A model of distributed sensors' scheduling and self-adaptive probability particle swarm optimization algorithm

被引：0

作者：

Ren, Jun-Liang ^{[1
]}

Xing, Qing-Hua ^{[1
]}

Li, Long-Yue ^{[1
]}

Jia, Zhe ^{[2
]}

机构：

[1] Air and Missile Defense College, Air Force Engineering University, Xi'an, 710051, Shaanxi

[2] Air Force Command College, Beijing

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2015年 / 43卷 / 09期

关键词：

Distributed computing; Particle swarm optimization; Scheduling; Self-adaptive; Sensor;

D O I：

10.3969/j.issn.0372-2112.2015.09.012

中图分类号：

学科分类号：

摘要：

This paper introduced a distributed computing method to study the scheduling problem in multi-sensor systems. According to the features of these sensors which were usually deployed at different locations, it redesigned C2 (Command and Control) module and sensor module, and discussed information interaction procedure between two modules. Then it established a sensor-target detection match degree computing model which involved task decomposition and minimal scheduling period methods. A self-adaptive probability particle swarm optimization (SAPPSO) algorithm for scheduling program was given. In SAPPSO, particle fitness value was based on different probabilities, which reflected the thinking of particle during iteration process. Experimental results showed that SAPPSO algorithm converged quickly, especially in the previous iteration period, which enabled SAPPSO to fulfill the requirements of real-time and high efficiency for scheduling. ©, 2015, Chinese Institute of Electronics. All right reserved.

引用

页码：1756 / 1762

页数：6

共 15 条

[1]

Yan Z.-W., Niu Y.-F., Li H.-L., Study of sensor scheduling for early warning satellite based on parallel tabu geneticalgorithm (PTGA), Journal of Astronautics, 24, 6, pp. 598-603, (2003)

[2]

Jiang W., Li Y.-J., Heuristic optimization of resourceallocation in space based early-warning system, Systems Engineering-Theory & Practice, 30, 10, pp. 1834-1840, (2010)

[3]

Guo H.-B., Wang Y.-L., Zeng H., Sensor scheduling for missile early-warning satellite based on genetic and simulated annealing algorithm, Electronics Optics & Control, 13, 4, pp. 71-74, (2006)

[4]

Cheng H.-W., Wang B., An W., A sensor scheduling method of LEO constellation based on information decision tree, Acta Electronica Sinica, 38, 11, pp. 2630-2633, (2010)

[5]

Tang S.-X., Yi X.-Q., Luo X.-S., An improved particle swarm optimization algorithm for early warning satellites scheduling problems, Systems Engineering, 30, 1, pp. 116-121, (2012)

[6]

Tang S.-X., Research on Satellite Sensor Resources Management and Detection Tasks Scheduling Problem for LEO Constellation of Space-based Early Warning System, (2011)

[7]

Feng M.-Y., Li G.-H., Yi X.-Q., Rule-based scheduling method for sensors on early warning satellites basing on task analysis, Computer Applications, 27, 12, pp. 3095-3098, (2007)

[8]

Zhang R.-T., Astudy on multi-radar tracking ballistic missile engagement prediction technique, Modem Radar, 32, 8, pp. 31-33, (2010)

[9]

Ma L., Wang X.-S., Li Y.-Z., Et al., Influence ofradar location on the measurability of micro-doppler detecting for precession target, Journal of National University of Defense Technology, 33, 2, pp. 54-59, (2011)

[10]

Li L.-Y., Liu F.-X., Mei Y.-Y., Attractor particle swarm optimization for anti-TBM fire power-target match modeling in terminal phase, Systems Engineering and Electronics, 35, 5, pp. 993-999, (2013)

← 1 2 →