Vol. 145
Latest Volume
All Volumes
PIERC 150 [2024] PIERC 149 [2024] PIERC 148 [2024] PIERC 147 [2024] PIERC 146 [2024] PIERC 145 [2024] PIERC 144 [2024] PIERC 143 [2024] PIERC 142 [2024] PIERC 141 [2024] PIERC 140 [2024] PIERC 139 [2024] PIERC 138 [2023] PIERC 137 [2023] PIERC 136 [2023] PIERC 135 [2023] PIERC 134 [2023] PIERC 133 [2023] PIERC 132 [2023] PIERC 131 [2023] PIERC 130 [2023] PIERC 129 [2023] PIERC 128 [2023] PIERC 127 [2022] PIERC 126 [2022] PIERC 125 [2022] PIERC 124 [2022] PIERC 123 [2022] PIERC 122 [2022] PIERC 121 [2022] PIERC 120 [2022] PIERC 119 [2022] PIERC 118 [2022] PIERC 117 [2021] PIERC 116 [2021] PIERC 115 [2021] PIERC 114 [2021] PIERC 113 [2021] PIERC 112 [2021] PIERC 111 [2021] PIERC 110 [2021] PIERC 109 [2021] PIERC 108 [2021] PIERC 107 [2021] PIERC 106 [2020] PIERC 105 [2020] PIERC 104 [2020] PIERC 103 [2020] PIERC 102 [2020] PIERC 101 [2020] PIERC 100 [2020] PIERC 99 [2020] PIERC 98 [2020] PIERC 97 [2019] PIERC 96 [2019] PIERC 95 [2019] PIERC 94 [2019] PIERC 93 [2019] PIERC 92 [2019] PIERC 91 [2019] PIERC 90 [2019] PIERC 89 [2019] PIERC 88 [2018] PIERC 87 [2018] PIERC 86 [2018] PIERC 85 [2018] PIERC 84 [2018] PIERC 83 [2018] PIERC 82 [2018] PIERC 81 [2018] PIERC 80 [2018] PIERC 79 [2017] PIERC 78 [2017] PIERC 77 [2017] PIERC 76 [2017] PIERC 75 [2017] PIERC 74 [2017] PIERC 73 [2017] PIERC 72 [2017] PIERC 71 [2017] PIERC 70 [2016] PIERC 69 [2016] PIERC 68 [2016] PIERC 67 [2016] PIERC 66 [2016] PIERC 65 [2016] PIERC 64 [2016] PIERC 63 [2016] PIERC 62 [2016] PIERC 61 [2016] PIERC 60 [2015] PIERC 59 [2015] PIERC 58 [2015] PIERC 57 [2015] PIERC 56 [2015] PIERC 55 [2014] PIERC 54 [2014] PIERC 53 [2014] PIERC 52 [2014] PIERC 51 [2014] PIERC 50 [2014] PIERC 49 [2014] PIERC 48 [2014] PIERC 47 [2014] PIERC 46 [2014] PIERC 45 [2013] PIERC 44 [2013] PIERC 43 [2013] PIERC 42 [2013] PIERC 41 [2013] PIERC 40 [2013] PIERC 39 [2013] PIERC 38 [2013] PIERC 37 [2013] PIERC 36 [2013] PIERC 35 [2013] PIERC 34 [2013] PIERC 33 [2012] PIERC 32 [2012] PIERC 31 [2012] PIERC 30 [2012] PIERC 29 [2012] PIERC 28 [2012] PIERC 27 [2012] PIERC 26 [2012] PIERC 25 [2012] PIERC 24 [2011] PIERC 23 [2011] PIERC 22 [2011] PIERC 21 [2011] PIERC 20 [2011] PIERC 19 [2011] PIERC 18 [2011] PIERC 17 [2010] PIERC 16 [2010] PIERC 15 [2010] PIERC 14 [2010] PIERC 13 [2010] PIERC 12 [2010] PIERC 11 [2009] PIERC 10 [2009] PIERC 9 [2009] PIERC 8 [2009] PIERC 7 [2009] PIERC 6 [2009] PIERC 5 [2008] PIERC 4 [2008] PIERC 3 [2008] PIERC 2 [2008] PIERC 1 [2008]
2024-07-29
Modified Adaptive RFT with Sample Covariance Matrix Inversion Recursive Estimation
By
Progress In Electromagnetics Research C, Vol. 145, 181-187, 2024
Abstract
Radon-Fourier transform (RFT) is able to effectively overcome the coupling between the range cell migration (RCM) effect and Doppler modulation by searching along range and velocity dimensions jointly for the moving target, which depends on envelope alignment and Doppler phase compensation. However, without effective clutter suppression, clutter would also be intergraded via RFT. Thus, the adaptive RFT (ARFT) has been proposed to clutter suppression by introducing an optimal filter weight, which is determined from the clutter's covariance matrix as well as the steering vector for the moving target with the consideration of RCM effect. Nevertheless, the ARFT needs to address the difficulty for real implementation, i.e., computational complexity is too high to a large number of pulse samples. It is known that to obtain the inversion the sample covariance matrix (Rcn-1) is order M3, i.e., O(M3), in which $M$ is the order of the matrix. It is the most complexity consumed step in ARFT. In this paper, we propose a modified adaptive RFT (MARFT) method to obtain Rcn-1 with recursive computation, which takes the complexity order M2, i.e., O(M2). Simulations show that the proposed method has the same clutter suppression results as the conventional ARFT method, where the computational complexity is much lower.
Citation
Haibo Wang, Wenhua Huang, Haichuan Zhang, Tao Ba, and Zhiqiang Yang, "Modified Adaptive RFT with Sample Covariance Matrix Inversion Recursive Estimation," Progress In Electromagnetics Research C, Vol. 145, 181-187, 2024.
doi:10.2528/PIERC24051602
References

