@inproceedings{9066e3cb881745ab97464efda734670a,
title = "Time-frequency Preprocessing Method of Radiation Source Signal based on Cyclic Statistics",
abstract = "To improve the real-time performance of radiation source signal time-frequency analysis, a fast radiation source signal time-frequency feature extraction method based on low-order cyclic statistics and Choi-Williams transform is proposed. First, the low-order cyclic statistics of the radiation source signal are calculated. Second, the IFFT is used to recover the cyclic domain signal, and the number of sampling points of the radiation source signal is adaptively controlled by the cyclic mean and IFFT point number. Finally, the recovered signal is used for CWD time-frequency analysis. In order to highlight the time-frequency image features, the geometric features of the signal modulation type are extracted using binary image transformation. Simulation analysis shows that the proposed time-frequency preprocessing method can significantly reduce the computational complexity while maintaining the accuracy of time-frequency features and improving real-time performance, providing effective support for subsequent sorting and recognition and parameter estimation work.",
keywords = "Time-frequency analysis, cycle statistics, noise suppression, signal preprocessing",
author = "Dingkun Huang and Xiaopeng Yan and Minghui Lv",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; 2023 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2023 ; Conference date: 24-02-2023 Through 26-02-2023",
year = "2023",
doi = "10.1117/12.2681035",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Paulo Batista and Pachori, {Ram Bilas}",
booktitle = "International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2023",
address = "United States",
}