The University of Southampton
University of Southampton Institutional Repository

Multi-Sensor Data Fusion for Helicopter Guidance

Multi-Sensor Data Fusion for Helicopter Guidance
Multi-Sensor Data Fusion for Helicopter Guidance
Helicopter crews are required to carry out a wide range of duties most of which involve operational safety. One particular flight safety problem that has relevance to a variety of different types of rotorcraft is assisting the pilot in obstacle avoidance. Obstacles, in this context, may typically include other aircraft, terrain features, buildings, bridges, overhead cables and poles. The problem is especially difficult in bad weather conditions, (such as fog, snow, and heavy rain), in the presence of heavy smoke, or dust, or at night. Also it must be noted that the dynamics of the moving obstacles (other aircraft etc) will not in general be linear or indeed even known a priori. In order to avoid obstacles effectively a system must have knowledge about the obstacles' positions and likely future positions relative the system's own aircraft. Since the information being provided by the sensors will be imperfect, (i.e. it will have some uncertainty associated with it), and since the process model, which must be used to predict any future positions, will also be uncertain, the required positions must be robustly estimated. Since the dynamics of moving obstacles will be a priori unknown, it will be necessary to learn process models for them. Since the dynamics of the obstacles may not be linear, the process models must be capable of reflecting non-linear behaviour. The uncertain information produced by the various sensors will be related to required knowledge about the obstacles by a sensor model, however this relationship need not be linear, and may even have to be learnt. The information sources of interest may be distributed over many platforms, therefore the architecture of the data fusion system must reflect the spatially distributed nature of the problem. In essence then, the main aim of this research is the design an estimator which is capable of dealing with non-linear process and sensor models, which may result from learning processes, and with distributed information sources.
Doyle, R.S.
faf69033-0b7a-4a87-af90-80589a133b62
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Doyle, R.S.
faf69033-0b7a-4a87-af90-80589a133b62
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Doyle, R.S. and Harris, C.J. (1995) Multi-Sensor Data Fusion for Helicopter Guidance

Record type: Monograph (Project Report)

Abstract

Helicopter crews are required to carry out a wide range of duties most of which involve operational safety. One particular flight safety problem that has relevance to a variety of different types of rotorcraft is assisting the pilot in obstacle avoidance. Obstacles, in this context, may typically include other aircraft, terrain features, buildings, bridges, overhead cables and poles. The problem is especially difficult in bad weather conditions, (such as fog, snow, and heavy rain), in the presence of heavy smoke, or dust, or at night. Also it must be noted that the dynamics of the moving obstacles (other aircraft etc) will not in general be linear or indeed even known a priori. In order to avoid obstacles effectively a system must have knowledge about the obstacles' positions and likely future positions relative the system's own aircraft. Since the information being provided by the sensors will be imperfect, (i.e. it will have some uncertainty associated with it), and since the process model, which must be used to predict any future positions, will also be uncertain, the required positions must be robustly estimated. Since the dynamics of moving obstacles will be a priori unknown, it will be necessary to learn process models for them. Since the dynamics of the obstacles may not be linear, the process models must be capable of reflecting non-linear behaviour. The uncertain information produced by the various sensors will be related to required knowledge about the obstacles by a sensor model, however this relationship need not be linear, and may even have to be learnt. The information sources of interest may be distributed over many platforms, therefore the architecture of the data fusion system must reflect the spatially distributed nature of the problem. In essence then, the main aim of this research is the design an estimator which is capable of dealing with non-linear process and sensor models, which may result from learning processes, and with distributed information sources.

This record has no associated files available for download.

More information

Published date: 1995
Additional Information: 1995/6 Research Journal Address: Department of Electronics and Computer Science
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250098
URI: http://eprints.soton.ac.uk/id/eprint/250098
PURE UUID: 39a46d05-370d-4f56-9983-7641a6534715

Catalogue record

Date deposited: 04 May 1999
Last modified: 20 Feb 2024 17:55

Export record

Contributors

Author: R.S. Doyle
Author: C.J. Harris

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×