Vol. 110
Latest Volume
All Volumes
PIERM 130 [2024] PIERM 129 [2024] PIERM 128 [2024] PIERM 127 [2024] PIERM 126 [2024] PIERM 125 [2024] PIERM 124 [2024] PIERM 123 [2024] PIERM 122 [2023] PIERM 121 [2023] PIERM 120 [2023] PIERM 119 [2023] PIERM 118 [2023] PIERM 117 [2023] PIERM 116 [2023] PIERM 115 [2023] PIERM 114 [2022] PIERM 113 [2022] PIERM 112 [2022] PIERM 111 [2022] PIERM 110 [2022] PIERM 109 [2022] PIERM 108 [2022] PIERM 107 [2022] PIERM 106 [2021] PIERM 105 [2021] PIERM 104 [2021] PIERM 103 [2021] PIERM 102 [2021] PIERM 101 [2021] PIERM 100 [2021] PIERM 99 [2021] PIERM 98 [2020] PIERM 97 [2020] PIERM 96 [2020] PIERM 95 [2020] PIERM 94 [2020] PIERM 93 [2020] PIERM 92 [2020] PIERM 91 [2020] PIERM 90 [2020] PIERM 89 [2020] PIERM 88 [2020] PIERM 87 [2019] PIERM 86 [2019] PIERM 85 [2019] PIERM 84 [2019] PIERM 83 [2019] PIERM 82 [2019] PIERM 81 [2019] PIERM 80 [2019] PIERM 79 [2019] PIERM 78 [2019] PIERM 77 [2019] PIERM 76 [2018] PIERM 75 [2018] PIERM 74 [2018] PIERM 73 [2018] PIERM 72 [2018] PIERM 71 [2018] PIERM 70 [2018] PIERM 69 [2018] PIERM 68 [2018] PIERM 67 [2018] PIERM 66 [2018] PIERM 65 [2018] PIERM 64 [2018] PIERM 63 [2018] PIERM 62 [2017] PIERM 61 [2017] PIERM 60 [2017] PIERM 59 [2017] PIERM 58 [2017] PIERM 57 [2017] PIERM 56 [2017] PIERM 55 [2017] PIERM 54 [2017] PIERM 53 [2017] PIERM 52 [2016] PIERM 51 [2016] PIERM 50 [2016] PIERM 49 [2016] PIERM 48 [2016] PIERM 47 [2016] PIERM 46 [2016] PIERM 45 [2016] PIERM 44 [2015] PIERM 43 [2015] PIERM 42 [2015] PIERM 41 [2015] PIERM 40 [2014] PIERM 39 [2014] PIERM 38 [2014] PIERM 37 [2014] PIERM 36 [2014] PIERM 35 [2014] PIERM 34 [2014] PIERM 33 [2013] PIERM 32 [2013] PIERM 31 [2013] PIERM 30 [2013] PIERM 29 [2013] PIERM 28 [2013] PIERM 27 [2012] PIERM 26 [2012] PIERM 25 [2012] PIERM 24 [2012] PIERM 23 [2012] PIERM 22 [2012] PIERM 21 [2011] PIERM 20 [2011] PIERM 19 [2011] PIERM 18 [2011] PIERM 17 [2011] PIERM 16 [2011] PIERM 14 [2010] PIERM 13 [2010] PIERM 12 [2010] PIERM 11 [2010] PIERM 10 [2009] PIERM 9 [2009] PIERM 8 [2009] PIERM 7 [2009] PIERM 6 [2009] PIERM 5 [2008] PIERM 4 [2008] PIERM 3 [2008] PIERM 2 [2008] PIERM 1 [2008]
2022-06-02
Sea-Surface Slow Small Target Detection Based on Polarimetric Multi-Domain Feature Fusion
By
Progress In Electromagnetics Research M, Vol. 110, 185-195, 2022
Abstract
A target detection method based on polarimetric multi-domain feature fusion is proposed in this paper to improve the detection performance of slow small targets on the sea. Firstly, a complex symmetric matrix was established based on the Pauli scattering vector. On the basis of an analysis on the matrix, the Takagi decomposition method was adopted to extract the normalized polarimetric maximum eigenvalue to characterize the echo signal. Secondly, a real symmetric Hurst exponent matrix was constructed by processing the echo signal of the polarimetric radar, and the normalized polarimetric Hurst exponent was extracted by the eigenvalue decomposition method. Thirdly, the normalized polarimetric Doppler peak height was extracted through the Doppler peak height algorithm. Finally, by fusing multi-domain features, a false alarm controllable detector was constructed through the convex hull algorithm. The results of experimental analysis on the measured datasets indicate that when the parameters are the same, compared with the traditional detection methods based on polarimetric features, the proposed method presents better robustness in the case of short observation time and low signal to clutter rate.
Citation
Chun-Ling Xue, Fei Cao, Qing Sun, Jian-Feng Xu, and Xiao-Wei Feng, "Sea-Surface Slow Small Target Detection Based on Polarimetric Multi-Domain Feature Fusion," Progress In Electromagnetics Research M, Vol. 110, 185-195, 2022.
doi:10.2528/PIERM22031701
References

