As the demand of indoor Location-Based Services (LBSs) increases, there is a growing interest in developing an accurate indoor positioning and tracking system on mobile devices. The core location determination problem can be reformulated as a sparse natured problem and thus can be solved by applying the Compressive Sensing (CS) theory. This thesis proposes a compact received signal strength (RSS) based real-time indoor positioning and tracking systems using CS theory that can be implemented on personal digital assistants (PDAs) and smartphones, which are both limited in processing power and memory compared to laptops. The proposed tracking system, together with a simple navigation module is implemented on Windows Mobile-operated smart devices and their performance in different experimental sites are evaluated. Experimental results show that the proposed system is a lightweight real-time algorithm that performs better than other traditional fingerprinting methods in terms of accuracy under constraints of limited processing and memory resources.

At Flight and Systems test Saab AB, a post-processing software is used to process GPS data. A new software by the name GrafNav has been purchased and the purpose of this master thesis therefore became, partly to make a judgment regarding GrafNav’s ability to estimate position, velocity and accuracy, partly to if needed improve the estimate and finally find one or several methods to estimate the position and velocity accuracy. The judgment of GrafNav was performed partly by a comparison to the former post-processing software (PNAV) and partly by a comparison to the airplane’s inertial navigation system (INS). The experiments showed that GrafNav’s ability to estimate the position is comparable with PNAV:s, but its capacity to estimate the velocity is considerably worse. The velocity estimate even showed a more noisy behavior than the original velocity from the receiver. More effort is needed to judge GrafNav’s ability to estimate the accuracy thru its quality signals. A few trials were made to improve the velocity estimate thru Kalman filtering (Rauch-Tung-Striebel smoothing). The filtering was first made using only the position data from GrafNav as measurements and afterwards both position and velocity data from GrafNav was used. The outcome of the Kalman filtering showed that the best result is obtained when solely position data is used and that the estimate in general is comparable with PNAV:s estimate, but considerable big deviations is obtained in conjunction to interruptions in position data. More over, is more effort needed using both position and velocity data when performing the smoothing and also replacing the stationary Kalman filter with an adaptive filter. Finally a method was brought out to estimate the position precision and a method to estimate the velocity accuracy. Both methods use the INS velocity to perform an estimation.

As the demand of indoor Location-Based Services (LBSs) increases, there is a growing interest in developing an accurate indoor positioning and tracking system on mobile devices. The core location determination problem can be reformulated as a sparse natured problem and thus can be solved by applying the Compressive Sensing (CS) theory. This thesis proposes a compact received signal strength (RSS) based real-time indoor positioning and tracking systems using CS theory that can be implemented on personal digital assistants (PDAs) and smartphones, which are both limited in processing power and memory compared to laptops. The proposed tracking system, together with a simple navigation module is implemented on Windows Mobile-operated smart devices and their performance in different experimental sites are evaluated. Experimental results show that the proposed system is a lightweight real-time algorithm that performs better than other traditional fingerprinting methods in terms of accuracy under constraints of limited processing and memory resources.

This thesis considers the combination of Fuzzy logic and Kalman Filtering that have traditionally been considered to be radically different. The former is considered heuristic and the latter statistical. In this thesis a philosophical justification for their combination is presented. Kalman Filtering is revised to enable the incorporation of fuzzy logic in its formulation. This formulation is subsequently referred to as the Revised-Kalman Filter. Heuristic membership functions are then used in the Revised-Kalman Filter to substitute for the system and measurement covariance matrices to form a fuzzy rendition of the Kalman Filter. The Fuzzy Kalman Filter formulation is further revised according to a concept referred to as the Parallel Distributed Compensation to allow for further heuristic adjustment of the corrective gain. This formulation is referred to as the Parallel Distributed Compensated-Fuzzy Kalman Filter. <p> Simulated implementations of the above filters reveal that a tuned Kalman Filter provides the best performance. However, if conditions change, the Kalman filters performance degrades and a better performance is obtained from the two versions of the Fuzzy Kalman Filters.

