• Journal of Terahertz Science and Electronic Information Technology
  • Vol. 20, Issue 3, 213 (2022)
WUMinghua*, RAO Bin, and WANG Wei
Author Affiliations
  • [in Chinese]
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    DOI: 10.11805/tkyda2021354 Cite this Article
    WUMinghua, RAO Bin, WANG Wei. Radar target number estimation method based on deep residual network[J]. Journal of Terahertz Science and Electronic Information Technology , 2022, 20(3): 213 Copy Citation Text show less
    References

    [1] WANG Z,LI M,CHEN H,et al. Adaptive detection of a subspace signal in signal-dependent interference[J]. IEEE Transactions on Signal Processing, 2017,65(18):4812-4820.

    [2] WANG Z,ZHAO Z,REN C,et al. CFAR subspace detectors with multiple observations in system-dependent clutter background[J]. Signal Processing, 2018,153:58-70.

    [3] GHOJAVAND K,DERAKHTIAN M,BIGUESH M. Rao-based detectors for adaptive target detection in the presence of signal-dependent interference[J]. IEEE Transactions on Signal Processing, 2020,68:1662-1672.

    [4] YAN L,ADDABBO P,HAO C,et al. New ECCM techniques against noiselike and/or coherent interferers[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020,56(2):1172-1188.

    [5] HAN S,YAN L,ZHANG Y,et al. Adaptive radar detection and classification algorithms for multiple coherent signals[J]. IEEE Transactions on Signal Processing, 2021,69:560-572.

    [6] RANGASWAMY M. Non-homogeneity detector for Gaussian and non-Gaussian interference scenarios[C]// Sensor Array & Multichannel Signal Processing Workshop. Rosslyn,VA,USA:IEEE, 2002:528-532.

    [7] RANGASWAMY M. Statistical analysis of the nonhomogeneity detector for non-Gaussian interference backgrounds[J]. IEEE Transactions on Signal Processing, 2005,53(6):2101-2111.

    [8] JIANG L, WANG T. Robust non-homogeneity detector based on reweighted adaptive power residue[J]. IET Radar, Sonar & Navigation, 2016,10(8):1367-1374.

    [9] LIU J, MASSAROD, ORLANDO D, et al. Radar adaptive detection architectures for heterogeneous environments[J]. IEEE Transactions on Signal Processing, 2020,68:4307-4319.

    [10] HE K,ZHANG X,REN S,et al. Deep residual learning for image recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). [S.l.]:IEEE Xplore Digital Library, 2016:770-778.

    WUMinghua, RAO Bin, WANG Wei. Radar target number estimation method based on deep residual network[J]. Journal of Terahertz Science and Electronic Information Technology , 2022, 20(3): 213
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