Using neural networks as part of a system to recognise formations of aircraft

P R Zanelli; J Austin

Using neural networks as part of a system to recognise formations of aircraft

P R Zanelli, J Austin

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper describes a technique for recognising formations of aircraft from data that has been gathered by a number of independent sensors, tl;en fused together to form a single representation of the environment. The task of recognising formations is formulated as a 3-D deformable template matching problem. The amount and type of deformation allowable by each template is learned from noisy examples of the template, using probability density estimation techniques. We compare a simple neural network approach to probability density estimation to a classical statistical approach. A more elaborate density estimation scheme is then presented that has been developed using ideas from both the classical statistical and neural network fields. Results are presented for all three techniques on simulated real world data (1).

Original language	English
Title of host publication	FIFTH INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS
Place of Publication	EDISON
Publisher	INST ELECTRICAL ENGINEERS INSPEC INC
Pages	152-157
Number of pages	6
ISBN (Print)	0-85296-690-3
Publication status	Published - 1997
Event	5th International Conference on Artificial Neural Networks - CAMBRIDGE Duration: 7 Jul 1997 → 9 Jul 1997

Conference

Conference	5th International Conference on Artificial Neural Networks
City	CAMBRIDGE
Period	7/07/97 → 9/07/97

Cite this

@inproceedings{dad99f06574a4950a9528d04395c1850,

title = "Using neural networks as part of a system to recognise formations of aircraft",

abstract = "This paper describes a technique for recognising formations of aircraft from data that has been gathered by a number of independent sensors, tl;en fused together to form a single representation of the environment. The task of recognising formations is formulated as a 3-D deformable template matching problem. The amount and type of deformation allowable by each template is learned from noisy examples of the template, using probability density estimation techniques. We compare a simple neural network approach to probability density estimation to a classical statistical approach. A more elaborate density estimation scheme is then presented that has been developed using ideas from both the classical statistical and neural network fields. Results are presented for all three techniques on simulated real world data (1).",

author = "Zanelli, {P R} and J Austin",

year = "1997",

language = "English",

isbn = "0-85296-690-3",

pages = "152--157",

booktitle = "FIFTH INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS",

publisher = "INST ELECTRICAL ENGINEERS INSPEC INC",

note = "5th International Conference on Artificial Neural Networks ; Conference date: 07-07-1997 Through 09-07-1997",

}

TY - GEN

T1 - Using neural networks as part of a system to recognise formations of aircraft

AU - Zanelli, P R

AU - Austin, J

PY - 1997

Y1 - 1997

N2 - This paper describes a technique for recognising formations of aircraft from data that has been gathered by a number of independent sensors, tl;en fused together to form a single representation of the environment. The task of recognising formations is formulated as a 3-D deformable template matching problem. The amount and type of deformation allowable by each template is learned from noisy examples of the template, using probability density estimation techniques. We compare a simple neural network approach to probability density estimation to a classical statistical approach. A more elaborate density estimation scheme is then presented that has been developed using ideas from both the classical statistical and neural network fields. Results are presented for all three techniques on simulated real world data (1).

AB - This paper describes a technique for recognising formations of aircraft from data that has been gathered by a number of independent sensors, tl;en fused together to form a single representation of the environment. The task of recognising formations is formulated as a 3-D deformable template matching problem. The amount and type of deformation allowable by each template is learned from noisy examples of the template, using probability density estimation techniques. We compare a simple neural network approach to probability density estimation to a classical statistical approach. A more elaborate density estimation scheme is then presented that has been developed using ideas from both the classical statistical and neural network fields. Results are presented for all three techniques on simulated real world data (1).

M3 - Conference contribution

SN - 0-85296-690-3

SP - 152

EP - 157

BT - FIFTH INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS

PB - INST ELECTRICAL ENGINEERS INSPEC INC

CY - EDISON

T2 - 5th International Conference on Artificial Neural Networks

Y2 - 7 July 1997 through 9 July 1997

ER -