Sidelobe Suppression Algorithm for Chaotic FM Signal Based on Neural Network
The chaotic FM signal is used to improve the Electronic Counter-Counter Measure (ECCM) capabilities of radar.However,the sidelobe level of this signal after matching processing is very high,thus would greatly debase the radars performance.Based on the Radial Basis Function (RBF) network,a novel range sidelobe processing technique is proposed,in which the quantum-behaved particle swarm optimization (QPSO) algorithm is applied to realize the optimization computing.A multidimensional vector composed of RBF network parameters is regarded as a particle to evolve.Then,the feasible sampling space is searched for the global optima.The simulation results show that this algorithm has easier computation and more rapid convergence compared with traditional algorithms.This method can also successfully suppress the sidelobe with good numerical stability.
QinYan Tan Yaoliang Song
School of Electronic Engineering & Optoelectronic Technology,Nanjing University of Science &Technology,Nanjing 210094,Jiangsu,China
国际会议
9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)
北京
英文
2008-10-26（万方平台首次上网日期，不代表论文的发表时间）