Passive source localization in wireless sensor networks (WSNs) is an important field of research with numerous applications in signal processing and wireless communications. One purpose of a WSN is to determine the position of a signal emitted from a source. This position is estimated based on received noisy measurements from sensors (anchor nodes) that are distributed over a geographical area. In most cases, the sensor positions are assumed to be known exactly, which is not always reasonable. Even if the sensor positions are measured initially, they can change over time. Due to the sensitivity of source location estimation accuracy with respect to the a priori sensor position information, the source location estimates obtained can vary significantly regardless of the localization method used. Therefore, the sensor position uncertainty should be considered to obtain accurate estimates. Among the many localization approaches, signal strength based methods have the advantages of low cost and simple implementation. The received signal energy mainly depends on the transmitted power and path loss exponent which are often unknown in practical scenarios. In this dissertation, three received signal strength difference (RSSD) based methods are presented to localize a source with unknown transmit power. A nonlinear RSSD-based model is formulated for systems perturbed by noise. First, an effective low complexity constrained weighted least squares (CWLS) technique in the presence of sensor uncertainty is derived to obtain a least squares initial estimate (LSIE) of the source location. Then, this estimate is improved using a computationally efficient Newton method. The Cramer-Rao lower bound (CRLB) is derived to determine the effect of sensor location uncertainties on the source location estimate. Results are presented which show that the proposed method achieves the CRLB when the signal to noise ratio (SNR) is sufficiently high. Least squares (LS) based methods are typically used to obtain the location estimate that minimizes the data vector error instead of directly minimizing the unknown parameter estimation error. This can result in poor performance, particularly in noisy environments, due to bias and variance in the location estimate. Thus, an efficient two stage estimator is proposed here. First, a minimax optimization problem is developed to minimize the mean square error (MSE) of the proposed RSSD-based model. Then semidefinite relaxation is employed to transform this nonconvex and nonlinear problem into a convex optimization problem. This can be solved e ciently to obtain the optimal solution of the corresponding semidefinite programming (SDP) problem. Performance results are presented which con rm the e ciency of the proposed method which achieves the CRLB. Finally, an extended total least squares (ETLS) method is developed for blind localization which considers perturbations in the system parameters as well as the constraints imposed by the relation between the observation matrix and data vector. The corresponding nonlinear and nonconvex RSSD-based localization problem is then transformed to an ETLS problem with fewer constraints. This is transformed to a convex semidefinite programming (SDP) problem using relaxation. The proposed ETLS-SDP method is extended to the case with an unknown path loss exponent. The mean squared error (MSE) and corresponding CRLB are derived as performance benchmarks. Performance results are presented which show that the RSSD-based ETLS-SDP method attains the CRLB for a sufficiently large SNR. / Graduate / 0544 / lohrasbi@uvic.ca

This diploma thesis consist of overview for position measuring methods and is mainly focused on design of non-standard inductive and incremental sensor. Specifications of those sensors is based on requirements set by MESING company. Also contains verification and testing measurement of completed sensor.

Room models, currently used for controller tests, assume the room air to be perfectly mixed. A new room model is developed, assuming non-homogeneous room conditions and distinguishing between different sensor positions. From measurement in real test rooms and detailed CFD simulations, a list of convective phenomena is obtained that has to be considered in the development of a model for a room equipped with different HVAC systems. The zonal modelling approach that divides the room air into several sub-volumes is chosen, since it is able to represent the important convective phenomena imposed on the HVAC system. The convective room model is divided into two parts: a zonal model, representing the air at the occupant zone and a second model, providing the conditions at typical sensor positions. Using this approach, the comfort conditions at the occupant zone can be evaluated as well as the impact of different sensor positions. The model is validated for a test room equipped with different HVAC systems. Sensitivity analysis is carried out on the main parameters of the model. Performance assessment and energy consumption are then compared for different sensor positions in a room equipped with different HVAC systems. The results are also compared with those obtained when a well-mixed model is used. A main conclusion of these tests is, that the differences obtained, when changing the position of the controller's sensor, is a function of the HVAC system and controller type. The differences are generally small in terms of thermal comfort but significant in terms of overall energy consumption. For different HVAC systems the cases are listed, in which the use of a simplified model is not recommended. This PhD has been submitted in accordance to the conditions for attaining both the French and the German degree of a PhD, on a co-national basis, in the frame of a statement of the French government from January 18th, 1994. The research has been carried out in the Automation and Energy Management Group (AGE), Department of Sustainable Development (DDD), at the "Centre Scientifique et Technique du Bâtiment" (CSTB) in Marne la Vallée, France, in collaboration with the "Centre Energétique" (CENERG) at the "Ecole Nationale Supérieure des Mines de Paris" (ENSMP), Paris, France and the Technical University of Dresden (TUD), Germany.

Room models, currently used for controller tests, assume the room air to be perfectly mixed. A new room model is developed, assuming non-homogeneous room conditions and distinguishing between different sensor positions. From measurement in real test rooms and detailed CFD simulations, a list of convective phenomena is obtained that has to be considered in the development of a model for a room equipped with different HVAC systems. The zonal modelling approach that divides the room air into several sub-volumes is chosen, since it is able to represent the important convective phenomena imposed on the HVAC system. The convective room model is divided into two parts: a zonal model, representing the air at the occupant zone and a second model, providing the conditions at typical sensor positions. Using this approach, the comfort conditions at the occupant zone can be evaluated as well as the impact of different sensor positions. The model is validated for a test room equipped with different HVAC systems. Sensitivity analysis is carried out on the main parameters of the model. Performance assessment and energy consumption are then compared for different sensor positions in a room equipped with different HVAC systems. The results are also compared with those obtained when a well-mixed model is used. A main conclusion of these tests is, that the differences obtained, when changing the position of the controller's sensor, is a function of the HVAC system and controller type. The differences are generally small in terms of thermal comfort but significant in terms of overall energy consumption. For different HVAC systems the cases are listed, in which the use of a simplified model is not recommended. This PhD has been submitted in accordance to the conditions for attaining both the French and the German degree of a PhD, on a co-national basis, in the frame of a statement of the French government from January 18th, 1994. The research has been carried out in the Automation and Energy Management Group (AGE), Department of Sustainable Development (DDD), at the "Centre Scientifique et Technique du Bâtiment" (CSTB) in Marne la Vallée, France, in collaboration with the "Centre Energétique" (CENERG) at the "Ecole Nationale Supérieure des Mines de Paris" (ENSMP), Paris, France and the Technical University of Dresden (TUD), Germany.

For some years, inertial sensors have become increasingly popular in various sports applications due to their small size and weight. However – due to the problem of sensor drift – additional sensors are usually required to obtain reliable position estimates. In this paper, we present an approach for position estimation in bouldering that relies solely on inertial sensors and domain knowledge that is modeled as a virtual sensor. / Seit einigen Jahren erfreuen sich Inertialsensoren aufgrund ihrer geringen Größe und ihres geringen Gewichts zunehmender Beliebtheit in verschiedenen Sportanwendungen. Aufgrund des Problems des Sensordrifts sind jedoch in der Regel zusätzliche Sensoren erforderlich, um zuverlässige Positionsschätzungen zu erhalten. In diesem Beitrag stellen wir einen Ansatz zur Positionsschätzung beim Bouldern vor, der sich ausschließlich auf Inertialsensoren und Domänenwissen stützt, welches als virtueller Sensor modelliert wird.