No description available.

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.

No description available.

Land vehicle navigation is primarily dependent upon the Global Positioning System (GPS) which provides accurate navigation in open sky. However, in urban and rural canyons GPS accuracy degrades considerably. To help GPS in such scenarios, it is often integrated with inexpensive inertial sensors. Such sensors have complex stochastic errors which are difficult to mitigate. In the presence of speed measurements from land vehicle, a reduced number of inertial sensors can be used which improve performance and termed as the Reduced Inertial Sensor System (RISS). Existing low-cost RISS/GPS integrated algorithms have limited accuracy due to use of approximations in error models and employment of a Linearized Kalman Filter (LKF). This research developed an enhanced error model for RISS which was integrated with GPS using an Extended Kalman Filter (EKF) for improved navigation of land vehicles. The proposed system was tested on several road experiments and the results confirmed the sustainable performance of the system during long GPS outages. To further increase the accuracy, Differential GPS (DGPS) is employed where carrier phase measurements are typically used. This requires resolution of Integer Ambiguity (IA) that comes at computational and time expense. This research uses pseudorange measurements for DGPS which mitigate large biases due to atmospheric errors and obviate the resolution of IA. These measurements are integrated with the enhanced RISS to filter increased noise and help GPS during signal blockages. The performance of the proposed system was compared with two other algorithms employing undifferenced GPS measurements where atmospheric effects are mitigated using either the Klobuchar model or dual frequency receivers. The proposed system performed better than both the algorithms in positional accuracy, multipath and GPS outages. This research further explored the reduction of Time-to-Fix Ambiguities (TTFA) for land vehicle navigation. To reduce the TTFA through inertial aiding, previous research used high-end Inertial Measurement Units (IMUs). This research uses MEMS grade IMU by integrating the enhanced RISS with carrier phase measurements using EKF. This algorithm was also tested on three road trajectories and it was shown that this integration helps reduce the TTFA as compared to the GPS-only case when fewer satellites are visible. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2014-06-23 11:30:58.036

Fault detection and isolation (FDI) are essential in ensuring safe and reliable operations in industrial systems. Extensive research has been carried out on FDI for one dimensional (1-D) systems, where variables vary only with time. The existing FDI strategies are mainly focussed on 1-D systems and can generally be classified as model based and process history data based methods. In many industrial systems, the state variables change with space and time (e.g., sheet forming, fixed bed reactors, and furnaces). These systems are termed as distributed parameter systems (DPS) or two dimensional (2-D) systems. 2-D systems have been commonly represented by the Roesser Model and the F-M model. Fault detection and isolation for 2-D systems represent a great challenge in both theoretical development and applications and only limited research results are available. In this thesis, model based fault detection strategies for 2-D systems have been investigated based on the F-M and the Roesser models. A dead-beat observer based fault detection has been available for the F-M model. In this work, an observer based fault detection strategy is investigated for systems modelled by the Roesser model. Using the 2-D polynomial matrix technique, a dead-beat observer is developed and the state estimate from the observer is then input to a residual generator to monitor occurrence of faults. An enhanced realization technique is combined to achieve efficient fault detection with reduced computations. Simulation results indicate that the proposed method is effective in detecting faults for systems without disturbances as well as those affected by unknown disturbances.The dead-beat observer based fault detection has been shown to be effective for 2-D systems but strict conditions are required in order for an observer and a residual generator to exist. These strict conditions may not be satisfied for some systems. The effect of process noises are also not considered in the observer based fault detection approaches for 2-D systems. To overcome the disadvantages, 2-D Kalman filter based fault detection algorithms are proposed in the thesis. A recursive 2-D Kalman filter is applied to obtain state estimate minimizing the estimation error variances. Based on the state estimate from the Kalman filter, a residual is generated reflecting fault information. A model is formulated for the relation of the residual with faults over a moving evaluation window. Simulations are performed on two F-M models and results indicate that faults can be detected effectively and efficiently using the Kalman filter based fault detection. In the observer based and Kalman filter based fault detection approaches, the residual signals are used to determine whether a fault occurs. For systems with complicated fault information and/or noises, it is necessary to evaluate the residual signals using statistical techniques. Fault detection of 2-D systems is proposed with the residuals evaluated using dynamic principal component analysis (DPCA). Based on historical data, the reference residuals are first generated using either the observer or the Kalman filter based approach. Based on the residual time-lagged data matrices for the reference data, the principal components are calculated and the threshold value obtained. In online applications, the T2 value of the residual signals are compared with the threshold value to determine fault occurrence. Simulation results show that applying DPCA to evaluation of 2-D residuals is effective.

The following dissertation attempts to highlight and address the implementation and performance of a Kalman filter on an FPGA. The reasons for choosing the Kalman filter and the platform for implementation are highlighted as well as an in depth explanation of the components and theory behind both are given. A controller system which allows the optimal performance of the Kalman filter on it is developed in VHDL. The design of the controller is dictated by the analysis of the Kalman filter which ensures only the most necessary components and operations are built into the instruction set. The controller is made up of several components including the loader, the ALU, Data RAM, KF IO, Control Store and the Branch Unit. The components working in conjunction allows the system to interface though a handshaking protocol with a peripheral of arbitrary latency. The control store is loaded with program code that is determined by converting human readable assembler into machine code through a Perl encoder. The controller system is tested and verified though an extensive testbench environment that emulates all outside signals and views internal operations. The controller system is capable of five matrix operations which are computed in parallel due to the FPGA development environment, which is far superior in this case to the alternative: a software solution, due to the vector operations inherent in the Kalman filter algorithm. The Kalman filter operation is analyzed and simulated in a MATLAB environment and this analysis confirms the need for the parallel processing power of the FPGA system upon which the controller has been built. FPGA statistical analysis confirms the successful implementation of the system meeting all criteria set at the outset of the project, including memory usage, IO usage and performance and accuracy benchmarks. / Graduate

