DOA Estimation Method Based on Unsupervised Learning Network With Threshold Capon Spectrum Weighted Penalty

In complex electronic countermeasure environment, direction-of-arrival (DOA) is very important for targets detection, localization and tracking. However, the power of interference is usually stronger than that of signal, which degrades the DOA estimation performance severely, and even makes DOA estimation failure. To solve this issue, this paper proposes a DOA estimation method based on unsupervised learning network with threshold Capon spectrum weighted penalty. In this work, an unsupervised network is proposed to obtain the DOA estimation spectrum, in which labels are no longer required. Furthermore, deep unfolded layers are introduced to remove the iterative solution of sparse recovery and increase the depth of network. Additionally, loss function contains reconstruction error and penalty term is developed to generate zero traps in direction of interference and signal, overcoming the influence of strong interference. Both numerical simulations and experiments demonstrate the effectiveness of the proposed method.