基于随机矩阵理论和最小描述长度的机载前视阵雷达杂波自由度估计

doi:10.11999/JEIT160132

Abstract
Figure/Table
References (21)
Related Citation (15)

Download: PDF (919 KB)
Export: BibTeX | EndNote (RIS)

Abstract

Owing to the heavy spread of eigenspectrum of the population covariance matrix under finite training samples condition, it is a challenge to estimate the clutter Degrees of Freedom (DoF) in airborne forward-looking radar. In this work, a method for estimation the clutter’s DoF is proposed. In order to estimate the clutter’s DoF, an idea from sources detection by Minimum Description Length (MDL) criterion is borrowed, and the parametric probability model is formed based on the eigenvalue’s statistical distribution properties from Random Matrix Theory (RMT). The proposed method is effective to estimate the clutter’s DoF under finite training samples condition, and the simulation results verify the efficiency of the proposed method.

Key words： Forward-looking radar Clutter Degrees of Freedom (CoF) Random Matrix Theory (RMT) Minimum Description Length (MDL) Covariance matrix

Received: 29 January 2016 Published: 08 September 2016

PACS:

TN957

Fund:

The National Natural Science Foundation of China (61471365, 61571442, 61231017), The National University’s Basic Research Foundation of China (3122015B002), The Foundation for Sky Young Scholars of Civil Aviation University of China

Corresponding Authors: LI Hai E-mail: haili@cauc.edu.cn

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LI Hai
	LIU Xinlong
	JIANG Ting
	WU Renbiao

Cite this article:

LI Hai,LIU Xinlong,JIANG Ting等. Estimation of Clutter Degrees of Freedom in Airborne Forward-looking Radar via Random Matrix Theory and Minimum Description Length Criteria[J]. JEIT, 2016, 38(12): 3224-3229.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT160132 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I12/3224

[1]	GUERCI J R. Space-Time Adaptive Processing for Radar [M]. Norwood, Artech House, 2014: 1-74.
[2]	FERTIG L B. Analytical expressions for Space-Time Adaptive Processing (STAP) performance[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(1): 42-53. doi: 10.1109/TAES.2014.130676.
[3]	DEGURSE J F, SAVY L, and MARCOS S. Reduced-rank STAP for target detection in heterogeneous environments[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1153-1162. doi: 10.1109/TAES.2014.120414.
[4]	BRENNAN L E and STAUDAHER F M. Subclutter visibility demonstration[R]. Technical Report RL-TR-92-21, Adaptive Sensors Incorporated, 1992.
[5]	SHLENS J. A tutorial on principal component analysis[OL]. http://arxiv.org/abs/1404. 1100, 2014.
[6]	同压龙, 王彤, 文才, 等. 一种稳健的机载非正侧视阵雷达杂波抑制方法[J]. 电子与信息学报, 2015, 37(5): 1044-1050. doi: 10.11999/JEIT141222.
	TONG Yalong, WANG Tong, WEN Cai, et al. A robust clutter suppression method for airborne non-sidelooking radar[J]. Journal of Electronics & Information Technology, 2015, 37(5): 1044-1050. doi: 10.11999/JEIT141222.
[7]	BAI Z D and SILVERSTEIN J W. Spectral Analysis of Large Dimensional Random matrices[M]. New York: Springer, 2010: 1-13.
[8]	Random matrices and their applications[OL]. http://djalil. chafai.net/wiki/hk2015:start.2015.
[9]	ZWINGELSTEIN C M and DEBBAH M. Random matrix theory based resource allocation in correlated MIMO systems with ARQ feedback[J]. IEEE Communications Letters, 2014, 18(5): 793-796. doi: 10.1109/LCOMM.2014.031414.140214.
[10]	KRITCHMAN S and NADLER B. Non-parametric detection of the number of signals: hypothesis testing and random matrix theory[J]. IEEE Transactions on Signal Processing, 2009, 57(10): 3930-3941. doi: 10.1109/TSP.2009.2022897.
[11]	VALLET P, LOUBATON P, and MESTRE X. Improved subspace estimation for multivariate observations of high dimension: the deterministic signals case[J]. IEEE Transactions on Information Theory, 2012, 58(2): 1043-1068.
[12]	AHMED A, HU Y F, NORAS J M, et al. Random matrix theory based spectrum sensing for cognitive radio networks [C] Internet Technologies and Applications, Wrexham, 2015: 479-483.
[13]	WAX M and KAILATH T. Detection of signals by information theoretic criteria[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1985, 33(2): 387-392. doi: 10.1109/TASSP.1985.1164557.
[14]	BARRON A, RISSANEN J, and YU B. The minimum description length principle in coding and modeling[J]. IEEE Transactions on Information Theory, 1998, 44(6): 2743-2760. doi: 10.1109/18.720554.
[15]	HUANG L and SO H C. Source enumeration via MDL criterion based on linear shrinkage estimation of noise subspace covariance matrix[J]. IEEE Transactions on Signal Processing, 2013, 61(19): 4806-4821. doi: 10.1109/TSP. 2013.2273198.
[16]	HAIMOVICH A M and AYOUB T F. The eigencanceler: Space time adaptive radar by eigenanalysis methods[R]. New Jersey Inst of Tech Newark, 1999.
[17]	MARCENKO V A and PASTUR L A. Distribution of eigenvalues for some sets of random matrices[J]. Sbornik: Mathematics, 1967, 1(4): 457-483.
[18]	NADAKUDITI R R and EDELMAN A. Sample eigenvalue based detection of high-dimensional signals in white noise using relatively few samples[J]. IEEE Transactions on Signal Processing, 2008, 56(7): 2625-2638. doi: 10. 1109/TSP.2008. 917356.
[19]	BAI Z D and SILVERSTEIN J W. CLT for linear spectral statistics of large-dimensional sample covariance matrices[J]. Annals of Probability, 2004, 32(1): 553-605.
[20]	WARD J. Space-time adaptive processing for airborne radar [R]. Technical Report 1015, MIT Lincoln Laboratory, 1994.
[21]	REED I S, MALLETT J D, and BRENNAN L E. Rapid convergence rate in adaptive arrays[J]. IEEE Transactions on Aerospace and Electronic Systems, 1974, 10(4): 853-863. doi: 10.1109/TAES.1974.307893.