Secondary Beam Attention Estimation Algorithm

Deconvolution algorithms exhibit significant limitations in complex environments. To enhance the accuracy of direction-of-arrival (DOA) estimation, this article proposes an estimation algorithm that integrates deep learning with deconvolution processing. The algorithm preprocesses the received data using a deconvolution approach and employs quadratic programming and compressed sensing algorithms to obtain the spatial power distribution vector and the input data for the neural network. To improve the accuracy of covariance matrix estimation, a stacked convolutional autoencoder model is constructed, which suppresses noise in the covariance matrix through model pretraining. In addition, we design a data selection operation to enhance the network’s focus on target locations. Furthermore, an attention mechanism tailored for beamforming results is introduced, utilizing multiple attention taps and bias taps to process data, enhancing the accuracy of error estimation. By integrating the attention mechanism with a multimodal network, the proposed algorithm achieves high-precision DOA estimation. Simulation and experimental results validate that the algorithm demonstrates superior estimation accuracy and resolution.