Robust Height Finding Based on Fractional Lower Order Moments for An Interferometric Array Very High Frequency Radar

Genhua CHEN; Baixiao CHEN; Yong QIN

doi:10.11999/JEIT190946

Volume 43 Issue 6

Jun. 2021

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(6): 1676-1682

Genhua CHEN, Baixiao CHEN, Yong QIN. Robust Height Finding Based on Fractional Lower Order Moments for An Interferometric Array Very High Frequency Radar[J]. Journal of Electronics & Information Technology, 2021, 43(6): 1676-1682. doi: 10.11999/JEIT190946

Citation:

Genhua CHEN, Baixiao CHEN, Yong QIN. Robust Height Finding Based on Fractional Lower Order Moments for An Interferometric Array Very High Frequency Radar[J]. Journal of Electronics & Information Technology, 2021, 43(6): 1676-1682. doi: 10.11999/JEIT190946

Citation:

PDF( 3192 KB)

Robust Height Finding Based on Fractional Lower Order Moments for An Interferometric Array Very High Frequency Radar

doi: 10.11999/JEIT190946

1.
School of Information Engineering, Nanchang Institute of Technology, Nanchang 310099, China
2.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (61401187), The Science Research Project of Department of Education of Jiangxi Provincial (GJJ170990)

Received Date: 2019-11-27
Rev Recd Date: 2021-02-23

Available Online: 2021-03-12

Publish Date: 2021-06-18

Abstract

Abstract

Two key factors limiting the performance of height finding of low-elevation targets for Very High Frequency (VHF) are the wider receive beamwidth and complex multipath signals. An T-shaped interferometric array is proposed to extend the receive aperture and increase the Degrees Of Freedom(DOF) for improving the angle resolution. A robust height finding Algorithm based on the Fractional Low Order Moments(FLOM) is proposed. Owing to the non-Gaussian diffuse component, the Covariation Matrix(CM) is demonstrated theoretically for the array manifold reservation by the fractional lower order moments. Then the decorrelation for the generalized signal covariation matrix is performed with spatial smoothing and real-valued transform. The robust low-elevation altitude estimation is achieved by the two-dimensional Unitary ESPRIT algorithm based on the dual-size spatial shift-invariance of the interferometric array. The proposed method increases the resolution between the direct signal and specular multipath. The three-region baseline method is also proposed theoretically. Simulation results demonstrate the validation of the interferometric structure and robust height finding method as well as the theoretical correctness of the three-region baseline method.
- Very High Frequency (VHF) radar,
- Fractional Low Order Moments(FLOM),
- Diffuse component,
- Covariation Matrix(CM),
- Height finding

FullText(HTML)

References(13)

References

[1]	BILLINGSLEY J B. Low-angle Radar Land Clutter: Measurements and Empirical Models[M]. Norwich: William Andrew Publishing, 2002: 300–350.
[2]	BARTON D K. Radar Equations for Modern Radar[M]. Norwood: Artech House, 2013: 80–82.
[3]	LIU Yuan, LIU Hongwei, XIA Xianggen, et al. Target localization in multipath propagation environment using dictionary-based sparse representation[J]. IEEE Access, 2019, 7: 150583–150597. doi: 10.1109/ACCESS.2019.2947497
[4]	KABZINSKI T and HABETS E A P. A least squares narrowband DOA estimator with robustness against phase wrapping[C]. 27th European Signal Processing Conference, A Coruna, Spain, 2019: 1–5.
[5]	陈根华, 陈伯孝. 复杂多径信号下基于空域变换的米波雷达稳健测高算法[J]. 电子与信息学报, 2020, 42(5): 1297–1302. doi: 10.11999/JEIT190554 CHEN Genhua and CHEN Baixiao. Robust altitude estimation based on spatial sign transform in the presence of diffuse multipath for very high frequency radar[J]. Journal of Electronics &Information Technology, 2020, 42(5): 1297–1302. doi: 10.11999/JEIT190554
[6]	PAL P and VAIDYANATHAN P P. Nested arrays: A novel approach to array processing with enhanced degrees of freedom[J]. IEEE Transactions on Signal Processing, 2010, 58(8): 4167–4181. doi: 10.1109/TSP.2010.2049264
[7]	ELYOUNCHA A, ERIKSSON L E B, and ROMEISER R. Measurements of sea surface currents in the baltic sea region using spaceborne along-track InSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 8584–8599. doi: 10.1109/TGRS.2019.2921705
[8]	马济通, 邱天爽, 李蓉, 等. 基于概率密度函数匹配与分数低阶矩的并行盲均衡算法[J]. 电子与信息学报, 2017, 39(7): 1532–1538. doi: 10.11999/JEIT160841 MA Jitong, QIU Tianshuang, LI Rong, et al. Concurrent blind equalization algorithm based on probability density function matching and fractional lower order moments[J]. Journal of Electronics &Information Technology, 2017, 39(7): 1532–1538. doi: 10.11999/JEIT160841
[9]	WONG K T and ZOLTOWSKI M D. Direction-finding with sparse rectangular dual-size spatial invariance Array[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(4): 1320–1336. doi: 10.1109/7.722717
[10]	XU Weichao, CHEN Changrun, DAI Jisheng, et al. Detection of known signals in additive impulsive noise based on Spearman’s rho and Kendall’s tau[J]. Signal Processing, 2019, 161: 165–179. doi: 10.1016/j.sigpro.2019.03.017
[11]	LIU T H and MENDEL J M. A subspace-based direction finding algorithm using fractional lower order statistics[J]. IEEE Transactions on Signal Processing, 2001, 49(8): 1605–1613. doi: 10.1109/78.934131
[12]	OPPENHEIM A V, WILLSKY A S, and NAWAB S H. Signals & Systems[M]. 2nd ed. Beijing: Prentice-Hall Press, 2014: 654–720.
[13]	ZUO Weiliang, XIN Jingmin, LIU Wenyi, et al. Localization of near-field sources based on linear prediction and oblique projection operator[J]. IEEE Transactions on Signal Processing, 2019, 67(2): 415–430. doi: 10.1109/TSP.2018.2883034