Poster: Two-Phase KalmanNet Based Collaborative Detection Framework
- Resource Type
- Conference
- Authors
- Zhou, Tianlong; Rao, Weixiong; Ye, Feng
- Source
- 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS) ICDCS Distributed Computing Systems (ICDCS), 2023 IEEE 43rd International Conference on. :1071-1072 Jul, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Location awareness
Target tracking
Computational modeling
Neural networks
Collaboration
Estimation
Reinforcement learning
Multi agent reinforcement learning
Kalman Filter
Collaborative Detection
- Language
- ISSN
- 2575-8411
The collaborative detection problem has been widely used in many applications. Existing works typically exploit a Kalman Filter or its variant to estimate target state with an impractical assumption that the state space and environment are fully known. To address this issue, we propose a novel multi-agent reinforcement learning (MARL) based collaborative detection framework. The key is (1) a Two-Phase Kalman Neural Network (TPKN) to estimate target state and (2) a reinforcement learning (RL) model by taking the target estimation state as input and generating an action to track targets. Our initial evaluation with 4 pursuer agents and 4 targets demonstrates that our framework outperforms the state-of-the-art by a much higher tracking ability and lower localization error. 1 1 Weixiong Rao and Feng Ye are joint corresponding authors of the paper.