Virtual array element beamforming algorithm based on LSTM neural network

Abstract

Virtual array element beamforming technology can improve the angular resolution of conventional beamforming algorithm. However, when the number of real array elements is limited, the beam pattern of the virtual array element beamforming algorithm based on linear prediction(the VAEBF-LP) is slightly distorted, the root mean square error(RMSE) of Direction of Arrival(DOA) is larger and so is the main lobe width. In response to this problem, A virtual array element beamforming algorithm based on the LSTM neural network(the VAEBF-LSTM) is proposed. This algorithm uses the LSTM neural network to learn the temporal characteristics between the received data of real array elements and the virtual array elements. Obtaining the received data of virtual array elements, this algorithm enables the beam pattern to be free of distortion and achieve higher angular resolution detection of targets with a relatively low RMSE of DOA in this context. The simulation results show that the VAEBF-LSTM not only makes the beam pattern distortion disappear but also makes the main lobe smaller in width than that of the conventional beamforming algorithm with the guarantee that the RMSE of DOA remains considerably low when the number of real array elements is limited.