基于同伦稀疏STAP的低空风切变风速估计

doi:10.12305/j.issn.1001-506X.2022.04.13

Abstract

Abstract:

In airborne weather radar front view array, due to the distance dependence of clutter, the training samples of independent identically distributed (IID) are seriously insufficient. The performance of traditional space-time adaptive processing (STAP) algorithm is reduced, and wind field velocity estimation is inaccurate.To solve this problem, this paper proposes a low-altitude wind-shear wind speed estimation method based on homotopy sparse STAP. This method only needs a small number of IID training samples to achieve a better wind field velocity estimation effect.Firstly, the homotopy sparse recovery algorithm is used to estimate the clutter covariance matrix, and then the high resolution space-time spectrum of clutter is reconstructed. Then, the optimal weight vector of STAP processor is solved to filter the clutter adaptively. Finally, the wind speed is estimated accurately.Simulation results show the effectiveness of the proposed method.

Key words: airborne weather radar, homotopy sparse space-time adaptive processing (STAP), low-altitude wind-shear, wind speed estimation

CLC Number:

TN959.4

Hai LI, Weijie CHENG, Ruijie XIE. Wind speed estimation of low-altitude wind-shear based on homotopy sparse STAP[J]. Systems Engineering and Electronics, 2022, 44(4): 1174-1181.

Figures/Tables 11

Fig.1

Fig.2

Table 1

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Table 2

Fig.8

Table 3

References 24

1	RTCA/DO-220 . Minimum operational performance standards for airborne weather radar with forward-looking windshear capability[J]. Washington: Ratio Technical Commission for Aeronautics Incoporated, 2016,
2	李海, 宋迪, 呼延泽, 等. LFMCW体制下基于TDPC-JDL的低空风切变风速估计方法[J]. 系统工程与电子技术, 2020, 42 (7): 1504- 1509.
	LI H , SONG D , HUYAN Z , et al. Wind speed estimation of low-altitude wind-shear based on TDPC-JDL under LFMCW system[J]. Systems Engineering and Electronics, 2020, 42 (7): 1504- 1509.
3	AUBRY A , DE M A , PALLOTTA L , et al. Covariance matrix estimation via geometric barycenters and its application to radar training data selection[J]. IET Radar, Sonar & Navigation, 2013, 7 (6): 600- 614.
4	REED I S , MALLETT J D , BRENNAN L E . Rapid convergence rate in adaptive arrays[J]. IEEE Trans.on Aerospace and Electronic Systems, 1974, 10 (6): 853- 863.
5	PECKHAM C D , HAIMOVICH A M , AYOUB T F , et al. Reduced-rank STAP performance analysis[J]. IEEE Trans.on Aerospace & Electronic Systems, 2000, 36 (2): 664- 676.
6	LI H, ZHOU M, WU R B, et al. Wind speed estimation of low-altitude wind-shear based on multiple Doppler channels joint adaptive processing[C]//Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2016: 3116-3120.
7	马泽强, 王希勤, 刘一民, 等. 基于稀疏恢复的空时二维自适应处理技术研究现状[J]. 雷达学报, 2014, 3 (2): 217- 228.
	MA Z Q , WANG X Q , LIU Y M , et al. An overview on sparse recovery-based STAP[J]. Journal of Radars, 2014, 3 (2): 217- 228.
8	段克清, 袁华东, 许红, 等. 稀疏恢复空时自适应处理技术研究综述[J]. 电子学报, 2019, 47 (3): 748- 756.
	DUAN K Q , YUAN H D , XU H , et al. An overview on sparse recovery space-time adaptive processing technique[J]. Acta Electronica Sinica, 2019, 47 (3): 748- 756.
9	DONOHO D L , ELAD M , TEMLYAKOV V N . Stable recovery of sparse overcomplete resentalations in the presence of noise[J]. IEEE Trans.on Information Theory, 2005, 52 (1): 6- 18.
10	BAI G H , TAO R , ZHAO J J , et al. Parameter-searched OMP method for eliminating basis mismatch in space-time spectrum estimation[J]. Signal Processing, 2017, 138 (3): 11- 15.
11	GORODNITSKY I F , RAO B D . Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm[J]. IEEE Trans.on Signal Processing, 2002, 45 (3): 600- 616.
12	KLEMM R . Principle of space-time adaptive processing[M]. 3rd ed. London: Institution of Engineering and Teohnology Publisher, 2006.
13	姚晖. 分布式信号源参数估计技术研究[D]. 郑州: 解放军信息工程大学, 2013.
	YAO H. Research on parameter estimation method for distri-buted sources[D]. Zhengzhou: Information Engineering University of PLA, 2013.
14	王彤. 机载雷达简易STAP方法及其应用[D]. 西安: 西安电子科技大学, 2001.
	WANG T. Study of simplified STAP and its application to airborne radar[D]. Xi'an: Xidian University, 2001.
15	YANG T Y , ZHANG L J , JIN R , et al. A simple homotopy proximal mapping algorithm for compressive sensing[J]. Machine Learning, 2019, 108 (6): 1019- 1056. doi: 10.1007/s10994-018-5772-7
16	孙珂, 张颢, 李刚, 等. 基于杂波谱稀疏恢复的空时自适应处理[J]. 电子学报, 2011, 39 (6): 1389- 1393.
	SUN K , ZHANG H , LI G , et al. STAP via sparse recovery of clutter spectrum[J]. Acta Electronica Sinica, 2011, 39 (6): 1389- 1393.
17	WANG G B , WEI X , YU B Z , et al. An efficient proximal block coordinate homotopy method for large-scale sparse least squares problems[J]. SIAM Journal on Entific Computing, 2020, 42 (1): 395- 423. doi: 10.1137/19M1243828
18	YANG Z C , LI X , WANG H Q , et al. Sparsity-based space-time adaptive processing using complex-valued homotopy technique for airborne radar[J]. Signal Processing Letters, 2014, 8 (5): 552- 564.
19	阳召成. 基于稀疏性的空时自适应处理理论和方法[D]. 长沙: 国防科学技术大学, 2013.
	YANG Z C. Theory and methods of sparsity-based space-time adaptive processing[D]. Changsha: National University of Defense Technology, 2013.
20	PETERSEN K , PEDERSEN M . The matrix cookbook[M]. Lyngby, Denmark: Technical University of Denmark Publisher, 2012.
21	DAI J C , XU W , ZHANG J L , et al. Homotopy algorithm for L1-norm minimisation problems[J]. Signal Processing Letters, 2015, 9 (1): 1- 9.
22	YU C X , ZHAO J T , WANG Y F , et al. Sparse diffraction imaging method using an adaptive reweighting homotopy algorithm[J]. Journal of Geophysics & Engineering, 2017, 14 (1): 26- 40.
23	ZHANG Z , XU Y , YANG J , et al. A survey of sparse representation: algorithms and applications[J]. IEEE Access, 2015, 3 (5): 490- 530.
24	ASIF M S , ROMBERG J . Sparse recovery of streaming signals using L1-homotopy[J]. IEEE Trans.on Signal Processing, 2014, 62 (16): 4209- 4223. doi: 10.1109/TSP.2014.2328981

