基于先验知识嵌入LSTM-PPO模型的智能干扰决策算法

张静克; 杨凯; 李超; 王洪雁

doi:10.11959/j.issn.1000-436x.2024270

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基于先验知识嵌入LSTM-PPO模型的智能干扰决策算法

学术通信 | 更新时间：2025-01-14

- 基于先验知识嵌入LSTM-PPO模型的智能干扰决策算法
- Intelligent interference decision algorithm with prior knowledge embedded LSTM-PPO model
- 通信学报 2024年45卷第12期页码：227-239
- 作者机构：
  
  1.电子信息系统复杂电磁环境效应国家重点实验室，河南洛阳 471032
  2.浙江理工大学信息科学与工程学院，浙江杭州 310018
  3.浙江理工大学计算机科学与技术学院，浙江杭州 310018
- 作者简介：
  
  [ "张静克（1988- ），男，河南洛阳人，博士，电子信息系统复杂电磁环境效应国家重点实验室助理研究员，主要研究方向为雷达对抗。" ]
  [ "杨凯（1999- ），男，浙江绍兴人，浙江理工大学硕士生，主要研究方向为电子对抗。" ]
  [ "李超（1986- ），男，河南洛阳人，博士，电子信息系统复杂电磁环境效应国家重点实验室助理研究员，主要研究方向为雷达对抗。" ]
  [ "王洪雁（1979- ），男，河南南阳人，博士，浙江理工大学特聘教授，主要研究方向为雷达对抗、MIMO雷达信号处理、机器视觉等。" ]
- 基金信息：
  
  电子信息系统复杂电磁环境效应国家重点实验室基金资助项目(CEMEE2023K0301)
- DOI：10.11959/j.issn.1000-436x.2024270
  中图分类号： TN92
- 收稿：2024-07-19，
  
  修回：2024-12-02，
  
  纸质出版：2024-12-25
- 稿件说明：
移动端阅览
张静克,杨凯,李超等.基于先验知识嵌入,LSTM-PPO模型的智能干扰决策算法[J].通信学报,2024,45(12):227-239.

ZHANG Jingke,YANG Kai,LI Chao,et al.Intelligent interference decision algorithm with prior knowledge embedded LSTM-PPO model[J].Journal on Communications,2024,45(12):227-239.
张静克,杨凯,李超等.基于先验知识嵌入,LSTM-PPO模型的智能干扰决策算法[J].通信学报,2024,45(12):227-239. DOI： 10.11959/j.issn.1000-436x.2024270.

ZHANG Jingke,YANG Kai,LI Chao,et al.Intelligent interference decision algorithm with prior knowledge embedded LSTM-PPO model[J].Journal on Communications,2024,45(12):227-239. DOI： 10.11959/j.issn.1000-436x.2024270.

摘要

针对基于传统强化学习模型的多功能雷达（MFR）干扰决策算法决策效率及有效性低、策略不稳定的问题，提出基于先验知识嵌入长短期记忆（LSTM）网络-近端策略优化（PPO）模型的智能干扰决策算法。所提算法首先将MFR干扰决策问题定义为马尔可夫决策过程（MDP）。其次，基于收益塑造理论将干扰领域先验知识嵌入PPO模型的奖励函数，利用重塑所得奖励函数引导智能体快速收敛从而提升决策效率。而后，基于LSTM优异的时序特征抽取能力，捕捉回波数据的动态特征以有效刻画雷达工作状态。最后，将所抽取动态特征输入PPO模型，经由所嵌入先验知识的引导，从而可快速获得有效干扰决策。仿真实验表明，相较于传统深度干扰决策算法，所提算法具有较高的决策效率以及有效性，且可高效稳健地达成MFR干扰决策算法。

Abstract

Focusing on the issues of low efficiency and effectiveness in decision-making as well as the instability of traditional reinforcement learning model-based multi-function radar (MFR) jamming decision algorithms

a prior knowledge embedded long short-term memory (LSTM) network-proximal policy optimization (PPO) model based intelligent interference decision algorithm was developed. Firstly

the MFR interference decision problem was regarded as a Markov decision process (MDP). Furthermore

by incorporating prior knowledge associated with the interference domain into the reward function of the PPO model using revenue shaping theory

a reshaped reward function was obtained to guide agent converge quickly so as to improve decision-making efficiency. Besides

leveraging LSTM’s excellent temporal feature extraction ability enables capturing dynamic characteristics of echo data effectively to describe radar working states. Finally

these extracted dynamic features were inputted into the PPO model. With guidance from embedded prior knowledge

an effective interference decision can be achieved rapidly. Simulation results demonstrate that compared to traditional reinforcement learning model based interference decision algorithms

higher efficiency and effectiveness in decision-making can be attained via the proposed algorithms and the MFR interference decision can be efficiently and robustly achieved.

关键词

Keywords

references

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