Efficient network training method for two-dimension DOA estimation
Hongguang Chen, Biao Li, Zhenkang Shen · 2004
Training set is vary large in two-dimensional (2D) direction estimation which prevents neural network from being widely used in 2D DOA estimation. A dimension-degraded training (DDT) method is proposed in this paper to reduce the training set. In the DDT method, elevation and azimuth are estimated in two separate neural networks respectively. We construct two 1-dimensional training sets that are much smaller than the original 2-dimensional one while keeping the high resolution of angle. Simulations show the validity of the proposed method.