Coronal mass ejections (CMEs) are some of the extraterrestrialevents most impactful to earth. Eorts to model and predict theireects have seen new possibilities in the two most recent decades dueto multiple new spacecrafts providing a wider range of data than everbefore. Models of these events suer from a number of inaccuracies,one of them being the density ratio between the CME and the ambientsolar wind. Since the arrival time for potentially harmful disturbancespredicted by models has been proved to be highly sensitive to thisparameter we therefore take care to set it as accurately as possible.Traditionally this value is either set to a default, justied by denitionand theory, or set to the density ratio between the bulk if the ejectedgas and the surrounding medium. A proposition has been made tomeasure density enhancement dierently, using a reference point at theshock wave preceding the CME for each event. This method strives toimprove arrival time predictions and was in this paper tested for onecoronal mass ejection event. Two runs if the model WSA-ENLIL+Conewas made; one with the default value of density enhancement, onewith a value determined through the revised method using coronographdata. Running the model with the revised value improved the predictedarrival time by moving it forwards in time by 4h, which was still tooearly. Other input data into the model run was then discussed as apossible cause of the remaining inaccuracy. / Koronamassutkastningar är ett av solfenomenen som påverkar jorden mest.Nya rymdfarkoster med instrument som satts i arbete de senaste två decenniernahar gett data som gjort det möjligt att modellera och förutse dessaevent till en högre precision än någonsin. Alla dessa modeller lider av någonform av felkälla, en av vilka är kvoten mellan densitet för massutkastningenoch den omgivande miljön. Eftersom förutsedda ankomsstider för potentielltskadliga störningar har visat sig vara särskilt känsliga för denna parameterså tar vi särskild hänsyn till att ange den så precist som möjligt. Vanligtvissätts detta värde till ett fast standardvärde, som anges av dess denitionoch bakomliggande teori, eller till kvoten mellan utkastningens bulk ochomgivningen. Ett förslag har dock lagts fram om att omdeniera parametern.Denna metod strävar efter att förbättra förutsedda ankomsttider ochhar i denna text testats för en koronamassutkastning. Två körningar avmodellen WSA-ENLIL+Cone gjordes; en med defaultvärdet för densitetsratiot,en med värdet satt genom mätning av empirisk cononagrafdata enligtden föreslagna metoden. Att köra modellen med den nya parametern förbättrade den förutsedda ankomsttiden genom att ytta den framåt i tidenmed 4 timmar, vilket fortfarande är för tidigt. Andra inputdata i modellendiskuterades då som möjliga orsaker till den återstående diskrepansen.

<p>This thesis is concerned with detection of transient signal families and detectors in nonlinear static sensor systems. The detection problems are treated within the framework of likelihood ratio based binary hypothesis testing.</p><p>An analytical solution to the noncoherent detection problem is derived, which in contrast to the classical noncoherent detector, is optimal for wideband signals. An optimal detector for multiple transient signals with unknown arrival times is also derived and shown to yield higher detection performance compared to the classical approach based on the generalized likelihood ratio test.</p><p>An application that is treated in some detail is that of ultrasonic nondestructive testing, particularly pulse-echo detection of defects in elastic solids. The defect detection problem is cast as a composite hypothesis test and a methodology, based on physical models, for designing statistically optimal detectors for cracks in elastic solids is presented. Detectors for defects with low computational complexity are also formulated based on a simple phenomenological model of the defect echoes. The performance of these detectors are compared with the physical model-based optimal detector and is shown to yield moderate performance degradation.</p><p>Various aspects of optimal detection in static nonlinear sensor systems are also treated, in particular the stochastic resonance (SR) phenomenon which, in this context, implies noise enhanced detectability. Traditionally, SR has been quantified by means of the signal-to-noise ratio (SNR) and interpreted as an increase of a system's information processing capability. Instead of the SNR, rigorous information theoretic distance measures, which truly can support the claim of noise enhanced information processing capability, are proposed as quantifiers for SR. Optimal detectors are formulated for two static nonlinear sensor systems and shown to exhibit noise enhanced detectability.</p>

