Tampere University of Technology

TUTCRIS Research Portal

Incorporating Aircraft Kinematics and Radar Cross Section into the Performance Prediction of Air Surveillance

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Standard

Incorporating Aircraft Kinematics and Radar Cross Section into the Performance Prediction of Air Surveillance. / Jylhä, Juha; Ruotsalainen, Marja; Väilä, Minna; Perälä, Henna.

FUSION 2019 - 22nd International Conference on Information Fusion. IEEE, 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Jylhä, J, Ruotsalainen, M, Väilä, M & Perälä, H 2019, Incorporating Aircraft Kinematics and Radar Cross Section into the Performance Prediction of Air Surveillance. in FUSION 2019 - 22nd International Conference on Information Fusion. IEEE, International Conference on Information Fusion, Ottawa, Canada, 2/07/19.

APA

Jylhä, J., Ruotsalainen, M., Väilä, M., & Perälä, H. (2019). Incorporating Aircraft Kinematics and Radar Cross Section into the Performance Prediction of Air Surveillance. In FUSION 2019 - 22nd International Conference on Information Fusion IEEE.

Vancouver

Jylhä J, Ruotsalainen M, Väilä M, Perälä H. Incorporating Aircraft Kinematics and Radar Cross Section into the Performance Prediction of Air Surveillance. In FUSION 2019 - 22nd International Conference on Information Fusion. IEEE. 2019

Author

Jylhä, Juha ; Ruotsalainen, Marja ; Väilä, Minna ; Perälä, Henna. / Incorporating Aircraft Kinematics and Radar Cross Section into the Performance Prediction of Air Surveillance. FUSION 2019 - 22nd International Conference on Information Fusion. IEEE, 2019.

Bibtex - Download

@inproceedings{4cca3475041d412eaac5265637376b65,
title = "Incorporating Aircraft Kinematics and Radar Cross Section into the Performance Prediction of Air Surveillance",
abstract = "The evolution of modern radar is heading toward a networked, multifunctional, adaptive, and cognitive system. The network of software-controllable fast-adapting radars follows a highly complex control and operation logic. It is not straightforward to assess its instantaneous capability to detect, track, and recognize targets. To be able to predict or optimize the system performance, one has to understand its behavior not only on a general level, but also in various operating conditions and considering the target behavior and properties accurately. In this paper, we propose the fusion of radar and tracker recordings with an extensive database of cooperative aircraft navigation recordings and radar cross section data to assess and learn the performance measures for the air surveillance. The main contribution of this paper is the incorporation of the aircraft kinematics, orientation, and radar cross section into an automated measurement-based analysis. We consider the employment of the measurement-based metrics and machine learning in the performance prediction. Simulations and experiments with real-life data demonstrate the feasibility and potential of the proposed concept.",
keywords = "artificial intelligence, machine learning, radar, radar cross sections, system analysis and design, systems modeling",
author = "Juha Jylh{\"a} and Marja Ruotsalainen and Minna V{\"a}il{\"a} and Henna Per{\"a}l{\"a}",
year = "2019",
month = "7",
day = "1",
language = "English",
booktitle = "FUSION 2019 - 22nd International Conference on Information Fusion",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Incorporating Aircraft Kinematics and Radar Cross Section into the Performance Prediction of Air Surveillance

AU - Jylhä, Juha

AU - Ruotsalainen, Marja

AU - Väilä, Minna

AU - Perälä, Henna

PY - 2019/7/1

Y1 - 2019/7/1

N2 - The evolution of modern radar is heading toward a networked, multifunctional, adaptive, and cognitive system. The network of software-controllable fast-adapting radars follows a highly complex control and operation logic. It is not straightforward to assess its instantaneous capability to detect, track, and recognize targets. To be able to predict or optimize the system performance, one has to understand its behavior not only on a general level, but also in various operating conditions and considering the target behavior and properties accurately. In this paper, we propose the fusion of radar and tracker recordings with an extensive database of cooperative aircraft navigation recordings and radar cross section data to assess and learn the performance measures for the air surveillance. The main contribution of this paper is the incorporation of the aircraft kinematics, orientation, and radar cross section into an automated measurement-based analysis. We consider the employment of the measurement-based metrics and machine learning in the performance prediction. Simulations and experiments with real-life data demonstrate the feasibility and potential of the proposed concept.

AB - The evolution of modern radar is heading toward a networked, multifunctional, adaptive, and cognitive system. The network of software-controllable fast-adapting radars follows a highly complex control and operation logic. It is not straightforward to assess its instantaneous capability to detect, track, and recognize targets. To be able to predict or optimize the system performance, one has to understand its behavior not only on a general level, but also in various operating conditions and considering the target behavior and properties accurately. In this paper, we propose the fusion of radar and tracker recordings with an extensive database of cooperative aircraft navigation recordings and radar cross section data to assess and learn the performance measures for the air surveillance. The main contribution of this paper is the incorporation of the aircraft kinematics, orientation, and radar cross section into an automated measurement-based analysis. We consider the employment of the measurement-based metrics and machine learning in the performance prediction. Simulations and experiments with real-life data demonstrate the feasibility and potential of the proposed concept.

KW - artificial intelligence

KW - machine learning

KW - radar

KW - radar cross sections

KW - system analysis and design

KW - systems modeling

M3 - Conference contribution

BT - FUSION 2019 - 22nd International Conference on Information Fusion

PB - IEEE

ER -