Robust Bayesian attention belief network for radar work mode recognition

This is the original implementation of the paper Robust Bayesian attention belief network for radar work mode recognition:

@article{DU2023103874,
title = {Robust Bayesian attention belief network for radar work mode recognition},
journal = {Digital Signal Processing},
volume = {133},
pages = {103874},
year = {2023},
issn = {1051-2004},
doi = {https://doi.org/10.1016/j.dsp.2022.103874},
url = {https://www.sciencedirect.com/science/article/pii/S1051200422004912},
author = {Mingyang Du and Ping Zhong and Xiaohao Cai and Daping Bi and Aiqi Jing},
}

1. Environment

Pytorch

2. Dataset

There are two self-built datasets used in this repository, which contains different inter-pulse modulation patterns. Please refer to the paper for further details. Both datasets comprise collections of PDW sequences defined by three parameters: RF, PW, and PRI. Additionally, you can experiment with your own datasets that include more radar signal parameters, such as pulse amplitude (PA) and direction of arrival (DOA).

3. Contact

If you have any question about our work or code, please feel free to email dumingyang17@nudt.edu.cn.