CAJ | 학술논문

为了提高雷达的抗干扰(ECCM)能力,采用混沌调频(FM)信号进行传输,但其回波经传统匹配滤波处理后,存在较高的随机分布的距离旁瓣,严重影响了雷达的性能.提出了一种以量子粒子群优化算法(QPSO)训练径向基函数(RBF)神经网络进行混沌FM信号旁瓣抑制的新方法.将RBF神经网络的参数组成一个多维向量,作为算法中的粒子进行进化,由此可在全局空间中搜索最优解.仿真结果表明,该方法计算简单,收敛速度快,并具有较好的数值稳定性,能很好的抑制距离旁瓣.
위료제고뢰체적항간우(ECCM)능력,채용혼돈조빈(FM)신호진행전수,단기회파경전통필배려파처리후,존재교고적수궤분포적거리방판,엄중영향료뢰체적성능.제출료일충이양자입자군우화산법(QPSO)훈련경향기함수(RBF)신경망락진행혼돈FM신호방판억제적신방법.장RBF신경망락적삼수조성일개다유향량,작위산법중적입자진행진화,유차가재전국공간중수색최우해.방진결과표명,해방법계산간단,수렴속도쾌,병구유교호적수치은정성,능흔호적억제거리방판.
The chaotic frequency modulation (FM) signal is used to improve the electronic counter-countermeasure(ECCM) capabilities of radar. However,the sidelobe level of the signal after matching processing is very high,which greatly debases the radar's performance. Based on the radial basis function (RBF) network,a novel range sidelobe processing technique was 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 simulated results show that the algorithm has easier computation,more rapid convergence compared with traditional algorithms,and can also successfully suppress the sidelobe with good numerical stability.