• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.61-69
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• 2018

In this paper, we propose a method for recognizing ground target using dual polarization channels in millimeter waveband air-to-surface radar. First, the Push-Broom target detection method is described and the received signal is modeled considering flight-path scenario of air-to-surface radar. The scattering centers were extracted using the RELAX algorithm, which is a time domain spectral estimation technique, and the feature vector of the target was generated. Based on this, a DB for 4 targets is constructed. As a result of the proposed method, it is confirmed that the target classification rates is improved by more than 15% than the single channel using the data of the dual polarization channel.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2505-2511
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• 2013

Many studies in detection and classification of the targets in the underwater environments have been conducted for military purposes, as well as for non-military purpose. Due to the complicated characteristics of underwater acoustic signal reflecting multipath environments and spatio-temporal varying characteristics, active sonar target classification technique has been considered as a difficult technique. And it has difficulties in collecting actual underwater data. In this paper, we synthesized active target echoes based on ray tracing algorithm using target model having 3-dimensional highlight distribution. Then, Fractional Fourier transform was applied to synthesized target echoes to extract feature vector. Recognition experiment was performed using neural network classifier.

• The Journal of the Acoustical Society of Korea
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• v.34 no.1
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• pp.75-81
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• 2015

In active sonar system, clutters degrade performance of target detection/tracking and overwhelm sonar operators in ASW (Antisubmarine Warfare). Conventional clutter reduction algorithms using consistency of local peaks are studied in multi-ping data and tracking filter research for active sonar was conducted. However these algorithms cannot classify target and clutters in single ping data. This paper suggests a single ping clutter reduction approach to reduce clutters in mid-frequency active sonar system using echo shape features. The algorithm performance test is conducted using real sea-trial data in heavy clutter density environment. It is confirmed that the number of clutters was reduced by about 80 % over the conventional algorithm while retaining the detection of target.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.6
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• pp.1-8
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• 2001

Recently, Ryu et al. proposed a multiple target angle tracking algorithm using the angular innovation extracted from the estimated signal subspace. This algorithm obtains the angles of targets and associates data simultaneously. Therefore, it has a simple structure without data association problem. However it requires the calculation of the inverse of a real matrix with dimension (2N+1)${\times}$(2N+1) to obtain the angular innovations of N targets. In this paper, a new linear equation for angular innovation is proposed using the fact that the projection error is zero when the target steering vector is projected onto the signal subspace. As a result, the proposed algorithm dose not require the matrix inversion and is computationally efficient.

• The Journal of the Acoustical Society of Korea
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• v.24 no.5
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• pp.294-300
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• 2005

Generally, traditional approaches to track the target position are to estimate ranges and bearings by 2-D MUSIC (MUltiple 519na1 Classification) method. and to associate estimates of 2-D MUSIC made at different time points with the right targets by JPDA (Joint Probabilistic Data Association) filter in the near field. However, the disadvantages of these approaches are that these have the data association Problem in tracking multiple targets. and that these require the heavy computational load in estimating a 2-D range/bearing spectrum. In case multiple targets are adjacent. the tracking performance degrades seriously because the estimate of each target's Position has a large error. In this paper, we proposed a new tracking algorithm using Position innovations extracted from the senor output covariance matrix in the near field. The proposed algorithm is demonstrated by the computer simulations dealing with the tracking of multiple closing and crossing targets.

• Journal of radiological science and technology
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• v.34 no.1
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• pp.51-58
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• 2011

