De Freitas, AFocke, Richard WDe Villiers, P2019-01-312019-01-312018-07De 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 Kingdom978-0-9964527-6-2978-1-5386-4330-3https://ieeexplore.ieee.org/document/8455815DOI: 10.23919/ICIF.2018.8455815http://hdl.handle.net/10204/10658Copyright: 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 websiteSensor 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.enProcess refinementSensor managementSchedulingRadar networksSurveillanceResponse surface modelingConference PresentationDe Freitas, A., Focke, R. W., & De Villiers, P. (2018). Response surface modeling for networked radar resource allocation. IEEE. http://hdl.handle.net/10204/10658De Freitas, A, Richard W Focke, and P De Villiers. "Response surface modeling for networked radar resource allocation." (2018): http://hdl.handle.net/10204/10658De Freitas A, Focke RW, De Villiers P, Response surface modeling for networked radar resource allocation; IEEE; 2018. http://hdl.handle.net/10204/10658 .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 -