DoA Estimation using Cascaded Neural Networks and Angle Classification for Coherent Signals

This paper focuses on the direction of arrival (DoA) estimation for two coherent sources using a uniform linear array (ULA). It is demonstrated that the DoA estimation error increases as the angle of the incoming signal moves away from the center in the range (-90°, 90°) for existing schemes. In addition, this paper also focuses on improving the DoA estimation performance in low signal to noise ratio (SNR). Therefore a cascaded neural network (CaNN) is proposed to improve the DoA estimation consisting of two stages of neural networks (NNs). The first NN is the enhanced SNR (ESNR) classifier, which is used to improve performance for different SNRs. The second NN is the angle estimator, which improves the performance for different angle ranges. For overcoming the issue of coherent signals, a spatially smoothened auto covariance matrix is fed to the SNR classifier and angle estimator blocks. A performance comparison with an existing scheme such as spatial smoothing- multiple signal classification (SS-MUSIC) and ESNR CaNN shows that the proposed CaNN for different angles and SNR ranges performs better than the existing schemes.