The use of ubiquitous network has made real time tracking of objects, animals and human beings easy through the use of radio frequency identification system (RFID). Localization techniques in RFID rely on accurate estimation of the read range between the reader and the tags. The tags consist of a small chip and a printed antenna which receives from and transmits information to the reader. The range information about the distance between the tag and the reader is obtained from the received signal strength indication (RSSI). Accuracy of the read range using RSSI can be very complicated especially in complicated propagation environment due to the nature and features of the environment. There are different kinds of localisation systems and they are Global Positioning System (GPS) which can be used for accurate outdoor localization; while technologies like artificial vision, ultrasonic signals, infrared and radio frequency signals can be employed for indoor localization. This project focuses on the location estimation in RFID Non Line-of-Sight (NLOS) environment using Real Time Localization System (RTLS) with passive tags, in carrying out passengers and baggage tracking at the airport. Indoor location radio sensing suffers from reflection, refraction and diffractions due to the nature of the environment. This unfavourable phenomenon called multipath leads to delay in the arrival of signal and the strength of signal received by receiving antenna within the propagation channel which in turns affects the RSSI, yielding inaccurate location estimation. RTLS based on time difference of arrival and error compensation technique and extended Kalman filter technique were employed in a NLOS environment to determine the location of tag. The better method for location estimation in a NLOS between the Kalman filtering and extended Kalman filtering is investigated. According to simulation results, the extended Kalman filtering technique is more suitable to be applied to RTLS.

A navigation system for precision lunar descent and landing is presented and analyzed. The navigation algorithm is based upon the extended Kalman Filter and employs measurements from an inertial measurement unit to propagate the vehicle position, velocity, and attitude forward in time. External measurements from an altimeter, star camera, terrain camera, and velocimeter are utilized in state estimate updates. The navigation algorithm also attempts to estimate the values of uncertain parameters associated with the sensors. The navigation algorithm also estimates the map-tie angle of the landing site which is a measure of the misalignment of the actual landing site location on the surface of the Moon versus the estimated position of the landing site. The navigation algorithm is subject to a sensitivity analysis which investigates the contribution of each error source to the total estimation performance of the navigation system. Per the results of the sensitivity analysis, it is found that certain error sources need not be actively estimated to achieve similar estimation performance at a reduced computational burden. A new, reduced-order system is presented and tested through covariance analysis and a monte carlo analysis. The new system is shown to have comparable estimation performance at a fraction of the computer run-time, making it more suitable for a real-time implementation. / text

In this thesis, an algorithm that improves the performance of the extended Kalman filter (EKF) on the mobile robot localization issue is proposed, which is aided by the cooperation of neural network and fuzzy logic. An EKF is used to fuse the information acquired from both the robot optical encoders and the external sensors in order to estimate the current robot position and orientation. Then the error covariance of the EKF is tracked by the covariance matching technique. When the output of the matching technique does not meet the desired condition, a fuzzy logic is employed to adjust the error covariance matrix to modify it back to the desired value range. Since the fuzzy logic is lack of the capability of learning, a neural network is presented in the algorithm to train the EKF. The simulation results demonstrate that, with the comparison to the odometry and the standard EKF method under the same error divergence condition, the proposed extended Kalman filter effectively improves the accuracy of the localization of the mobile robot system and effectively prevents the filter divergence.

