# An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning  
[PERSON]  
1 College of Electronic Engineering, National University of Defense Technology, Hefei 230031, China; [EMAIL_ADDRESS] (M.Z. ); [EMAIL_ADDRESS] (C.W. ); [EMAIL_ADDRESS] (Y.S.) [PERSON]  
1 College of Electronic Engineering, National University of Defense Technology, Hefei 230031, China; [EMAIL_ADDRESS] (M.Z. ); [EMAIL_ADDRESS] (C.W. ); [EMAIL_ADDRESS] (Y.S.) Wenli Zhu  
2X'an Satellite Control Center, Xi'an 710043, China 243_2  
based on machine learning are studied [11; 12; 13; 14; 15; 16]. In recent years, there have been some deep learning-based methods in DOA estimation. However, the application of deep learning in DOA estimation is not yet widespread [17]. Recently, works have mainly used deep neural networks (DNN) [16; 18; 19] and convolutional neural networks (CNN) [20; 21; 22; 23] to construct DOA estimation models. In existing deep learning-based methods, researchers have focused on how to learn the complex mapping between signals and their azimuths and how to enhance the DOA estimation performance based on DNNs and CNNs. However, the existing deep learning-based methods mostly work under ideal conditions. Due to the various error factors in the actual environment, the accuracy of DOA estimation in the practical applications of these methods will be greatly reduced. In order to make DNN-based DOA estimation models work well in the actual environment, a large number of actual signal samples need to be collected, however, this is very difficult. In addition, when the environment changes, a large number of samples need to be re-collected and the model needs to be re-trained. Therefore, it is a great challenge to quickly calibrate errors and to make the DNN-based models efficiently applicable in the actual environment. To address the above issue, in this paper, we propose an intelligent DOA estimation error calibration method based on transfer learning. Transfer learning [24; 25; 26; 27] can adaptively update the trained model according to the new samples, and transfer the model to new tasks. The idea of transfer learning suits the task of DOA estimation. The large number of ideal signal samples generated by computer simulation can be used to train a DNN-based DOA estimation model to learn the mapping between signals and their azimuths. The small number of actual signal samples collected from the actual environment can be used to fine-tune the model based on transfer learning. The original DOA estimation capability of the model is retained while the model can learn new error knowledge during transfer learning. As such, only a small number of actual signal samples need to be collected, and the model can calibrate the errors and be quickly applied in the actual environment. When the environment changes, the model can continuously learn the error knowledge in new environments by learning from a small number of new actual signal samples. Specifically, the proposed method is implemented as follows. First, we construct an intelligent DOA estimation model based on CNN. Second, we generate a large number of ideal simulation signal samples to train the CNN model. Third, we use a small number of actual signal samples collected in the actual environment to fine tune the CNN model. The proposed method has the following advantages: (i) It is a one-stage error calibration method that is different from the traditional two-stage methods. Figure 1 shows the traditional two-stage error calibration methods. These use calibration algorithms to process the input actual signals. The calibrated signals are input to the DOA estimation algorithm to predict the directions of signals. The error calibration and the DOA estimation are two separate modules and the error calibration module can only consider a single or partial error factor. Figure 2 shows the proposed method. The DOA estimation model trained on ideal signal samples is fine-tuned with actual signal samples. It implements both error calibration and DOA estimation on the same model.