Airborne behaviour monitoring using Gaussian processes with map information

Date published

2013-07-31T00:00:00Z

Free to read from

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

Institution of Engineering and Technology

Department

Type

Article

ISSN

1751-8784

Format

Citation

Hyondong Oh, Hyo-Sang Shin, Seungkeun Kim, Antonios Tsourdos, Brian A. White, Airborne behaviour monitoring using Gaussian processes with map information, IET Radar, Sonar & Navigation, Volume 7, Issue 4, April 2013, Pages 393 – 400.

Abstract

This paper proposes an airborne behaviour monitoring methodology of ground vehicles based on a statistical learning approach with domain knowledge given by road map information. To monitor and track the moving ground target using UAVs aboard a moving target indicator, an interactive multiple model (IMM) filter is firstly applied. {\color{red}The IMM filter consists of an on-road moving mode using a road-constrained filter and an off-road moving mode using a conventional filter.} Mode probability is also calculated from the IMM filter, and it provides deviation of the vehicle from the road. Then, a novel hybrid algorithm for anomalous behaviour recognition is developed using a Gaussian process regression on velocity profile along the one-dimensionalised position of the vehicle, as well as the deviation of the vehicle. To verify the feasibility and benefits of the proposed approach, a numerical simulation is performed using realistic car trajectory data in a city traffic.

Description

Software Description

Software Language

Github

Keywords

DOI

Rights

This paper is a postprint of a paper submitted to and accepted for publication in IET Radar, Sonar & Navigation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library."

Relationships

Relationships

Supplements

Funder/s