Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-27T08:03:07.509Z Has data issue: false hasContentIssue false

Minimum redundancy MIMO array synthesis with a hybrid method based on cyclic difference sets and ACO

Published online by Cambridge University Press:  24 July 2015

Jian Dong*
Affiliation:
School of Information Science and Engineering, Central South University, 410083, Changsha, China. Phone: +86 1580 2654 984
Ronghua Shi
Affiliation:
School of Information Science and Engineering, Central South University, 410083, Changsha, China. Phone: +86 1580 2654 984
Ying Guo
Affiliation:
School of Information Science and Engineering, Central South University, 410083, Changsha, China. Phone: +86 1580 2654 984
*
Corresponding author: J. Dong Email: dongjian@csu.edu.cn

Abstract

As a recently proposed concept, multiple-input multiple-output (MIMO) radars exhibit much higher spatial resolution than traditional transmitter based radars because of the synthesized virtual array. In this paper, the problem of minimum redundancy (MR)-MIMO array synthesis is addressed, which seeks to maximize the virtual array aperture of MIMO radars for a given number of transmitting and receiving elements. A hybrid method combining autocorrelation property of cyclic difference sets (CDSs) and global search characteristics of ant colony optimization (ACO) is proposed for a rapid and numerically-effective exploration of MR-MIMO array configurations. Numerical experiments validate the proposed method, showing improvements in convergence rate and computational cost with respect to bare ACO-based search as well as improvements in the generality and configuration variety with respect to the CDS-based method.

