基于深度学习的DTMB外辐射源雷达参考信道估计

参考数据的纯度影响着地面数字广播多媒体(DTMB)外辐射源雷达的探测能力,而参考信道估计的精度是影响参考数据恢复的关键因素。针对此问题,文章将基于深度学习理论的信道估计方法引入外辐射源雷达参考信道估计过程。利用自回归模型对参考信道进行建模,并搭建参考信道估计网络。通过迭代训练后,估计得到参考信道响应。相比于传统算法,基于深度学习的参考信道估计精度得到有效提升,改善了雷达的探测性能。

DTMB Passive Radar Reference Channel Estimation Based on Deep Learning

The purity of reference data affects the detection capability of DTMB passive radar, and the accuracy of reference channel estimation is a key factor to affect the recovery of reference data. In view of this problem, the channel estimation method based on the deep learning theory is introduced into the passive radar reference channel estimation process. The reference channels are modeled by the autoregressive model and the reference channel estimation network is built. After iterative training, the reference channel response is estimated. Compared with the traditional algorithm, the estimation accuracy of reference channel based on deep learning is improved effectively, and the detection performance of radar is improved.