Coherent DOA estimation method based on angle interval separation learning

In face of the accuracy degradation of traditional model-driven algorithms when processing spatially close coherent source signals and the large training sample requirement of data-driven algorithms, a high-precision direction of arrival (DOA) estimation method based on angle interval separation learning (AISL) is proposed. It leverages the sparsity of signal angle intervals and employs spatial filters to estimate angle interval information using the concept of area separation. The signals are then partitioned into corresponding angle interval areas, followed by DOA estimation through deep neural network (DNN) multi-label classifiers. Additionally,sparse autoencoder (SAE) technology is introduced to compress input data and extract key features, effectively reducing computational complexity while filtering out interference. Simulation results demonstrate that compared to other data-driven algorithms, this method achieves superior estimation accuracy and generalization ability in spatially close angle domains under limited training sample conditions.