THE JOURNAL OF KOREAN INSTITUTE OF ELECTROMAGNETIC ENGINEERING AND SCIENCE Jun.; 27(6),

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1 THE JOURNAL OF KOREAN INSTITUTE OF ELECTROMAGNETIC ENGINEERING AND SCIENCE Jun.; 27(6), ISSN (Print)ISSN X (Online) Analysis of Target Identification Performances Using Bistatic Images 이승재 이성현 강민석 양은정 김경태 Seung-Jae LeeSeong-Hyeon LeeMin-Seok KangEunjung Yang*Kyung-Tae Kim 요약 (Inverse Synthetic Aperture Radar: ) 2,., -. DB. (database: DB) (High Resolution Range Profile: HRRP) DB,, DB. DB,. Abstract Inverse synthetic aperture radar() image generated from bistatic radar(bi-) represents two-dimensional scattering distribution of a target, and the Bi- can be used for bistatic target identification. However, Bi- has large variability in scattering mechanisms depending on bistatic configurations and do not represent exact range-doppler information of a target due to inherent distortion. Thus, an efficient training DB construction is the most important factor in target identification using Bi-s. Recently, a database construction method based on realistic flight scenarios of a target, which provides a reliable identification performance for the monostatic target identification, was applied to target identification using high resolution range profiles(hrrps) generated from bistatic radar(bi- HRRPs), to construct efficient training DB under bistatic configurations. Consequently, high identification performance was achieved using only small amount of training Bi-HRRPs, when the target is a considerable distance away from the bistatic radar. Thus, flight scenarios based training DB construction is applied to target identification using Bi-s. Then, the capability and efficiency of the method is analyzed. Key words: Bistatic Radar, Database Construction, Image, Target Identification. 서론 (stealth) (Bistatic) /IR. (Department of Electrical Engineering, Pohang University of Science and Technology) *(Agency for Defense Development) Manuscript received April 19, 2016 ; Revised June 21, 2016 ; Accepted June 22, (ID No ) Corresponding Author: Kyung-Tae Kim ( 566 c Copyright The Korean Institute of Electromagnetic Engineering and Science. All Rights Reserved.

2 [1],[2]. (Monostatic).,. (high resolution rnage Profile: HRRP) (Inverse Synthetic Aperture Radar: ), (database: DB)., HRRP. (). DB., DB [3]. DB, DB., DB HRRP DB [4]., DB., HRRP, HRRP DB. DB,. HRRP DB DB,.. 3D CAD (Physical Optics: PO).,,.. 바이스태틱 M. Martorella [1] (modeling),. 1, 그림 1. [1] Fig. 1. Bistatic geometry [1]. 567

3 THE JOURNAL OF KOREAN INSTITUTE OF ELECTROMAGNETIC ENGINEERING AND SCIENCE. vol. 27, no. 6, Jun rect rect (2),, (Coherent Processing Interval: CPI), x,., (rectangular),,. (Image Projection Plane: IPP),, (Effective Rotation vector: ERV)., A B (3) (4). (1) (3) (4),, A, B, A (Line of Sight: LOS), B LOS. A B, B A. (5) cos, LOS,. (1) (motion compensation: MOCOM)., (ideal point scatterer)., IPP, (6). sin cos (6) (7) 1., (6), sin cos 1 (6) (8). cos sin (7), (8) (1) (9). (6) (8) (5) (9) (9). (9) (inverse Fourier transform) 568

4 LOS 1 HRRP. sinc.,.. 2 RCS 1 m, m rect (10) (range-migration). ( ),. (10), 2. (11) (a) (a) Bistatic geometry sin sin (12) (11) (point spread function: PSF) (smearing), (Doppler),,, (convolution).,. (11) (12). (, ), (b) (b) Bistatic image for initial (c) (c) Bistatic image for initial 그림 2. Fig. 2. Bistatic geometry and bistatic image for two ideal point scatterers. 569

5 THE JOURNAL OF KOREAN INSTITUTE OF ELECTROMAGNETIC ENGINEERING AND SCIENCE. vol. 27, no. 6, Jun (a) Tx, Rx., 10 GHz, 300 MHz, deg (0.5 m) deg. 2(b), deg deg, RLOS deg,. 2. 2(c) 2(b), deg deg. 2(b), (c) (11), ( ). 2(b), (c)..., DB DB [3].,, DB. DB,., DB HRRP [4], DB. DB. DB,,.. 3. 비행시나리오기반 DB 구축 DB (elevation angle) (azimuth angle),., 2(b), (c),. DB 그림 3. Fig. 3. Flight scenario based bistatic target identification. 570

