dc.contributor.author |
De Freitas, A
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dc.contributor.author |
Focke, Richard W
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dc.contributor.author |
De Villiers, P
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dc.date.accessioned |
2019-01-31T13:00:26Z |
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dc.date.available |
2019-01-31T13:00:26Z |
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dc.date.issued |
2018-07 |
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dc.identifier.citation |
De Freitas, A., Focke, R.W. and De Villiers, P. 2018. Response surface modeling for networked radar resource allocation. International Conference on Information Fusion, 10-13 July 2018, Cambridge, United Kingdom |
en_US |
dc.identifier.isbn |
978-0-9964527-6-2 |
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dc.identifier.isbn |
978-1-5386-4330-3 |
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dc.identifier.uri |
https://ieeexplore.ieee.org/document/8455815
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dc.identifier.uri |
DOI: 10.23919/ICIF.2018.8455815
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dc.identifier.uri |
http://hdl.handle.net/10204/10658
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dc.description |
Copyright: 2018 ISIF. Due to copyright restrictions, the attached PDF file contains the accepted version of the published paper. For access to the published item, please consult the publisher's website |
en_US |
dc.description.abstract |
Sensor management is an important function of any data fusion center as the output of a fusion system is dependent on the quality of the information collected. In this paper, the scheduling aspect of sensor management function is implemented using Response Surface Modeling (RSM). Applying RSM requires formulating the sensor management function as an objective function. The benefit of RSM over prior global optimization approaches is the simplification of the evaluation of this objective function to find global optima. This leads to either reduced computational requirements and/ or shorter due times for creating sensor schedules. This work shows the utility of RSM towards scheduling multiple sensors, and seeks to introduce RSM to the sensor management community. It is shown that the RSM scheduler provides a significant improvement towards reducing the number of missed targets in a surveillance radar network. This is compared to performing a uniform scanning regime (or sequential stepped scan) often employed. Very few iterations are required to provide this gain. The RSM technique also quickly determines where the most effective use of sensor resources needs to be applied. Consequently, it spends more radar dwell time on these beam locations. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.ispartofseries |
Worklist;21024 |
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dc.subject |
Process refinement |
en_US |
dc.subject |
Sensor management |
en_US |
dc.subject |
Scheduling |
en_US |
dc.subject |
Radar networks |
en_US |
dc.subject |
Surveillance |
en_US |
dc.subject |
Response surface modeling |
en_US |
dc.title |
Response surface modeling for networked radar resource allocation |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
De Freitas, A., Focke, R. W., & De Villiers, P. (2018). Response surface modeling for networked radar resource allocation. IEEE. http://hdl.handle.net/10204/10658 |
en_ZA |
dc.identifier.chicagocitation |
De Freitas, A, Richard W Focke, and P De Villiers. "Response surface modeling for networked radar resource allocation." (2018): http://hdl.handle.net/10204/10658 |
en_ZA |
dc.identifier.vancouvercitation |
De Freitas A, Focke RW, De Villiers P, Response surface modeling for networked radar resource allocation; IEEE; 2018. http://hdl.handle.net/10204/10658 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - De Freitas, A
AU - Focke, Richard W
AU - De Villiers, P
AB - Sensor management is an important function of any data fusion center as the output of a fusion system is dependent on the quality of the information collected. In this paper, the scheduling aspect of sensor management function is implemented using Response Surface Modeling (RSM). Applying RSM requires formulating the sensor management function as an objective function. The benefit of RSM over prior global optimization approaches is the simplification of the evaluation of this objective function to find global optima. This leads to either reduced computational requirements and/ or shorter due times for creating sensor schedules. This work shows the utility of RSM towards scheduling multiple sensors, and seeks to introduce RSM to the sensor management community. It is shown that the RSM scheduler provides a significant improvement towards reducing the number of missed targets in a surveillance radar network. This is compared to performing a uniform scanning regime (or sequential stepped scan) often employed. Very few iterations are required to provide this gain. The RSM technique also quickly determines where the most effective use of sensor resources needs to be applied. Consequently, it spends more radar dwell time on these beam locations.
DA - 2018-07
DB - ResearchSpace
DP - CSIR
KW - Process refinement
KW - Sensor management
KW - Scheduling
KW - Radar networks
KW - Surveillance
KW - Response surface modeling
LK - https://researchspace.csir.co.za
PY - 2018
SM - 978-0-9964527-6-2
SM - 978-1-5386-4330-3
T1 - Response surface modeling for networked radar resource allocation
TI - Response surface modeling for networked radar resource allocation
UR - http://hdl.handle.net/10204/10658
ER -
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en_ZA |