• International Journal of Contents
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• 제15권1호
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• pp.32-38
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• 2019

In an environment where a large number of weapons are operated compared to a large number of ground targets, it is important to monitor and manage the targets to set up a fire plan, and through their multilateral analysis, to equip them with a priority order process for targets having a high threat level through the quantitative calculation of the threat level. Existing studies consider the anti-aircraft and anti-ship targets only, hence, it is impossible to apply the existing algorithm to ground weapon system development. Therefore, we proposed an effective threat evaluation algorithm for multiple ground targets in multi-target and multi-weapon environments. Our algorithm optimizes to multiple ground targets by use of unique ground target features such as proximity degree, sorts of weapons and protected assets, target types, relative importance of the weapons and protected assets, etc. Therefore, it is possible to maximize an engagement effect by deducing an effective threat evaluation model by considering the characteristics of ground targets comprehensively. We carried out performance evaluation and verification through simulations and visualizations, and confirmed high utility and effect of our algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3762-3781
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• 2020

Infrared pedestrian target recognition is a vital problem of significant interest in computer vision. In this work, a novel infrared pedestrian target recognition method that uses heterogeneous features with multi-kernel transfer learning is proposed. Firstly, to exploit the characteristics of infrared pedestrian targets fully, a novel multi-scale monogenic filtering-based completed local binary pattern descriptor, referred to as MSMF-CLBP, is designed to extract the texture information, and then an improved histogram of oriented gradient-fisher vector descriptor, referred to as HOG-FV, is proposed to extract the shape information. Second, to enrich the semantic content of feature expression, these two heterogeneous features are integrated to get more complete representation for infrared pedestrian targets. Third, to overcome the defects, such as poor generalization, scarcity of tagged infrared samples, distributional and semantic deviations between the training and testing samples, of the state-of-the-art classifiers, an effective multi-kernel transfer learning classifier called MK-TrAdaBoost is designed. Experimental results show that the proposed method outperforms many state-of-the-art recognition approaches for infrared pedestrian targets.

• 한국멀티미디어학회논문지
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• 제24권5호
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• pp.659-666
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• 2021

With the continuous development of deep learning, human behavior recognition algorithms have achieved good results. However, in a multi-person recognition environment, the complex behavior environment poses a great challenge to the efficiency of recognition. To this end, this paper proposes a multi-person pose estimation model. First of all, the human detectors in the top-down framework mostly use the two-stage target detection model, which runs slow down. The single-stage YOLOv3 target detection model is used to effectively improve the running speed and the generalization of the model. Depth separable convolution, which further improves the speed of target detection and improves the model's ability to extract target proposed regions; Secondly, based on the feature pyramid network combined with context semantic information in the pose estimation model, the OHEM algorithm is used to solve difficult key point detection problems, and the accuracy of multi-person pose estimation is improved; Finally, the Euclidean distance is used to calculate the spatial distance between key points, to determine the similarity of postures in the frame, and to eliminate redundant postures.

• 한국전자파학회논문지
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• 제22권10호
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• pp.966-980
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• 2011

레이더는 표적으로부터 반사된 신호의 크기, 도플러 속도로부터 표적의 거리, 속도 정보를 알 수 있으며, 이런 반사 신호의 특징들은 표적의 반사 특성과 기동에 의해 결정된다. 표적의 위치 정보에 대한 각도 오차는 합채널에 대한 차채널의 크기 비로부터 추출된다. 본 논문에서는 다기능 레이더의 성능을 평가하고 분석하기 위한 레이더 반사 신호 모의 장치(RSSE)에 대해 소개하였다. 개발된 레이더 반사 신호 모의 장치는 다채널 직접 디지털 합성(MC-DDS)을 이용하여, 재밍 신호를 포함한 다중 표적 환경을 모사할 수 있도록 구현되었으며, 효율적인 하드웨어 구조 설계를 통해 모사할 수 있는 표적의 수를 용이하게 확장할 수 있도록 설계되었다. 개발된 모의 장치의 요구 성능 및 기능을 시험 환경에서 확인하였으며, 신호 처리기(RSP)와의 연동 시험 구성에서 표적 탐지 성능을 입증하였다.

• 한국전자파학회논문지
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• 제15권6호
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• pp.579-589
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• 2004

일반적인 시간 영역에서의 레이더 신호들은 표적의 관측각에 민감하게 변화한다. 이로 인하여 각도가 넓어짐에 따라서 표적 구분의 정확도가 상당히 감소하게 된다. 이러한 문제를 해결하기 위하여 본 논문에서는 다중각도 정보를 이용하여 표적 구분 성능을 향상시키기 위한 방법을 제시한다. 먼저, 대표적인 시간영역 레이더신호인 1차원 range profile로부터 central moments와 PCA를 결합하여 특성백터를 추출한다. 추출된 특성백터에 다중 각도 정보를 사용하는 구분기를 적용시켜 넓은 관측각에서 표적 인식 성능을 향상시킬 수 있다. 다중 각도정보를 이용하는 기법에는 독립방식과 종속방식이 있으며, 본 논문에서는 두 기법의 성능을 비교한다. 성능 비교 실험에는 포항공대 단축거리 무반향실에서 측정된 여섯 개의 항공기 모델에 대한 레이더가 단면적 데이터가 이용된다.

