Advanced navigation algorithms for precision landing

A detailed analysis of autonomous navigation algorithms to achieve autonomous precision landing is presented. The problem of integrated attitude determination and inertial navigation is solved. The theoretical results are applied and tested in three different applications. Optimality conditions for constrained quaternion estimation using the Kalman filter are derived. It is common in spacecraft applications to separate the attitude determination from the inertial navigation system. While this approach has worked in the past, it inevitably degrades the navigation performance when the correlations between the two systems are not correctly accounted for. It is shown how to optimally include an attitude determination algorithm into the Kalman filter. When the conditions to achieve optimality are not met, it is shown how to achieve sub-optimality by properly accounting for the correlation. The traditional approach to inertial navigation is to employ the inertial measurement unit (IMU) outputs to propagate the estimated states forward in time, rather then use them to update the state. A detailed covariance analysis of deadreckoning Mars entry navigation is performed. The contribution of various sources of IMU errors are explicitly accounted for and the filter performance is validated through Monte Carlo analysis. The drawback of dead-reckoning is that this approach prevents the inertial measurements from reducing the uncertainty of the estimated states. While this shortcoming can be compensated by the availability of other measurements, it becomes crucial when the IMU is the only sensor to provide measurements. Such a situation arises, for example, during Mars atmospheric entry. In the second application of this work, IMU measurements from a NASA mission are processed in an extended Kalman filter, and the results are compared to dead-reckoning. It is shown that is possible to reduce the uncertainty of the inertial states by filtering the IMU. The final application is lunar descent to landing navigation. In this example the IMU is filtered and the algorithms to include an attitude estimate into the Kalman filter are tested. The design performance is confirmed by Monte Carlo analysis.Aerospace Engineering and Engineering Mechanic

Enhanced indoor positioning utilising wi-fi fingerprint and QR calibration techniques

The growing interest in location-based services (LBS), due to the demand for its application in personal navigation, billing and information enquiries, has expedited the research development for indoor positioning techniques. The widely used global positioning system (GPS) is a proven technology for positioning, navigation, but it performs poorly indoors. Hence, researchers seek alternative solutions, including the concept of signal of opportunity (SoOP) for indoor positioning. This research planned to use cheap solutions by utilizing available communication system infrastructure without the need to deploy new transmitters or beacons for positioning purposes. Wi-Fi fingerprinting has been identified for potential indoor positioning due to its availability in most buildings. In unplanned building conditions where the available number of APs is limited and the locations of APs are predesignated, certain positioning algorithms do not perform well consistently. In addition, there are several other factors that influence positioning accuracy, such as different path movements of users and different Wi-Fi chipset manufacturers. To overcome these challenges, many techniques have been proposed, such as collaborative positioning techniques, data fusion of radio-based positioning and mobile-based positioning that uses sensors to sense the physical movement activity of users. A few researchers have proposed combining radio-based positioning with vision-based positioning while utilizing image sensors. This work proposed integrated layers of positioning techniques, which is based on enhanced deterministic method; Bayesian estimation and Kalman filter utilising dynamic localisation region. Here, accumulated accuracy is proposed with distribution of error location by estimation at each test point on path movement. The error distribution and accumulated accuracy have been presented in graphs and tables for each result. The proposed algorithm has been enhanced by location based calibration with additional QR calibration. It allows not only correction of the actual position but the control of the errors from being accumulated by utilizing the Bayesian technique and dynamic localisation region. The position of calibration point is determined by analysing the error distribution region. In the last part, modification on Kalman filter step for calibration algorithm did further improve the location error compared to other deterministic algorithms with calibration point. The CDF plots have shown all developed techniques that provide accuracy improvement for indoor positioning based on Wi-Fi fingerprinting and QR calibration

Underwater Remotely Operated Vehicle featuring advances in rotational actuation, communication, and localization

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 101-103).The design for a spherical Remotely Operated Vehicle (ROV) with a camera, called the Eyeball ROV due to motions similar to the human eye, is presented in this thesis. The ROV features an actuation scheme that utilizes a two-axis gimbal for changing the location of the center of mass of the ROV. This creates continuous and unlimited rotations in place on the part of the ROV, allowing the camera to be panned and tilted. A model of the ROV is presented, and control was tested in both simulation and experiments. In addition, a dual-use system for both communication and localization of the ROV is presented. This novel dual-use system uses visible blue light (-470nm) to relay data in addition to providing a beacon with which the orientation and position in space of the ROV was estimated. This localization algorithm was implemented using an Extended Kalman Filter (EKF), and was tested in both simulations and experiments.by Ian Charles Rust.S.M

