Abstract
Multiple-input multiple-output (MIMO) radars have attracted lots of attention for their special advantages. As a key issue, target localization method for MIMO radar has also been studied by lots of researchers. However, most of these methods are based on the ideal array model, and the practical unknown mutual coupling will make them have great performance degradation or even fail to work. Sparse representation (SR) has obtained a rapid development in the field of array signal processing over the past few years for its loose requirement for data amount and high-resolution feature. But only one-dimensional angle is considered, and the unknown mutual coupling will make the dictionary hard to construct when apply it for MIMO radar target localization. Even if the mutual coupling is compensated, the two-dimensional angle in MIMO radar will make the dictionary very huge, as well as enhance the inter-correlation of the dictionary. As a result, it brings higher complexity without performance improvement. However, according to our study, both the influences of mutual coupling and two-dimensional angle are occurred in space domain, but the output data of MIMO radar contains both the information in space and time domains. Thus, the influenced space domain is avoided and Doppler frequency in the time domain is utilized in this paper and not only the mutual coupling is avoided, but also a one-dimensional dictionary for the Doppler frequency can be obtained. The Doppler frequency can be firstly obtained via the sparse recovery technique, and meanwhile, the influenced direction matrix can be acquired from the non-zero rows of the recovered matrix. According to the special characteristic of the mutual coupling in uniform linear array (ULA), select the special rows of the direction matrix to eliminate the effect of mutual coupling and estimate the angles. The estimated target parameters (two-dimensional angles and Doppler frequency) are automatically paired, and the proposed algorithm has better estimation performance and can detect more targets than the subspace-based method and SR-based method. Simulation results verify the effectiveness of the algorithm.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Fishler E, Haimovich A, Blum RS, Cimini LJ, Chizhik D, Valenzuela RA (2004) MIMO radar: an idea whose time has come. In: Proceedings of IEEE radar conference, pp 71–78
Sharma R (2010) Analysis of MIMO radar ambiguity functions and implications on clear region. In: Proceedings of IEEE radar conference, pp 544–548
Li J, Liao G, Griffiths H (2011) Bistatic MIMO radar space-time adaptive processing. In: Proceedings of IEEE international radar conference, pp 498–502
Bekkerman I, Tabrikian J (2006) Target detection and localization using MIMO radars and sonars. IEEE Trans Signal Process 5(10):3873–3883
Zhang X, Xu L, Xu L, Xu D (2010) Direction of departure (DOD) and direction of arrival (DOA) estimation in MIMO radar with reduced-dimension MUSIC. IEEE Commun Lett 14(12):1161–1163
Bencheikh ML, Wang YD, He HY (2010) Polynomial root finding technique for joint DOA DOD estimation in bistatic MIMO radar. Signal Process 90(9):2723–2730
Duofang C, Baixiao C, Guodong Q (2008) Angle estimation using ESPRIT in MIMO radar. Electron Lett 44(12):770–771
Jinli C, Hong G, Weimin S (2008) Angle estimation using ESPRIT without pairing in MIMO radar. Electron Lett 44(24):1422–1423
Yunhe C (2010) Joint estimation of angle and Doppler frequency for bistatic MIMO radar. Electron Lett 46(2):170–172
Zhang X, Xu Z, Xu L, Xu D (2011) Trilinear decomposition-based transmit angle and receive angle estimation for multiple-input multiple-output radar. IET Radar Sonar Navig 5(6):626–631
Nion D, Sidiropoulos ND (2009) Adaptive algorithms to track the PARAFAC decomposition of a third-order tensor. IEEE Trans Signal Process 57(6):2299–2310
Malioutov D, Cetin M, Willsky AS (2005) A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Trans Signal Process 53(8):3010–3022
Liu Y, Wu M, Wu S (2010) Fast OMP algorithm for 2D angle estimation in MIMO radar. Electron Lett 46(6):444–445
Zheng ZD, Zhang J, Zhang JY (2012) Joint DOD and DOA estimation of bistatic MIMO radar in the presence of unknown mutual coupling. Signal Process 92(12):3039–3048
Dai J, Zhao D, Ji X (2012) A sparse representation method for DOA estimation with unknown mutual coupling. IEEE Antennas Wirel Propag Lett 11:1210–1213
Berg EV, Friedlander MP (2007) SPGL1: a solver for large-scale sparse reconstruction. http://www.cs.ubc.ca/labs/scl/spgl1
Stoica P, Nehorai A (1990) Performance study of conditional and unconditional direction-of-arrival estimation. IEEE Trans Signal Process 38(10):1783–1795
Acknowledgments
This work is supported by China NSF Grants (61371169,61071164), Funding of Jiangsu Innovation Program for Graduate Education (CXZZ13_0165), Funding for Outstanding Doctoral Dissertation in NUAA (BCXJ13-09), Priority academic program development of Jiangsu high education institutions and the Fundamental Research Funds for the Central Universities (NS2013024,kfjj130114)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, J., Zhang, X. (2014). Target Localization for MIMO Radar with Unknown Mutual Coupling Based on Sparse Representation. In: Sun, J., Jiao, W., Wu, H., Lu, M. (eds) China Satellite Navigation Conference (CSNC) 2014 Proceedings: Volume III. Lecture Notes in Electrical Engineering, vol 305. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54740-9_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-54740-9_41
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54739-3
Online ISBN: 978-3-642-54740-9
eBook Packages: EngineeringEngineering (R0)