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RCS에 대한 최적 주파수를 적용한 주파수 가변형 레이더 성능 분석
임형용(Hyeongyong Lim),이태우(Taewoo Lee),윤동원(Dongweon Yoon) 한국정보기술학회 2017 한국정보기술학회논문지 Vol.15 No.1
Radar cross section (RCS) represents the instantaneous amount of target reflection as a standardized planar area when a radar detects a target using microwaves. It changes according to the parameters including target’s shape, size, material, direction of observation, and radar operating frequency, and influences radar’s received signal strength and detection probability. Therefore, detection probability of radar can be enhanced by operating a radar system using the optimal frequency which maximizes an RCS value of each target. In this paper, we propose a method to improve detection probability of a radar system by finding the optimal frequency based on RCS values of various targets over variable frequencies. The proposed method is verified by calculating root mean square error of 3-dimensional trajectory data estimated using the variable optimal frequency method with distance and angle inaccuracy of target in multipath fading environment.
임형용(Hyeongyong Lim),박기홍(Gihong Park),윤동원(Dongweon Yoon) 대한전자공학회 2017 전자공학회논문지 Vol.54 No.11
최대우도 (ML: Maximum Likelihood) 검출은 다중 입출력 (MIMO: Multiple-Input Multiple-Output) 시스템에서 최적 신호검출 방법이지만 다중 입력의 개수나 변조 차수가 커질수록 계산 복잡도가 지수적으로 증가하여 실제 구현이 거의 불가능해진다. 본 논문은 MIMO 시스템에서 Breadth-First 알고리즘을 기반으로 최소 구현 복잡도를 가지면서도 정확히 ML 검출이 가능한 알고리즘을 제시하고 증명하며, 컴퓨터 모의 실험을 통해 검증한다. In multiple-input multiple-output (MIMO) systems, maximum likelihood (ML) detection is well known as an optimal detection for minimizing the average error probability of MIMO systems. The main drawback of ML detection, however, is that the computational complexity grows exponentially with the number of inputs and the modulation order. In this paper, we propose a simple ML detection algorithm based on the Breadth-First algorithm for MIMO systems. We show that the proposed algorithm is equivalent to the exact ML detection with the minumum computational complexity of the Breadth-First algorithm and it is validiated via computer simulation.
박기홍(Gihong Park),임형용(Hyeongyong Lim),윤동원(Dongweon Yoon) 한국정보기술학회 2017 한국정보기술학회논문지 Vol.15 No.10
In multiple-input multiple-output (MIMO) systems, maximum likelihood (ML) detection is an optimal detection method. However, there is a problem for actual implementation since the computational complexity increases exponentially as the dimension of the MIMO systems or the modulation order becomes large. Metric Feedback Detection (MFD) is an detection algorithm which can perform ML detection with the low computational complexity through using the adaptive threshold. If the threshold calculation layer in MFD is changed according to the channel environment, the efficiency of the algorithm can be more increased. In this paper, we propose an efficient algorithm, which has the similar error performance to the ML detection, by applying clipping and adaptive layer techniques into the MFD in order to reduce the computational complexity. We verify the algorithm via computer simulation.