Bo Du
ABSTRACT
In the area of direction-of-arrival (DOA) estimate research, Unknown mutual coupling (UMC) causes significant interference with the array’s localization performance. Although the sparsity of the structure of the sparse array can weaken the mutual coupling (MC) to some extent, the residual MC still has a substantial effect on the localization performance. In this paper, a sparsity-based localization method for mixed near-field (NF) and far-field (FF) sources with UMC is developed. On the basis of the rank reduction (RARE) principle, multiple parameters of the source are decoupled. Therefore the one-dimensional (1-D) search is necessary to estimate the aforementioned factors. The sparse array localization performance is greatly improved by the offsetting of MC parameters using proposed method. Simulation experiments show that this method significantly improves the performance of mixed NF and FF sources localization under the UMC environment.
- [1] H. Jiang, L. Li, and X. Li, “Gsna: A novel sparse array design achieving enhanced degree of freedom for noncircular sources,” Wireless Communications and Mobile Computing, vol. 2022, 2022.Google Scholar
Digital Library
- [2] Y. Liu, H. Cao, Y. Wu, and K. Wang, “Fast and accurate approach for doa estimation of coherent signals,” Wireless Communications and Mobile Computing, vol. 2022, 2022.Google Scholar
- [3] Y. Lv, J. Zhen, and B. Guo, “Accessorial locating for internet of vehicles based on doa estimation in industrial transportation,” Wireless Communications and Mobile Computing, vol. 2021, 2021.Google Scholar
- [4] H. Xu, W. Cui, F. Mei, B. Ba, and C. Jian, “The design of a novel sparse array using two uniform linear arrays considering mutual coupling,” Journal of Sensors, vol. 2021, 2021.Google ScholarCross Ref
- [5] D. Wu, F. Liu, Z. Li, and Z. Han, “Coherent target direction-of-arrival estimation for coprime arrays: From spatial smoothing perspective,” Wireless Communications and Mobile Computing, vol. 2021, 2021.Google Scholar
- [6] R. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE transactions on antennas and propagation, vol. 34, no. 3, pp. 276–280, 1986.Google Scholar
- [7] F. Sun, J. Wu, H. Fan, L. Chen, and P. Lan, “Simple direction-of-arrival estimation under nonuniform noise scenarios,” Wireless Communications and Mobile Computing, vol. 2022, 2022.Google Scholar
- [8] S. Argentieri, P. Danes, and P. Soueres, “Modal analysis based beamforming for nearfield or farfield speaker localization in robotics,” in 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2006, pp. 866–871.Google Scholar
- [9] R. Mukai, H. Sawada, S. Araki, and S. Makino, “Frequency-domain blind source separation of many speech signals using near-field and far-field models,” EURASIP Journal on Advances in Signal Processing, vol. 2006, pp. 1–13, 2006.Google ScholarDigital Library
- [10] A. A. Ebrahimi, H. R. Abutalebi, and M. Karimi, “Localisation of mixed near-field and far-field sources using the largest aperture sparse linear array,” IET Signal Processing, vol. 12, no. 2, pp. 155–162, 2018.Google ScholarCross Ref
- [11] J. Liang and D. Liu, “Passive localization of mixed near-field and far-field sources using two-stage music algorithm,” IEEE Transactions on Signal Processing, vol. 58, no. 1, pp. 108–120, 2009.Google Scholar
- [12] Y. Wang, W. Cui, Y. Du, B. Ba, and F. Mei, “A novel sparse array for localization of mixed near-field and far-field sources,” International Journal of Antennas and Propagation, vol. 2021, 2021.Google ScholarCross Ref
- [13] A. J. Weiss and B. Friedlander, “Mutual coupling effects on phase-only direction finding,” IEEE transactions on antennas and propagation, vol. 40, no. 5, pp. 535–541, 1992.Google Scholar
- [14] B. Friedlander and A. J. Weiss, “Direction finding in the presence of mutual coupling,” IEEE transactions on antennas and propagation, vol. 39, no. 3, pp. 273–284, 1991.Google Scholar
- [15] T. Svantesson, “Mutual coupling compensation using subspace fitting,” in Proceedings of the 2000 IEEE Sensor Array and Multichannel Signal Processing Workshop. SAM 2000 (Cat. No. 00EX410). IEEE, 2000, pp. 494–498.Google ScholarCross Ref
