基于稀疏化LS-SVM的宽频段波达方向估计

Broadband Direction of Arrival Estimation Based on Sparse Least Squares Support Vector Machines

天线阵列的宽频段测向特性十分复杂,使采用智能学习的方法对波达方向进行估计时,面临着一个海量数据的复杂学习问题.采用LS-SVM建立来波方位估计模型,对LS-SVM的支持向量进行稀疏化,利用支持度高的支持向量作为训练样本,并通过二次学习获取了天线阵列的复杂测向能力,实现了宽频段波达方向的估计.实验结果表明,用稀疏化的支持向量进行二次学习,能显著提高来波方位估计的精度,在宽频段来波方位估计中有巨大的应用价值.

Broadband direction finding characteristic of antenna is very complex,so the efficient learning algorithm and the optimal training sets are very important when using smart learning method to estimate the direction of arrival.The paper use LS-SVM algorithm to build DOA estimation model which can solve the problem faster,prune the non support vectors of the LS-SVM,then construct the DOA estimation model use high support vectors as training sets for second learning.Experimental results show that the second learning with the sparse vectors can prove the DOA precision,and has a broad application value in broadband DOA estimation.