Abstract
Compressed sensing (CS)-based frequency agile radar (FAR) is attractive due to its superior data rate and target measurement performance. However, traditional frequency strategies for CS-based FAR are not cognitive enough to adapt well to the increasingly severe active interference environment. In this paper, we propose a cognitive frequency design method for CS-based FAR using reinforcement learning (RL). Specifically, we formulate the frequency design of CS-based FAR as a model-free partially observable Markov decision process (POMDP) to cope with the non-cooperation of the active interference environment. Then, a recognizer-based belief state computing method is proposed to relieve the storage and computation burdens in solving the model-free POMDP. This method is independent of the environmental knowledge and robust to the sensing scenario. Finally, the double deep Q network-based method using the exploration strategy integrating the CS-based recovery metric into the ϵ-greedy strategy (DDQN-CSR-ϵ-greedy) is proposed to solve the model-free POMDP. This can achieve better target measurement performance while avoiding active interference compared to the existing techniques. A number of examples are presented to demonstrate the effectiveness and advantage of the proposed design.
Highlights
In electronic warfare scenarios, hostile jammers emit active interference by intercepting and imitating radar signals [1,2], having a significant negative effect on radar functioning.it is necessary to equip radar systems with anti-jamming techniques
We propose a recognizer-based belief state computing method to represent the historical information of the model-free partially observable Markov decision process (POMDP)
To eliminate the limitation brought by the pulse width on the frequency step, the linear frequency modulated (LFM) signal is transmitted in the pulse
Summary
Hostile jammers emit active interference by intercepting and imitating radar signals [1,2], having a significant negative effect on radar functioning. (2) Compared to prior RL-based radar frequency strategy designs in active interference, our work provides a more realistic modeling method. It only requires observations to implement the proposed recognizer-based belief state computing method (4) We propose the DDQN-CSR-e-greedy method to solve the model-free POMDP This is able to achieve better target measurement performance in active interference than the state-of-art methods. The DDQN-CSR-e-greedy method takes actions based on the agent state and output posterior probability, which is independent of the environmental model This method uses the CSR metric to guide both anti-interference action exploration and exploitation phases.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have