A time-frequency feature prediction network for time-varying radio frequency interference

一种面向时变射频干扰的时频特征预测网络

¹ Aviation University of Air Force, Changchun 130000, China
² College of Air and Missile Defense, Air Force Engineering University, Xi'an 710051, China
³ Air Force Logistics Academy, Xuzhou 221000, China

Received: 4 July 2022

Abstract

The time-varying radio frequency interference has strong nonlinear dynamic characteristics, which is difficult to be predicted by linear method effectively, making the anti-interference decision without sufficient information support. To solve this problem, a recurrent neural network for spectrum prediction based on time-frequency correlation features is proposed. A sliding window is used to characterize the two-dimensional correlation of time-frequency series, and the spectrum prediction problem is transformed into a problem similar to spatiotemporal sequence prediction. A gradient bridge structure across time frames is added to reduce the attenuation of the gradient in the long time and multi-level network propagation. The training efficiency and network performance are improved by the loss function with better matching. Simulation and experimental results verify the validity of the network prediction results.

摘要

时变射频干扰非线性强, 难以用线性方法进行有效预测, 进而使抗干扰决策缺少信息支撑。针对该问题提出了基于时频相关特征的频谱预测递归神经网络, 通过滑窗模型表征时频序列的二维相关性, 将频谱预测问题转化为类似于空时序列预测的问题, 增加跨时间帧的梯度桥结构以减轻梯度在长时间、多层级网络传播时的衰减现象, 用匹配性更高的损失函数提高训练效率和网络性能。仿真和实验结果验证了该网络预测结果的有效性。