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论文:2023,Vol:41,Issue(3):587-594 |
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引用本文： |
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万鹏程, 冯为可, 童宁宁, 韦伟. 一种面向时变射频干扰的时频特征预测网络[J]. 西北工业大学学报 |
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WAN Pengcheng, FENG Weike, TONG Ningning, WEI Wei. A time-frequency feature prediction network for time-varying radio frequency interference[J]. Journal of Northwestern Polytechnical University |
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| 一种面向时变射频干扰的时频特征预测网络 |
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| 万鹏程1, 冯为可2, 童宁宁2, 韦伟3 |
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1. 空军航空大学, 吉林 长春 130000;
2. 空军工程大学 防空反导学院, 陕西 西安 710051;
3. 空军勤务学院, 江苏 徐州 221000 |
| 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。 |
| 关键词:
频谱预测
射频干扰
深度神经网络
循环神经网络
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| A time-frequency feature prediction network for time-varying radio frequency interference |
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| WAN Pengcheng1, FENG Weike2, TONG Ningning2, WEI Wei3 |
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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 |
| 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. |
| Key words:
spectrum prediction
radio frequency interference
deep neural network
recurrent neural network
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| 收稿日期: 2022-07-04
修回日期:
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| DOI: 10.1051/jnwpu/20234130587 |
| 基金项目: 国家自然科学基金面上项目（62001507）资助 |
| 通讯作者: 冯为可(1992—),空军工程大学讲师,主要从事雷达信号处理研究。e-mail:fengweike007@163.com
Email：fengweike007@163.com |
| 作者简介: 万鹏程(1993—),空军航空大学讲师,主要从事目标探测与识别研究。
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