@inproceedings{164b960721b446449eb910b53349b293,
title = "Adaptive waveform selection algorithm based on reinforcement learning for cognitive radar",
abstract = "Cognitive radar is a newly emerging intelligent radar that can adaptively change the transmitted signal waveform according to changes in the target and environment to improve the accuracy of target state estimation. In this paper, the running process of cognitive radar adaptive transmission is analyzed, the tracking waveform parameter selection is correlated with the target state estimation and the reinforcement learning model is established. The problem of unknown target state space is solved by the 'prioritized sweeping' method and the computational efficiency is improved by replacing 'eligibility trace'. The simulation results show that the indirect reinforcement learning method is better than the fixed waveform and the waveform selection algorithm based on the minimum mean square error for the tracking accuracy and state estimation error of the target.",
keywords = "Cognitive radar, Indirect reinforcement learning, Transmit adaptive, Waveform selection",
author = "Xin Cao and Zhe Zheng and Di An",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2nd IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2019 ; Conference date: 22-11-2019 Through 24-11-2019",
year = "2019",
month = nov,
doi = "10.1109/AUTEEE48671.2019.9033413",
language = "English",
series = "Proceedings of 2019 IEEE 2nd International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "208--213",
booktitle = "Proceedings of 2019 IEEE 2nd International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2019",
address = "United States",
}