Abstract
Considering the computational complexity and redundancy of traditional array signal arrival angle (DOA) estimation algorithms, the compressed sensing technology was used to improve the real-time and accurate performance of the DOA estimation algorithm, in which, the space sparse signals were reconstructed from the array data by means of array manifold matrix. Compared with the classical MUSIC algorithm, the compressed sensing DOA estimation method could effectively improve the direction finding accuracy and angle resolution with low SNR and snapshot deficiency. Moreover, the proposed algorithm could achieve the coherent signal estimation correctly, and the simulation results show that its performance was superior to that of traditional algorithm.
Similar content being viewed by others
References
Hu, N., Ye, Z., Xu, X., et al.: DOA estimation for sparse array via sparse signal reconstruction. IEEE Trans. Aerospace Electron. Syst. 49(2), 760–773 (2013)
Xi, N., Liping, L.: A computationally efficient subspace algorithm for 2-D DOA estimation with L-shaped array. IEEE Signal Process. Lett. 21(8), 971–974 (2014)
Yan, F.G., Jin, M., Qiao, X.: Low-complexity DOA estimation based on compressed MUSIC and Its performance analysis. IEEE Trans. Signal Process. 61(8), 1915–1930 (2013)
Rahman, M.U.: Performance analysis of MUSIC DOA algorithm estimation in multipath environment for automotive radars. Int. J. Appl. Sci. Eng. 14(2), 125–132 (2016)
Ren, S., Ma, X., Yan, S., et al.: 2-D unitary ESPRIT-like direction of arrival estimation for coherent signals with a uniform rectangular array. Sensors 13(4), 4272–4288 (2013)
Qian, C., Huang, L., So, H.C.: Computationally efficient ESPRIT algorithm for direction-of-arrival estimation based on Nyström method. Signal Process. 94, 74–80 (2014)
Gu, J.F., Zhu, W.P., Swamy, M.N.S.: Joint 2-D DOA estimation via sparse L-shaped array. IEEE Trans. Signal Process. 63(5), 1171–1182 (2015)
Qian, C., Huang, L., So, H.C.: Improved unitary root-MUSIC for DOA estimation based on pseudo-noise resampling. IEEE Signal Process. Lett. 21(2), 140–144 (2014)
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Malloy, M.L., Nowak, R.D.: Near-optimal adaptive compressed sensing. IEEE Trans. Inf. Theory 60(7), 4001–4012 (2014)
Malioutov, D., Cetin, M., Willsky, A.: A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Trans. Signal Process. 53(8), 3010–3022 (2005)
Gribonval, R., Cevher, V., Davies, M.E.: Compressible distributions for high-dimensional statistics. IEEE Trans. Inf. Theory 58(8), 5016–5034 (2012)
Rossi, M., Haimovich, A.M., Eldar, Y.C.: Spatial compressive sensing for MIMO radar. IEEE Trans. Signal Process. 62(2), 419–430 (2014)
Kim, L.O., Ye, J.: Compressive music: revisiting the link between compressive sensing and array signal processing. IEEE Trans. Inf. Theory 58(1), 278–301 (2012)
Liu, Z.M., Huang, Z.T., Zhou, Y.Y.: Sparsity-inducing direction finding for narrowband and wideband signals based on array covariance vectors. IEEE Trans. Wirel. Commun. 12(8), 3896–3907 (2013)
Bo, L., Zeng-Hui, Z., Ju-Bo, Z.: Sparsity model and performance analysis of DOA estimation with compressive sensing. J. Electron. Inf. Technol. 36(3), 589–594 (2014)
Donoho, D.L., Huo, X.: Uncertainty principles and ideal atomic decomposition. IEEE Trans. Inf. Theory 47, 2845–2862 (2001)
Gribonval, R., Nielsen, M.: Sparse representations in unions of bases. IEEE Trans. Inf. Theory 49, 3320–3325 (2003)
Cai, T., Wang, L., Xu, G.W.: Stable recovery of sparse signals and an oracle inequality. IEEE Trans. Inf. Theory 56, 3516–3522 (2010)
Dheringe, N.A., Bansode, B.N.: Performance evaluation and analysis of direction of arrival estimation using MUSIC, TLS ESPRIT and Pro ESPRIT algorithms. Perform. Eval. 4(6), 4948–4958 (2015)
Acknowledgements
The authors wish to thank for the financial support of Natural Science Foundation of China (61573253, 61271321), Tianjin Natural Science Foundation (16JCYBJC16400), Tianjin Enterprise Science and Technology Project of Special Correspondent (17JCTPJC54700), Tianjin Science and Technology Project (16YFZCGX00360,16ZXZNGX00080), National Training Programs of Innovation and Entrepreneurship for Undergraduates (201610069007, 201710069023). The corresponding author is Professor Zhang Liyi.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yong, Z., Li-Yi, Z., Jian-Feng, H. et al. A new DOA estimation algorithm based on compressed sensing. Cluster Comput 22 (Suppl 1), 895–903 (2019). https://doi.org/10.1007/s10586-018-1752-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-018-1752-8