In this dissertation, we design and develop a novel direction-of-arrival (DOA) finding system. We investigate the problems of DOA finding using canonical and crystallographic antenna array structures, develop a novel integrated algorithm consisting of combined multiple signal classification (MUSIC) algorithm, Kalman Filter and Kent Distribution to improve the accuracy and robustness of DOA estimation, design and conduct the real time testing of DOA and verify the accuracy and efficiency of the designed DOA finding system. We first examine the ability of mitigating the aliasing and enhancing the DOA estimation of different antenna structures, including canonical and crystallographic antenna structures. Our results show that the crystallographic antenna array has a better performance of overcoming aliasing in many circumstances, improving the estimation accuracy and covering more spatial region of DOA estimation. Then we propose a novel integrated algorithm to achieve a more robust DOA finding with higher accuracy. We show that the DOA estimation using MUSIC algorithm can be strongly influenced by the size, spacing and distributions of elements of the receiving antenna array as well as noise and mutual coupling. We propose a combined MUSIC and Kalman Filter algorithm to reduce the noise and enhance the robustness of the DOA estimation. Further more we map the DOA estimation onto the sphere and use Kent distribution to characterize the spread of DOA points on the sphere. We calculate the mean direction of Kent distribution to present the DOA vector, which further improves the accuracy of DOA finding. At last, we design and build a multi-channel and real time automated measurement system to validate the proposed antenna structure and integrated algorithms. Our testing results indicate that the designed DOA finding system can work practically and efficiently, with higher accuracy and stronger robustness.

Estimation theory is a branch of statistics and probability that derives information about random variables based on known information. In process engineering, state estimation is used for a variety of purposes, such as: soft sensing, digital filter design, model predictive control and performance monitoring. In literature, there exist numerous estimation algorithms. In this study, we provide guidelines for choosing the appropriate estimator for a system under consideration. Various estimators are compared and their advantages and disadvantages are highlighted. This has been done through case studies which use examples from process engineering. We also address certain robustness issues of application of estimation techniques to chemical processes. Choice of estimator in case of high plant-model mismatch has also been discussed. The study is restricted to unconstrained nonlinear estimators. / Process Control

The complementary advantages of GPS and INS have been the principle driving factor to integrate these two navigation systems as an integrated GPS/INS system in various architectural forms to provide robust positioning. Although the loosely coupled and tightly coupled GPS/INS systems have been in existence for over a decade or two and performed reasonably well, nevertheless, the tracking performance was still a concern in non-benign environments such as dynamic scenarios, indoor environments, urban areas, under foliages etc., where the GPS tracking loops lose lock due to the signals being weak, subjected to excessive dynamics or completely blocked. The motivation of this research, therefore, was to address these limitations with an integrated GPS/Pseudolite/INS system using ultra-tight integration architecture. The main research contributions are summarised as below: (a) The performance of the tracking loops in dynamic scenarios were analysed in detail with both conventional and ultra-tight software receivers. The stochastic modelling of the INS-derived Doppler is of utmost importantance in enhancing the benefits of ultra-tight integration, and therefore, two popular stochastic techniques??? Gauss Markov (GM) and Autoregressive (AR), were investigated to model the Doppler signal. The simulation results demonstrate that the AR method is capable of producing better accuracies and is more efficient. The algorithms to determine the AR parameters (order and coefficients) were also provided. (b) The various mathematical relationships that elicit the understanding of the ultra-tightly integrated system were derived in detail. The Kalman filter design and its implementation were also provided. Various simulation and real-time experiments were conducted to study the performance of the filter, and the results confirm the underlying assumptions in the theoretical analyses and the mathematical derivations. Covariance analysis was also performed to study the convergence and stability effects of the filter. (c) Interpolator design using signal processing techniques were proposed to increase the sampling rate of the INS-derived Doppler. To efficiently realise the interpolator transfer function, two optimal techniques were investigated ??? Polyphase and Cascaded Integrator Comb (CIC), and our results show that CIC was more efficient than polyphase in accuracy and real-time implementations. (d) The integration of Pseudolites (PL) with INS in ultra-tight configuration was analysed for an indoor environment. The acquisition and tracking performances of ???Pseudolites-only??? and ???Pseudolite/INS??? modes were compared to study the impact of the inertial signals aiding. The results demonstrate that aiding of the inertial signals with the baseband loops (acquisition and tracking) improve the overall tracking performance. An overview on the effects of the pseudolite signal propagation is also given. (e) Simulation and real-time experiments have been conducted to evaluate the proposed algorithms and the overall design of the ultra-tightly integrated system. A comparison was also done between GPS/PL/INS and GPS/INS integrated systems to study the potential advantages of the pseudolite integration. The details of the field experiment are provided. The data from a real-time experiment was processed to further evaluate the robustness of the system. The results confirm that the developed mathematical models and algorithms are correct.

Zugl.: Berlin, Techn. Univ., Diss., 2006