Localization and mapping are two of the most central tasks when it comes to autonomous robots. It has often been performed using expensive, accurate sensors but the fast development of consumer electronics has made similar sensors available at a more affordable price. In this master thesis a TurtleBot, robot and a Microsoft Kinect, camera are used to perform Simultaneous Localization And Mapping, SLAM. The thesis presents modifications to an already existing open source SLAM algorithm. The original algorithm, based on visual odometry, is extended so that it can also make use of measurements from wheel odometry and asingle axis gyro. Measurements are fused using an Extended Kalman Filter, EKF, operating in a multirate fashion. Both the SLAM algorithm and the EKF are implemented in C++ using the framework Robot Operating System, ROS. The implementation is evaluated on two different data sets. One set is recorded in an ordinary office room which constitutes an environment with many landmarks. The other set is recorded in a conference room where one of the walls is flat and white. This gives a partially sparse featured environment. The result by providing additional sensor information is a more robust algorithm. Periods without credible visual information does not make the algorithm lose its track and the algorithm can thus be used in a larger variety of environments including such where the possibility to extract landmarks is low. The result also shows that the visual odometry can cancel out drift introduced by wheel odometry and gyro sensors.

The United Kingdom’s Civil Aviation Authority published the national aviation forecast in 2008. The forecast predicts that domestic traffic will increase by 3.5% per year, and that international traffic will grow, on average, by 4.5% during 2010-2020. Based on this prediction, the traffic density will increase dramatically in the future, and airspace will be more and more congested. Usually, there are two potential solutions to deal with this situation: improving the ability of air traffic flow management is one solution; reducing the separation minimum of aircraft is another solution. However, this thesis focuses on the second solution, based on constraints of communication, navigation and surveillance systems (CNS). Cont?d.

As wireless devices become more common, the ability to position a wireless device has become a topic of importance. Accurate positioning through technologies such as the Global Positioning System is possible for outdoor environments. Indoor environments pose a different challenge, and research continues to position users indoors. Due to the prevalence of wireless local area networks (WLANs) in many indoor spaces, it is prudent to determine their capabilities for the purposes of positioning. Signal strength and time based positioning systems have been studied for WLANs. Direction or angle of arrival (AOA) based positioning will be possible with multiple antenna arrays, such as those included with upcoming devices based on the IEEE 802.11n standard. The potential performance of such a system is evaluated. The positioning performance of such a system depends on the accuracy of the AOA estimation as well as the positioning algorithm. Two different maximum-likelihood (ML) derived algorithms are used to determine the AOA of the mobile user: a specialized simple ML algorithm, and the space- alternating generalized expectation-maximization (SAGE) channel parameter estimation algorithm. The algorithms are used to determine the error in estimating AOAs through the use of real wireless signals captured in an indoor office environment. The statistics of the AOA error are used in a positioning simulation to predict the positioning performance. A least squares (LS) technique as well as the popular extended Kalman filter (EKF) are used to combine the AOAs to determine position. The position simulation shows that AOA- based positioning using WLANs indoors has the potential to position a wireless user with an accuracy of about 2 m. This is comparable to other positioning systems previously developed for WLANs.

