequency domain. The reported experimental results confirm the possibility of using Kalman Filter in estimating high-frequency forces generated in VAD process. / Graduate