mlsanityData to 14 Oct 2026

I Can Hear You Coming: RF Sensing for Uncooperative Satellite Evasion

arXiv 2504.03983 ↗First posted 4 Apr 2025cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

This work presents a novel method for leveraging intercepted Radio Frequency (RF) signals to inform a constrained Reinforcement Learning (RL) policy for robust control of a satellite operating in contested environments. Uncooperative satellite engagements with nation-state actors prompts the need for enhanced maneuverability and agility on-orbit. However, robust, autonomous and rapid adversary avoidance capabilities for the space environment is seldom studied. Further, the capability constrained nature of many space vehicles does not afford robust space situational awareness capabilities that can be used for well informed maneuvering. We present a "Cat & Mouse" system for training optimal adversary avoidance algorithms using RL. We propose the novel approach of utilizing intercepted radio frequency communication and dynamic spacecraft state as multi-modal input that could inform paths for a mouse to outmaneuver the cat satellite. Given the current ubiquitous use of RF communications, our proposed system can be applicable to a diverse array of satellites. In addition to providing a comprehensive framework for training and implementing a constrained RL policy capable of providing control for robust adversary avoidance, we also explore several optimization based methods for adversarial avoidance. These methods were then tested on real-world data obtained from the Space Surveillance Network (SSN) to analyze the benefits and limitations of different avoidance methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 62 references that are in the map. First posted on arXiv 4 Apr 2025.

Builds on

12 of 62 references.

References · 12

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  3. 2017Constrained Policy Optimizationcited by 983
  4. 2015End-to-End Training of Deep Visuomotor Policiescited by 875
  5. 2019Solving Rubik's Cube with a Robot Handcited by 734
  6. 2017Safe Reinforcement Learning via Shieldingcited by 449
  7. 2021Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observabilitycited by 109
  8. 2021Understanding Domain Randomization for Sim-to-real Transfercited by 80
  9. 2024Dynamic Model Predictive Shielding for Provably Safe Reinforcement Learningcited by 15
  10. 2023Deep Reinforcement Learning for Autonomous Spacecraft Inspection using Illuminationcited by 8
  11. 2023Dexterous In-hand Manipulation by Guiding Exploration with Simple Sub-skill Controllerscited by 4
  12. 2019Bayesian Cooperative Localization Using Received Signal Strength With Unknown Path Loss Exponent: Message Passing Approachescited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Satellite Chasers: Divergent Adversarial Reinforcement Learning to Engage Intelligent Adversaries on Orbit
  2. 2025Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environments
  3. 2026Transformer-Based Reinforcement Learning for Autonomous Orbital Collision Avoidance in Partially Observable Environments
  4. 2025OrbitZoo: Real Orbital Systems Challenges for Reinforcement Learning
  5. 2025ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attacks
  6. 2025Toward Trusted Onboard AI: Advancing Small Satellite Operations using Reinforcement Learning
  7. 2026Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learning
  8. 2024Intercepting Unauthorized Aerial Robots in Controlled Airspace Using Reinforcement Learning