6 바이스태틱 을 이용한 표적식별 성능 분석 이스태틱 표적식별의 개념도를 나타낸다. Ⅳ. 시뮬레이션 결과 비행시나리오 기반 훈련 DB 구축을 위해, 송신기와 수 신기는 각각 x y z X 와 x y z X 에 위치한다고 가정하였고, X 는 모노스태틱 기하구조와 바이스태틱 기하구조를 형성하도록 각각 km 와 km 로 정하였다. 또한, 송신파의 중심 주파수는 10 GHz, 주파 수 대역폭은 200 MHz로 설정하였다. 바이스태틱 의 왜곡에 영향을 주는 와 는 표적과 레이 다 사이의 거리에 따라 변하기 때문에, 표적과 레이다의 거리가 가까운 비행 시나리오 SET-1과 표적과 레이다의 거리가 먼 비행 시나리오 SET-2로 나누어 이에 해당하는 훈련 DB를 각각 구성하였다. SET-1과 SET-2에 해당하는 표적의 비행 파라미터는 표 1과 표 2에 각각 제시되어 있 다. SET-1과 SET-2는 크게 3 가지 비행 방향으로 구성되 고, 각 시나리오에는 80개의 비행 시작점이 존재한다. 표 적은 비행 시작점으로부터 300 m/s의 속도로 직선 비행하 였고, 레이다 수신신호는 바이스태틱 내 수직 거리 해상도 이 거리 해상도와 과 같아지도록 획득되었다. 표적에 대한 바이스태틱 수신신호를 획득하 기 위해서는, 전자기적 수치해석 기법인 PO에 기반하여 자체 개발한 시뮬레이터를 이용하였다. 그 후, 바이스태 틱 수신신호들 중 100개의 시간에서 바이스태틱 수신신 호들을 샘플링하였고, 해당 바이스태틱 수신신호에 2차 원 역퓨리에 변환을 적용하여 바이스태틱 을 형성하여 훈련 DB를 구축하였다. 즉, SET-1과 SET-2에서 각 훈련 DB를 구성하는 바이스태틱 의 총 개 수는 4(표적의 수) 3(비행방향 수) 80(각 비행방향 내 비행 수) = 960개이다. 표적식별 성능 분석을 위한 표적 으로는 F117, F16, Mig25, Yf 23을 선택하였고, 3D CAD 모 델을 그림 4와 같이 제작하였다. SET-2 내 비행방향 에서 x km, y km, z km, X km 일 때 각 표적의 바이스태틱 은 그림 5에 도시되어 있다. 그림 6은 SET-2 내 훈련 바이스태틱 을 형성 할 시, 각 비행방향에서 샘플링된 시간에 해당하는 (a) F117 (b) F16 표적의 모델 (c) Mig25 (d) Yf23 그림 4. 3D CAD Fig. 4. CAD models of targets. (a) F117 (b) F16 (c) Mig25 (d) Yf23 표적의 바이스태틱 그림 5. Fig. 5. Bistatic images of targets. 에 대한 표적 비행 파라미터 표 1. SET-1 Table 1. Target flight parameters for SET-1. Flight direction Initial position: x 24, 8, 8, 24 km Initial position: y 30, 40, 50, 60, 70 km Initial position: z 1, 3, 5, 7 km 에 대한 표적 비행 파라미터 표 2. SET-2 Table 2. Target flight parameters for SET-2. Flight direction Initial position: x 24, 8, 8, 24 km Initial position: y 80, 90, 100, 110, 120 km Initial position: z 1, 3, 5, 7 km 571

