Intelligent Waveform Optimization for Target Tracking Based on Fuzzy Reinforcement Learning In Radar Sensor Networks

Radar sensor networks (RSNs) have more degrees of freedom than single radar system, and significantly improve the target angular resolution and parameter identifiability. This work proposes an intelligent waveform optimization strategy based on fuzzy Q learning (FQL) for multi-target tracking (MTT) of RSN in a cluttered environment. Specifically, this method adopts a distributed fusion architecture, each radar node independently detects and tracks multiple targets, and uses the covariance intersection (CI) fusion algorithm to solve the unknown correlation of each radar node. Combined with the target error covariance predicted by the Riccati equation, an FQL waveform optimization method is designed to select the best transmission from the waveform library. It integrates the radar and targets into a closed loop and updates the transmit waveform in real time according to the status of the targets to maximize global MTT performance. Simulation verifies that the proposed method’s tracking performance and CPU time are superior to the Q-learning.