The standard way for both ground and aerial vehicles to navigate is to use anInertial Navigation System, INS, containing an Inertial Measurement Unit, IMU,measuring the acceleration and angular rate, and a GPS measuring the position.The IMU provides high dynamic measurements of the acceleration and the angularrate, which the INS integrates to velocity, position and attitude, respectively.While being completely impossible to jam, the dead-reckoned estimates will driftaway, i.e., the errors are unbounded. In conjunction with a GPS, providing lowdynamic updates with bounded errors, a highly dynamic system without any driftis attained. The weakness of this system is its integrity, since the GPS is easilyjammed with simple equipment and powered only by a small standard battery.When the GPS is jammed this system falls back into the behavior of the INS withunbounded errors. To counter this integrity problem a camera can be used aseither a back up to the GPS or as its replacement. The camera provides imageswhich are then matched versus a reference, e.g., a map or an aerial photo, to getsimilar estimates as the GPS would provide. The camera can of course also bejammed by blocking the view of the camera with smoke. Bad visibility can alsooccur due to bad weather, but a camera based navigation system will definitelybe more robust than one using GPS.This thesis presents two ways to fuse the measurements from the camera and theIMU, both of them utilizing the Harris corner detector to find point correspondencesbetween the camera image and an aerial photo. The systems are evaluatedby simulated data mimicking both a low and a high accuracy IMU and a camerataking snapshots of the aerial photo. Results show that for the simulated cameraimages the implemented corner detector works fine and that the overall result iscomparable to using a GPS. / Standardsättet för både flygande och markgående fordon att navigera är att användaett tröghetsnavigeringssystem, innehållande en IMU som mäter acceleration ochvinkelhastighet, tillsammans med GPS. IMU:n tillhandahåller högfrekventa mätningarav acceleration och vinkelhastighet som integreras till hastighet, positionoch attityd. Ett sådant system är omöjligt att störa, men lider av att de dödräknadestorheterna hastighet, position och attityd, med tiden, kommer att driva ivägifrån de sanna värdena. Tillsammans med GPS, som ger lågfrekventa mätningarav positionen, erhålls ett system med god dynamik och utan drift. Svagheten i ettvvisådant system är dess integritet, då GPS enkelt kan störas med enkel och billigutrustning. För att lösa integritetsproblemet kan en kamera användas, antingensom stöd eller som ersättare till GPS. Kameran tar bilder som matchas gentemoten referens ex. en karta eller ett ortofoto. Det ger liknande mätningar som de GPSger. Ett kamerabaserat system kan visserligen också störas genom att blockerasynfältet för kameran med exempelvis rök. Dålig sikt kan också uppkomma pågrund av dåligt väder eller dimma, men ett kamerabaserat system kommer definitivtatt vara robustare än ett som använder GPS.Det här examensarbetet presenterar två sätt att fusionera mätningar från etttröghetssystem och en kamera. Gemensamt för båda är att en hörndetektor, Harriscorner detector, används för att hitta korresponderande punkter mellan kamerabildernaoch ett ortofoto. Systemen utvärderas på simulerat data. Resultatenvisar att för simulerade data så fungerar den implementerade hörndetektorn ochatt prestanda i nivå med ett GPS-baserat system uppnås.

This thesis approaches the problem of estimating the arm angles of an industrial robot with flexibilities in joints and links. Due to cost-cutting efforts in the industrial robots industry, weaker components and more cost-effective structures have been introduced which in turn has led to problems with flexibilities, nonlinearities and friction. In order to handle these challenging dynamic problems and achieve high accuracy this study introduces state observers to estimate the tool position.The observers use measurements of the motor angles and an accelerometer and the different evaluated observers are based on an Extended Kalman Filter and a deterministic variant. They have been evaluated in experiments on an industrial robot with two degrees of freedom. The experimental verification shows that the state estimates can be highly accurate for medium frequency motions, ranging from 3-30Hz. For this interval the estimate were also robust to model inaccuracies.The estimation of low-frequency motions was relatively poor, due to problemswith drift for the accelerometer, and it also showed a significant dependence on the accuracy of the model. For industrial robots it is mainly the medium frequency motions which are hard to estimate with existing techniques and these observers therefore carries great potential for increased precision.

A permanent magnet synchronous motor is traditionally controlled from measured values of the angular velocity and position of the rotor. However, there is a wish from SAAB Avitronics to investigate the possibility of estimating this angular velocity and position from the current measurements. The rotating rotor will affect the currents in the motor’s stator depending on the rotor’s angular velocity, and the observer estimates the angular velocity and angular position from this effect. There are several methods proposed in the article database IEEE Xplore to observe this angular velocity and angular position. The methods of observation chosen for study in this thesis are the extended Kalman filter and a phase locked loop algorithm based on the back electro motive force augmented by an injection method at low velocities. The extended Kalman filter was also programmed to be run on a digital signal processor in SAAB Avitronics’ developing hardware. The extended Kalman filter performs well in simulations and shows promise in hardware implementation. The algorithm for hardware implementation suffers from poor resolution in calculations involving the covariance matrices of the Kalman filter due to the use of 16-bit integers, yielding an observer that only functions in certain conditions. As simulations with 32-bit integer algorithm performs well it is likely that a 32- bit implementation of the extended Kalman filter would perform well on a motor, making sensorless control possible in a wide range of operations.