The main idea of this thesis is to de ne and formulate the role of bias estimation in multitarget{multisensor scenarios as a general framework for various measurement types. After a brief introduction of the work that has been done in this thesis, three main contributions are explained in detail, which exercise the novel ideas. Starting with radar measurements, a new bias estimation method that can estimate o set and scaling biases in large network of radars is proposed. Further, Cram er{Rao Lower Bound is calculated for the bias estimation algorithm to show the theoretical accuracy that can be achieved by the proposed method. In practice, communication loss is also part of the distributed systems, which sometimes can not be avoided. A novel technique is also developed to accompany the proposed bias estimation method in this thesis to compensate for communication loss at di erent rates by the use of tracklets. Next, bearing{only measurements are considered. Biases in this type of measurement can be di cult to tackle because the measurement noise and systematic biases are normally larger than in radar measurements. In addition, target observability is sensitive to sensor{target alignment and can vary over time. In a multitarget{ multisensor bearing{only scenario with biases, a new model is proposed for the biases that is decoupled form the bearing{only measurements. These decoupled bias measurements then are used in a maximum likelihood batch estimator to estimate the biases and then be used for compensation. The thesis is then expanded by applying bias estimation algorithms into video sensor measurements. Video sensor measurements are increasingly implemented in distributed systems because of their economical bene ts. However, geo{location and geo{registration of the targets must be considered in such systems. In last part of the thesis, a new approach proposed for modeling and estimation of biases in a two video sensor platform which can be used as a standalone algorithm. The proposed algorithm can estimate the gimbal elevation and azimuth biases e ectively. It is worth noting that in all parts of the thesis, simulation results of various scenarios with di erent parameter settings are presented to support the ideas, the accuracy, mathematical modelings and proposed algorithms. These results show that the bias estimation methods that have been conducted in this thesis are viable and can handle larger biases and measurement errors than previously proposed methods. Finally, the thesis conclude with suggestions for future research in three main directions. / Thesis / Doctor of Philosophy (PhD)

Tracking systems are already encountered in everyday life in numerous applications, but many algorithms from the existing literature rely on assumptions that do not always hold in realistic scenarios, or can only be applied in niche circumstances. Therefor this thesis is motivated to develop new approaches that relax assumptions and restrictions, improve tracking performance, and are applicable in a broad range of scenarios. In the area of terrain-aided tracking this an algorithm is proposed to track targets using a Gaussian mixture measurement distribution to better represent multimodal distributions that can arise due to terrain conditions. This allowed effective use in a wider range of terrain conditions than existing approaches, which assume a unimodal Gaussian measurement distribution. Next, the problem of estimating and compensating for sensor biases is considered in the context of terrain-aided tracking. Existing approaches to bias estimation cannot be easily reconciled with the nonlinear converted measurement model applied in terrain-aided tracking. To address this, a novel efficient bias estimation algorithm is proposed that can be applied to a wide range of measurement models and operational scenarios, allowing for effective bias estimation and measurement compensation to be performed in situations that cannot be handled by existing algorithms. Finally, to address scenarios where converted measurement tracking is not possible or desired, the problem of sensor motion compensation when tracking in pixel coordinates is considered. Existing approaches compensate for sensor motion by transforming state estimates between frames, but are only able to achieve partial transformation of the state estimate and its covariance matrix. This thesis proposes a novel algorithm used to transform the full state estimate and its covariance matrix, improving tracking performance when tracking with a low frame rate and when tracking targets moving with a nearly coordinated turn motion model. Each of the proposed algorithms are evaluated in several simulated scenarios and compared against existing approaches and baselines to demonstrate their efficacy. / Thesis / Doctor of Philosophy (PhD)

No description available.

In this work, a practical approach to assessing bias and uncertainty using patient samples in a clinical laboratory is presented. The scheme is essentially a splitsample setup where one instrument is appointed to being the “master” instrument which other instruments are compared to. The software presented automatically collects test results from a Laboratory Information System in production and couples together the results of pairwise measurements. Partitioning of measurement results by user-defined criteria and how this can facilitate isolation of variation sources are also discussed. The logic and essential data model are described and the surrounding workflows outlined. The described software and workflow are currently in considerable practical use in several Swedish large-scale distributed laboratory organizations. With the appropriate IT-support, split-sample testing can be a powerful complement to external quality assurance.

No description available.

