High-resolution Direction of Arrival Estimation Method Based on Sparse Arrays with Minimum Number of Elements

Authors

DOI:

https://doi.org/10.26636/jtit.2021.143720

Keywords:

compressed sensing, direction of arrival (DOA) estimation, sparse array

Abstract

Regular fully filled antenna arrays have been widely used in direction of arrival (DOA) estimation. However, practical implementation of these arrays is rather complex and their resolutions are limited to the beamwidth of the array pattern. Therefore, higher resolution and simpler methods are desirable. In this paper, the compressed sensing method is first applied to an initial fully filled array to randomly select the most prominent and effective elements which are used to form the sparse array. To keep the dimension of the sparse array equal to that of the fully filled array, the first and the last elements were excluded from the sparseness process. In addition, some constraints on the sparse spectrum are applied to increase estimation accuracy. The optimization problem is then solved iteratively using the iterative reweighted l1 norm. Finally, a simple searching algorithm is used to detect peaks in the spectrum solution that correspond to the directions of the arriving signals. Compared with the existing scanned beam methods, such as the minimum variance distortionless response (MVDR) technique, and with subspace approaches, such as multiple signal classification (MUSIC) and ESPIRT algorithms, the proposed sparse array method offers better performance even with a lower number of array elements and in severely noisy environments. Effectiveness of the proposed sparse array method is verified via computer simulations.

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References

[1] M. Guo, Y. D. Zhang, and T. Chen, „DOA estimation using compressed sparse array", IEEE Trans. on Sig. Process., vol. 66, no. 15, pp. 4133-4146, 2018. DOI: https://doi.org/10.1109/TSP.2018.2847645
View in Google Scholar

[2] P. Gong, X. Zhang, and T. Ahmed, „Computationally efficient DOA estimation for coprime linear array: A successive signal subspace fitting algorithm", Int. J. of Electron., vol. 107, no. 8, pp. 1216-1238, 2020. DOI: https://doi.org/10.1080/00207217.2020.1726485
View in Google Scholar

[3] B. D. van Veen and K. M. Buckley, „Beamforming: A versatile approach to spatial filtering", IEEE Sig. Process. Mag., vol. 5, no. 2, pp. 4-24, 1988. DOI: https://doi.org/10.1109/53.665
View in Google Scholar

[4] M. Carlin, P. Rocca, G. Oliveri, F. Viani, and A. Massa, „Directions-of-arrival estimation through Bayesian compressive sensing strategies", IEEE Trans. on Antenn. Propag., vol. 61, no. 7, pp. 3828-3838, 2013. DOI: https://doi.org/10.1109/TAP.2013.2256093
View in Google Scholar

[5] A. D. Lonkeng and J. Zhuang, „Two-dimensional DOA estimation using arbitrary arrays for massive MIMO systems", Int. J. of Antenn. and Propag., vol. 2017, Article ID 6794920, 2017. DOI: https://doi.org/10.1155/2017/6794920
View in Google Scholar

[6] A. B. Gershman, M. Rubsamen, and M. Pesavento, „One- and twodimensional direction-of-arrival estimation: an overview of searchfree techniques", Sig. Process., vol. 90, no. 5, pp. 1338-1349, 2010. DOI: https://doi.org/10.1016/j.sigpro.2009.12.008
View in Google Scholar

[7] T. C. Yang, „Deconvolved conventional beamforming for a horizontal line array", IEEE J. of Oceanic Engin., vol. 43, no. 1, pp. 160-172, 2018. DOI: https://doi.org/10.1109/JOE.2017.2680818
View in Google Scholar

[8] J. Capon, „High-resolution frequency-wavenumber spectrum analysis", Proc. of the IEEE, vol. 57, no. 8, pp. 1408-1418, 1969. DOI: https://doi.org/10.1109/PROC.1969.7278
View in Google Scholar

[9] R. O. Schmidt, „Multiple emitter location and signal parameter estimation", IEEE Trans. on Antenn. and Propag., vol. 34, no. 3, pp. 276-280, 1986. DOI: https://doi.org/10.1109/TAP.1986.1143830
View in Google Scholar

