Antinoise Robustness Scalable Joint Estimation of Frequency and DOA for Sparse Arrays

doi:10.3969/j.issn.0372-2112.2019.01.016

PDF(914 KB)

ACTA ELECTRONICA SINICA ›› 2019, Vol. 47 ›› Issue (1) : 122-128. DOI: 10.3969/j.issn.0372-2112.2019.01.016

Antinoise Robustness Scalable Joint Estimation of Frequency and DOA for Sparse Arrays

HUANG Xiang-dong^1,2, YANG Lin¹, YANG Meng-kai¹, HUANG Guang-ming²

Author information +

History +

Abstract

To improve the antinoise robustness of frequency and direction of arrival in the temporal-spatial undersampling case,this paper presents two aspects of improvements.On one hand,in the configuration of sparse array arrangement,this paper constructs a relaxed coprime sparse array consisting of 3 sensors,whose element spacings are configured in terms of TRRNS (Towards Robustness in Residue Number System) reconstruction algorithm;On the other hand,in the design of recovery algorithm,the original CRT (Chinese Remainder Theorem) based algorithm is replaced by the TRRNS algorithm,from which the mechanism of the anti-noise robustness scalable adjustment will be derived and verified by numerical simulations.Compared to the original CRT based joint estimator,the proposed estimator at least achieves 9dB improvement of the SNR threshold without increasing the hardware complexity and system cost,which presents vast applications in radar,remote sensing and other passive sensing fields.

Key words

signal processing / undersampling / direction of arrival estimation / frequency estimation / antinoise robustness / relaxed coprime array

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

HUANG Xiang-dong, YANG Lin, YANG Meng-kai, HUANG Guang-ming. Antinoise Robustness Scalable Joint Estimation of Frequency and DOA for Sparse Arrays[J]. Acta Electronica Sinica, 2019, 47(1): 122-128. https://doi.org/10.3969/j.issn.0372-2112.2019.01.016

References

[1] Xu L,Li J,Stoica P.Target detection and parameter estimation for MIMO radar systems[J].IEEE Transactions on Aerospace and Electronic Systems,2009,44(3):927-939.
[2] Rappaport T S.Wireless Communications:Principles and Practice[M].Prentice Hall PTR New Jersy,1996.
[3] Li Y,Seshadri N,Ariyavisitakul S.Channel estimation for OFDM systems with transmitter diversity in mobile wireless channels[J].IEEE Journal on Selected Areas in Communications,1999,17(3):461-471.
[4] Gustafsson F,Gunnarsson F.Positioning using time-difference of arrival measurements[A].Proceedings of the 2003 IEEE International Conference on Acoustics,Speech,and Signal Processing[C].Piscataway,NJ:IEEE,2003.553-556.
[5] Poisel R.Electronic Warfare Target Location Methods[M].Artech House,2012.
[6] Moffet A.Minimum-redundancy linear arrays[J].IEEE Transactions on Antennas and Propagation,2003,16(2):172-175.
[7] Abajo F J,Garcia D.Colloquium:Light scattering by particle and hole arrays[J].Review of Modern Physics,2009,79(4):1267-1290.
[8] Pal P,Vaidyanathan P P.Coprime sampling and the MUSIC algorithm[A].Proceedings of the 2011 Digital Signal Processing and Signal Processing Education Meeting[C].Piscataway,NJ:IEEE,2011.289-294.
[9] Vaidyanathan P P,Pal P.Sparse sensing with co-prime samplers and arrays[J].IEEE Transactions on Signal Processing,2011,59(2):573-586.
[10] Vaidyanathan P P,Pal P.Theory of sparse coprime sensing in multiple dimensions[J].IEEE Transactions on Signal Processing,2011,59(8):3592-3608.
[11] Liu C L,Vaidyanathan P P.Remarks on the spatial smoothing step in coarray MUSIC[J].IEEE Signal Processing Letters,2015,22(9):1438-1442.
[12] Pal P,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.
[13] Liu C L,Vaidyanathan P P.Super nested arrays:Linear sparse arrays with reduced mutual coupling-part I:Fundamentals[J].IEEE Transactions on Signal Processing,2016,64(15):3997-4012.
[14] Liu C L,Vaidyanathan P P.Super nested arrays:Linear sparse arrays with reduced mutual coupling-part Ⅱ:High-order extensions[J].IEEE Transactions on Signal Processing,2016,64(16):4203-4217.
[15] 刘亮,陶建武,黄家才.基于稀疏对称阵列的近场源定位[J].电子学报,2009,37(6):1307-1312. LIU Liang,TAO Jian-wu,HUANG Jia-cai.Near-field source localization based on sparse symmetric array[J].Acta Electronica Sinica,2009,37(6):1307-1312.(in Chinese)
[16] 王彪,朱志慧,戴跃伟.基于具有时序结构的稀疏贝叶斯学习的水声目标DOA估计研究[J].电子学报,2016,44(3):693-698. WANG Biao,ZHU Zhi-hui,DAI Yue-wei.Direction ofarrival estimation research for underwater acoustic target based on sparse Bayesian learning with temporally correlated source vectors[J].Acta Electronica Sinica,2016,44(3):693-698.(in Chinese)
[17] 梁红,张恒.空时欠采样下多目标频率和方位联合估计新方法[J].西北工业大学学报,2012,30(5):694-698. LIANG Hong,ZHANG Heng.An effective method for joint estimation of frequency and DOA with sub-Nyquist spatial-temporal signals based on GRCRT for multiple numbers[J].Journal of Northwestern Polytechnical University,2012,30(5):694-698.(in Chinse)
[18] Li X,Liang H,Xia X G.A robust Chinese remainder theorem with its applications in frequency estimation from undersampled waveforms[J].IEEE Transactions on Signal Processing,2009,57(11):4314-4322.
[19] 黄翔东,刘明卓,等.单次空时域并行欠采样下的频率和到达角联合估计[J].物理学报,2017,66(18):188401. HUANG Xiang-dong,LIU Ming-zhuo,et al.Joint estimation of frequency and direction of arrival under the single-and-parallel spatial-temporal undersampling condition[J].Acta Physica Sinica,2017,66(18):188401.(in Chines)
[20] Wang W,Xia X G.A closed-form robust Chinese remainder theorem and its performance analysis[J].IEEE Transactions on Signal Processing,2010,58(11):5655-5666.
[21] Xiao L,Xia X G,Huo H.Towards robustness in residue number systems[J].IEEE Transactions on Signal Processing,2017,65(6):1497-1510.
[22] Qin S,Zhang Y D,Amin M G.Frequency diverse coprime arrays with coprime frequency offsets for multi-target localization[J].IEEE Journal of Selected Topics in Signal Processing,2017,11(2):321-335.