This thesis proposes a set of methods for the purpose of improving the calibration of three-axis magnetometers. Specifically, these methods aim to improve the accuracy of the bias estimation methods currently in use. The first proposed method utilizes a constrained optimization problem based on norm preserving. The second proposed method finds the same bias estimate as the first method, but in a computationally more efficient manner. The last proposed method tackles the case where the value of the local geomagnetic field is only imprecisely known. Computer simulations demonstrate the viability of the proposed methods.

abstract: Earth-system models describe the interacting components of the climate system and technological systems that affect society, such as communication infrastructures. Data assimilation addresses the challenge of state specification by incorporating system observations into the model estimates. In this research, a particular data assimilation technique called the Local Ensemble Transform Kalman Filter (LETKF) is applied to the ionosphere, which is a domain of practical interest due to its effects on infrastructures that depend on satellite communication and remote sensing. This dissertation consists of three main studies that propose strategies to improve space- weather specification during ionospheric extreme events, but are generally applicable to Earth-system models: Topic I applies the LETKF to estimate ion density with an idealized model of the ionosphere, given noisy synthetic observations of varying sparsity. Results show that the LETKF yields accurate estimates of the ion density field and unobserved components of neutral winds even when the observation density is spatially sparse (2% of grid points) and there is large levels (40%) of Gaussian observation noise. Topic II proposes a targeted observing strategy for data assimilation, which uses the influence matrix diagnostic to target errors in chosen state variables. This strategy is applied in observing system experiments, in which synthetic electron density observations are assimilated with the LETKF into the Thermosphere-Ionosphere- Electrodynamics Global Circulation Model (TIEGCM) during a geomagnetic storm. Results show that assimilating targeted electron density observations yields on average about 60%–80% reduction in electron density error within a 600 km radius of the observed location, compared to 15% reduction obtained with randomly placed vertical profiles. Topic III proposes a methodology to account for systematic model bias arising ifrom errors in parametrized solar and magnetospheric inputs. This strategy is ap- plied with the TIEGCM during a geomagnetic storm, and is used to estimate the spatiotemporal variations of bias in electron density predictions during the transitionary phases of the geomagnetic storm. Results show that this strategy reduces error in 1-hour predictions of electron density by about 35% and 30% in polar regions during the main and relaxation phases of the geomagnetic storm, respectively. / Dissertation/Thesis / Doctoral Dissertation Applied Mathematics 2018

In this thesis, the Recursive-Random Sample Consensus (R-RANSAC) algorithm is applied to a vision-based, cooperative target tracking system. Unlike previous applications, which focused on a single camera platform tracking targets in the image frame, this work uses multiple camera platforms to track targets in the inertial or world frame. The process of tracking targets in the inertial frame is commonly referred to as geolocation.In practical applications sensor biases cause the geolocated target estimates to be biased from truth. The method for cooperative estimation developed in this thesis first estimates the relative rotational and translational biases that exist between tracks from different vehicles. It then accounts for the biases and performs the track-to-track association, which determines if the tracks originate from the same target. The track-to-track association is based on a sliding window approach that accounts for the correlation between tracks sharing common process noise and the correlation in time between individual estimation errors, yielding a chi-squared distribution. Typically, accounting for the correlation in time requires the inversion of a Nnx x Nnx covariance matrix, where N is the length of the window and nx is the number of states. Note that this inversion must occur every time the track-to-track association is to be performed. However, it is shown that by making a steady-state assumption, the inverse has a simple closed-form solution, requiring the inversion of only two nx x nx matrices, and can be calculated offline. Distributed data fusion is performed on tracks where the hypothesis test is satisfied. The proposed method is demonstrated on data collected from an actual vision-based tracking system.A novel method is also developed to cooperatively estimate the location and size of occlusions. This capability is important for future target tracking research involving optimized path planning/gimbal pointing, where a geographical map is unavailable. The method is demonstrated in simulation.