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The ability to acquire accurate information of the surrounding road environment is crucial for autonomous driving and advanced driver assistance systems. A method to estimate the shape of the road has been developed and evaluated. The estimate is based on fusion of data from a road marking detector, a radar tracker, map data, GPS, and inertial sensors. The method is intended for highway use and focus has been on increasing the availability of a sufficiently accurate road shape estimate in the event of sensor failures. To make use of past sensor measurements, an extended Kalman filter has been used together with dynamical models for the road and the ego vehicle. Results from a performance evaluation show that the road shape estimate clearly benefits from being based on a fusion of sensor data. The different sensors have also proven to be of various importance to the different parameters that describe the road shape. / Fordon som kan köra autonomt, det vill säga utan förare, är ett mål för fordonsindustrin och en dröm för många bilägare. Det skulle möjliggöra för förare att använda tiden till annat och minska personalkostnader för transportbolag. Säkerheten på våra vägar skulle även kunna förbättras eftersom att ett sådant system har möjlighet att reagera snabbare än någon människa och drabbas inte av trötthet eller störs av andra passagerare. Förmåga att kunna inhämta och tolka information om den omkringliggande trafiksituationen är ytterst nödvändigt för att kunna utveckla autonoma fordon och behövs även för mer avancerade moderna säkerhetssytem, som till exempel kollissionsvarningssystem. En viktig del i detta är att kunna uppfatta hur formen på vägen ser ut. Målet med detta examensarbete är att utveckla en algoritm som estimerar vägens form baserat på ett antal sensorer monterade på ett fordon och information från en kartdatabas. Den största vikten har legat på att algoritmen alltid ska kunna leverera en tillräckligt bra skattning, även i perioder när sensormätningar inte finns tillgängliga på grund av att sensorer fallerar. Den tänkta miljön är motorvägskörning, främst därför att det innebär en hel del förenklingar i jämförelse med andra typer av vägar. Det stora problemet för sådana algoritmer ligger ofta i att sensorer lider av olika typer av nackdelar. De mäter bara en viss specifik sak, kan ha stora mätfel, är känsliga för olika förhållanden och har begränsingar i räckvidd. För att uttnyttja sensorernas olika styrkor och mildra effekten av deras brister har ett flertal sensorer använts tillsammans. Examensarbetet har utförts på Scania och testats på deras lastbilar. De typer av sensorer som har använts är redan, eller är på god väg att bli, standardutrustning i deras lastbilar och i många andra moderna fordon. Algoritmen använder sig av mätningar från en vägmarkeringsdetektor, som tillhandahåller formen på de två närmaste väglinjerna, en radar, som ger position och rörelse hos framförvarande bilar, en kartdatabas, som tillsammans med en GPS ger tidigare uppmätt kurvatur vid fordonets position, och interna sensorer som mäter det egna fordonets rörelser. För att kunna fortsätta ge en skattning när mätningar inte finns tillgängliga och för att göra algoritmen robustare mot dålig data, har en metod använts som uttnyttjar informationen i tidigare mätvärden, ett så kallat Extended Kalman filter. Denna metod kräver en matematisk beskrivning av hur formen på vägen framför fordonet förväntas förändras över tid, baserat på hur fordonet rör sig. De olika typerna av mätvärden från sensorerna kombineras i metoden och viktas olika beroende på hur tillförlitliga man anser att sensorerna är. Algoritmen har utvärderats på mätningar från allmänna motorvägar utanför Södertälje. Resultatet från denna utvärdering visar att det är väldigt fördelaktigt att kombinera flera olika typer av sensorer för att kunna leverera en bra skattning så ofta som möjligt. Det visar sig även att de olika typerna av sensorer är av olika stor betydelse för olika vägformsparametrar.

This thesis provides a novel approach to the problem of state estimation for discrete-time nonlinear systems in the presence of large model uncertainties. Though classical nonlinear Kalman filters such as the extended Kalman filter (EKF) can handle uncertainties by increasing the value of noise covariances, this is only applicable to systems with small uncertainties. To this end, a discretetime robust extended Kalman filter (REKF) is formulated and applied to examples from the fields of aerospace engineering and signal processing with an emphasis on attitude estimation for small unmanned aerial vehicles (UAVs) and image processing under the influence of atmospheric turbulence. The robust filter is an approximate set-valued state estimator where the Riccati and filter equations are obtained as an approximate solution to a reverse-time optimal control problem defining the set-valued state estimator. The advantages of the REKF over the classical EKF are investigated for examples from the fields aerospace engineering and signal processing where large model uncertainties are introduced. In the case of small UAVs, an alternative attitude estimation algorithm based on the REKF is proposed in the event of gyroscopic failure and the inability of the vehicle to carry redundant sensors due to limited payload capabilities. In the case of image reconstruction under atmospheric turbulence, a robust pixel-wandering (random shifts) scheme is proposed to aid the process of image reconstruction. Also, problems pertaining to platform vibration analysis for aerospace vehicles and a frequency demodulation process in the presence of channel-induced uncertainties is also discussed.