参数	参数值	参数	参数值
载机高度/m	600	阵元数	8
载机速度/(m/s)	75	相干脉冲数	32
波长/m	0.032	主瓣角度/(°)	(60, 0)
脉冲重复频率/Hz	7 000	距离分辨率/m	150
杂噪比/dB	40	信噪比/dB	5
风场中心距离/km	12.5	风场中心方向/(°)	(60, 0)
风场水平方向范围/km	8.5~16.5	风场垂直方向范围/km	0~0.6
风场水平风速范围/(m/s)	-40~40	风场垂直风速范围/(m/s)	-20~20

IID样本数	风速估计方法	均方根误差/(m/s)
100	LSMI	2.039 4
	M-CAP	1.659 1
	OMP	1.573 4
	同伦稀疏STAP	1.553 5
50	LSMI	22.532 5
	M-CAP	1.787 6
	OMP	1.602 3
	同伦稀疏STAP	1.584 7
10	LSMI	31.838 1
	M-CAP	24.799 4
	OMP	7.903 9
	同伦稀疏STAP	1.642 8

杂噪比/dB	风速估计方法	均方根误差/(m/s)
40	LSMI	2.039 4
	M-CAP	1.659 1
	OMP	1.573 4
	同伦稀疏STAP	1.553 5
50	LSMI	14.478 0
	M-CAP	7.670 2
	OMP	1.825 5
	同伦稀疏STAP	1.762 6
60	LSMI	21.302 6
	M-CAP	11.962 3
	OMP	2.568 2
	同伦稀疏STAP	2.453 3

[1]	Hai LI, Ze HUYAN, Zhijie MAO. Low-altitude wind-shear velocity ambiguity resolution based on compressed sensing under strong clutter [J]. Systems Engineering and Electronics, 2022, 44(10): 3029-3036.
[2]	Hai LI, Di SONG, Ze HUYAN, Qing FENG, Zibo ZHUANG. Wind speed estimation of low-altitude wind-shear based on TDPC-JDL under LFMCW system [J]. Systems Engineering and Electronics, 2020, 42(7): 1504-1509.
[3]	Hai LI, Zhixin LIU, Weijie CHENG, Zibo ZHUANG, Yi FAN. Low-altitude wind shear wind speed estimation method based on MBMC under sea clutter [J]. Systems Engineering and Electronics, 2020, 42(11): 2481-2487.
[4]	LI Hai, WANG Jie. Low-altitude wind-shear wind speed estimation based on CMCAP [J]. Systems Engineering and Electronics, 2019, 41(3): 529-533.
[5]	ZHANG Qiang, XIAO Gang, LAN Yiqun. Turbulence target detection based on BP neural network multilevel classification [J]. Systems Engineering and Electronics, 2018, 40(7): 1486-1490.
[6]	SHI Haijie, LI Jinghua, YUE Lu. Meteorological target detection method and simulation of airborne weather radar in cruise stage [J]. Systems Engineering and Electronics, 2018, 40(2): 280-286.
[7]	YU Ying-jie, LI Yong. Method for detecting and simulating 3D turbulence field of airborne weather radar [J]. Systems Engineering and Electronics, 2016, 38(2): 293-297.
[8]	HAN Yan-fei, LIU Xia, LI Hai, WU Ren-biao. Microphysics based radar signal simulation for the three dimensional low altitude wind shear [J]. Systems Engineering and Electronics, 2016, 38(2): 298-304.
[9]	LU Xiaoguang1,2， WU Renbiao2. Nonlinear least-squares spectral moment estimation algorithm [J]. Systems Engineering and Electronics, 2014, 36(3): 447-452.

Wind speed estimation of low-altitude wind-shear based on homotopy sparse STAP

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 24

Related Articles 9

Recommended Articles

Metrics

Comments