[10] R. Roy and T. Kailath, „ESPRIT-estimation of signal parameters via rotational invariance techniques", IEEE Trans. on Sig. Process., vol. 37, no. 7, pp. 984-995, 1989. DOI: https://doi.org/10.1109/29.32276
View in Google Scholar

[11] X. Fan, L. Pang, P. Shi, G. Li, and X. Zhang, „Application of bee evolutionary genetic algorithm to maximum likelihood direction-ofarrival estimation", Mathem. Probl. in Engin., vol. 2019, Article ID 6035870, 2019. DOI: https://doi.org/10.1155/2019/6035870
View in Google Scholar

[12] H. Chen, S. Li, J. Liu, F. Liu, and M. Suzuki, „A novel modification of PSO algorithm for SML estimation of DOA", Sensors, vol. 16, no. 12, 2016. DOI: https://doi.org/10.3390/s16122188
View in Google Scholar

[13] S. Zhao, Y. S. Shmaliy, and C. K. Ahn, „Iterative maximum likelihood FIR estimation of dynamic systems with improved robustness", IEEE/ASME Trans. on Mechatron., vol. 23, no. 3, pp. 1467-1476, 2018. DOI: https://doi.org/10.1109/TMECH.2018.2820075
View in Google Scholar

[14] K. H. Sayidmarie and J. R. Mohammed, „Performance of a wide angle and wideband nulling method for phased arrays", Progr. In Electromag. Res. M, vol. 33, pp. 239-249, 2013. DOI: https://doi.org/10.2528/PIERM13100603
View in Google Scholar

[15] J. R. Mohammed, „Design of printed Yagi antenna with additional driven element for WLAN applications", Progr. in Electromag. Res. C, vol. 37, pp. 67-81, 2013. DOI: https://doi.org/10.2528/PIERC12121201
View in Google Scholar

[16] J. R. Mohammed, „Element selection for optimized multi-wide nulls in almost uniformly excited arrays", IEEE Antenn. and Wirel. Propag. Lett., vol. 17, no. 4, pp. 629-632, 2018. DOI: https://doi.org/10.1109/LAWP.2018.2807371
View in Google Scholar

[17] J. R. Mohammed and K. H. Sayidmarie, „Sidelobe cancellation for uniformly excited planar array antennas by controlling the side elements", IEEE Antenn. and Wirel. Propag. Lett., vol. 13, pp. 987-990, 2014. DOI: https://doi.org/10.1109/LAWP.2014.2325025
View in Google Scholar

[18] J. Zhang, Z. Duan, Y. Zhang, and J. Liang, „Compressive sensing approach for DOA estimation based on sparse arrays in presence of mutual coupling", in Communications, Signal Processing, and Systems. Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, Q. Liang et al., Eds. Lecture Notes in Electrical Engineering, vol. 516. Springer, 2020. DOI: https://doi.org/10.1007/978-981-13-6504-1_151
View in Google Scholar

[19] H. Li, C. Wang, and X. Zhu, „Compressive sensing for high resolution direction-of-arrival estimation via iterative optimization on sensing matrix", Int. J. of Antenn. and Propag., vol. 2015, no. 1, Article ID 713930, 2015. DOI: https://doi.org/10.1155/2015/713930
View in Google Scholar

[20] S. F. Cotter, B. D. Rao, K. Engan, and K. Kreutz-Delgado, „Sparse solutions to linear inverse problems with multiple measurement vectors", IEEE Trans. on Sig. Process., vol. 53, no. 7, pp. 2477-2488, 2005. DOI: https://doi.org/10.1109/TSP.2005.849172
View in Google Scholar

[21] I. F. Gorodnitsky and B. D. Rao, „Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum norm algorithm", IEEE Trans. on Sig. Process., vol. 45, no. 3, pp. 600-616, 1997. DOI: https://doi.org/10.1109/78.558475
View in Google Scholar

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Submitted

2023-05-12

Published

2021-03-30

Issue

No. 1 (2021)

Section

ARTICLES FROM THIS ISSUE

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How to Cite

[1]
J. R. Mohammed, “High-resolution Direction of Arrival Estimation Method Based on Sparse Arrays with Minimum Number of Elements”, JTIT, vol. 83, no. 1, pp. 8–14, Mar. 2021, doi: 10.26636/jtit.2021.143720.

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