The manifold matrix of the received signals can be destroyed when the array is with the gain and phase errors,which will affect the performance of the traditional direction of arrival(DOA)estimation approaches.In this...The manifold matrix of the received signals can be destroyed when the array is with the gain and phase errors,which will affect the performance of the traditional direction of arrival(DOA)estimation approaches.In this paper,a novel active array calibration method for the gain and phase errors based on a cascaded neural network(GPECNN)was proposed.The cascaded neural network contains two parts:signal-to-noise ratio(SNR)classification network and two sets of error estimation subnetworks.Error calibration subnetworks are activated according to the output of the SNR classification network,each of which consists of a gain error estimation network(GEEN)and a phase error estimation network(PEEN),respectively.The disadvantage of neural network topology architecture is changing when the number of array elements varies is addressed by the proposed group calibration strategy.Moreover,due to the data characteristics of the input vector,the cascaded neural network can be applied to arrays with arbitrary geometry without repetitive training.Simulation results demonstrate that the GPECNN not only achieves a better balance between calibration performance and calibration complexity than other methods but also can be applied to arrays with different numbers of sensors or different shapes without repetitive training.展开更多
基金supported by the Key R&D Program of Shandong Province(2020CXGC010109)the Beijing Municipal Science and Technology Project(Z181100003218015)。
文摘The manifold matrix of the received signals can be destroyed when the array is with the gain and phase errors,which will affect the performance of the traditional direction of arrival(DOA)estimation approaches.In this paper,a novel active array calibration method for the gain and phase errors based on a cascaded neural network(GPECNN)was proposed.The cascaded neural network contains two parts:signal-to-noise ratio(SNR)classification network and two sets of error estimation subnetworks.Error calibration subnetworks are activated according to the output of the SNR classification network,each of which consists of a gain error estimation network(GEEN)and a phase error estimation network(PEEN),respectively.The disadvantage of neural network topology architecture is changing when the number of array elements varies is addressed by the proposed group calibration strategy.Moreover,due to the data characteristics of the input vector,the cascaded neural network can be applied to arrays with arbitrary geometry without repetitive training.Simulation results demonstrate that the GPECNN not only achieves a better balance between calibration performance and calibration complexity than other methods but also can be applied to arrays with different numbers of sensors or different shapes without repetitive training.