This thesis is concerned with detection of transient signal families and detectors in nonlinear static sensor systems. The detection problems are treated within the framework of likelihood ratio based binary hypothesis testing. An analytical solution to the noncoherent detection problem is derived, which in contrast to the classical noncoherent detector, is optimal for wideband signals. An optimal detector for multiple transient signals with unknown arrival times is also derived and shown to yield higher detection performance compared to the classical approach based on the generalized likelihood ratio test. An application that is treated in some detail is that of ultrasonic nondestructive testing, particularly pulse-echo detection of defects in elastic solids. The defect detection problem is cast as a composite hypothesis test and a methodology, based on physical models, for designing statistically optimal detectors for cracks in elastic solids is presented. Detectors for defects with low computational complexity are also formulated based on a simple phenomenological model of the defect echoes. The performance of these detectors are compared with the physical model-based optimal detector and is shown to yield moderate performance degradation. Various aspects of optimal detection in static nonlinear sensor systems are also treated, in particular the stochastic resonance (SR) phenomenon which, in this context, implies noise enhanced detectability. Traditionally, SR has been quantified by means of the signal-to-noise ratio (SNR) and interpreted as an increase of a system's information processing capability. Instead of the SNR, rigorous information theoretic distance measures, which truly can support the claim of noise enhanced information processing capability, are proposed as quantifiers for SR. Optimal detectors are formulated for two static nonlinear sensor systems and shown to exhibit noise enhanced detectability.

We consider time-of-arrival based robust geolocation in harsh line-of-sight/non-line-of-sight environments. Herein, we assume the probability density function (PDF) of the measurement error to be completely unknown and develop an iterative algorithm for robust position estimation. The iterative algorithm alternates between a PDF estimation step, which approximates the exact measurement error PDF (albeit unknown) under the current parameter estimate via adaptive kernel density estimation, and a parameter estimation step, which resolves a position estimate from the approximate log-likelihood function via a quasi-Newton method. Unless the convergence condition is satisfied, the resolved position estimate is then used to refine the PDF estimation in the next iteration. We also present the best achievable geolocation accuracy in terms of the Cramér-Rao lower bound. Various simulations have been conducted in both real-world and simulated scenarios. When the number of received range measurements is large, the new proposed position estimator attains the performance of the maximum likelihood estimator (MLE). When the number of range measurements is small, it deviates from the MLE, but still outperforms several salient robust estimators in terms of geolocation accuracy, which comes at the cost of higher computational complexity.

To increase real estate values, developers often apply designs on the land. In the case of a single family housing development, the designs are applied to the unit of subdivisions. In this study, the designs are defined as “value creation concepts,” which increase housing values at the subdivision level. The value creation concepts are classified into five categories – the sense of arrival, product mix, walkability, circulation system, and amenity. This cross-sectional study focuses on exploring the effects of value creation concepts in the subdivision. Two methodologies – the Hedonic Price Model (HPM) and the Hierarchical Linear Model (HLM) – are used to test whether or not the value creation concepts would increase or decrease single family housing values. The study sample is composed of 6,562 single family houses nested in 85 subdivisions in College Station, Texas. Data are composed of two levels: the housing level and the subdivision level. The scores of the sense of arrival were provided by sixtyone graduate students at Texas A&M University using photograph evaluations. Most structural variables were obtained from the Brazos County Appraisal District, and physical environmental variables were objectively measured using the Geographical Information System. In the both models, sense of arrival, greenway connectivity, sidewalk connectivity, and median length of cul-de-sac variables have positive effects on single family housing values while phased project, the number of accessible entrances, street density, single family density, and median length of block variables have negative effects on single family housing values. At the housing level, several structural variables (e.g. bathrooms, attached garage, porches, etc), attached to a golf course, sports facilities, network distance from the nearest elementary school, population density, and personal variables (i.e., tenure, workable age, employment) were significant (p<.05) predictors of single family housing value. Findings support that the value creation concepts have effects on increasing housing values at the subdivision level, which would provide thoughtful insights for developers in residential areas. In addition, the HLM can be used as the complement of the HPM by controlling interaction terms between housing variables and subdivision variables, or among the subdivision variables themselves.

In this thesis, DOA estimation using array interpolation in randomly distributed sensor arrays is considered. Array interpolation is a technique in which a virtual array is obtained from the real array and the outputs of the virtual array, computed from the real array using a linear transformation, is used for direction of arrival estimation. The idea of array interpolation techniques is to make simplified and computationally less demanding high resolution direction finding methods applicable to the general class of non-structured arrays.In this study,we apply an interpolation technique for arbitrary array geometries in an attempt to extend root-MUSIC algorithm to arbitrary array geometries.Another issue of array interpolation related to direction finding is spatial smoothing in the presence of multipath sources.It is shown that due to the Vandermonde structure of virtual array manifold vector obtained from the proposed interpolation method, it is possible to use spatial smoothing algorithms for the case of multipath sources.

