An Effective Approach to Improving Low-Cost GPS Positioning Accuracy in Real-Time Navigation

Positioning accuracy is a challenging issue for location-based applications using a low-cost global positioning system (GPS). This paper presents an effective approach to improving the positioning accuracy of a low-cost GPS receiver for real-time navigation. The proposed method precisely estimates position by combining vehicle movement direction, velocity averaging, and distance between waypoints using coordinate data (latitude, longitude, time, and velocity) of the GPS receiver. The previously estimated precious reference point, coordinate translation, and invalid data check also improve accuracy. In order to evaluate the performance of the proposed method, we conducted an experiment using a GARMIN GPS 19xHVS receiver attached to a car and used Google Maps to plot the processed data. The proposed method achieved improvement of 4–10 meters in several experiments. In addition, we compared the proposed approach with two other state-of-the-art methods: recursive averaging and ARMA interpolation. The experimental results show that the proposed approach outperforms other state-of-the-art methods in terms of positioning accuracy

Reliable Navigation for SUAS in Complex Indoor Environments

Indoor environments are a particular challenge for Unmanned Aerial Vehicles (UAVs). Effective navigation through these GPS-denied environments require alternative localization systems, as well as methods of sensing and avoiding obstacles while remaining on-task. Additionally, the relatively small clearances and human presence characteristic of indoor spaces necessitates a higher level of precision and adaptability than is common in traditional UAV flight planning and execution. This research blends the optimization of individual technologies, such as state estimation and environmental sensing, with system integration and high-level operational planning. The combination of AprilTag visual markers, multi-camera Visual Odometry, and IMU data can be used to create a robust state estimator that describes position, velocity, and rotation of a multicopter within an indoor environment. However these data sources have unique, nonlinear characteristics that should be understood to effectively plan for their usage in an automated environment. The research described herein begins by analyzing the unique characteristics of these data streams in order to create a highly-accurate, fault-tolerant state estimator. Upon this foundation, the system built, tested, and described herein uses Visual Markers as navigation anchors, visual odometry for motion estimation and control, and then uses depth sensors to maintain an up-to-date map of the UAV\u27s immediate surroundings. It develops and continually refines navigable routes through a novel combination of pre-defined and sensory environmental data. Emphasis is put on the real-world development and testing of the system, through discussion of computational resource management and risk reduction

