Research Article
A Novel Direction Noise Detection Method of HFSWR Based on Space-Time Multi-eigenvalue Synthesis
@INPROCEEDINGS{10.1007/978-3-031-86203-8_3, author={Dezhu Xiao and Xin Zhang and Shuaida Zhao and Qiang Yang}, title={A Novel Direction Noise Detection Method of HFSWR Based on Space-Time Multi-eigenvalue Synthesis}, proceedings={Wireless and Satellite Systems. 14th EAI International Conference, WiSATS 2024, Harbin, China, August 23--25, 2024, Proceedings, Part II}, proceedings_a={WISATS PART 2}, year={2025}, month={3}, keywords={HFSWR Directional noise Eigenvalue detection Direction detection}, doi={10.1007/978-3-031-86203-8_3} }- Dezhu Xiao
Xin Zhang
Shuaida Zhao
Qiang Yang
Year: 2025
A Novel Direction Noise Detection Method of HFSWR Based on Space-Time Multi-eigenvalue Synthesis
WISATS PART 2
Springer
DOI: 10.1007/978-3-031-86203-8_3
Abstract
High Frequency Surface Wave Radar (HFSWR) is one of the main emerging technologies in the field of modern ocean exploration and monitoring remote sensing. However, with the increasing complexity of the HFSWR detection environment, the spatial distribution of its external environmental noise intensity presents a directional distribution, called directional noise. Directional noise has a complex source, and its noise base will be elevated by 10 to 15dB. Accurate detection of directional noise is pivotal for enhancing the detection performance of HFSWR. The eigenvalue is used as a measure to characterize directional noise. In this paper, a directional noise detection method based on space-time eigenvalue synthesis is proposed. The construction of the space-time covariance matrix relies on sample selection within the local processing region (LPR) of the angle-doppler joint domain and the accumulation of multiple snapshot samples in the range units. Subsequently, the detection statistics are formulated based on the eigenvalue distribution characteristics of the space-time covariance matrix. Finally, the effectiveness of the proposed algorithm in locating directional noise is validated using measured data.
