Sensor Fusion for Navigation of Autonomous Underwater Vehicle Using Kalman Filtering

An Autonomous Underwater Vehicle (AUV) is a robot that can travel underwater without requiring any intervention from the operator. As opposed to AUV, Remotely Operated Vehicle (ROV) is an underwater robot which requires manual control through a tethered wire connected to a base ship or a station. AUV finds tremendous applications in the field of defense, underwater mine detection, study of ocean floor, repair of undersea cables and is also pursued as a hobby. For an automated vehicle to travel from point A to point B, requires three interrelated technologies: Navigation, Guidance and Control. This thesis mainly focuses on the Navigation aspect of the AUV. Inertial Navigation System (INS) use accelerometers and gyroscopes to measure acceleration and attitude (orientation) rates respectively to estimate position, velocity and attitude in three orthogonal directions. Global Navigation Satellite System (GNSS) uses a cluster of satellites to estimate the position of GNSS receiver close to the surface of the earth. INS gives accurate short term navigation solutions yet its accuracy diminishes overthe long run because of accumulation of errors. The precision of GNSS navigation solution is not so good when contrasted with INS but they don't corrupt over the long run. When these two navigation systems are fused or integrated using a Kalman filter, the subsequent system performs better than either of the individualsystems even when sensors of lower cost and lower performance are used. One disadvantage of using GNSS is that the GNSS signals are lost whenever the AUV dives inside the water. But by using an integrated GNSS/INS system, an INS is allowed to navigate with improved initial error even when GNSS signals are lost, thus achieving the desired standalone performance. Moreover, whenever the GNSS signals are available, the system utilizes the INS data to decrease the signal reacquisition time for GNSS. Thus each system supports the other system to achieve the desired performance. The thesis focuses on the design and implementation of Kalman filters for these applications. First of all, dynamic model and sensor error model for strapdown INS has been developed. The effectiveness of the model was studied using Schuler oscillation test, bias error test and stationary INS test. Next, an error model for GNSS has been developed. Subsequently, various vi types of vehicle dynamic model for GNSS receivers has been developed and its error characteristics were compared using a simulated Figure‐8 track (track in the shape of 8). Finally, performance analysis of INS, GNSS and integrated GNSS/INS is studied on a Figure‐8 simulated track. Effect of loss of GNSS signals on the performance is also studied

Computerised accelerometric machine learning techniques and statistical developments for human balance analysis

Balance maintenance is crucial to participating in the activities of daily life. Balance is often considered as the ability to maintain the centre of mass (COM) position within the base of support. Primarily, to maintain balance, reliance is placed on the balance related sensory systems i.e., the visual, proprioceptive and vestibular. Several factors can affect a person’s balance such as neurological diseases, ageing, medication and obesity etc. To gain insight into the balance operations, studies rely on statistical and machine learning techniques. Statistical techniques are used for inferencing while machine learning techniques proved effective for interpretation. The focus of this study was on the issues encountered in human balance analysis such as the quantification of balance by relevant features, the relationships between COM and ground projected body sway, the performance of various sensory systems in balance analysis, and their relationships between the directions of body sway (i.e., mediolateral (ML) and anteriorposterior (AP)). A portable wireless accelerometry device was developed, balance analysis methods based on the inverted pendulum were devised and evaluated for their accuracy and reliability against a setup designed to allow manual balance measurements. Balance data were collected from 23 healthy adult subjects with the mean (standard deviation) of the age, height and weight: 24.5 (4.0) years, 173.6 (6.8) cm, and 72.7 (9.9) kg respectively. The accelerometry device was attached to the subjects at the approximate position of the illac crest, while they performed 30 seconds trials of the four conditions associated with a standard balance test called the modified Clinical Test of Sensory Interaction and Balance (mCTSIB). These required standing on a hard (ground) surface with the eyes open, standing on hard surface with the eyes closed, standing on a compliant surface (sponge, 10 cm thick) with the eyes open and standing on a compliant surface with the eyes closed. Statistical and machine learning techniques such as t-test, Wilcoxon signed-rank test, the Mann-Whitney U test, Analysis of variance (ANOVA), Kruskal-Wallis test, Friedman test, correlation analysis, linear regression, Bland and Altman analysis, principal component analysis (PCA), K-means clustering, and Kohonen neural network (KNN) were employed for interpreting the measurements. The findings showed close agreement between the developed balance analysis methods and the related measurements from the manual setup for balance analysis. The COM was observed to be responsible for differing amount of sway across the subjects and could affect both the angle and ground projected sway. The AP direction was more sensitive to sway than the ML direction. The subjects were observed to depend more on their proprioceptive system to control balance. The proprioceptive system was observed to have a greater impact in controlling the AP velocity of the subjects as compared to their visual system. The proprioceptive system had no impact on the ML velocity. The visual system was responsible for the control of the ML velocity and for reducing the acceleration in both directions. It was concluded that for comparison of postural sway information, subjects with closely related COM positions should be compared, comparison should be carried out in respect to the base of their support. The sway normalisation by dividing with COM position should be performed to reduce the obscuring effect of the COM. Enhancement of the proprioceptive system should be carried out to reduce the AP velocity while enhancement of the visual system should be used to reduce the ML sway and acceleration in ML and AP directions. The velocity in the AP direction should be used to examine the performance of the proprioceptive system while the ML velocity and acceleration should be used for the visual system. The vestibular system characterised sway more in the AP direction, and hence, the AP direction should be used to examine its performance in balance

Aeronautical Engineering: a Continuing Bibliography with Indexes (Supplement 243)

This bibliography lists 423 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1989. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

On a wildlife tracking and telemetry system : a wireless network approach

Includes abstract.Includes bibliographical references (p. 239-261).Motivated by the diversity of animals, a hybrid wildlife tracking system, EcoLocate, is proposed, with lightweight VHF-like tags and high performance GPS enabled tags, bound by a common wireless network design. Tags transfer information amongst one another in a multi-hop store-and-forward fashion, and can also monitor the presence of one another, enabling social behaviour studies to be conducted. Information can be gathered from any sensor variable of interest (such as temperature, water level, activity and so on) and forwarded through the network, thus leading to more effective game reserve monitoring. Six classes of tracking tags are presented, varying in weight and functionality, but derived from a common set of code, which facilitates modular tag design and deployment. The link between the tags means that tags can dynamically choose their class based on their remaining energy, prolonging lifetime in the network at the cost of a reduction in function. Lightweight, low functionality tags (that can be placed on small animals) use the capabilities of heavier, high functionality devices (placed on larger animals) to transfer their information. EcoLocate is a modular approach to animal tracking and sensing and it is shown how the same common technology can be used for diverse studies, from simple VHF-like activity research to full social and behavioural research using wireless networks to relay data to the end user. The network is not restricted to only tracking animals – environmental variables, people and vehicles can all be monitored, allowing for rich wildlife tracking studies

Motorless flight research, 1972

The proceedings of a symposium on motorless flight research are presented. The subjects discussed are: (1) glider aerodynamic and design, (2) instrumentation, (3) structural concepts and materials, (4) soaring meteorology, (5) self-launching and ultralight sailplanes, and (6) performance testing

Living and working in space. A history of Skylab

The history of Skylab is examined with emphasis on program development from previous Apollo missions, modifications to spacecraft, onboard experiments, and flight crew training. A listing of the missions and an evaluation of results are included with a brief description of the workshop's reentry