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The current method to compute the wellbore while drilling is based on stationary surveys at the desired station. This is done by measuring the inclination and the azimuth of the borehole between the current and the previously surveyed stations. Using a mathematical model based on assumptions of the shape of the drilled section, the coordinates of the borehole can be derived. This current method neglects the actual trajectory between the two surveying stations. Exploration and production companies demand cost effective drilling operations. Thus, demand has been rising for a continuous survey that captures the actual trajectory between the stationary surveying stations. This provides an actual estimate of the curvature "dogleg" along the well trajectory. In addition, this allows a better estimation of the casing and cementing of the borehole. Therefore, in this development the wellbore trajectory between the two surveying stations is continuously surveyed using three accelerometers and three MEMS gyroscopes. The computation algorithm is based on strap down Inertial Navigation System mechanization and Kalman filtering. The inputs to the continuous drilling survey system are the accelerometers and gyroscopes measurements, while the outputs are position, tool face, inclination and azimuth of the drill bit. This wellbore survey system will exhibit an unlimited growth of position, and azimuth errors if there are no external observations to update the surveying system. Two external update schemes can limit this error growth while drilling. The first is based on the continuous source of drilled pipe length measurements while the second is the zero velocity update. The Kalman filter continuous surveying system was successfully applied to drilling tests. External updates of the drill pipe length were utilized to reduce measurement error drift. When the drilling process was stopped to connect new drill pipe stands, zero velocity updates were employed by the Kalman filter.

The premise in using principles of vibration-based damage identification in structural health monitoring (SHM) is that when damage occurs, the mechanical properties of the structure will be altered and thus the vibration response of the structure to externally applied loads and environmental conditions will be changed. The low frequency of civil structures and the limited effect of local damage on vibration characteristics of these structures make the development of efficient SHM systems challengeable.

In antisubmarine warfare (ASW), fast and accurate angle of arrival (AOA) estimation is required in order to localize fast motion targets such as torpedoes. For this purpose we introduce in this paper a new approach called warped delay-and-sum (WDAS) beamforming to estimate the AOA of a narrowband source with uniform linear array (ULA). The basic idea of warped delay-and-sum (WDAS) beamforming is based on calculating the spatial spectrum of the conventional delay-and-sum (DAS) beamforming at nonuniform spaced angles obtained by a spatial frequency transformation using a first order allpass function with a complex valued parameter. This research proves the perfection of the proposed method through simulation work. Moreover, a comparative study will be conducted to compare the performance with the conventional DAS beamforming.

Navigation solutions have become a vital part of our lives due to their use in numerous applications. Among these is land vehicle navigation, where navigation systems are embedded in passenger cars, ambulances, police cars, farming vehicles, fire trucks, among others. Two popular navigation systems employed in such an application are the Global Positioning System (GPS) and the Inertial Navigation System (INS). The GPS and INS have been integrated for their complementary characteristics to provide a more accurate and robust navigation solution than either stand-alone system. Based on the required accuracy, the simplicity of the desired system, and other factors, the two systems are integrated using any of the three common approaches of integration with the deepest level being the ultra-tightly coupled GPS/INS. The significance of research in this paper is two-fold. First, it introduces a new ultra-tight GPS/INS integrated system that enhances a receiver’s tracking robustness and sensitivity in challenging scenarios and consequently improves navigation availability. Second, it presents a novel GPS/INS trajectory data simulation podium. The latter proposed system can generate simulation data sets for all levels of GPS/INS integration and is used in this paper to verify the operation and performance of the newly implemented ultra-tight GPS/INS integrated system. The software receiver approach has been adopted as a platform for implementing the proposed algorithm; however, the proposed methodology can be applied in conventional hardware receivers as well. The performance of the suggested system(s) has been tested using a variety of simulation trajectory data sets in various conditions and scenarios.

The global navigation satellite system (GNSS) is often considered as the solution of choice for navigating in outdoor environments. However, in the specific care of urban environments, the information delivered by a GNSS receiver is not very precise and may even be unavailable. For this reason, in most cases, the positioning task is realized by coupling of a satellite geo-referencing system with sensors such as odometers, inertial units, etc. To reduce the cost of such navigation solution for land vehicles, the three-dimensional reduced inertial sensor system (3D-RISS) algorithm has been developed for integration with GNSS to provide integrated navigation solution for land vehicles. The inertial sensors are accurate in the short term but drift over time. A solution is then to use other exteroceptive sensors such as cameras and Light Detection and Ranging (LiDAR). The estimation of camera’s egomotion over time is known as visual odometry (VO). Although pose estimation can be achieved by evaluating the best estimate for the essential matrix using Nister’s 5-point algorithm, iterative estimation from feature correspondences always causes errors that will accumulate over time and lead to drifts that even robust pose-graph optimization algorithms cannot avoid. To remedy this problem, an optimization process such as bundle adjustment is generally applied to optimize the estimation of camera poses and 3D-landmark parameters simultaneously. To make use of this optimization method, we employ structure-from-motion (SfM) techniques by exploiting the projective geometry between 3D-landmarks and their respective projections into 2D imagery. The integration of structure-from-motion algorithms for a single camera with other positioning sensors have been rarely studied, therefore the ultimate goal of this paper is to develop and present results for an integrated SfM/3D-RISS/GNSS system. The resulting solution exhibits improved positioning accuracy over the conventional 2D-2D motion estimation method under certain conditions. Experimental verification of the proposed solution is illustrated through two real road trajectories that were performed on two different land vehicles. A description is provided to show the performance of such integration in details during GNSS outages that last up to 10 minutes.