Open Access
Issue
JNWPU
Volume 41, Number 3, June 2023
Page(s) 587 - 594
DOI https://doi.org/10.1051/jnwpu/20234130587
Published online 01 August 2023
  1. NGUYEN L H, TRAN T D, DO T T. Sparse models and sparse recovery for ultra-wideband SAR applications[J]. IEEE Trans on Aerospace and Electronic Systems, 2014, 50(2): 940–958. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  2. HUANG Y, ZHANG L, LI J, et al. Reweighted tensor factorization method for SAR narrowband and wideband interference mitigation using smoothing multiview tensor model[J]. IEEE Trans on Geoscience and Remote Sensing, 2020, 58(5): 3298–3313. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  3. YIN Jiapeng, LI Jianbing, PANG Chen, et al. A radio frequency interference mitigation method for polarimetric doppler weather radars[J]. Journal of Radars, 2021, 10(6): 905–918. [Article] (in Chinese) [Google Scholar]
  4. YANG Z, DU W, LIU Z, et al. WBI suppression for sar using iterative adaptive method[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(3): 1008–1014. [Article] [CrossRef] [Google Scholar]
  5. HUANG Y, ZHANG L, LI J, et al. A novel tensor technique for simultaneous narrowband and wideband interference suppression on single-channel SAR system[J]. IEEE Trans on Geoscience and Remote Sensing, 2019, 57(12): 9575–9588. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  6. AUBRY A, CAROTENUTO V, MAIO A D, et al. High range resolution profile estimation via a cognitive stepped frequency technique[J]. IEEE Trans on Aerospace and Electronic Systems, 2019, 55(1): 444–458. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  7. LIU Bo, BAI Xiaodong, ZHANG Gengxin, et al. Review of deep learning in cognitive radio[J]. Journal of East China Normal University, 2021(1): 36–52. [Article] (in Chinese) [Google Scholar]
  8. KIRK B H, NARAYANAN R M, GALLAGHER K A, et al. Avoidance of time-varying radio frequency interference with software-defined cognitive radar[J]. IEEE Trans on Aerospace and Electronic Systems, 2019, 55(3): 1090–1107. [Article] [CrossRef] [Google Scholar]
  9. KOVARSKIY J A, KIRK B H, MARTONE A F, et al. Evaluation of real-time predictive spectrum sharing for cognitive radar[J]. IEEE Trans on Aerospace and Electronic Systems, 2021, 57(1): 690–705. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  10. SHEN Bin, WANG Xin, CHEN Siji, et al. Machine learning based primary user transmit mode classification for spectrum sensing in cellular cognitive radio network[J]. Journal of Electronics & Information Technology, 2021, 43(1): 92–100. [Article] (in Chinese) [Google Scholar]
  11. WANG Yaping, WEI Guanghui, SHANG Zaifei, et al. Out-of-band multi-frequency interference prediction method and verification for communication station[J]. Transactions of Beijing Institute of Technology, 2020, 40(11): 1230–1237. [Article] (in Chinese) [Google Scholar]
  12. ZHANG Qinglong, WANG Yuming, CHENG Erwei, et al. Investigation on the effect law and prediction method of out-of-band electromagnetic interference in navigation receiver[J]. System Engineering and Electronics, 2021, 43(9): 2588–2593. [Article] (in Chinese) [Google Scholar]
  13. WANG Xinxin, WANG Xiang, FAN Jianchao, et al. Analysis of RF interference characteristics of broadcasting satellite TV receivers to SMAP satellite L-band microwave radiometer[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2292–2299. [Article] (in Chinese) [Google Scholar]
  14. STINCO P, GRECO M, GINI F, et al. Cognitive radars in spectrally dense environments[J]. IEEE Aerospace and Electronic Systems Magazine, 2016, 31(10): 20–27 [CrossRef] [Google Scholar]
  15. YU L, WANG Q, GUO Y, et al. Spectrum availability prediction in cognitive aerospace CO[C]//IEEE 2017 Cognitive Communications for Aerospace Applications Workshop, 2017 [Google Scholar]
  16. SHAWEL B S, WOLDEGEBREAL D H, POLLIN S. Convolutional LSTM-based long-term spectrum prediction for dynamic spectrum access[C]//European Signal Processing Conference, 2019: 1–5 [Google Scholar]
  17. XING J S, CHEN Z, WANG H, et al. Convolutional LSTM network: a machine learning approach for precipitation nowcasting[C]//Twenty-ninth Conference on Neural Information Processing Systems, 2015: 802–810 [Google Scholar]
  18. LI X, LIU Z, CHEN G, et al. Deep Learning for spectrum prediction from spatial-temporal-spectral data[J]. IEEE Communications Letters, 2020, 25(4): 1216–1220 [Google Scholar]
  19. HE K, SUN J. Convolutional neural networks at constrained time cost[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015: 5353–5360 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.