Xiaoyu Li
ABSTRACT
With the dramatic development of information technology and rapid growth of computation performances, artificial intelligent techniques have been gradually applied in all aspects of industrial research, especially in radar signal processing. However, deep learning methods utilized in radar sea clutter are just beginning, and related researches on Doppler characteristics of sea clutter remain sparse. In this paper, artificial intelligent research on sea clutter Doppler parameters prediction is developed based on real data. Firstly, classical signal processing methods for sea clutter spectral parameters extraction are introduced. Secondly, a deep neural network model is built to predict sea clutter Doppler parameters. Finally, the raised DNN model is compared to three other classical machine learning models which are widely used in regression prediction. After comprehensive comparisons with other models in different metrics, it can be concluded that DNN model built in this paper achieves better prediction results.
- Li Qingliang Measurement and Modeling of Radar Clutter from Land and Sea[M]. 2017: National Defense Industry PressGoogle Scholar
- Keith Ward Sea Clutter: Scattering, the K distribution and Radar Performance, 2nd Edition[M]. the Institude of Engineering and Technology, London, United Kingdom, 2013Google Scholar
- Ann Marie Raynal Doppler Characteristics of Sea Clutter[J], 2010: Sandia National LaboratoriesGoogle Scholar
- Simon Watts The Doppler Spectra of Medium Grazing Angle Sea Clutter[J]. 2016: IET Radar, Sonar & NavigationGoogle Scholar
- Luke Rosenberg Characteristics of low-PRF X-band sea-clutter Doppler spectra [J]. 2008 International Conference on RadarGoogle Scholar
- Jin-Peng Zhang Estimation of sea clutter inherent Doppler spectrum from shipborne Sband radar sea echo[J]. 2020 Chinese Phys. B 29 068402Google Scholar
- Leung, H.et al. Chaotic radar signal processing over the sea. IEEE J. Ocean. Eng. 1993, 18, 287–295.Google Scholar
- McDonald, M.K Chaotic behavior and non-linear prediction of airborne radar sea clutter data. In Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No. 02CH37322), Long Beach, CA, USA, 25–25 April 2002.Google ScholarCross Ref
- Machado Fernandez Improved shape parameter estimation in K clutter with neural networks and deep learning. 2016: International Journal of Interact. Multimedia Artificial IntelligenceGoogle Scholar
- Liwen Ma Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network[J]. 2019: Remote SensingGoogle Scholar
- Penglang Shui GRNN-Based Predictors of UHF-Band Sea Clutter Reflectivity at Low Grazing Angle[J]. 2021: IEEE Geoscience and Remote Sensing LettersGoogle Scholar
- Jordan Michelet Geometric segmentation of sea clutter in coherent radar images: Range-Doppler Map versus Range-Time Intensity Map[J]. 2021: IEEE Transactions on aerospace and electronic systemsGoogle Scholar
- Zhang Bolin Yule-Walker method and Burg method based on AR model power spectrum estimate performance analysis [J]. 2016: Computer & Digital EngineeringGoogle Scholar
- Zhang Xiaofeng The experiments and analysis of polarization characteristics of the ground and sea clutters at Ku band[J]. 2019: Chinese Journal of Radio ScienceGoogle Scholar
Index Terms
- A DNN-Based Method for Sea Clutter Doppler Parameters Prediction
Recommendations
Combined Adaptive Normalized Matched Filter Detection of Moving Target in Sea Clutter
In this paper, combined adaptive normalized matched filter (ANMF) detector is proposed to detect moving target in sea clutter. For the long integration time, moving target suffers from Doppler frequency changes in individual range cells, and the sea ...
Chaotic characterization of sea clutter: new experimental results and novel applications
ASILOMAR '95: Proceedings of the 29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set)The paper begins by summarizing previously reported research findings on the chaotic characterization of sea clutter, obtained using an instrument-quality radar. Then, after a brief review of chaos theory, experimental results are presented using data ...
Multifractal correlation characteristic for radar detecting low-observable target in sea clutter
This paper studies the multifractal correlation characteristic of sea clutter which can be applied to radar low-observable target detection in sea clutter. Multifractal correlation is a generalization of multifractal 'single point' statistic, and takes ...
Comments