1. Aubry, Augusto, Antonio De Maio, Vincenzo Carotenuto, and Alfonso Farina, "Radar phase noise modeling and effects --- Part I: MTI filters," IEEE Transactions on Aerospace and Electronic Systems, Vol. 52, No. 2, 698-711, 2016.

2. Xu, Jia, Ji Yu, Ying-Ning Peng, and Xiang-Gen Xia, "Radon-Fourier transform for radar target detection, I: Generalized Doppler filter bank," IEEE Transactions on Aerospace and Electronic Systems, Vol. 47, No. 2, 1186-1202, 2011.

3. Lang, Ping, Xiongjun Fu, Jian Dong, and Jian Yang, "An efficient Radon-Fourier transform-based coherent integration method for target detection," IEEE Geoscience and Remote Sensing Letters, Vol. 20, 1-5, 2023.

4. Hussain, Musadiq, Rehan Ahmed, and Hammad M. Cheema, "Segmented Radon-Fourier transform for long time coherent radars," IEEE Sensors Journal, Vol. 23, No. 9, 9582-9594, 2023.

5. Longman, Oren and Igal Bilik, "Spectral Radon-Fourier transform for automotive radar applications," IEEE Transactions on Aerospace and Electronic Systems, Vol. 57, No. 2, 1046-1056, 2021.

6. Xu, Jia, Xiang-Gen Xia, Shi-Bao Peng, Ji Yu, Ying-Ning Peng, and Li-Chang Qian, "Radar maneuvering target motion estimation based on generalized Radon-Fourier transform," IEEE Transactions on Signal Processing, Vol. 60, No. 12, 6190-6201, 2012.

7. Wu, Wei, Guo Hong Wang, and Jin Ping Sun, "Polynomial Radon-polynomial Fourier transform for near space hypersonic maneuvering target detection," IEEE Transactions on Aerospace and Electronic Systems, Vol. 54, No. 3, 1306-1322, 2017.

8. Qian, L.-C., J. Xu, X.-G. Xia, W.-F. Sun, T. Long, and Y.-N. Peng, "Fast implementation of generalised Radon-Fourier transform for manoeuvring radar target detection," Electronics Letters, Vol. 48, No. 22, 1427-1428, 2012.

9. Zhang, Zhenghe, Nan Liu, Yongning Hou, Shiyu Zhang, and Linrang Zhang, "A coherent integration segment searching based GRT-GRFT hybrid integration method for arbitrary fluctuating target," Remote Sensing, Vol. 14, No. 11, 2695, 2022.

10. Xu, J., J. Yu, Y.-N. Peng, X.-G. Xia, and T. Long, "Space-time Radon-Fourier transform and applications in radar target detection," IET Radar, Sonar & Navigation, Vol. 6, No. 9, 846-857, 2012.

11. Qian, Li-Chang, Jia Xu, Xiang-Gen Xia, Wen-Feng Sun, Teng Long, and Ying-Ning Peng, "Wideband‐scaled Radon‐Fourier transform for high‐speed radar target detection," IET Radar, Sonar & Navigation, Vol. 8, No. 5, 501-512, 2014.

12. Xu, Jia, Liang Yan, Xu Zhou, Teng Long, Xiang-Gen Xia, Yong-Liang Wang, and Alfonso Farina, "Adaptive Radon-Fourier transform for weak radar target detection," IEEE Transactions on Aerospace and Electronic Systems, Vol. 54, No. 4, 1641-1663, 2018.

13. Pillai, S. Unnikrishna, Array Signal Processing, Springer-Verlag, 1989.
doi:10.1007/978-1-4612-3632-0

14. Guerci, Joseph R., Space-Time Adaptive Processing for Radar, 2nd Ed., Artech House, 2015.

15. Carlson, Blair D., "Covariance matrix estimation errors and diagonal loading in adaptive arrays," IEEE Transactions on Aerospace and Electronic Systems, Vol. 24, No. 4, 397-401, 1988.

16. Reed, Irving S., John D. Mallett, and Lawrence E. Brennan, "Rapid convergence rate in adaptive arrays," IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-10, No. 6, 853-863, 1974.