This study proposed a numerical algorithm for estimating the angular misalignments between sensors of an ultra short baseline (USBL) positioning system. The algorithm is based on positioning a seabed transponder by moving a vessel along a predetermined straight-line path. Under the scheme of straight-line survey, mathematical representations of positioning error arising from heading, pitch, and roll misalignments were derived, respectively. The effect of each misalignment angle and how the differences can be used to calibrate each misalignment angle in turn are presented. A USBL calibration procedure that takes advantage of the geometry of position errors resulting from angular misalignments is then developed. During the USBL measurement, temporal and spatial variations of sound speed structure in water column are the major error sources. Therefore, this study used the sound speed profile together with a ray tracing method to correct observations of the USBL measurement. In addition, this study developed a method to compensate the effects of cross-track error on the estimation of alignment errors, and this makes the proposed algorithm applicable for using a vessel without dynamic positioning (DP) systems to collect USBL observations. The performance of the algorithm is evaluated through simulation and field experiment. The simulation and experimental results have demonstrated the effectiveness and robustness of the iterative scheme in finding alignment errors. The proposed algorithm yields a very rapid convergence of the solution series; usually the estimates obtained in the first iteration approximate to true values, and only a few iterations are necessary to achieve fairly accurate solutions.

On paper and pencil multiple choice tests, the potential for examinees to mark their answers in incorrect locations presents a serious threat to the validity of test score interpretations. When an examinee skips one or more items (i.e., answers out of sequence) but fails to accurately reflect the size of that skip on their answer sheet, that can trigger a string of misaligned responses called shift errors. Shift errors can result in correct answers being marked as incorrect, leading to possible underestimation of an examinee's true ability. Despite movement toward computerized testing in recent years, paper and pencil multiple choice tests are still pervasive in many high stakes assessment settings, including K 12 testing (e.g., MCAS) and college entrance exams (e.g., SAT), leaving a continuing need to address issues that arise within this format. Techniques for detecting aberrant response patterns are well established but do little to recognize reasons for the aberrance, limiting options for addressing the misfitting patterns. While some work has been done to detect and address specific forms of aberrant response behavior, little has been done in the area of shift error detection, leaving great room for improvement in addressing this source of aberrance. The opportunity to accurately detect construct irrelevant errors and either adjust scores to more accurately reflect examinee ability or flag examinees with inaccurate scores for removal from the dataset and retesting would improve the validity of important decisions based on test scores, and could positively impact model fit by allowing for more accurate item parameter and ability estimation. The purpose of this study is to investigate new algorithms for shift error detection that employ IRT models for probabilistic determination as to whether misfitting patterns are likely to be shift errors. The study examines a matrix of detection algorithms, probabilistic models, and person parameter methods, testing combinations of these factors for their selectivity (i.e., true positives vs. false positives), sensitivity (i.e., true shift errors detected vs. undetected), and robustness to parameter bias, all under a carefully manipulated, multifaceted simulation environment. This investigation attempts to provide answers to the following questions, applicable across detection methods, bias reduction procedures, shift conditions, and ability levels, but stated generally as: 1) How sensitively and selectively can an IRT based probabilistic model detect shift error across the full range of probabilities under specific conditions?, 2) How robust is each detection method to the parameter bias introduced by shift error?, 3) How well does the detection method detect shift errors compared to other, more general, indices of person fit?, 4) What is the impact on bias of making proposed corrections to detected shift errors?, and 4) To what extent does shift error, as detected by the method, occur within an empirical data set? Results show that the proposed methods can indeed detect shift errors at reasonably high detection rates with only a minimal number of false positives, that detection improves when detecting longer shift errors, and that examinee ability is a huge determinant factor in the effectiveness of the shift error detection techniques. Though some detection ability is lost to person parameter bias, when detecting all but the shortest shift errors, this loss is minimal. Application to empirical data also proved effective, though some discrepancies in projected total counts suggest that refinements in the technique are required. Use of a person fit statistic to detect examinees with shift errors was shown to be completely ineffective, underscoring the value of shift error specific detection methods.

Synchronization between base stations is a fundamental part of any operating telecommunication system. With 5G and future generations of mobile networks, the data speeds are getting higher, which creates the need for fast and accurate synchronization. In wireless systems, the transmitted signals are affected by the environment. Both moving and stationary objects can cause a transmitted signal to be scattered or reflected, causing the receiver to receive multiple instances of one signal. If a synchronization signal is transmitted from one base station and received in multiple instances by another, it is hard for the receiving base station to know which of the received instances that should be used for calculating the synchronization error between the base stations. In this thesis, multiple different algorithms for selecting a synchronization signal pair between two base stations to be used for calculating time alignment error have been tested. The results have been evaluated based on their accuracy of selecting a correct matching signal pair. It is shown that the proposed algorithms in this thesis all perform significantly better than the method currently in use. Further, the advantages and disadvantages of each of the new algorithms are discussed, and finally new concepts for future studies are suggested. / Synkronisering mellan basstationer är en fundamental del av ett fungerande telekommunikationssystem. Med 5G och framtida generationer av mobila nätverk så ökas datahastigheter, vilket skapar behovet av en snabb och precis synkronisering. I trådlösa system påverkas skickade signaler av dess omgivning. Både stationära och icke-stationära objekt i omgivningen kan splittra eller reflektera signaler, vilket ger upphov till en flervägskanal. Detta gör att en mottagare kan ta emot flera instanser av en skickad signal. Om en synkroniseringssignal skickas från en basstation via en flervägskanal till en mottagande basstation, så kommer mottagaren att ta emot flera instanser av den skickade signalen vid olika tidpunkter. Det kan då vara svårt för mottagaren att avgöra vilken av de mottagna signalerna som ska användas vid beräkning av tidsfelet mellan basstationerna. I detta examensarbete testas ett flertal olika algoritmer för att välja vilket synkroniseringssignalpar som ska användas vid beräkning av tidsfelet mellan två basstationer. Resultatet utvärderas baserat på hur hög precision algoritmen har i att välja ett korrekt matchat synkroniseringssignalpar. Resultatet visar att de algoritmer som presenteras i denna uppsats presterar märkbart bättre än den algoritm som används i systemen just nu. Vidare diskuteras fördelar och nackdelar med de olika algoritmerna och förslag på vidareutveckling av algoritmerna läggs fram.