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计算机工程 ›› 2010, Vol. 36 ›› Issue (06): 204-205.doi: 10.3969/j.issn.1000-3428.2010.06.069

• 人工智能及识别技术 • 上一篇    下一篇

基于改进LS-SVM的来波方位估计

李鹏飞1,2，张 旻1,2   

  1. (1. 解放军电子工程学院309研究室，合肥 230037；2. 安徽省电子制约技术重点实验室，合肥 230037)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2010-03-20 发布日期:2010-03-20

Incoming Wave Direction Estimation Based on Improved LS-SVM

LI Peng-fei1,2, ZHANG Min1,2   

  1. (1. Research Laboratory 309, PLA Electronic Engineering Institute, Hefei 230037; 2. Anhui Province Key Laboratory of Electronic Restricting Technique, Hefei 230037)
  • Received:1900-01-01 Revised:1900-01-01 Online:2010-03-20 Published:2010-03-20

PDF

925

被引次数

3 | 1

摘要： 提取已知方位信号的协方差矩阵的上三角部分作为样本特征，构建方位估计模型。针对最小二乘支持向量机最优参数难以选定的问题，采用实值编码的启发式遗传算法，以模型的来波方位估计性能为目标，实现基于高斯核函数的SVM参数优化，提高了来波方位估计精度。实验结果表明，该方法估计精度较高、实时性好，在无线电测向领域具有广阔应用前景。

关键词: 最小二乘支持向量机, 遗传算法, 来波方位, 估计

Abstract: This paper extracts the upper triangular half of the covariance matrix of knowing direction signals to construct the direction estimation model. Aiming at the problem that the best parameter of Least Squares-Support Vector Machine(LS-SVM) is hard to select, it uses real-coded heuristic genetic algorithm. Aiming at the approximate performance estimation of model incoming wave direction, it optimizes the parameters of LS-SVM with Gauss kernel function. The estimation precision is improved. Experimental results show that this method has high precision, high real-time performance and a broad application future in wireless direction finding field.

Key words: Least Squares-Support Vector Machine(LS-SVM), genetic algorithm, incoming wave direction, estimation

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