DOA Estimation Based on RBFNN and LPP
Rongxi Wang · Science Technology and Engineering · 2013
An effective dimension reduction method is proposed to improve the performance of direction of arrival(DOA) estimation.The method applies Locality Preserving Projection(LPP) to optimize the neural network for the DOA estimation.The purpose of LPP is to reduce the training samples and the complexity of the neural network.Compared with the commonly used upper triangular half of the covariance matrix,the method can reduce the feature dimension without losing any DOA information.Simulation results indicate that the performance of the proposed method based on LPP and RBF neural network is much better than that of the traditional methods in terms of estimation precision and efficiency.Furthermore,it is not sensitive about the noise.The proposed method can satisfy the real-time requirements of the DOA estimation.