• ICROS
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• v.21 no.4
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• pp.27-33
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• 2015

단일 센서 기반의 표적 탐지 문제에서 센서의 한계 요소에 의해 탐지 성능이 제한된다. 따라서, 최근 단일 센서 기반의 표적 탐지 성능을 향상시키기 위한 방안으로 각 센서의 강점을 효과적으로 융합하는 다중 센서 정보 융합 기반의 표적 탐지 기법에 대한 연구가 활발히 진행되고 있다. 센서 정보 융합을 위해서는 각 센서별 영상 획득, 각 영상의 기하학적 정합, 센서 정보 융합 기반의 표적 탐지 기술이 필요하며, 본 논문에서는 이에 대한 기술 및 개발 동향을 소개한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.454-460
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• 1998

본 논문은 전장에서의 표적 식별을 위해 다수의 센서가 사용되는 환경에서 요구되는 융합방법론에 대해 간단히 살표 보고 이에 대한 차이점을 비교한다. 다수의 센서를 사용함으로써 각각의 센서가 가진 중복성, 보완성을 활용하여 센서가 제공하는 정보의 불확실성을 줄일수 있는 가능성을 기대할 수 있다. 본 논문에서는 베이지안 알고리즘, Dempster-Shafer 이론 그리고 퍼지 융합 방법 등에 대한 간단히 소개하고 임의의 표적과 특성값을 설정하여 융합 알고리즘간의 성능을 비교하였다.

• 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.

• Journal of Korea Multimedia Society
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• v.15 no.3
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• pp.291-298
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• 2012

In automatic target recognition(ATR) systems, target extraction techniques are very important because ATR performance depends on segmentation result. So, this paper proposes a multi-sensor image fusion method based on adaptive weights. To incorporate the FLIR image and CCD image, we used information such as the bi-modality, distance and texture. A weight of the FLIR image is derived from the bi-modality and distance measure. For the weight of CCD image, the information that the target's texture is more uniform than the background region is used. The proposed algorithm is applied to many images and its performance is compared with the segmentation result using the single image. Experimental results show that the proposed method has the accurate extraction performance.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.131-134
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• 2000

본 논문에서는 각 지역의 수중 음향 센서로부터 중앙의 정보 융합 센터로 전송되어진 동일한 또는 상이한 표적의 Identity 정보들을 종합해 최종적으로 표적의 Identity를 결정하는 Decision Fusion 기법을 다룬다. 기존의 연구는 표적의 속성 정보로부터 정보 융합을 통해 표적의 Identity를 선택하는 기법을 주로 다루고 있다. 그러나 본 논문에서는 기존의 연구보다 한 단계 나아가 선택된 표적의 Identity들로부터 운용자가 가장 합리적인 결정을 내릴 수 있도록 하는 표적의 Identity 결정을 위한 Decision Fusion 기법을 제안한다. 이러한 수중 음향 표적 식별 시스템에서의 Identity Decision Fusion 기법으로 Voting 기법, 센서 정보의 신뢰도를 고려한 Weighted Voting 기법, 그리고 다 기준 의사 결정 기법인 Analytic Hierarchy Process (AHP) 기법을 제안하고 그 성능을 평가한다

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.556-561
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• 2011

It is generally known that particle filters can produce consistent target tracking performance in comparison to the Kalman filter for non-linear and non-Gaussian systems. In this paper, I propose a Rao-Blackwellized multiple model particle filter(RBMMPF) to enhance computational efficiency of the particle filters as well as to reduce sensitivity of modeling. Despite that the Rao-Blackwellized particle filter needs less particles than general particle filter, it has a similar tracking performance with a less computational load. Comparison results for performance is listed for the using single sensor information RBMMPF and using multisensor data fusion RBMMPF.

• Journal of Korea Multimedia Society
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• v.15 no.2
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• pp.195-203
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• 2012

This paper proposes ground target classification algorithm based on decision fusion and feature extraction method using multi-sensor images. The decisions obtained from the individual classifiers are fused by applying a weighted voting method to improve target recognition rate. For classifying the targets belong to the individual sensors images, features robust to scale and rotation are extracted using the difference of brightness of CM images obtained from CCD image and the boundary similarity and the width ratio between the vehicle body and turret of target in FLIR image. Finally, we verity the performance of proposed ground target classification algorithm and feature extraction method by the experimentation.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.335-343
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• 2012

This paper presents an target tracking algorithm for fusion of radar and infrared(IR) sensor measurement data. Generally, fusion methods with Kalman filter assume that processing data obtained by radar and IR sensor are synchronized. It has much limitation to apply the fusion methods to real systems. A key point which is taken into account in the proposed algorithm is the fact that two asynchronous dissimilar data are fused by compensating the time difference of the measurements using radar's ranges and track state vectors. The proposed fusion algorithm in the paper is evaluated via a computer simulation with the existing track fusion and measurement fusion methods.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.31-37
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• 2021

In this paper, we propose an outlier detection algorithm called C-SCGP to prevent the degradation of localization performance based on RSS (Received Signal Strength) and AOA (Angle of Arrival) in the presence of outliers in wireless sensor networks. Since the accuracy of target estimation can significantly deteriorate due to various cause of outliers such as malfunction of sensor, jamming, and severe noise, it is important to detect and filter out all outliers. The single cluster graph partitioning (SCGP) algorithm has been widely used to remove such outliers. The proposed continuous-SCGP (C-SCGP) algorithm overcomes the weakness of the SCGP that requires the threshold and computing probability of outliers, which are impratical in many applications. The results of numerical simulations show that the performance of C-SCGP without setting threshold and probability computation is the same performance of SCGP.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.3
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• pp.34-42
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• 2007

It is very important for the combat system to process extensive data exactly at short time for the better situation awareness compared with the threats in these days. This paper suggests to add radial velocity on the decision factor of sensor data fusion in the existing algorithm for the accuracy enhancement of the sensor data fusion in the combat system.