L'estimation de la pose (position et l'attitude) en temps réel est une fonction clé pour les véhicules autonomes routiers. Cette thèse vise à étudier des systèmes de localisation pour ces véhicules en utilisant des capteurs automobiles à faible coût. Trois types de capteurs sont considérés : des capteurs à l'estime qui existent déjà dans les automobiles modernes, des récepteurs GNSS mono-fréquence avec antenne patch et une caméra de détection de la voie regardant vers l’avant. Les cartes très précises sont également des composants clés pour la navigation des véhicules autonomes. Dans ce travail, une carte de marquage de voies avec une précision de l’ordre du décimètre est considérée. Le problème de la localisation est étudié dans un repère de travail local Est-Nord-Haut. En effet, les sorties du système de localisation sont utilisées en temps réel comme entrées dans un planificateur de trajectoire et un contrôleur de mouvement pour faire en sorte qu’un véhicule soit capable d'évoluer au volant de façon autonome à faible vitesse avec personne à bord. Ceci permet de développer des applications de voiturier autonome aussi appelées « valet de parking ». L'utilisation d'une caméra de détection de voie rend possible l’exploitation des informations de marquage de voie stockées dans une carte géoréférencée. Un module de détection de marquage détecte la voie hôte du véhicule et fournit la distance latérale entre le marquage de voie détecté et le véhicule. La caméra est également capable d'identifier le type des marquages détectés au sol (par exemple, de type continu ou pointillé). Comme la caméra donne des mesures relatives, une étape importante consiste à relier les mesures à l'état du véhicule. Un modèle d'observation raffiné de la caméra est proposé. Il exprime les mesures métriques de la caméra en fonction du vecteur d'état du véhicule et des paramètres des marquages au sol détectés. Cependant, l'utilisation seule d'une caméra a des limites. Par exemple, les marquages des voies peuvent être absents dans certaines parties de la zone de navigation et la caméra ne parvient pas toujours à détecter les marquages au sol, en particulier, dans les zones d’intersection. Un récepteur GNSS, qui est obligatoire pour le démarrage à froid, peut également être utilisé en continu dans le système de localisation multi-capteur du fait qu’il permet de compenser la dérive de l’estime. Les erreurs de positionnement GNSS ne peuvent pas être modélisées simplement comme des bruits blancs, en particulier avec des récepteurs mono-fréquence à faible coût travaillant de manière autonome, en raison des perturbations atmosphériques sur les signaux des satellites et les erreurs d’orbites. Un récepteur GNSS peut également être affecté par de fortes perturbations locales qui sont principalement dues aux multi-trajets. Cette thèse étudie des modèles formeurs de biais d’erreur GNSS qui sont utilisés dans le solveur de localisation en augmentant le vecteur d'état. Une variation brutale due à multi-trajet est considérée comme une valeur aberrante qui doit être rejetée par le filtre. Selon le flux d'informations entre le récepteur GNSS et les autres composants du système de localisation, les architectures de fusion de données sont communément appelées « couplage lâche » (positions et vitesses GNSS) ou « couplage serré » (pseudo-distance et Doppler sur les satellites en vue). Cette thèse étudie les deux approches. En particulier, une approche invariante selon la route est proposée pour gérer une modélisation raffinée de l'erreur GNSS dans l'approche par couplage lâche puisque la caméra ne peut améliorer la performance de localisation que dans la direction latérale de la route. / Estimating the pose (position and attitude) in real-time is a key function for road autonomous vehicles. This thesis aims at studying vehicle localization performance using low cost automotive sensors. Three kinds of sensors are considered : dead reckoning (DR) sensors that already exist in modern vehicles, mono-frequency GNSS (Global navigation satellite system) receivers with patch antennas and a frontlooking lane detection camera. Highly accurate maps enhanced with road features are also key components for autonomous vehicle navigation. In this work, a lane marking map with decimeter-level accuracy is considered. The localization problem is studied in a local East-North-Up (ENU) working frame. Indeed, the localization outputs are used in real-time as inputs to a path planner and a motion generator to make a valet vehicle able to drive autonomously at low speed with nobody on-board the car. The use of a lane detection camera makes possible to exploit lane marking information stored in the georeferenced map. A lane marking detection module detects the vehicle’s host lane and provides the lateral distance between the detected lane marking and the vehicle. The camera is also able to identify the type of the detected lane markings (e.g., solid or dashed). Since the camera gives relative measurements, the important step is to link the measures with the vehicle’s state. A refined camera observation model is proposed. It expresses the camera metric measurements as a function of the vehicle’s state vector and the parameters of the detected lane markings. However, the use of a camera alone has some limitations. For example, lane markings can be missing in some parts of the navigation area and the camera sometimes fails to detect the lane markings in particular at cross-roads. GNSS, which is mandatory for cold start initialization, can be used also continuously in the multi-sensor localization system as done often when GNSS compensates for the DR drift. GNSS positioning errors can’t be modeled as white noises in particular with low cost mono-frequency receivers working in a standalone way, due to the unknown delays when the satellites signals cross the atmosphere and real-time satellites orbits errors. GNSS can also be affected by strong biases which are mainly due to multipath effect. This thesis studies GNSS biases shaping models that are used in the localization solver by augmenting the state vector. An abrupt bias due to multipath is seen as an outlier that has to be rejected by the filter. Depending on the information flows between the GNSS receiver and the other components of the localization system, data-fusion architectures are commonly referred to as loosely coupled (GNSS fixes and velocities) and tightly coupled (raw pseudoranges and Dopplers for the satellites in view). This thesis investigates both approaches. In particular, a road-invariant approach is proposed to handle a refined modeling of the GNSS error in the loosely coupled approach since the camera can only improve the localization performance in the lateral direction of the road. Finally, this research discusses some map-matching issues for instance when the uncertainty domain of the vehicle state becomes large if the camera is blind. It is challenging in this case to distinguish between different lanes when the camera retrieves lane marking measurements.As many outdoor experiments have been carried out with equipped vehicles, every problem addressed in this thesis is evaluated with real data. The different studied approaches that perform the data fusion of DR, GNSS, camera and lane marking map are compared and several conclusions are drawn on the fusion architecture choice.