Browsing by Author "Silson, P."
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Item Open Access Attitude determination from single camera vector observations(2010-09-17) Tsourdos, Antonios; Zadovski, V.; Silson, P.Attitude determination is of major importance in Guidance and Control Systems of the Unmanned Aerial Vehicles (lIAVαs). Supplying wrong or imprecise attitude can very often be catastrophic for the UAVαs. Vision sensors are nowadays essential as they provide a rich source of information given as relative measurements between the vehicle navigation parameters (position, velocity and attitude) and the environment. This paper presents a framework for attitude determination from single camera vector observations. We assume a known environment in a form of a map and true vehicle positions from which each observation has been taken. Two different methods for attitude determination are presented: an iterative numerical solution based on Gauss Newton's method and an exact method known as the Davenport q-method. Pros and cons ofthe both solutions are presented.Item Open Access An Evaluation of Sensor and Data Fusion Technologies for Application within an Integrated Base Defence System(2009-11-11T17:45:23Z) Tsourdos, Antonios; Silson, P.; White, B.; Spillings, J.; Burke, P.The purpose of this paper is to communicate the technical issues involved in integrating data fusion technologies in the development of an integrated base defence system. Options are presented, with possible alternatives, when selecting the sensor systems to support specific mission goals. The document then provides a proposed candidate system architecture that integrates the independent systems to support the commander’s information needs.Item Open Access Merging probabilistic data of multiple targets detected by multiple sensors(2009-11-11T17:54:28Z) Spillings, J.; Tsourdos, Antonios; Silson, P.; White, B.The aim of this work is to present an extension to current data fusion techniques and associated results for the implementation of a collaborative multi-platform, multi-target detection system. A multi-sighting data fusion algorithm has been simulated. The groundbased platforms have been assumed autonomous, with fully operational guidance systems. The attached sensors have associated errors that are controlled through the simulation. The target sightings and errors are translated into estimates with associated covariances in both the major and minor axes represented by 3-dimensional Gaussian distributions. Data merging is performed in two stages using the Jointly Gaussian Probability Density Function (JGPDF) with global alignment and minimal acceptable distance calculations. Empirically data highlights that, when using this approach a better estimate of the target’s location can be obtained when more observations are made, along with distinguishing between multiple targets. This paper aims to explore the issues surrounding localising detected targets within a know region from data gathered by multiple platforms. The problem is addressed by using a platform, having a known map to perform self-localisation, to detect and localise a target with respect to itself. The technique of interest for localisation is Simultaneous Localisation and Map building (SLAM) while research will be conducted into data fusion techniques for merging of the resulting target acquisition data. The main objectives of this work are to: - gain a theoretical understanding of SLAM and the surrounding issues, - compare and contrast the estimation techniques employed within SLAM, - perform a short study into appropriate sensor suites and fusion techniques and - develop a practically feasible solution to the described SLAM problem.