7 THE JOURNAL OF KOREAN INSTITUTE OF ELECTROMAGNETIC ENGINEERING AND SCIENCE. vol. 27, no. 6, Jun 및 를 보여준다. 표적이 바이스태틱 레이다의 정면 으로 다가오는 비행방향인 의 경우, 다른 비 행 방향에 비해 시간에 따른 관측각의 변화가 크지 않다. 따라서 거리 해상도와 동일한 수직거리 해상도를 획득하 기 위하여 긴 CPI 동안 표적을 관측해야 하고, 바이스태 틱 형성 시, 와 가 커짐을 알 수 있 다. 따라서 상기 비행방향 내 형성된 바이스태틱 영 상은 다른 방향에 비해 심하게 왜곡될 것이다. 그림 7은 SET-2 내 각 비행방향에서 x km y km z km X km 일 때, F16 표적의 모노스태틱 및 바이스태틱 을 도시한다. 그림 7에 의하면 비행방향 의 경우, 다른 비행방향 에 비해 바이스태틱 의 왜곡이 심하여 모노스 태틱 과 크게 달라짐을 확인할 수 있다. 이는 그림 6에서 보인 바와 같이, 비행방향 의 경 의 모노스태 틱 의 모노스 내 (b) 틱 의 바이스 (c) (d) (c) Monostatic image for (d) Bistatic image for 태틱 (a) 의 바이스태 (a) (b) (a) Monostatic image for (b) Bistatic image for 내 의 모노스태 태틱 의 바이스태 (e) (f) (e) Monostatic image for (f) Bistatic image for 틱 틱 내 각 비행방향에 대한 F16 표적의 모노 스태틱 및 바이스태틱 그림 7. SET-2 (c) (e) 내 내 각 비행방향 내 내 (d) (f) 및 내 그림 6. Fig. 6. and for each flight direction. 572 Fig. 7. Monostatic and bistatic image of F16 for each flight direction in SET-2. 우, 와 가 크기 때문이다. 즉, 비행 시나리오 기 반 표적식별을 이용하여 훈련 DB를 구축하더라도 이는 훈련 바이스태틱 과 시험 은 크게 왜 곡되고, 상기 비행방향에서의 바이스태틱 표적식별 성능 은 모노스태틱 표적식별 성능에 비해 크게 저하될 것임 을 예측할 수 있다. 시험 DB를 형성하기 위한 표적의 비행 시작점은 각 시 나리오의 훈련 DB 공간 내에서 임의로 50개를 선택하였

8 . DB ± ± ±. 300 m/s, , 100, 2, DB. SET-1 SET-2 DB 4( ) 3( ) 50( )=600. DB DB, (center of mass: COM)., (principal component analysis: PCA) [5], nearest neighbor (NN)., (signal to noise ratio: SNR) 0 db 30 db 5 db (additive white gaussian noise: AWGN). (13). 올바르게구분한수 전체시험데이터수 (13) 8 9 SET-1 SET-2 SNR., SET. HRRP [4] SET-1 SET-2. HRRP LOS., HRRP SET-1., SET-1 SET-2,, SET. HRRP, SET-1 SET-2 [4], HRRP., SET-1 SET-2,.,,., SET-1 SET-2 SET-2.. 결론및향후연구방향 DB.., CPI,. DB., CPI IS- AR. 573

9 THE JOURNAL OF KOREAN INSTITUTE OF ELECTROMAGNETIC ENGINEERING AND SCIENCE. vol. 27, no. 6, Jun (a) : (a) Flight direction: (a) : (a) Flight direction: (b) : (b) Flight direction: (b) : (b) Flight direction: (c) : (c) Flight direction: 그림 8. SET-1 SNR Fig. 8. Probability of correct identification versus SNR for SET-1. (c) : (c) Flight direction: 그림 9. SET-2 SNR Fig. 9. Probability of correct identification versus SNR for SET

10 . SET-1,. HRRP [4], HRRP.,,. References [1] M. Martorella, J. Palmer, J. Homer, B. Littleton and L. D. Longstaff, "On bistatic inverse synthetic aperture radar", IEEE Transactions on Aerospace and Electronic Systems, vol. 43, no. 3, pp , Jul [2] M. Martorella, D. Cataldo, and S. Brisken, "Bistatically equivalent monostatic approximation for bistatic ", Proc IEEE Radar Conference, pp. 1-5, Apr./May [3] G. -G. Choi, S. -K. Han, and K. -T. Kim, "Efficient construction of training database for identification of aircraft images", Proc IEEE CIE Int. Conf. Radar, pp , Oct [4],,,,, " ",, 27(2), pp , [5] M. A. Turk, A. P. Pentland, "Face recognition using eigen faces", Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR), pp , Jun : () : () : [ 주관심분야 ],,,, : ( ) : () : [ 주관심분야 ],,, : () : () : [ 주관심분야 ],, DOA : () : () : () : [ 주관심분야 ], 575

11 THE JOURNAL OF KOREAN INSTITUTE OF ELECTROMAGNETIC ENGINEERING AND SCIENCE. vol. 27, no. 6, Jun : () : () : () : : : /IR [ 주관심분야 ],, RCS 576

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