@inproceedings{e8ccdc0612d64beab909c24a152466a6,
title = "Identification and suppression of clutter using machine learning method",
abstract = "A clutter suppression method using machine learning method, i.e. random forest, is proposed, which can greatly reduce the false alarm rate in radar target detection. The ground clutter causes great interference to the target detection of the low elevation measurement radar. While the traditional Doppler technology fails to meet the demand of clutter suppression especially in radar measuring small targets with low speed. Polarization information is another important information of the target which is helpful to identify clutter and then to suppress the clutter. The random forest is explored with nine different radar measurement combinations comprising of twelve various polarimetric signatures as input vectors. The results reveal that the random forest can obtain around 93% accuracy when all radar input signatures are used. The application of the method shows that the clutter is well identified and suppressed after filtering.",
keywords = "AI techniques, clutter suppression, ground clutter, polarimetric signatures, polarized radar",
author = "Meiqin Liu and Rui Wang and Cheng Hu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019 ; Conference date: 11-12-2019 Through 13-12-2019",
year = "2019",
month = dec,
doi = "10.1109/ICSIDP47821.2019.9173283",
language = "English",
series = "ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019",
address = "United States",
}