Target Prioritization for Multi-Function Radar Using Artificial
                        Neural Network Based on Steepest Descent Method

Jeong, Nam-Hoon; Lee, Seong-Hyeon; Kang, Mi Jin; Gu, Chang-Woo; Kim, Cheol-Ho; Kim, Kyung‐Tae

doi:10.5515/kjkiees.2018.29.1.68

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…함정용 MFR의 자원 관리는 크게 표적 우선순위 할당과 빔 스케줄링으로 구분할 수 있다. 표적 우선순위 할당은 수집된 정보로부터 각 표적에 대한 레이다의 우선순위를 결정하는 과정으로, 퍼지 논리 기반 알고리즘 [2] , 동적 계획 법 기반 알고리즘 [3] , 인공 신경망 기반 알고리즘 [4] 등이 개 발되어 있다. 또한, 빔 스케줄링은 표적 우선순위 할당 알 고리즘으로부터 얻어진 각 표적의 우선순위를 바탕으로 탐색 및 추적 빔을 시간적으로 적절하게 할당하는 과정이 다.…”

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Beam Scheduling for Shipborne Multi-Function Radar Based on Job Shop Scheduling Theory

Jeong¹,

Jin²,

Choi³

et al. 2019

J. Korean Inst. Electromagn. Eng. Sci.

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Beam scheduling is necessary for a multifunction radar(MFR) to perform multiple tasks and manage the resources of the radar efficiently. In this paper, we introduce the concept of the job shop problem to solve the beam-scheduling problem, and apply heuristic algorithms to enable reasonable beam allocation by the MFR. Then, we select three indicators, frame time, total weighed tardiness, and the number of targets dropped out, by considering realistic operation of the MFR. Based on the simulation results, we analyze the scheduling of each algorithm according to the number of targets and compare the performance of the algorithms.

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Beam Scheduling for Shipborne Multi-Function Radar Based on Job Shop Scheduling Theory

Jeong¹,

Jin²,

Choi³

et al. 2019

J. Korean Inst. Electromagn. Eng. Sci.

Self Cite

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Radar Beam Scheduling Using Pareto Optimal Point to Optimize Dual Cost Function

Jin¹,

Jeong²,

Choi³

et al. 2019

J. Korean Inst. Electromagn. Eng. Sci.

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Achieving efficient mission performance of multi-function radars is a challenge because of resource limitations; thus, in this study, we examine a specific radar resource management technique. Previous studies have optimizing a single cost function to improve radar performance; however, other cost functions that include trade-offs must also be optimized. In this study, the relevant task is first vectorized, and the mission selection vector searches for the task to be performed. Then, using the Pareto optimal point, a tracking period that satisfies both dual cost functions of the task is found. The scheduling is finally performed by updating the new start time of the task for the corresponding tracking period. Simulation results show that it is possible to improve the radar cost value while minimizing the loss of the cost function using a trade-off relationship for optimizing the dual cost function.

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Target Prioritization for Multi-Function Radar Using Artificial Neural Network Based on Steepest Descent Method

Cited by 2 publications

References 4 publications

Beam Scheduling for Shipborne Multi-Function Radar Based on Job Shop Scheduling Theory

Beam Scheduling for Shipborne Multi-Function Radar Based on Job Shop Scheduling Theory

Radar Beam Scheduling Using Pareto Optimal Point to Optimize Dual Cost Function

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