We introduce Simultaneous Localization and Tracking (SLAT), the problem of tracking a target in a sensor network while simultaneously localizing and calibrating the nodes of the network. Our proposed solution, LaSLAT, is a Bayesian filter providing on-line probabilistic estimates of sensor locations and target tracks. It does not require globally accessible beacon signals or accurate ranging between the nodes. When applied to a network of 27 sensor nodes, our algorithm can localize the nodes to within one or two centimeters.

A mobile robot needs to know its pose (position and orientation) in order to navigate and perform useful tasks. The problem of determining this pose with respect to a global or local frame is called localisation, and is a key component in providing autonomy to mobile robots. Thus, localisation answers the question Where am I? from the robot’s perspective. Localisation involving a single robot is a widely explored and documented problem in mobile robotics. The basic idea behind most documented localisation techniques involves the optimum combination of noisy and uncertain information that comes from various robot’s sensors. However, many complex robotic applications require multiple robots to work together and share information among themselves in order to successfully and efficiently accomplish certain tasks. This leads to research in collaborative localisation involving multiple robots. Several studies have shown that when multiple robots collaboratively localise themselves, the resulting accuracy in their estimated positions and orientations outperforms that of a single robot, especially in scenarios where robots do not have access to information about their surrounding environment. This thesis presents the main theme of most of the existing collaborative, multi-robot localisation solutions, and proposes an alternative or complementary solution to some of the existing challenges in multirobot localisation. Specifically, in this thesis, a heuristically tuned Extended Kalman Filter is proposed to localise a group of mobile robots. Simulations show that when certain conditions are met, the proposed tuning method significantly improves the accuracy and reliability of poses estimated by the Extended Kalman Filter. Real world experiments performed on custom-made robotic platforms validate the simulation results.

Due to some technical and environmental constraints, expanding the current electric power generation and transmission system is being challenged by even increasing the deployment of distributed renewable generation and storage systems. Energy storage can be used to store energy from utility during low-demand (off-peak) hours and deliver this energy back to the utility during high-demand (on-peak) hours. Furthermore, energy storage can be used with renewable sources to overcome some of their limitations such as their strong dependence on the weather conditions, which cannot be perfectly predicted, and their unmatched or out-of-synchronization generation peaks with the demand peaks. Generally, energy storage enhances the performance of distributed renewable sources and increases the efficiency of the entire power system. Moreover, energy storage allows for leveling the load, shaving peak demands, and furthermore, transacting power with the utility grid. This research proposes an energy management system (EMS) to manage the operation of distributed grid-tied battery micro-storage systems for stationary applications when operated with and without renewable sources. The term "micro" refers to the capacity of the energy storage compared to the grid capacity. The proposed management system employs four dynamic models; economic model, battery model, and load and weather forecasting models. These models, which are the main contribution of this research, are used in order to optimally control the operation of the micro-storage system (MSS) to maximize the economic return for the end-user when operated in an electricity spot market system. Chapter 1 presents an introduction to the drawbacks of the current power system, the role of energy storage in deregulated electricity markets, limitations of renewable sources, ways for participating in spot electricity markets, and an outline of the main contributions in this dissertation. In Chapter 2, some hardware design considerations for distributed micro-storage systems as well as some economic analyses are presented. Chapters 3 and 4 propose a battery management system (BMS) that handles three main functions: battery charging, state-of-charge (SOC) estimation and state-of-health (SOH) estimation. Chapter 5 proposes load and weather forecasting models using artificial neural networks (ANNs) to develop an energy management strategy to control the operation of the MSS in a spot market system when incorporated with other renewable energy sources. Finally, conclusions and future work are presented in Chapter 6.

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