Fuzzy Compensation Support Vector Classification for Direction of Arrival Estimation
Xiang He, Zemin Liu, Bin Jiang · 2009
This paper presents a new direction of arrival (DOA) estimation method based on a multi-class implementation of fuzzy compensation support vector machine (SVM). The proposed method can achieve higher accurate estimates for DOA while avoiding the all-direction peak value searching technique used in other traditional DOA estimation methods. Meanwhile, compared with other SVM-based DOA estimation, like LS-SVM algorithm, this approach reduces the training and testing time and performs better with larger data, so is easier to implement in real-time applications. Computer simulation results show the effectiveness of the proposed method.