The aim of the study is to analyze the dose delivery error depending on the depth variation according to target positions and arc trajectories by comparing the simulated treatment planning with the actual dose delivery in conformal arc therapy. We simulated the conformal arc treatment planning with the three target positions (center, 2.5 cm, and 5 cm in the phantom). For the experiments, IMRT body phantom (I’mRT Phantom, Wellhofer Dosimetry, Germany) was used for treatment planning with CT (Computed Tomography, Light speed 16, GE, USA). The simulated treatment plans were established by three different target positions using treatment planning system (Eclipse, ver. 6.5, VMS, Palo Alto, USA). The radiochromic film (Gafchromic EBT2, ISP, Wayne, USA) and dose analysis software (OmniPro-IMRT, ver. 1.4, Wellhofer Dosimetry, Germany) were used for the measurement of the planned arc delivery using 6 MV photon beam from linear accelerator (CL21EX, VMS, Palo Alto, USA). Gamma index (DD: 3%, DTA: 2 mm) histogram and dose profile were evaluated for a quantitative analysis. The dose distributions surrounded by targets were also compared with each plans and measurements by conformity index (CI), and homogeneity index (HI). The area covered by 100% isodose line was compared to the whole target area. The results for the 5 cm-shifted target plan show that 23.8%, 35.6%, and 37% for multiple conformal arc therapy (MCAT), single conformal arc therapy (SCAT), and multiple static beam therapy, respectively. In the 2.5 cm-shifted target plan, it was shown that 61%, 21.5%, and 14.2%, while in case of center-located target, 70.5%, 14.1%, and 36.3% for MCAT, SCAT, and multiple static beam therapy, respectively. The values were resulted by most superior in the MCAT, except the case of the 5 cm-shifted target. In the analysis of gamma index histogram, it was resulted of 37.1, 27.3, 29.2 in the SCAT, while 9.2, 8.4, 10.3 in the MCAT, for the target positions of center, shifted 2.5 cm and 5 cm, respectively. The fail proportions of the SCAT were 2.8 to 4 times as compared to those of the MCAT. In conclusion, dose delivery error could be occurred depending on the target positions and arc trajectories. Hence, if the target were located in the biased position, the accurate dose delivery could be performed through the optimization of depth according to arc trajectory.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.7
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• pp.749-757
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• 1992

In this paper, as a more effective approach for maneuvering target tracking a realtime optical tracking system based of optical JTC(Joint Transform Correlator) which is capable of transforming the massive input target data into a few correlation peaks is implemented. And for real-time implementation the high resolution LCD(Liquid Crystal Display) spatialight modulator is used to construct the optical JTC system, and the mean binarization method is used to reduce the effects of background noises on correlation signal. From the good experimental results on maneuvering targets, the possibility of real-time moving target tarcking system based on optical JTC is a suggested.

• Journal of IKEEE
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• v.22 no.4
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• pp.1044-1049
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• 2018

CNN (Convolutional Neural Networks) is a neural network that models animal visual information processing. And it shows good performance in various fields. In this paper, we use CNN to classify target and non-target data by analyzing the spectrogram of active sonar signal. The data were divided into 8 classes according to the ratios containing the targets and used for learning CNN. The spectrogram of the signal is divided into frames and used as inputs. As a result, it was possible to classify the target and non-target using the characteristic that the classification results of the seven classes corresponding to the target signal sequentially appear only at the position of the target signal.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.9
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• pp.952-958
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• 2014

In this paper, we propose a parametric sparse recovery algorithm(SRA) applied to a radar signal model, based on the compressive sensing(CS), for the ISAR(Inverse Synthetic Aperture Radar) image reconstruction from an incomplete radar-cross-section(RCS) data and for the estimation of rotation rate of a target. As the SRA, the iteratively-reweighted-least-square(IRLS) is combined with the radar signal model including chirp components with unknown chirp rate in the cross-range direction. In addition, the particle swarm optimization(PSO) technique is considered for searching correct parameters related to the rotation rate. Therefore, the parametric SRA based on the IRLS can reconstruct ISAR image and estimate the rotation rate of a target efficiently, although there exists missing data in observed RCS data samples. The performance of the proposed method in terms of image entropy is also compared with that of the traditional interpolation methods for the incomplete RCS data.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.4
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• pp.404-413
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• 2015

The target classification for ballistic target(BT) is one of the most critical issues of ballistic defence mode(BDM) in multi-function radar(MFR). Radar responds to the target according to the result of classifying BT and air breathing target(ABT) on BDM. Since the efficiency and accuracy of the classification is closely related to the capacity of the response to the ballistic missile offense, effective and accurate classification scheme is necessary. Generally, JEM(Jet Engine Modulation), HRR(High Range Resolution) and ISAR(Inverse Synthetic Array Radar) image are used for a target classification, which require specific radar waveform, data base and algorithms. In this paper, the classification method that is applicable to a MFR system in a real environment without specific waveform is proposed. The proposed classifier adopts kinematic data as a feature vector to save radar resources at the radar time and hardware point of view and is implemented by fuzzy logic of which simple implementation makes it possible to apply to the real environment. The performance of the proposed method is verified through measured data of the aircraft and simulated data of the ballistic missile.