- [16] B. C. Ng and C. M. S. See, “Sensor-array calibration using a maximum-likelihood approach,” IEEE Transactions on Antennas and Propagation, vol. 44, no. 6, pp. 827–835, 1996.Google Scholar
- [17] B. Wang, Y. Zhao, and J. Liu, “Mixed-order music algorithm for localization of far-field and near-field sources,” IEEE Signal Processing Letters, vol. 20, no. 4, pp. 311–314, 2013.Google ScholarCross Ref
- [18] Z. Zheng, M. Fu, W.-Q. Wang, S. Zhang, and Y. Liao, “Localization of mixed near-field and far-field sources using symmetric double-nested arrays,” IEEE Transactions on Antennas and Propagation, vol. 67, no. 11, pp. 7059–7070, 2019.Google ScholarCross Ref
- [19] Z. Zheng, M. Fu, W.-Q. Wang, and H. C. So, “Symmetric displaced coprime array configurations for mixed near-and far-field source localization,” IEEE Transactions on Antennas and Propagation, vol. 69, no. 1, pp. 465–477, 2020.Google Scholar
- [20] Y. Wang, W. Cui, Y. Du, B. Ba, and F. Mei, “A novel sparse array for localization of mixed near-field and far-field sources,” International Journal of Antennas and Propagation, vol. 2021, 2021.Google ScholarCross Ref
- [21] J. Liu, Y. Zhang, Y. Lu, S. Ren, and S. Cao, “Augmented nested arrays with enhanced dof and reduced mutual coupling,” IEEE Transactions on Signal Processing, vol. 65, no. 21, pp. 5549–5563, 2017.Google ScholarDigital Library
- [22] C. H. BH Wang, YL Wang, “A robust doa estimation algorithm for uniform linear array in the presence of mutual coupling,” 2003.Google Scholar
- [23] C.-L. Liu and P. Vaidyanathan, “Super nested arrays: Linear sparse arrays with reduced mutual coupling—part i: Fundamentals,” IEEE Transactions on Signal Processing, vol. 64, no. 15, pp. 3997–4012, 2016.Google Scholar
- [24] Liu, Chun-Lin and Vaidyanathan, PP, “Super nested arrays: Linear sparse arrays with reduced mutual coupling—part ii: High-order extensions,” IEEE Transactions on Signal Processing, vol. 64, no. 16, pp. 4203–4217, 2016.Google Scholar
- [25] M. C. Dogan and J. M. Mendel, “Applications of cumulants to array processing. i. aperture extension and array calibration,” IEEE Transactions on Signal Processing, vol. 43, no. 5, pp. 1200–1216, 1995.Google ScholarDigital Library
- [26] P. Chevalier, L. Albera, A. Ferréol, and P. Comon, “On the virtual array concept for higher order array processing,” IEEE Transactions on Signal Processing, vol. 53, no. 4, pp. 1254–1271, 2005.Google ScholarDigital Library
- [27] N. Yuen and B. Friedlander, “Performance analysis of higher order esprit for localization of near-field sources,” IEEE transactions on Signal Processing, vol. 46, no. 3, pp. 709–719, 1998.Google ScholarDigital Library
Index Terms
- Sparsity-based localization for Mixed Near-Field and Far-Field Sources with unknown Mutual Coupling
Recommendations
Classification and localization of mixed near-field and far-field sources using mixed-order statistics
A novel algorithm for mixed sources classification and localization is proposed.More reasonable classification of the source types can be realized.An improved localization accuracy of near-field sources can be achieved.Parameter pair-matching and two-...
An effective localization method for mixed far-field and near-field strictly non-circular sources
AbstractIn this paper, an effective direction-of-arrival (DOA) and range estimations method for mixed far-field and near-field non-circular sources is proposed based on a large centrosymmetric uniform linear array (ULA). By exploiting the non-...
Mixed far-field and near-field source localization based on subarray cross-cumulant
Highlights- An efficient approach for mixed sources localization is proposed.
- An improved ...
AbstractThis paper presents a new algorithm for mixed far-field and near-field source localization using a uniform linear array (ULA). Firstly, the ULA is divided into two overlapping subarrays to construct two special cross-cumulant matrices ...
Comments