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Research on Efficient Reinforcement Learning for Adaptive Frequency-Agility Radar.

Xinzhi Li¹, Shengbo Dong¹

¹Beijing Institute of Remote Sensing Equipment, Beijing 100854, China.

Sensors (Basel, Switzerland)

|December 10, 2021

Summary

This study introduces an adaptive reinforcement learning (RL) model to enhance frequency-agile radar

Keywords:

Markov decision process (MDP)Markov game (MG)frequency-agility radar radar anti-jamming reinforcement learning (RL)

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Area of Science:

Electrical Engineering
Computer Science
Artificial Intelligence

Background:

Modern radar systems face complex and dynamic jamming environments.
Existing reinforcement learning (RL) models struggle with frequency-agile radar due to unclear environmental boundaries and non-sequential data.
Direct application of RL algorithms leads to low sample efficiency and computational challenges.

Purpose of the Study:

To develop an efficient and adaptive reinforcement learning (RL) model for frequency-agile radar anti-jamming.
To address the limitations of traditional RL in dynamic and uncertain radar environments.
To improve the adaptability and performance of frequency-agile radar against sophisticated jamming techniques.

Main Methods:

A radar-jammer system model based on Markov game (MG) was established.
The Nash equilibrium point was determined to define dynamic environment boundaries.
The state and behavioral structure of the RL model were improved for frequency-agile data processing.

Main Results:

The proposed adaptive RL model effectively improved anti-jamming performance.
Enhanced computational efficiency was observed in the frequency-agile radar system.
The model demonstrated superior adaptability in complex and changeable jamming scenarios.

Conclusions:

The novel RL model provides an effective solution for frequency-agile radar anti-jamming.
The approach enhances radar adaptability and efficiency in challenging electronic warfare environments.
This research contributes to advancing intelligent radar systems capable of real-time adaptation.