Deep multi-intentional inverse reinforcement learning for cognitive multi-function radar inverse cognition

TL;DR

This paper proposes a deep multi-intentional inverse reinforcement learning approach to identify and interpret complex multi-function radar behaviors by estimating multiple reward functions, enhancing electronic intelligence capabilities.

Contribution

It introduces a novel IRL method combined with EM to handle multiple reward functions for cognitive multi-function radars, improving behavior understanding.

Findings

Outperforms baseline methods in simulations

Accurately estimates multiple reward functions

Enhances radar behavior interpretation

Abstract

In recent years, radar systems have advanced significantly, offering environmental adaptation and multi-task capabilities. These developments pose new challenges for electronic intelligence (Elint) and electronic support measures (ESM), which need to identify and interpret sophisticated radar behaviors. This paper introduces a Deep Multi-Intentional Inverse Reinforcement Learning (DMIIRL) method for the identification and inverse cognition of cognitive multi-function radars (CMFR). Traditional Inverse Reinforcement Learning (IRL) methods primarily target single reward functions, but the complexity of CMFRs necessitates multiple reward functions to fully encapsulate their behavior. To this end, we develop a method that integrates IRL with Expectation-Maximization (EM) to concurrently handle multiple reward functions, offering better trajectory clustering and reward function estimation. Simulation results demonstrate the superiority of the proposed method over baseline approaches.