Type
Research Papers
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1] Bliss, D.W.; Forsythe, K.W.: Multiple-input multiple-output (MIMO) radar and imaging: degrees of freedom and resolution, in Proc. 37th Asilomar Conf. Signals, Systems and Computers, Pacific Grove, CA, 2003.Google Scholar
[2] Forsythe, K.W.; Bliss, D.W.; Fawcett, G.S.: Multiple-input multiple-output (MIMO) radar: performance issues, in Proc. 38th Asilomar Conf. Signals, Systems and Computers, Pacific Grove, CA, 2004.Google Scholar
[3] Takuya, T.; Masayuki, S.; Yukinobu, T.; Hiroki, S.: Hybrid SIMO and MIMO sparse array radar. Int. J. Microw. Wireless Technol., 6 (2014), 389395.Google Scholar
[4] Chen, C.Y.; Vaidyanathan, P.P.: MIMO radar space-time adaptive processing using prolate spheroidal wave functions. IEEE Trans. Signal Process., 56 (2008), 623635.Google Scholar
[5] Wang, W.Q.: Virtual antenna array analysis for MIMO synthetic aperture radars. Int. J. Antennas Propag., 1 (2012), 110.Google Scholar
[6] Wang, W.Q.; Shao, H.Z.; Cai, J.Y.: MIMO antenna array design with polynomial factorization. Int. J. Antennas Propag., 1 (2013), 19.Google Scholar
[7] Chen, C.Y.; Vaidyanathan, P.P.: Minimum redundancy MIMO radars, in Proc. Int. Symp. Circuits and Systems (ISCAS), Seattle, WA, 2008.Google Scholar
[8] Moffet, A.T.: Minimum-redundancy linear arrays. IEEE Trans. Antennas Propag., 16 (1968), 172175.Google Scholar
[9] Pillai, S.U.; Bar-Ness, Y.; Haber, F.: A new approach to array geometry for improved spatial spectrum estimation. Proc. IEEE, 73 (1985), 15221524.Google Scholar
[10] Karaman, M.; Wygant, I.O.; Oralkan, O.; Khuri-Yakub, B.T.: Minimally redundant 2-D array designs for 3-D medical ultrasound imaging. IEEE Trans. Med. Imag., 28 (2009), 10511061.Google Scholar
[11] Pal, Piya; Vaidyanathan, P.P.: Nested arrays: a novel approach to array processing with enhanced degrees of freedom. IEEE Trans. Signal Process., 58 (2010), 41674181.Google Scholar
[12] Rezer, R.; Gropengießer, W.; Jacob, A.F.: Particle swarm optimization of minimum-redundancy MIMO arrays, in Microwave Conf. (GeMIC), Darmstadt, 2011.Google Scholar
[13] Leeper, D.G.: Isophoric arrays—massively thinned phased arrays with well-controlled sidelobes. IEEE Trans. Antennas Propag., 47 (1999), 18251835.Google Scholar
[14] Caorsi, S.; Lommi, A.; Massa, A.; Pastorino, M.: Peak sidelobe level reduction with a hybrid approach based on gas and difference sets. IEEE Trans. Antennas Propag., 52 (2004), 11161121.Google Scholar
[15] Kwon, G.; Hwang, K.-C.; Park, J.-Y.; Kim, S.-J.; Kim, D.-H.: GA-enhanced thin square array with cyclic difference sets. IEICE TRANS. electron., E96-C (2013), 612614.Google Scholar
[16] Oliveri, G.; Donelli, M.; Massa, A.: Linear array thinning exploiting almost difference sets. IEEE Trans. Antennas Propag., 57 (2009), 38003812.Google Scholar
[17] Oliveri, G.; Manica, L.; Massa, A.: ADS-based guidelines for thinned planar arrays. IEEE Trans. Antennas Propag., 58 (2010), 19351948.Google Scholar
[18] Oliveri, G.; Massa, A.: Genetic algorithm (GA)-enhanced almost difference set (ADS)-based approach for array thinning. IET Microw. Antennas Propag., 5 (2011), 305315.Google Scholar
[19] Dong, J.; Li, Q.X.; Guo, W.: A combinatorial method for antenna array design in minimum redundancy MIMO radars. IEEE Antennas Wireless Propag. Lett., 8 (2009), 11501153.CrossRefGoogle Scholar
[20] Dong, J.; Shi, R.H.; Lei, W.T.; Guo, Y.: Minimum redundancy MIMO array synthesis by means of cyclic difference sets. Int. J. Antennas Propag., 1 (2013), 19.Google Scholar
[21] Baumert, L.D.: Cyclic Difference Sets, Lecture Notes in Math, vol. 182, Springer-Verlag, Berlin, Heidelberg, New York, 1971.Google Scholar
[22] Hall, M. Jr.: Combinatorial Theory, 2nd ed., John Wiley & Sons, Inc., New York, NY, USA, 1998.Google Scholar
[23] Gordon, D.: La Jolla Cyclic Difference Set Repository [online]. Available: http://www.ccrwest.org/diffsets.html.Google Scholar
[24] Oliveri, G.; Caramanica, F.; Migliore, M.D.; Massa, A.: Synthesis of non-uniform MIMO arrays through combinatorial sets. IEEE Antennas Wireless Propag. Lett., 11 (2012), 728731.Google Scholar
[25] Leech, J.: On the representation of 1,2,…,n by differences. J. London Math. Soc., 31 (1956), 60169.Google Scholar
[26] Linebarger, D.A.; Sudborough, I.H.; Tollis, I.G.: Difference bases and sparse sensor arrays. IEEE Trans. Inform. Theory, 39 (1993), 716721.Google Scholar
[27] Rocca, P.; Manica, L.; Massa, A.: Ant colony based hybrid approach for optimal compromise sum-difference patterns synthesis. Microw. Opt. Technol. Lett., 52 (2010), 128132.Google Scholar
[28] Óscar, Q.-T.; Eva, R.-I.: Ant colony optimization in thinned array synthesis with minimum sidelobe level. IEEE Antennas Wireless Propag. Lett., 5 (2006), 349352.Google Scholar
[29] Rocca, P.; Manica, L.; Massa, A.: An improved excitation matching method based on an ant colony optimization for suboptimal-free clustering in sum-difference compromise synthesis. IEEE Trans. Antennas Propag., 57 (2009), 22972306.Google Scholar
[30] Dong, J.; Li, Q.X.; Jin, R.; Zhu, Y.T.; Huang, Q.L.; Gui, L.Q.: A method for seeking low-redundancy large linear arrays for aperture synthesis microwave radiometers. IEEE Trans. Antennas Propag., 58 (2010), 19131921.Google Scholar
[31] Oliveri, G.; Rocca, P.; Poli, L.; Carlin, M.; Massa, A.: Evolutionary strategies for advanced array optimization, in IEEE Int. Symp. on Antennas and Propagation (APSURSI), Spokane, WA, 2011.Google Scholar
[32] Kopilovich, L.E.: Square array antennas based on hadamard difference sets. IEEE Trans. Antennas Propag., 56 (2008), 263266.Google Scholar
[33] Oliveri, G.; Caramanica, F.; Fontanari, C.; Massa, A.: Rectangular thinned arrays based on McFarland difference sets. IEEE Trans. Antennas Propag., 59 (2011), 15461552.CrossRefGoogle Scholar