Volume XL-1/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 431-436, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-431-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 431-436, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-431-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  25 Sep 2013

25 Sep 2013

NEWTONIAN IMPERIALIST COMPETITVE APPROACH TO OPTIMIZING OBSERVATION OF MULTIPLE TARGET POINTS IN MULTISENSOR SURVEILLANCE SYSTEMS

A. Afghan-Toloee, A. A. Heidari, and Y. Joibari A. Afghan-Toloee et al.
  • Dept. of Geomatic Engineering, University of Tehran, Amir-Abad Street, Tehran, Iran

Keywords: Newtonian imperialist competitive algorithm, multi-sensor, multiple target, collective intelligent, deployment

Abstract. The problem of specifying the minimum number of sensors to deploy in a certain area to face multiple targets has been generally studied in the literatures. In this paper, we are arguing the multi-sensors deployment problem (MDP). The Multi-sensor placement problem can be clarified as minimizing the cost required to cover the multi target points in the area. We propose a more feasible method for the multi-sensor placement problem. Our method makes provision the high coverage of grid based placements while minimizing the cost as discovered in perimeter placement techniques. The NICA algorithm as improved ICA (Imperialist Competitive Algorithm) is used to decrease the performance time to explore an enough solution compared to other meta-heuristic schemes such as GA, PSO and ICA. A three dimensional area is used for clarify the multiple target and placement points, making provision x, y, and z computations in the observation algorithm. A structure of model for the multi-sensor placement problem is proposed: The problem is constructed as an optimization problem with the objective to minimize the cost while covering all multiple target points upon a given probability of observation tolerance.