근접장 신호 위치추정을 위한 신경망을 이용한 원거리장 근사 기법

정성훈^*

해군 소령/해군사관학교 기계시스템공학과 조교수

A Far-field Approximation Method Using Neural Networks for Near-field Sources Localization

Sunghoon Jung^*

LCDR, ROK Navy/Assistant professor, Dept. of Mechanical System Engineering, Republic of Korea Naval Academy

Correspondence to: ^*Sunghoon Jung Dept. of Mechanical System Engineering, Republic of Korea Naval Academy 1 Jungwon-ro, Jinhae-gu, Changwon-si, Gyungsangnam-do, 51704, Republic of Korea Tel: +82-55-907-5320 E-mail: hun401@navy.ac.kr

초록

본 논문에서는 신경망을 이용한 근접장 신호의 원거리장 근사 기법을 제안한다. 기존의 근사 기법은 근사 오차로 모든 빔 형성기에 적합하지 않아 표적의 위치를 정확히 추정할 수 없다. 제안기법은 신경망 학습을 통해 근거리와 원거리장의 조향 벡터 간의 관계를 파악하여 효과적으로 원거리장 근사를 할 수 있다. 실험을 통해 제안기법은 모든 빔 형성기에 표적의 위치를 추정할 수 있는 응답을 충분히 나타낼 수 있음을 실험을 통해 확인하였다.

Abstract

This paper propose a far-field approximation method for near-field signals using a neural network. A conventional approximation method is not suitable for all beamformers due to approximation errors, so it cannot accurately estimate the target's location. The proposed method can effectively approximate the far field by identifying the relationship between the steering vectors in the near field and the far field through neural network. Through simulation, it was confirmed that the proposed method can sufficiently provide a response that can estimate the target's position in all beamformers.

Keywords:

SONAR, Source Localization, Near-field, Far-field Approximation, Beamformer

키워드:

소나, 위치추정, 근접장, 원거리장 근사, 빔 형성기

Acknowledgments

본 논문은 해군사관학교 해양연구소 학술연구과제 연구비 지원으로 수행된 연구임.

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