In this study, a neural network(NN) based algorithm is proposed for real time multiple source tracking problem based on a previously reported work. The proposed algorithm namely modified neural network based multiple source tracking algorithm (MN-MUST) performs direction of arrival(DoA) estimation in three stages which are the detection, filtering and DoA estimation stages. The main contributions of this proposed system are: reducing the input size for the uncorrelated source case (reducing the training time) of NN system without degradation of accuracy and insertion of a nonlinear spatial filter to isolate each one of the sectors where sources are present, from the others. MN-MUST algorithm finds the targets correctly no matter whether the targets are located within the same angular sector or not. In addition as the number of targets exceeds the number of antenna elements the algorithm can still perform sufficiently well. Mutual coupling in array does not influence MN-MUST algorithm performance. iv MN-MUST algorithm is further improved for a cylindrical microstrip patch antenna array by using the advantages of directive antenna pattern properties. The new algorithm is called cylindrical patch array MN-MUST(CMN-MUST). CMN-MUST algorithm consists of three stages as MN-MUST does. Detection stage is exactly the same as in MN-MUST. However spatial filtering and DoA estimation stage are reduced order by using the advantages of directive antenna pattern of cylindirical microstrip patch array. The performance of the algorithm is investigated via computer simulations, for uniform linear arrays, a six element uniform dipole array and a twelve element uniform cylindrical microstrip patch array. The simulation results are compared to the previously reported works and the literature. It is observed that the proposed algorithm improves the previously reported works. The algorithm accuracy does not degrade in the presence of the mutual coupling. A uniform cylindrical patch array is successfully implemented to the MN-MUST algorithm. The implementation does not only cover full azimuth, but also improv the accuracy and speed. It is observed that the MN-MUST algorithm provides an accurate and efficient solution to the targettracking problem in real time.

In this thesis, two-dimensional (2-D) direction-of-arrival (DOA) estimation problem is considered. Usually, DOA estimation is considered in one dimension assuming a fixed elevation angle. While this assumption simplifies the problem, both the azimuth and elevation angles, namely, the 2-D DOA estimates are required in practical scenarios. In this thesis, planar array structures are considered for 2-D DOA estimation. In this context, V-shaped arrays are discussed and some of the important features of these arrays are outlined. A new method for the design of V-shaped arrays is presented for both isotropic and directional beam patterns. The design procedure is simple and can be applied for both uniform and nonuniform V-shaped sensor arrays. Closed form expressions are presented for the V-angle in order to obtain isotropic angle performance. While circular arrays have the isotropic characteristics, V-shaped arrays present certain advantages due to their large aperture for the same number of sensors and inter-sensor distance. The comparison of circular and V-shaped arrays is done by considering the azimuth and elevation Cramer-Rao Bounds (CRB). It is shown that V-shaped and circular arrays have similar characteristics for the sensor position errors while the uniform isotropic (UI) V-array performs better when there is mutual coupling and the sources are correlated. In the literature, there are several techniques for 2-D DOA estimation. Usually, fast algorithms are desired for this purpose since a search in two dimensions is a costly process. These algorithms have a major problem, namely, the pairing of the azimuth-elevation couples for multiple sources. In this thesis, a new fast and effective technique for this purpose is proposed. In this technique, a virtual array output is generated such that when the ESPRIT algorithm is used, the eigenvalues of the rotational transformation matrix have the 2-D angle information in both magnitude and phase. This idea is applied in different scenarios and three methods are presented for these cases. In one case, given an arbitrary array structure, array interpolation is used to generate the appropriate virtual arrays. When the antenna mutual coupling is taken into account, a special type of array structure, such as circular, should be used in order to apply the array interpolation. In general, the array mutual coupling matrix (MCM) should have a symmetric Toeplitz form. It is shown that the 2-D DOA performance of the proposed method approaches to the CRB by using minimum number of antennas in case of mutual coupling. This method does not require the estimation of the mutual coupling coefficients. While this technique is effective, it has problems especially when the number of sources increases. In order to improve the performance, MCM is estimated in the third approach. This new approach performs better, but it cannot be used satisfactorily in case of multipath signals. In this thesis, the proposed idea for fast 2-D DOA estimation is further developed in order to solve the problem when mutual coupling and multipath signals jointly exist. In this case, real arrays with some auxiliary sensors are used to generate a structured mutual coupling matrix. It is shown that the problem can be effectively solved when the array structure has a special form. Specifically, parallel uniform linear arrays (PULA) are employed for this purpose. When auxiliary sensors are used, a symmetric banded Toeplitz MCM is obtained for the PULA. This allows the application of spatial smoothing and ESPRIT algorithm for 2-D DOA estimation. The proposed algorithm uses triplets and presents closed form paired 2-D DOA estimates in case of unknown mutual coupling and multipath signals. Several simulations are done and it is shown that the proposed array structure and the method effectively solve the problem.

