TY - JOUR
T1 - Optimal allocation of cooperative jamming resource based on hybrid quantum-behaved particle swarm optimisation and genetic algorithm
AU - Jiang, Haiqing
AU - Zhang, Yangrui
AU - Xu, Hongyi
N1 - Publisher Copyright:
© The Institution of Engineering and Technology.
PY - 2017
Y1 - 2017
N2 - The multi-dimension jamming resources allocation (JRA) problem is studied to enhance the jamming effectiveness for the cooperative jammers formation against radar net. The allocation strategies include the methods of allocating jamming resources and the mode selection for the jamming signal. First, jamming resource optimum allocation model is established based on the detection probability of the netted radar fusion centre. Second, a hybrid quantum-behaved particle swarm optimisation and self-adjustable genetic algorithm (HQPSOGA) is proposed to optimise the deployment of the jamming resource innovatively with multi-constrained conditions. Finally, the HQPSOGA is compared with the integer-value genetic algorithm, standard particle swarm optimisation (PSO) and quantum-behaved PSO in JRA problem regarding the solution quality, robustness, convergence rate and reliability by a general Monte Carlo simulation. Simulation results show that the proposed method is capable of developing better overall interference capacity efficiently for the jammers formation than any other tested algorithm.
AB - The multi-dimension jamming resources allocation (JRA) problem is studied to enhance the jamming effectiveness for the cooperative jammers formation against radar net. The allocation strategies include the methods of allocating jamming resources and the mode selection for the jamming signal. First, jamming resource optimum allocation model is established based on the detection probability of the netted radar fusion centre. Second, a hybrid quantum-behaved particle swarm optimisation and self-adjustable genetic algorithm (HQPSOGA) is proposed to optimise the deployment of the jamming resource innovatively with multi-constrained conditions. Finally, the HQPSOGA is compared with the integer-value genetic algorithm, standard particle swarm optimisation (PSO) and quantum-behaved PSO in JRA problem regarding the solution quality, robustness, convergence rate and reliability by a general Monte Carlo simulation. Simulation results show that the proposed method is capable of developing better overall interference capacity efficiently for the jammers formation than any other tested algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85017408679&partnerID=8YFLogxK
U2 - 10.1049/iet-rsn.2016.0119
DO - 10.1049/iet-rsn.2016.0119
M3 - Article
AN - SCOPUS:85017408679
SN - 1751-8784
VL - 11
SP - 185
EP - 192
JO - IET Radar, Sonar and Navigation
JF - IET Radar, Sonar and Navigation
IS - 1
ER -