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A RCS model of complex targets for radar performance prediction

Tutkimustuotosvertaisarvioitu

Standard

A RCS model of complex targets for radar performance prediction. / Väilä, Minna; Jylhä, Juha; Väisänen, Ville; Perälä, Henna; Visa, Ari; Harju, Mikko; Virtanen, Kai.

2017 IEEE Radar Conference (RadarConf). Seattle, WA, USA : IEEE, 2017. s. 430-435.

Tutkimustuotosvertaisarvioitu

Harvard

Väilä, M, Jylhä, J, Väisänen, V, Perälä, H, Visa, A, Harju, M & Virtanen, K 2017, A RCS model of complex targets for radar performance prediction. julkaisussa 2017 IEEE Radar Conference (RadarConf). IEEE, Seattle, WA, USA, Sivut 430-435, IEEE RADAR CONFERENCE, 1/01/00. https://doi.org/10.1109/RADAR.2017.7944241

APA

Väilä, M., Jylhä, J., Väisänen, V., Perälä, H., Visa, A., Harju, M., & Virtanen, K. (2017). A RCS model of complex targets for radar performance prediction. teoksessa 2017 IEEE Radar Conference (RadarConf) (Sivut 430-435). Seattle, WA, USA: IEEE. https://doi.org/10.1109/RADAR.2017.7944241

Vancouver

Väilä M, Jylhä J, Väisänen V, Perälä H, Visa A, Harju M et al. A RCS model of complex targets for radar performance prediction. julkaisussa 2017 IEEE Radar Conference (RadarConf). Seattle, WA, USA: IEEE. 2017. s. 430-435 https://doi.org/10.1109/RADAR.2017.7944241

Author

Väilä, Minna ; Jylhä, Juha ; Väisänen, Ville ; Perälä, Henna ; Visa, Ari ; Harju, Mikko ; Virtanen, Kai. / A RCS model of complex targets for radar performance prediction. 2017 IEEE Radar Conference (RadarConf). Seattle, WA, USA : IEEE, 2017. Sivut 430-435

Bibtex - Lataa

@inproceedings{e77fb6bffd014159b63f3a8207211653,
title = "A RCS model of complex targets for radar performance prediction",
abstract = "The objective of the radar performance prediction is to compute quantities of interest concerning the ability of the radar to observe its surroundings. Besides the properties of the radar system, the performance is affected by the target, whose radar cross section (RCS) is one of the predominant factors. The performance prediction is usually performed in relation to the target RCS characterized by a constant value or a particular statistical distribution. Such representations generalize real-life complex targets rendering them unsuitable for some objectives since the RCS is significantly influenced by the target aspect angle and is inherently stochastic by nature. Thus, a more dynamic description may be valuable e.g. for analyzing the radar performance on a flight path of interest. We propose representing the RCS with a histogram that includes such dynamic properties and is suitable for considering the target in different ways for performance prediction: in a more general manner or dependent on its aspect angle. We consider the case of traditional RCS with low spatial resolution and demonstrate the proposed approach through the probability of detection computed for a generic surveillance radar.",
keywords = "Radar cross-sections, Histograms, Predictive models, Aircraft, Computational modeling, Probability",
author = "Minna V{\"a}il{\"a} and Juha Jylh{\"a} and Ville V{\"a}is{\"a}nen and Henna Per{\"a}l{\"a} and Ari Visa and Mikko Harju and Kai Virtanen",
year = "2017",
month = "5",
day = "8",
doi = "10.1109/RADAR.2017.7944241",
language = "English",
isbn = "978-1-4673-8824-5",
publisher = "IEEE",
pages = "430--435",
booktitle = "2017 IEEE Radar Conference (RadarConf)",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - A RCS model of complex targets for radar performance prediction

AU - Väilä, Minna

AU - Jylhä, Juha

AU - Väisänen, Ville

AU - Perälä, Henna

AU - Visa, Ari

AU - Harju, Mikko

AU - Virtanen, Kai

PY - 2017/5/8

Y1 - 2017/5/8

N2 - The objective of the radar performance prediction is to compute quantities of interest concerning the ability of the radar to observe its surroundings. Besides the properties of the radar system, the performance is affected by the target, whose radar cross section (RCS) is one of the predominant factors. The performance prediction is usually performed in relation to the target RCS characterized by a constant value or a particular statistical distribution. Such representations generalize real-life complex targets rendering them unsuitable for some objectives since the RCS is significantly influenced by the target aspect angle and is inherently stochastic by nature. Thus, a more dynamic description may be valuable e.g. for analyzing the radar performance on a flight path of interest. We propose representing the RCS with a histogram that includes such dynamic properties and is suitable for considering the target in different ways for performance prediction: in a more general manner or dependent on its aspect angle. We consider the case of traditional RCS with low spatial resolution and demonstrate the proposed approach through the probability of detection computed for a generic surveillance radar.

AB - The objective of the radar performance prediction is to compute quantities of interest concerning the ability of the radar to observe its surroundings. Besides the properties of the radar system, the performance is affected by the target, whose radar cross section (RCS) is one of the predominant factors. The performance prediction is usually performed in relation to the target RCS characterized by a constant value or a particular statistical distribution. Such representations generalize real-life complex targets rendering them unsuitable for some objectives since the RCS is significantly influenced by the target aspect angle and is inherently stochastic by nature. Thus, a more dynamic description may be valuable e.g. for analyzing the radar performance on a flight path of interest. We propose representing the RCS with a histogram that includes such dynamic properties and is suitable for considering the target in different ways for performance prediction: in a more general manner or dependent on its aspect angle. We consider the case of traditional RCS with low spatial resolution and demonstrate the proposed approach through the probability of detection computed for a generic surveillance radar.

KW - Radar cross-sections

KW - Histograms

KW - Predictive models

KW - Aircraft

KW - Computational modeling

KW - Probability

U2 - 10.1109/RADAR.2017.7944241

DO - 10.1109/RADAR.2017.7944241

M3 - Conference contribution

SN - 978-1-4673-8824-5

SP - 430

EP - 435

BT - 2017 IEEE Radar Conference (RadarConf)

PB - IEEE

CY - Seattle, WA, USA

ER -