The performance of multi-channel Compressive Sensing (CS)-based Direction-of-Arrival (DOA) estimation algorithm degrades when the gains between Radio Frequency (RF) channels are inconsistent, and when target angle information mismatches with system sensing model. To solve these problems, a novel single-channel CS-based DOA estimation algorithm via sensing model optimization is proposed. Firstly, a DOA sparse sensing model using single-channel array considering the sensing model mismatch is established. Secondly, a new single-channel CS-based DOA estimation algorithm is presented. The basic idea behind the proposed algorithm is to iteratively solve two CS optimizations with respect to target angle information vector and sensing model quantization error vector, respectively. In addition, it avoids the loss of DOA estimation performance caused by the inconsistent gain between RF channels. Finally, simulation results are presented to verify the efficacy of the proposed algorithm.
References
[1]
Julio, M., Cuo, S. and Lawerence, C. (2013) Task-driven Adaptive Statistical Compressive Sensing of Gaussian Mixture Models. IEEE Trans. Signal Process, 61, 585-600. https://doi.org/10.1109/TSP.2012.2225054
[2]
Candes, E.J. and Wakin, M.B. (2008) An Introduction to Compressive Sampling. IEEE Signal Process. Mag., 25, 21-30. https://doi.org/10.1109/MSP.2007.914731
Romberg, J. (2008) Imaging via Compressive Sampling. IEEE Signal Process. Mag., 25, 14-20. https://doi.org/10.1109/MSP.2007.914729
[5]
Li, H.T., Wang, C.Y., Wang, K., He, Y.P. and Zhu, X.H. (2015) High Resolution Range Profile of Compressive Sensing Radar with Low Computational Complexity. IET Radar Sonar Navig., 9, 984-990. https://doi.org/10.1049/iet-rsn.2014.0454
[6]
Li, R.P., Zhao, Z.F., Zhang, Y., Palicot, J. and Zhang, H.G. (2014) Adaptive Multi-Task Compressive Sensing for Localisation in Wireless Local Area Networks. IET Communications, 8, 1736-1744. https://doi.org/10.1049/iet-com.2013.1019
[7]
Liu, Y., Mei, W.B., and Du, H.Q. (2014) Two Compressive Sensing-Based Estimation Schemes Designed for Rapidly Time-Varying Channels in Orthogonal Frequency Division Multiplexing Systems. IET Signal Process, 8, 291-299.
https://doi.org/10.1049/iet-spr.2013.0352
[8]
Hawes, M.B. and Liu, W. (2014) Compressive Sensing-Based Approach to the Design of Linear Robust Sparse Antenna Arrays with Physical Size Constraint. IET Microwaves, Antennas and Propag., 8, 736-746.
https://doi.org/10.1049/iet-map.2013.0469
[9]
Julian, W., Simon, H. and Martin, K. (2013) Analysis Based Blind Compressive Sensing. IEEE Signal Process. Lett., 20, 491-494.
https://doi.org/10.1109/LSP.2013.2252900
[10]
Krim, H. and Viberg, M. (1996) Two Decades of Array Signal Processing Research: The Parametric Approach. IEEE Signal Process. Mag., 13, 67-94.
https://doi.org/10.1109/79.526899
[11]
Yu, Y., Petropulu, A.P. and Poor, H.V. (2010) MIMO Radar Using Compressive Sampling. IEEE Journal of Selected Topic in Signal Process, 4, 146-163.
https://doi.org/10.1109/JSTSP.2009.2038973
[12]
Bilik, I. (2011) Spatial Compressive Sensing for Direction-Of-Arrival Estimation of Multiple Sources Using Dynamic Sensor Arrays. IEEE Trans. Aerosp. Electron. Syst., 47, 1757-1769. https://doi.org/10.1109/TAES.2011.5937263
[13]
Liu, Z., Wei, X. and Li, X. (2013) Aliasing-Free Moving Target Detection in Random Pulse Repetition Interval Radar Based on Compressed Sensing. IEEE Sensor Journal, 13, 2523-2534. https://doi.org/10.1109/JSEN.2013.2249762
[14]
Cotter, S.F., Rao, B.D. and Engan, K. (2005) Sparse Solution to Linear Inverse Problems with Multiple Measurement Vectors. IEEE Trans. Signal Process, 53, 2477-2488. https://doi.org/10.1109/TSP.2005.849172
[15]
Ali, C.G., Volkan, C. and James, H.M. (2012) Bearing Estimation via Spatial Sparsity Using Compressive Sensing. IEEE Trans. Aerosp. Electron. Syst., 48, 1358-1369.
https://doi.org/10.1109/TAES.2012.6178067
[16]
Liu Z.M. and Zhou, Y.Y. (2013) A Unified Framework and Sparse Bayesian Perspective for Direction-Of-Arrival Estimation in the Presence of Array Imperfections. IEEE Trans. Signal Process, 61, 3786-3798.
https://doi.org/10.1109/TSP.2013.2262682
[17]
Zhang, Y., Ye, Z., Xu, X. and Hu, N. (2014) Off-grid Doa Estimation Using Array Covariance Matrix and Block-Sparse Bayesian Learning. Signal Process, 98, 197-201. https://doi.org/10.1016/j.sigpro.2013.11.022
[18]
Yang, Z., Xie, L. and Zhang, C. (2013) Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference. IEEE Trans. Signal Process, 61, 38-43.
https://doi.org/10.1109/TSP.2012.2222378
[19]
Tan, Z. and Nehorai, A. (2014) Sparse Direction of Arrival Estimation Using Co-Prime Arrays with Off-Grid Targets. IEEE Signal Process. Lett., 21, 26-29.
https://doi.org/10.1109/LSP.2013.2289740
[20]
Mohimani, H., Massoud, B.Z. and Jutten, C. (2009) A Fast Approach for Over- complete Sparse Decomposition Based on Smoothed L0 Norm. IEEE Trans. Signal Process, 57, 289-301. https://doi.org/10.1109/TSP.2008.2007606
[21]
Tibshirani, R. (1996) Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society, 58, 267-288.
[22]
Nural, H.N., Tughrul, A. and Brian, W.F. (2013) Single-Channel Beamforming Al-gorithm for 3-Faceted Pahsed Array Antenna. IEEE Antennas Wirel. Propag. Lett., 12, 813-816. https://doi.org/10.1109/LAWP.2013.2271051
[23]
Mishali, M. and Eldar, Y.C. (2008) Reduce and Boost: Recovering Arbitrary Sets of Jointy Sparse Vectors. IEEE Trans. Signal Process, 56, 4692-4702.
https://doi.org/10.1109/TSP.2008.927802