Higher Order Statistics (HOS) and Second Order Statistics (SOS) approaches have certain advantages and disadvantages in signal processing applications. HOS approach provides more statistical information for non-Gaussian signals. On the other hand, SOS approach is more robust to the estimation errors than the HOS approach, especially when the number of observations is small. In this thesis, HOS and SOS approaches are jointly used in order to take advantage of both methods. In this respect, the joint use of HOS and SOS approaches are introduced for online calibration of sensor arrays with arbitrary geometries. Three different problems in online array calibration are considered and new algorithms for each of these problems are proposed. In the first problem, the positions of the randomly deployed sensors are completely unknown except the two reference sensors and HOS and SOS approaches are used iteratively for the joint Direction of Arrival (DOA) and sensor position estimation. Iterative HOS-SOS algorithm (IHOSS) solves the ambiguity problem in sensor position estimation by observing the source signals at least in two different frequencies and hence it is applicable for wideband signals. The conditions on these frequencies are presented. IHOSS is the first algorithm in the literature which finds the DOA and sensor position estimations in case of randomly deployed sensors with unknown coordinates. In the second problem, narrowband signals are considered and it is assumed that the nominal sensor positions are known. Modified IHOSS (MIHOSS) algorithm uses the nominal sensor positions to solve the ambiguity problem in sensor position estimation. This algorithm can handle both small and large errors in sensor positions. The upper bound of perturbations for unambiguous sensor position estimation is presented. In the last problem, an online array calibration method is proposed for sensor arrays where the sensors have unknown gain/phase mismatches and mutual coupling coefficients. In this case, sensor positions are assumed to be known. The mutual coupling matrix is unstructured. The two reference sensors are assumed to be perfectly calibrated. IHOSS algorithm is adapted for online calibration and parameter estimation, and hence CIHOSS algorithm is obtained. While CIHOSS originates from IHOSS, it is fundamentally different in many aspects. CIHOSS uses multiple virtual ESPRIT structures and employs an alignment technique to order the elements of rows of the actual array steering matrix. In this thesis, a new cumulant matrix estimation technique is proposed for the HOS approach by converting the multi-source problem into a single source one. The proposed algorithms perform well even in the case of correlated source signals due to the effectiveness of the proposed cumulant matrix estimate. The iterative procedure in all the proposed algorithms is guaranteed to converge. Closed form expressions are derived for the deterministic Cram&acute / er-Rao bound (CRB) for DOA and unknown calibration parameters for non-circular complex Gaussian noise with unknown covariance matrix. Simulation results show that the performances of the proposed methods approach to the CRB for both DOA and unknown calibration parameter estimations for high SNR.

Traditional naval vessels with steel structures have the benefit of large safety factors and a distinct material endurance limit. However, as performance requirements and budget constraints rise, the demand for lighter weight vessels increases. Reducing the mass of vessels is commonly achieved by the use of aluminum or composite structures, which requires closer attention to be paid to crack initiation and propagation. It is rarely feasible to require a lengthy inspection process that removes the vessel from service for an extended amount of time. Structural health monitoring (SHM), involving continuous measurement of the structural response to an energy source, has been proposed as a step towards condition-based maintenance. Furthermore, using a passive monitoring system with an array of sensors has several advantages: monitoring can take place in real-time using only ambient noise vibrations and neither deployment of an active source nor visual access to the inspected areas are required. Passive SHM on a naval vessel is not without challenge. The structures of ships are typically geometrically complex, causing scattering, multiple reflections, and mode conversion of the propagating waves in the vessel. And rather than a distinct and predictable input produced by controlled active sources, the vibration sources are hull impacts, smaller waves, and even onboard machinery and activity. This research summarizes findings from data collected onboard a Navy vessel and presents recommendations data processing techniques. The intent is to present a robust method of passive structural health monitoring for such a vessel using only ambient vibrations recordings.