Selected problems of MFR resources management

Komorniczak, W.; Pietrasinski, J.

doi:10.1109/ific.2000.859837

Cited by 18 publications

(9 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For surveillance tasks, there are four fuzzy variables including the original priority and number of threatening targets. In [6], [7], a neural network based approach is utilized for target ranking with respect to range, radial velocity, membership (friend or foe), acceleration and object rank (important or not important). Both neural network and fuzzy logic based approaches provide an adaptive priority assignment, in general.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Improvement of Time-Balance Radar Schedulers Through Decision Policies (Extended Version)

Çayır,

Candan

2017

Preprint

View full text Add to dashboard Cite

The resource management of a phase array system capable of multiple target tracking and surveillance is critical for the realization of its full potential. Present work aims to improve the performance of an existing method, time-balance scheduling, by establishing an analogy with a well-known stochastic control problem, the machine replacement problem. With the suggested policy, the scheduler can adapt to the operational scenario without a significant sacrifice from the practicality of the time-balance schedulers. More specifically, the numerical experiments indicate that the schedulers directed with the suggested policy can successfully trade the unnecessary track updates, say of non-maneuvering targets, with the updates of targets with deteriorating tracks, say of rapidly maneuvering targets, yielding an overall improvement in the tracking performance.

show abstract

Section: Introductionmentioning

confidence: 99%

“…However, the learning process is far from trivial [2]. The process includes training of several data sets in random order from the same initial starting point [6].…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of Time-Balance Radar Schedulers Through Decision Policies (Extended Version)

Çayır,

Candan

2017

Preprint

View full text Add to dashboard Cite

show abstract

“…과 Orman형 [2] 이 있으며, 레이다 빔 스케줄링을 위해 다양한 규칙을 비 교 분석한 논문 [3] [4], [5] 은닉 마르코프 모델(HMM)이나 마르 코프 결정 모델(MDP)구조 하에서 동적 프로그래밍을 통 해 문제를 해결하는 방법 [6] , QoS(quality of service)를 목적 함수로 두는 Q-RAM 알고리즘 [7]～ [9] , 레이다 부하 상태에 따라 빔 스케줄링 알고리즘을 선택적으로 적용할 수 있 는 방법 [10]…”

unclassified

Beam Scheduling Algorithm for Interleaved Mode of AESA Radar

Roh¹,

Won²,

Yoon³

2021

J. Korean Inst. Electromagn. Eng. Sci.

View full text Add to dashboard Cite

The modern active electronically scanned array (AESA) airborne radar features an interleaved mode, whereby situational awareness can be maximized via incorporation of the radar's electronic beam pointing capability. This interleaved mode can enable simultaneous operation of two or more modes, such as the air-to-air and sea surface-search modes, via the technique of time-sharing. One of the key requirements for successful realization of the interleaved mode is a beam scheduler design that facilitates effective control of the allocated resources for each mode. Thus, in this paper, we propose an effective beam scheduler that can control the allocated resources for each mode through the mode-switching option based on the radar's frames, bars, and beams. Furthermore, the proposed beam scheduler involves the mode-switching decision logic and calculates the processing ratio for each mode.

show abstract

“…Stoffel [4] 은 고정형 우선 순 위와 인공 신경망을 이용한 휴리스틱 탐색 기법을 비교하여 후자가 탐색과 추적 성능의 향상을 가져온 다고 보고하였다. 이외의 적응형 우선 순위 선정 방 식으로는 인공 신경망을 사용한 기법 [5] , 퍼지 추론 시스템(Fuzzy Inference System: FIS)을 이용한 기법 들이 있다 [6], [7] . 이러한 기존 적응형 기법 중에서 실 제 시스템에 적용된 예는 Barbaresco의 FIS [6] 가 있다.…”

unclassified

A Study of Fuzzy Inference System Based Task Prioritizations for the Improvement of Tracking Performance in Multi-Function Radar

Kim

Park²,

Kim³

et al. 2013

The Journal of Korean Institute of Electromagnetic Engineering

View full text Add to dashboard Cite

This paper presents the improvement of tracking performance using fuzzy inference system based task prioritizations for multi-function radars. The presented technique calculates elemental priorities using track information of a target and obtain the total priority from fuzzy inference system of each fuzzy set's membership function. In this paper, we proposed the task prioritization algorithms based on fuzzy inference system, and evaluated the tracking performance on multi-function radar scenario using it. As a result, we confirmed that excellent performance could be achieved when using the proposed algorithm.

show abstract

Selected problems of MFR resources management

Cited by 18 publications

References 1 publication

Performance Improvement of Time-Balance Radar Schedulers Through Decision Policies (Extended Version)

Performance Improvement of Time-Balance Radar Schedulers Through Decision Policies (Extended Version)

Beam Scheduling Algorithm for Interleaved Mode of AESA Radar

A Study of Fuzzy Inference System Based Task Prioritizations for the Improvement of Tracking Performance in Multi-Function Radar

Contact Info

Product

Resources

About