• 전자공학회논문지A
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• 제29A권11호
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• pp.70-77
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• 1992

Generally, the conventional tracking algorithms are very limited in the practical applications because of that the computation load is exponentially increased as the number of targets being tracked is increase. Recently, to overcome this kind of limitation, some new tracking methods based on neural network algorithms which have learning and parallel processing capabilities are introduced. By application of neural networks to multi-target tracking problems, the tracking system can be made computationally independent of the number of objects being tracked, through their characteristics of massive parallelism and dense interconnectivity. In this paper, a new neural network tracking algorithm, which has capability of adaptive target tracking with little increase of the amount of calculation under the clutter and noisy environments, is suggested and the possibility of real-time multi-target tracking system based on neural networks is also demonstrated through some good computer simulation results.

• 전자공학회논문지S
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• 제36S권7호
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• pp.34-42
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• 1999

다중표적 추적시스템은 여러 개의 표적물을 동시에 추적한다. 표적물의 추적에는 일반적으로 칼만필터를 사용하게 된다. 칼만필터는 최적의 특성을 지니고 있지만, 많은 계산량을 요구하는 단점이 있다. 따라서 여러 개의 표적물을 동시에 추적하는 다중표적 추적시스템의 실시간 구현을 위하여 칼만필터 대신에 계산량이 적은 다른 예측기를 사용하기도 한다. 본 논문에서는 계산량을 줄이기 위하여 칼만필터에서 사용하는 시스템의 모델을 줄이는 방법을 사용하여 보았다. 표적물의 운동을 등속운동으로 가정하여 사용된 모델은 표적물의 추적능력을 지니면서도 그 계산량을 줄일 수 있었다. 간단한 시뮬레이션과 실제의 영상정보에 적용한 결과는 등속운동을 가정한 칼만필터가 원래의 좋은 특성을 유지하면서 계산량을 줄일 수 있어 다중표적 추적시스템에 유리하게 사용될 수 있음을 보여주었다.

• International Journal of Control, Automation, and Systems
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• 제5권2호
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• pp.200-207
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• 2007

The passive direction finding cross localization method is widely adopted in passive tracking, therefore there will exist masses of false intersection points. Eliminating these false intersection points correctly and quickly is a key technique in passive localization. A new method is proposed for passive locating and tracking multi-jammer target in this paper. It not only solves the difficulty of determining the number of targets when masses of false intersection points existing, but also solves the initialization problem of elastic network. Thus this method solves the problem of multi-jammer target correlation and the elimination of static false intersection points. The method which dynamically establishes multiple hypothesis trajectory trees solves the problem of eliminating the remaining false intersection points. Simulation results show that computational burden of the method is lower, the elastic network can more quickly find all or most of the targets and have a more probability of locking the real targets. This method can eliminate more false intersection points.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권9호
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• pp.414-419
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• 2006

This paper presents a design of a multi-sensor data fusion filter for a Flight Test System. The multi-sensor data consist of positional information of the target from radars and a telemetry system. The data fusion filter has a structure of a federated Kalman filter and is based on the Singer dynamic target model. It consists of dedicated local filter for each sensor, generally operating in parallel, plus a master fusion filter. A fault detection and correction algorithms are included in the local filter for treating bad measurements and sensor faults. The data fusion is carried out in the fusion filter by using maximum likelihood estimation algorithm. The performance of the designed fusion filter is verified by using both simulation data and real data.

• Journal of Positioning, Navigation, and Timing
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• 제10권1호
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• pp.49-54
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• 2021

Indoor positioning system becomes of increasing interests due to the demands for accurate indoor location information where Global Navigation Satellite System signal does not approach. Wi-Fi access points (APs) built in many construction in advance helps developing a Wi-Fi Received Signal Strength Indicator (RSSI) based indoor localization. This localization method first collects pairs of position and RSSI measurement set, which is called fingerprint database, and then estimates a user's position when given a query measurement set by comparing the fingerprint database. The challenge arises from nonlinearity and noise on Wi-Fi RSSI measurements and complexity of handling a large amount of the fingerprint data. In this paper, machine learning techniques have been applied to implement Wi-Fi based localization. However, most of existing indoor localizations focus on single position estimation. The main contribution of this paper is to develop multi-target localization by using deep neural, which is beneficial when a massive crowd requests positioning service. This paper evaluates the proposed multilocalization based on deep learning from a multi-story building, and analyses its learning effect as increasing number of target positions.