Very improved KINematic gravimetry: a new approach to aerogravimetry

The regional gravity field modeling by means of classical remove-compute-restore procedures is nowadays widely used in different contexts: from geodetic applications for the regional gravimetric geoid determination to exploration geophysics applications to extrapolate gridded or sparse points values of gravity anomalies (Bouguer, free-air, isostatic, etc.), useful to understand and map geological structures in a specific region. However, the accuracies and resolutions required for such exploration activity, do not consent the exploitation of satellite only gravity field data but need the integration with observation acquired at lower altitude. Thanks to the development, in the late eighties and early nineties, of Global Navigation Satellite Systems (GNSS) and the consequent availability of accurate navigational data, techniques such as airborne gravimetry, that can provide this complementary information, started to spread worldwide. This technique represents nowadays one of the most efficient techniques ideal to collect gravity observations close to the Earth’s surface, in a fast and cost-effective way. Airborne gravimetry is capable of providing gravity measurements also in challenging environments which can be difficult to access otherwise, such as mountainous areas, rain forests and polar regions. However due to the relatively high acquisition velocity, the presence of atmospheric turbulence, aircraft vibration, instrumental drift, etc. airborne data are usually contaminated by a very high observation error. For this reason a proper procedure to filter the raw observations both in the low and high frequency should be applied to recover valuable information. In this work a new methodology to process airborne gravity measurements, named Very Improved KINematic Gravimetry (VIKING) is presented. The proposed procedure allows to pre-process the raw observations coming from both the GNSS receiver and the gravimeter, with the aim to optimally combine the derived accelerations to compute gravity disturbances. Furthermore it consents to process by means of a filtering and gridding procedure these latter raw gravity disturbances to predict the signal on other points (grids or sparse points). In details, the pre-processing deals with the manipulation of data acquired from the on board gravimeter and GNSS receiver to correct biases and derive gravity accelerations; while the processing regards the procedure to filter and grid the gravity accelerations data to obtain gravity anomalies/disturbances maps. The developed algorithms used to pre-process raw GNSS acquired data are basically obtained by manipulating the classical GNSS observation equation to derive a new expression sensitive to the receiver acceleration (which corresponds to the vehicle acceleration) but almost insensitive to its actual position, by means of the implementation of the variometric approach. Regarding the pre-processing of the gravimeter data, the two principal aims of the method are the computation of all the corrections to properly combine gravimeter observations with GNSS observations and the optimal sampling of the gravimeter data, characterized by a very high observation rate, in such a way to not have loss of valuable information in terms of gravity accelerations. The proposed solution to filter and grid raw airborne observations is a remove-compute-restore like procedure, and consists in a combination of an along track Wiener filter and a classical Least Squares Collocation technique. Basically the proposed procedure is an adaptation to airborne gravimetry of the Space-Wise approach, developed by Politecnico di Milano, to process data coming from the ESA satellite mission GOCE. Among the main differences with respect to the satellite application of this approach there is the fact that, while in processing GOCE data the stochastic characteristics of the observation error can be considered a-priori well known, in airborne gravimetry, due to the complex environment in which the observations are acquired, these characteristics are unknown and should be retrieved from the dataset itself. The presented VIKING methodology is suited for airborne data analysis in order to be able to quickly filter and grid gravity observations in an easy fast and accurate way. Some innovative theoretical aspects focusing in particular on the theoretical covariance modeling are presented too. An important part of the whole research project regarded the implementation of a suitable software for airborne gravity data processing. It has been developed in parallel C language and is organized in a set of toolboxes, which can be run independently from all the other ones, or in sequence to perform the whole processing. In order to evaluate the goodness of the whole procedure and its performances, various numerical tests have been performed on a real aerogravimetric dataset. The different tests were mainly focused on the analysis of the optimal choice of some parameters involved in the computation and on the performances in terms of accuracy and computational times of the various modules. The final result of the whole VIKING procedure, once calibrated the different parameters accordingly to the numerical tests performed, shows a predicted signal with ac- curacies of about 1.3 mGal. The obtained result in term of accuracy is in line with the expectations derived from the specific survey characteristics

An intelligent navigation system for an unmanned surface vehicle

Merged with duplicate record 10026.1/2768 on 27.03.2017 by CS (TIS)A multi-disciplinary research project has been carried out at the University of Plymouth to design and develop an Unmanned Surface Vehicle (USV) named ýpringer. The work presented herein relates to formulation of a robust, reliable, accurate and adaptable navigation system to enable opringei to undertake various environmental monitoring tasks. Synergistically, sensor mathematical modelling, fuzzy logic, Multi-Sensor Data Fusion (MSDF), Multi-Model Adaptive Estimation (MMAE), fault adaptive data acquisition and an user interface system are combined to enhance the robustness and fault tolerance of the onboard navigation system. This thesis not only provides a holistic framework but also a concourse of computational techniques in the design of a fault tolerant navigation system. One of the principle novelties of this research is the use of various fuzzy logic based MSDF algorithms to provide an adaptive heading angle under various fault situations for Springer. This algorithm adapts the process noise covariance matrix ( Q) and measurement noise covariance matrix (R) in order to address one of the disadvantages of Kalman filtering. This algorithm has been implemented in Spi-inger in real time and results demonstrate excellent robustness qualities. In addition to the fuzzy logic based MSDF, a unique MMAE algorithm has been proposed in order to provide an alternative approach to enhance the fault tolerance of the heading angles for Springer. To the author's knowledge, the work presented in this thesis suggests a novel way forward in the development of autonomous navigation system design and, therefore, it is considered that the work constitutes a contribution to knowledge in this area of study. Also, there are a number of ways in which the work presented in this thesis can be extended to many other challenging domains.DEVONPORT MANAGEMENT LTD, J&S MARINE LTD AND SOUTH WEST WATER PL

Technical Workshop: Advanced Helicopter Cockpit Design

Information processing demands on both civilian and military aircrews have increased enormously as rotorcraft have come to be used for adverse weather, day/night, and remote area missions. Applied psychology, engineering, or operational research for future helicopter cockpit design criteria were identified. Three areas were addressed: (1) operational requirements, (2) advanced avionics, and (3) man-system integration