• Title/Summary/Keyword: Target identification

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Target Identification using the Mahalanobis Distance and Geometric Parameters (마할라노비스 거리와 기하학적 파라메터에 의한 표적의 인식)

  • 이준웅;권인소
    • Journal of Institute of Control, Robotics and Systems
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    • v.5 no.7
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    • pp.814-820
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    • 1999
  • We propose a target identification algorithm for visual tracking. Target identification is realized by finding out corresponding line segments to the hypothesized model segments of the target. The key idea is the combination of the Mahalanobis distance with the geometrical relationship between model segments and extracted line segments.

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Target identification for visual tracking

  • Lee, Joon-Woong;Yun, Joo-Seop;Kweon, In-So
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10a
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    • pp.145-148
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    • 1996
  • In moving object tracking based on the visual sensory feedback, a prerequisite is to determine which feature or which object is to be tracked and then the feature or the object identification precedes the tracking. In this paper, we focus on the object identification not image feature identification. The target identification is realized by finding out corresponding line segments to the hypothesized model segments of the target. The key idea is the combination of the Mahalanobis distance with the geometrica relationship between model segments and extracted line segments. We demonstrate the robustness and feasibility of the proposed target identification algorithm by a moving vehicle identification and tracking in the video traffic surveillance system over images of a road scene.

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Analysis of Target Identification Performances Using Bistatic ISAR Images (바이스태틱 ISAR 영상을 이용한 표적식별 성능 분석)

  • Lee, Seung-Jae;Lee, Seong-Hyeon;Kang, Min-Seok;Yang, Eunjung;Kim, Kyung-Tae
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.27 no.6
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    • pp.566-576
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    • 2016
  • Inverse synthetic aperture radar(ISAR) image generated from bistatic radar(Bi-ISAR) represents two-dimensional scattering distribution of a target, and the Bi-ISAR can be used for bistatic target identification. However, Bi-ISAR 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-ISARs. 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-ISARs. Then, the capability and efficiency of the method is analyzed.

Analysis of Target Identification Performances Based on HRR Profiles against the Moving Targets (HRR Profile을 이용한 이동 표적에 대한 표적 식별 성능 분석)

  • Park, Jong-Il;Jung, Sang-Won;Kim, Kyung-Tae;Chun, Jong-Hoon;Bae, Jun-Woo
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.20 no.3
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    • pp.289-295
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    • 2009
  • HRR(High Resolution Range) profiles show one-dimensional radar images including electromagnetic scattering phenomena of a target. Thus, they are not only robust to noise, but also easily obtainable in a real-time. However, in order to construct a training database for the success of radar target identification, a huge amount of HRR profiles are needed because HRR profiles are highly dependent on the relative angle between the radar and the target. In order to alleviate this difficulty, a database construction method based on the scenarios of target's movement is proposed. The proposed method is able to provide a reliable target identification performance even with a small amount of training database.

Radar Target Recognition Using a Fusion of Monostatic/Bistatic ISAR Images (모노스태틱/바이스태틱 ISAR 영상 융합을 통한 표적식별 연구)

  • Cha, Sang-Bin;Yoon, Se-Won;Hwang, Seok-Hyun;Kim, Min;Jung, Joo-Ho;Lim, Jin-Hwan;Park, Sang-Hong
    • The Journal of Korean Institute of Information Technology
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    • v.16 no.12
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    • pp.93-100
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    • 2018
  • Inverse Synthetic Aperture Radar(ISAR) image is 2-dimensional radar cross section distributions of a target. For target approaching along radar's line of sight(LOS), the bistatic ISAR can compensate for the weakness of the monostatic ISAR which can not obtain the vertical resolution of the image. However, bistatic ISAR have longer processing times and variability in scattering mechanisms than monostatic ISAR, so target identification using only bistatic ISAR images can be inefficient. Therefore, this paper analyzes target identification performance using monostatic and bistatic ISAR images of targets approaching along radar's LOS and proposes a method of target identification through fusion of two radars. Simulation results demonstrate that identification performance through fusion is more efficient than identification performance using only monostatic, bistatic ISAR images.

A Study on the Comparision of One-Dimensional Scattering Extraction Algorithms for Radar Target Identification (레이더 표적 구분을 위한 1차원 산란점 추출 기법 알고리즘들의 성능에 관한 비교 연구)

  • Jung, Ho-Ryung;Seo, Dong-Kyu;Kim, Kyung-Tae;Kim, Hyo-Tae
    • Proceedings of the Korea Electromagnetic Engineering Society Conference
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    • 2003.11a
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    • pp.193-197
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    • 2003
  • Radar target identification can be achieved by using various radar signatures, such as one-dimensional(1-D) range profile, 2-D radar images, and 1-D or 2-D scattering centers on a target. In this letter, five 1-D scattering center extraction methods are discussed - TLS(Total Least Square)-Prony, Fast Root-MUSIC (Multiple Signal Classification), Matrix-Pencil, GEESE(GEneralized Eigenvalues utilizing Signal-subspace Eigenvalues), TLS-ESPRIT(Total Least Squares - Estimation of Signal Parameters via Rotational Invariance Technique), These methods are compared in the context of estimation accuracy as well as a computational efficiency using a noisy data. Finally these methods are applied to the target classification experiment with the measured data in the POSTECH compact range facility.

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De-Noising of HRRP Using EMD for Improvement of Target Identification Performance (표적 식별 성능 향상을 위한 EMD를 이용한 HRRP의 잡음 제거 기법)

  • Park, Joon-Yong;Lee, Seung-Jae;Yang, Eunjung;Kim, Kyung-Tae
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.28 no.4
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    • pp.328-335
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    • 2017
  • In this paper, we propose an efficient method to remove noise component contained in high resolution range profile(HRRP) to improve target identification performance. The proposed method can effectively eliminate the noise component using both the statistical characteristics of the noise component and EMD algorithm. Experimental results show that the proposed method can substantially improve the identification capability, removing the noise component effectively.

Target Identification Algorithm Using Fractal Dimension on Millimeter-Wave Seeker (프랙탈 차원을 이용한 밀리미터파 탐색기 표적인식 알고리즘 연구)

  • Roh, Kyung A;Jung, Jun Young;Song, Sung Chan
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.29 no.9
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    • pp.731-734
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    • 2018
  • Many studies have been conducted on the accurate detection and identification of targets from ground clutter, in order to improve the accuracy rate of land guided weapons. Due to the variety and complicated characteristics of the ground clutter signal compared to the target, an active target identification technique is needed. In this paper, we propose a new algorithm to identify targets and divide them into different types by extracting the unique characteristics of the target through fractal dimension calculation with the characteristics of self-similarity. In the simulation using the algorithm, the probabilities of identifying the tank and truck were 100 % and 98.89 %, respectively, and the type of the target could be identified with a probability of 98 % or more.