• Annual Conference of KIPS
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• 2013.05a
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• pp.1010-1011
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• 2013

본 논문에서는 국방과학연구소에서 운용하는 KOMSAR(Korea Miniature Synthetic Aperture Radar)장비로 측정한 실제 항공기의 데이터를 이용하여 효율적인 표적식별을 수행하였다. 표적식별과정은 수신된 모든 데이터에 대하여 거리측면도를 구한 다음 4개의 표적으로 분리한 후, 효과적인 특성벡터를 구성하여 nearest neighbor(NN) 구분기로 표적식별 성능을 수행하였다. 표적식별수행 결과, 높은 구분성능으로 구분이 가능하였다.

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.549-558
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• 2020

In this paper, the method is discussed such that the robustness of automatic initiated narrowband target tracking is improved in passive sonar. In the case of automatic tracking initiation as target in passive sonar, due to a number of clutter, the clutter is initiated as target and tracked which prohibits the operation capability. The associated probability and information entropy of measurements, extracted from detection data, is calculated to keep going on automatic target initiation and tracking of true target, but reduce the automatic initiation and tracking of clutter. If the association probability and information entropy of the extracted measurements is satisfied for the predefined conditions, the procedure of automatic initiation begins. Using sea-trial data, simulations are executed and the results from the proposed method indicate that it keeps the automatic target initiation and tracking of true target and suppresses the automatic target initiation and tracking of clutters in contrary to the conventional method.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• Journal of IKEEE
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• v.24 no.4
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• pp.1034-1038
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• 2020

In order to effectively process target recognition using radar, accurate signal information for the target is required. However, such a target signal is usually mixed with noise, and this part of the study is continuously carried out. Especially, image processing, target signal processing and target recognition for the target are examples. Since the field of target recognition is important from a military point of view, this paper carried out research on target recognition of aircraft using a tree-structured fuzzy neural networks. Fuzzy neural networks are learned by using reflected signal data for an aircraft to optimize the model, and then test data for the target are used for the optimized model to perform an experiment on target recognition. The effectiveness of the proposed method is verified by the simulation results.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.61-64
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• 2007

SAR 영상에 존재하는 군사표적은 광학 영상에 있는 군사표적에 비하여 쉽게 구별하기 힘들다. 이는 전체 영상에서 군사표적을 구성하는 픽셀의 수가 매우 적기 때문이다. 이러한 문제 때문에 SAR 영상 분석가들은 영상을 분석하는 것이 어렵다. 이 문제를 해결하기 위해서는 자동화된 분석 시스템이 필요하다. 본 논문에서는 기존에 연구된 SAR 영상을 이용한 자동표적추출 시스템을 분석하고 구현하였다. 구현된 자동표적추출 시스템을 MSTAR 데이터 셋을 이용하여 실험하여 결과를 도출하고， 그 결과를 분석하여 자동표적추출 시스템 각 단계의 성능을 분석하였다. 분석 결과 각 단계별로 최적의 성능을 보여주는 임계값을 알아낼 수 있었다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.5
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• pp.94-104
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• 2000

The accuracy and reliability of the target tracking is very critical issue in the design of automotive collision warning radar A significant problem in multi-target tracking (MTT) is the target-to-measurement data association If an incorrect measurement is associated with a target, the target could diverge the track and be prematurely terminated or cause other targets to also diverge the track. Most methods for target-to-measurement data association tend to coalesce neighboring targets Therefore, many algorithms have been developed to solve this data association problem. In this paper, a new multi-target data association method based on order statistics is described The new approaches. called the order statistics probabilistic data association (OSPDA) and the order statistics joint probabilistic data association (OSJPDA), are formulated using the association probabilities of the probabilistic data association (PDA) and the joint probabilistic data association (JPDA) filters, respectively Using the decision logic. an optimal or near optimal target-to-measurement data association is made A computer simulation of the proposed method in a heavy cluttered condition is given, including a comparison With the nearest-neighbor CNN). the PDA, and the JPDA filters, Simulation results show that the performances of the OSPDA filter and the OSJPDA filter are superior to those of the PDA filter and the JPDA filter in terms of tracking accuracy about 18% and 19%, respectively In addition, the proposed method is implemented using a developed digital signal processing (DSP) board which can be interfaced with the engine control unit (ECU) of car engine and with the d?xer through the controller area network (CAN)