1. Xu, S., J. Zhang, J. Pu, and P. Shui, "Sea-surface floating small target detection based on polarization features," IEEE Geoscience and Remote Sensing Letters, Vol. 15, No. 10, 1505-1509, October 2018.

2. Weinberg, G. V. and C. Tran, "Burr distribution for X-band maritime surveillance radar clutter," Progress In Electromagnetics Research B, Vol. 81, 183-201, 2018.
doi:10.2528/PIERB18061801

3. Park, J.-H., D.-H. Kim, D.-H. Kim, and S. Kim, "Variation of the shape parameter of K-distribution for sea clutter with the spatial correlation of sea surface," Progress In Electromagnetics Research Letters, Vol. 92, 25-30, 2020.
doi:10.2528/PIERL20042402

4. Yang, M., G. Zhang, C. Guo, and M. Sun, "A coarse-to-fine approach for ship detection in SAR image based on CFAR algorithm," Progress In Electromagnetics Research M, Vol. 35, 105-111, 2014.
doi:10.2528/PIERM14012201

5. Li, Q., H. Zhang, and R. Lai, "Research on analysis of high-order fractal characteristics of aircraft echoes and classification of targets in low-resolution radars," Progress In Electromagnetics Research M, Vol. 75, 61-68, 2018.

6. Liu, N., J. Guan, J. Song, G. Wang, and Y. He, "Application of target detection based on fractal theories," Modern Radar, Vol. 34, No. 2, 12-18, February 2012.

7. Shui, P., D. Li, and S. Xu, "Tri-feature based detection of floating small targets in sea clutter," IEEE Transactions on Aerospace and Electronic Systems, Vol. 50, No. 2, 1416-1430, April 2014.
doi:10.1109/TAES.2014.120657

8. Xue, C., F. Cao, Q. Sun, J. Qin, and X. Feng, "Sea-surface weak target detection based on multi-feature information fusion," Systems Engineering and Electronics, 2022.

9. Keith, W., T. Robert, and W. Simon, Sea Clutter: Scattering, the K Distribution and Radar Performance, Chapter 2, Publishing House of Electronic Industry, 2016.

10. Wu, P., J. Wang, and W. Wang, "Small target detection in sea clutter based on polarization characteristics decomposition," Journal of Electronics & Information Technology, Vol. 33, No. 4, 816-822, April 2011.
doi:10.3724/SP.J.1146.2010.00678

11. Chen, S., H. Gao, and F. Luo, "Target detection in sea clutter based on combined characteristics of polarization," Journal of Radars, Vol. 9, No. 4, 664-673, August 2020.

12. Xu, S. and J. Pu, "Floating small targets detection in sea clutter based on four-polarization-channels fusion," Journal of Signal Processing, Vol. 33, No. 3, 324-329, March 2017.

13. Haykin, S., "The McMaster IPIX radar sea clutter database,", http://soma.ece.mcmaster.ca/ipix, 2001.

14. Yan, H. and J. Zhou, "Factorizations of complex symmetric matrices and complex skew-symmetric matrices," Journal of Hohai University: Natural Sciences, Vol. 36, No. 2, 283-285, March 2008.

15. Guo, Z. and P. Shui, "Anomaly based sea-surface small target detection using K-nearest neighbor classification," IEEE Transactions on Aerospace and Electronic Systems, Vol. 56, No. 6, 4947-4964, August 2020.
doi:10.1109/TAES.2020.3011868