Rendezvous and Docking Strategy for Crewed Segment of the Asteroid Redirect Mission

This paper will describe the overall rendezvous, proximity operations and docking (RPOD) strategy in support of the Asteroid Redirect Crewed Mission (ARCM), as part of the Asteroid Redirect Mission (ARM). The focus of the paper is on the crewed mission phase of ARM, starting with the establishment of Orion in the Distant Retrograde Orbit (DRO) and ending with docking to the Asteroid Redirect Vechicle (ARV). The paper will detail the sequence of maneuvers required to execute the rendezvous and proximity operations mission phases along with the on-board navigation strategies, including the final approach phase. The trajectories to be considered will include target vehicles in a DRO. The paper will also discuss the sensor requirements for rendezvous and docking and the various trade studies associated with the final sensor selection. Building on the sensor requirements and trade studies, the paper will include a candidate sensor concept of operations, which will drive the selection of the sensor suite; concurrently, it will be driven by higher level requirements on the system, such as crew timeline constraints and vehicle consummables. This paper will address how many of the seemingly competing requirements will have to be addressed to create a complete system and system design. The objective is to determine a sensor suite and trajectories that enable Orion to successfully rendezvous and dock with a target vehicle in trans lunar space. Finally, the paper will report on the status of a NASA action to look for synergy within RPOD, across the crewed and robotic asteroid missions

Flight Mechanics/Estimation Theory Symposium, 1990

This conference publication includes 32 papers and abstracts presented at the Flight Mechanics/Estimation Theory Symposium on May 22-25, 1990. Sponsored by the Flight Dynamics Division of Goddard Space Flight Center, this symposium features technical papers on a wide range of issues related to orbit-attitude prediction, determination and control; attitude sensor calibration; attitude determination error analysis; attitude dynamics; and orbit decay and maneuver strategy. Government, industry, and the academic community participated in the preparation and presentation of these papers

Mathematical theory of the Goddard trajectory determination system

Basic mathematical formulations depict coordinate and time systems, perturbation models, orbital estimation techniques, observation models, and numerical integration methods

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included

Active control of turbulence-induced helicopter vibration

Helicopter vibration signatures induced by severe atmospheric turbulence have been shown to differ considerably from nominal, still air vibration. The perturbations of the transmission frequency have significant implications for the design of passive and active vibration alleviation devices, which are generally tuned to the nominal vibration frequency. This thesis investigates the existence of the phenomena in several realistic atmospheric turbulence environments, generated using Computational Fluid Dynamic (CFD) engineering software and assimilated within a high-fidelity rotorcraft simulation, RASCAL. The RASCAL simulation is modified to calculate blade element sampling of the gust, enabling thorough, high frequency analyses of the rotor response. In a final modification, a numerical, integration-based inverse simulation algorithm, GENISA is incorporated and the augmented simulation is henceforth referred to as HISAT. Several implementation issues arise from the symbiosis, principally because of the modelling of variable rotorspeed and lead-lag motion. However, a novel technique for increasing the numerical stability margins is proposed and tested successfully. Two active vibration control schemes, higher harmonic control 'HHC' and individual blade control 'IBC', are then evaluated against a 'worst-case' sharp-edged gust field. The higher harmonic controller demonstrates a worrying lack of robustness, and actually begins to contribute to the vibration levels. Several intuitive modifications to the algorithm are proposed but only disturbance estimation is successful. A new simulation model of coupled blade motion is derived and implemented using MATLAB and is used to design a simple IBC compensator. Following bandwidth problems, a redesign is proposed using H theory which improves the controller performance. Disturbance prediction/estimation is attempted using artificial neural networks to limited success. Overall, the aims and objectives of the research are met

MEMS Technology for Biomedical Imaging Applications

Biomedical imaging is the key technique and process to create informative images of the human body or other organic structures for clinical purposes or medical science. Micro-electro-mechanical systems (MEMS) technology has demonstrated enormous potential in biomedical imaging applications due to its outstanding advantages of, for instance, miniaturization, high speed, higher resolution, and convenience of batch fabrication. There are many advancements and breakthroughs developing in the academic community, and there are a few challenges raised accordingly upon the designs, structures, fabrication, integration, and applications of MEMS for all kinds of biomedical imaging. This Special Issue aims to collate and showcase research papers, short commutations, perspectives, and insightful review articles from esteemed colleagues that demonstrate: (1) original works on the topic of MEMS components or devices based on various kinds of mechanisms for biomedical imaging; and (2) new developments and potentials of applying MEMS technology of any kind in biomedical imaging. The objective of this special session is to provide insightful information regarding the technological advancements for the researchers in the community