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.1
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• pp.63-73
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• 2021

This paper presents a algorithm for automatic target recognition robust to the influence of the flame in order to track the target by EOTS(Electro-Optical Targeting System) equipped on UAV(Unmanned Aerial Vehicle) when there is aerial target or marine target with flame at the same time. The proposed method converts infrared images of targets and flames into a gradient vector field, and applies each gradient magnitude to a polynomial curve fitting technique to extract polynomial coefficients, and learns them in a shallow neural network model to automatically recognize targets and flames. The performance of the proposed technique was confirmed by utilizing the various infrared image database of the target and flame. Using this algorithm, it can be applied to areas where collision avoidance, forest fire detection, automatic detection and recognition of targets in the air and sea during automatic flight of unmanned aircraft.

• Current Industrial and Technological Trends in Aerospace
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• v.4 no.1
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• pp.83-91
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• 2006

추적레이다는 표적으로부터 반사되어 돌아오는 신호 또는 질의 신호에 대한 응답 신호를 수신하여 표적을 추적하는 장비이다. 추적레이다가 표적을 추적하는 범위는 일반적으로 좁게 한정되므로 이동하는 표적을 추적하기 위해서는 먼저 안테나 빔의 지향각과 거리를 표적에 맞추고, 표적이 획득된 후에는 안테나 빔을 연속적으로 이동하는 표적을 향해 방사하여 표적을 추적하게 된다. 일반적으로 추적레이다가 표적을 추적하는 경우에는 과정 잡음과 측정 잡음에 의해서 발생되는 부정확성과 관심없는 표적이나 클러터 등으로부터 생성된 측정 근원의 부정확성으로 인한 문제가 발생하게 된다. 이러한 표적 추적에 따른 문제를 해결하기 위해서 많은 추적 알고리듬들이 개발되어 왔다. 이 논문에서는 가장 기본적인 표준 칼만 필터와 측정 근원의 부정확성에 따른 데이터 연관 문제를 고려한 여러 추적 알고리듬에 대해서 기술하였다. 또한 한국항공우주연구원 우주센터의 우주발사체 추적용 추적레이다에 대한 간략한 설명과 우주발사체 추적에 사용되는 추적 알고리듬에 대해서 소개하였다.

• The Journal of the Acoustical Society of Korea
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• v.38 no.4
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• pp.415-426
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• 2019

In this paper, we discussed automatic target tracking algorithms for diver detection sonar that observes penetration forces of coastal military installations and major infrastructures. First of all, we analyzed sea trial data in diver detection sonar and composed automatic target tracking algorithms based on track existence probability as track quality measure in clutter environment. In particular, these are presented track management algorithms which include track initiation, confirmation, termination, merging and target tracking algorithms which include single target tracking IPDAF (Integrated Probabilistic Data Association Filter) and multitarget tracking LMIPDAF (Linear Multi-target Integrated Probabilistic Data Association Filter). And we analyzed performances of automatic target tracking algorithms using sea trial data and monte carlo simulation data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5C
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• pp.355-364
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• 2012

Target processing mechanisms are necessary to collect target information, real-time data fusion, and tactical environment recognition for cooperative engagement ability. Among these mechanisms, the target tracking starts from predicting state of speed, acceleration, and location by using sensors' measurements. However, it can be a problem to give the reliability because the measurements have a certain uncertainty. Thus, a technique which uses multiple sensors is needed to detect the target and increase the reliability. Also, data fusion technique is necessary to process the data which is provided from heterogeneous sensors for target tracking. In this paper, a target tracking algorithm is proposed based on probabilistic data association(PDA) by fusing radar and ESM sensor measurements. The radar sensor's azimuth and range measurements and the ESM sensor's bearing-only measurement are associated by the measurement fusion method. After gating associated measurements, state estimation of the target is performed by PDA filter. The simulation results show that the proposed algorithm provides improved estimation under linear and circular target motions.