DOA Estimation using Multi-head Self-Attention with Relative Positional Encoding and Hybrid Multi-Objective Grey Wolf Optimization

Abstract

Direction of Arrival (DOA) estimation can be defined as the process of estimating the incident angle of waves by an antenna array, a radiating or reflecting source. DOA estimation is of high importance in several areas, like navigation, remote sensing, radar, sonar and wireless communication. Nevertheless, noise, interference and multipath effects complicate the accurate estimation of DOA. The challenges can be overcome by advancing deep learning (DL) and simulating the intricate relationships in the signal data effecting. In the presented work, a new DOA estimation method utilizing hybrid LSTM-CNN feature extraction pipeline, multi-head attention GWO-mRMR based feature selection, and ANN is presented. The method includes improvements such as positional encoding in multi-head attention and adaptive learning rate in ANN. It is trained and evaluated on synthetic signal data generated by a Uniform Linear Array (ULA) model. In evaluation, it achieved exceptional results with MAE (2.15), MSE (15.67), RMSE (3.95), and R² score (0.97). The accuracy of DOA estimations can be attributed to the robust feature extraction pipeline that effectively captured spatial and temporal features and model’s focus on the major relevant and informative features. Additionally, the performance was superior to that of conventional existing techniques of MUSIC, SVR, and ESPRIT, establishing the efficacy of the suggested method for DOA estimation in realistic scenarios.