摘要
为了获取较高的宽带信号的DOA(direction-of-arrival)估计精度,提出了基于改进的广义回归神经网络(IGRNN,improved generalized regression neural network)和主成分分析(PCA,principalcomponent analysis)的宽带DOA估计算法。选用PCA方法对训练样本进行降维,以降低神经网络的复杂度;利用粒子群算法优化GRNN的参数;根据选取不同的聚焦角度确定粗估计、精估计的训练模型,通过粗估计得出目标的大致方位后,利用精估计模型得出最终的估计结果,避免了聚焦角度对估计精度的影响。仿真结果表明,本文提出的算法具有较好的估计精度和较高的工作效率。
A novel wideband direction-of-arrival estimation algorithm based on improved generalized regression neural network（IGRNN）and principal component analysis（PCA） is proposed in order to obtain high estimation precision.Firstly,the PCA method is used to lower the dimension of train samples in order to reduce the complexity of GRNN whose parameters are optimized by particle swarm optimization algorithm.At the same time,accurate and rough estimation models are established respectively according to different focusing angles.The accurate model is used for DOA estimation after rough estimation to avoide the influence of focusing angles.Simulation results show that our algorithm has high estimation precision and efficiency.
出处
《光电子．激光》
EI
CAS
CSCD
北大核心
2012年第4期692-696,共5页
Journal of Optoelectronics·Laser
基金
国家自然科学基金(61001150)
船舶工业国防科技预研基金(10J3.5.2)
江苏高校优势学科建设工程和江苏省研究生培养创新工程(CX10B-110Z)资助项目
