Phased array radar resource management: a comparison of scheduling algorithms

Miranda, Sergio L.; Baker, Chris; Woodbridge, K.; Griffiths, Hugh

doi:10.1109/nrc.2004.1316399

Cited by 16 publications

(12 citation statements)

References 5 publications

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“…The time is characterized www.ijacsa.thesai.org by the tactical requirements, the energy is limited by the transmitter energy, and the RRM limits the computational resource. All of those limitations have impacts on the performance of the radar resource management [1, 6,8,9]. …”

Section: B the Radar Resource Management Problemmentioning

confidence: 99%

“…Operator and Strategy: This module operates as a human-machine interface, allowing intervention to enable corrections in the behavior of the system [8].…”

Section: A Architecturementioning

confidence: 99%

“…Fuzzy logic approach and neural network approach for prioritizing radar tasks in changing environment conditions is introduced. Results suggest that the fuzzy approach is a valid means of evaluating the relative importance of the radar tasks; the resulting priorities have been adapted by the fuzzy logic prioritization method [6,8,9,13].…”

Section: Performance Evalutionmentioning

confidence: 99%

“…All the functions or the tasks are coordinated by a central component RRM in the radar system. This RRM component is critical to the success of a MFR since it maximizes the radar resource usage in order to achieve optimal performance [6,8,9].…”

Section: Introductionmentioning

confidence: 99%

“…It is also very important that the information in respect of radar resource management is presented to the radar operator at all times [8,9].…”

Section: A Introduction To Ai Techniquesmentioning

confidence: 99%

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A Comparative Study on different AI Techniques towards Performance Evaluation in RRM(Radar Resource Management)

Madhusudhan¹,

Nazim²

2012

IJARAI

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Abstract-The multifunction radar (MFR) has to make a decision as to which functions are to be performed first or which must be degraded or even not done at all when there are not enough resources to be allocated. The process of making these decisions and determining their allocation as a function of time is known as Radar Resource Management (RRM). The RRM has two basic issues: task prioritization and task scheduling. The task prioritization is an important factor in the task scheduler. The other factor is the required scheduling time, which is decided by the environment, the target scenario and the performance requirements of radar functions. The required scheduling time could be improved by using advanced algorithm [1, 6].

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Section: B the Radar Resource Management Problemmentioning

confidence: 99%

“…Operator and Strategy: This module operates as a human-machine interface, allowing intervention to enable corrections in the behavior of the system [8].…”

Section: A Architecturementioning

confidence: 99%

Section: Performance Evalutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…It is also very important that the information in respect of radar resource management is presented to the radar operator at all times [8,9].…”

Section: A Introduction To Ai Techniquesmentioning

confidence: 99%

See 3 more Smart Citations

A Comparative Study on different AI Techniques towards Performance Evaluation in RRM(Radar Resource Management)

Madhusudhan¹,

Nazim²

2012

IJARAI

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Interval Algebra – An effective means of scheduling surveillance radar networks

2015

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Interval Algebra provides an effective means to schedule surveillance radar networks, as it is a temporal ordering constraint language. Thus it provides a solution to a part of resource management, which is included in the revised Data Fusion Information Group model of information fusion. In this paper, the use of Interval Algebra to schedule mechanically steered radars to make multistatic measurements for selected targets of importance is shown.Interval Algebra provides a framework for incorporating a richer set of requirements, without requiring modifications to the underlying algorithms.The performance of Interval Algebra was compared to that of the Greedy Randomised Adaptive Search Procedure and the applicability of Interval Algebra to nimble scheduling was investigated using Monte-Carlo simulations of a binary radar system. The comparison was done in terms of actual performance as well as in terms of computation time required. The performance of the algorithms was quantified by keeping track of the number of targets that could be measured simultaneously. It was found that nimble scheduling is important where the targets are moving fast enough to rapidly change the recognised surveillance picture during a scan.Two novel approaches for implementing Interval Algebra for scheduling surveillance radars are presented. It was found that adding targets on the fly and improving performance by incrementally growing the network is more efficient than pre-creating the full network. The second approach stemmed from constraint ordering. It was found that for simple constraint sets, the Interval Algebra relationship matrix reduces to a single vector of interval sets.The simulations revealed that an Interval Algebra algorithm that utilises both approaches can perform as well as the Greedy Randomised Adaptive Search Procedure with similar processing time requirements. Finally, it was found that nimble scheduling is not required for surveillance radar networks where ballistic and supersonic targets can be ignored. Nevertheless, Interval Algebra can easily be used to perform nimble scheduling with little modification and may be useful in scheduling the scans of multifunction radars.

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