Combining Self-Organizing Map and Lipschitz Condition for Estimation in Direction of Arrival  

作　　者：Xiuhui Tan Peng Wang Hongping Hu Rong Cheng Yanping Bai Xiuhui Tan;Peng Wang;Hongping Hu;Rong Cheng;Yanping Bai(School of Mathematics, North University of China, Taiyuan, China)

机构地区：[1]School of Mathematics, North University of China, Taiyuan, China

出　　处：《Open Journal of Applied Sciences》2023年第7期1012-1028,共17页应用科学（英文）

摘　　要：There are many DOA estimation methods based on different signal features, and these methods are often evaluated by experimental results, but lack the necessary theoretical basis. Therefore, a direction of arrival (DOA) estimation system based on self-organizing map (SOM) and designed for arbitrarily distributed sensor array is proposed. The essential principle of this method is that the map from distance difference of arrival (DDOA) to DOA is Lipschitz continuity, it indicates the similar topology between them, and thus Kohonen SOM is a suitable network to classify DOA through DDOA. The simulation results show that the DOA estimation errors are less than 1° for most signals between 0° to 180°. Compared to MUSIC, Root-MUSIC, ESPRIT, and RBF, the errors of signals under signal-to-noise ratios (SNR) declines from 20 dB to 2 dB are robust, SOM is better than RBF and almost close to MUSIC. Further, the network can be trained in advance, which makes it possible to be implemented in real-time.There are many DOA estimation methods based on different signal features, and these methods are often evaluated by experimental results, but lack the necessary theoretical basis. Therefore, a direction of arrival (DOA) estimation system based on self-organizing map (SOM) and designed for arbitrarily distributed sensor array is proposed. The essential principle of this method is that the map from distance difference of arrival (DDOA) to DOA is Lipschitz continuity, it indicates the similar topology between them, and thus Kohonen SOM is a suitable network to classify DOA through DDOA. The simulation results show that the DOA estimation errors are less than 1° for most signals between 0° to 180°. Compared to MUSIC, Root-MUSIC, ESPRIT, and RBF, the errors of signals under signal-to-noise ratios (SNR) declines from 20 dB to 2 dB are robust, SOM is better than RBF and almost close to MUSIC. Further, the network can be trained in advance, which makes it possible to be implemented in real-time.

关 键 词：DOA Estimation Kohonen SOM Distance Difference of Arrival Topological Order Lipschitz Condition 

分 类 号：TN9[电子电信—信息与通信工程]