a539c633-2e23-4c7b-b304-a395fcf8376b20211116080231850naun:naunmdt@crossref.orgMDT DepositInternational Journal of Applied Mathematics and Informatics2074-127810.46300/91014http://www.naun.org/cms.action?id=3064319202131920211510.46300/91014.2021.15https://www.naun.org/cms.action?id=23306Bao GenXuJoint Lab of Array Signal Processing TongFang Electronic Science and Technology Co. Ltd. Jiujiang, ChinaYi HeWanJoint Lab of Array Signal Processing TongFang Electronic Science and Technology Co. Ltd. Jiujiang, ChinaSi LongTangJoint Lab of Array Signal Processing TongFang Electronic Science and Technology Co. Ltd. Jiujiang, ChinaXue KeDingJoint Lab of Array Signal Processing TongFang Electronic Science and Technology Co. Ltd. Jiujiang, ChinaQunWanDepartment of Electronic Engineering University of Electronic Science and Technology of China Chengdu, ChinaIn order to find the directions of coherent signals, a sparsity enhanced beam-forming method is proposed. Unlike the conventional minimum variance distortless response (MVDR) method, the minimum variance in the proposed method corresponds to the orthogonal relationship between the noise subspace and the sparse representation of the received signal vector, whereas the distortless response corresponds to the nonorthogonal relationship between the signal subspace and the sparse representation of the received signal vector. The proposed sparsity enhanced MVDR (SEMVDR) method is carried out by the iterative reweighted Lp-norm constraint minimization. for direction finding of coherent signals. Simulation results are shown that SEMVDR has better performance than the existing algorithms, such as MVDR and MUSIC, when coherent signals are present.111620211116202155589https://www.naun.org/main/UPress/ami/2021/a182013-009(2021).pdf10.46300/91014.2021.15.9https://www.naun.org/main/UPress/ami/2021/a182013-009(2021).pdf10.1049/cp.2013.0373Q. Wan, B. G. Xu, J. H. Yin, F. Fang, Y. H. Wan, S. L. Tang, "An approach to manifold estimation for antenna array in situation of interferences", IET International Radar Conference 2013, Xi An, China, Apr. 14-16, 2013, in press 10.1109/proc.1969.7278J. Capon, "High resolution frequency-wavenumber spectrum analysis," Proc. IEEE, vol. 57, no. 8, Aug. 1969, pp.1408-1418. 10.1049/el.2010.8730Y.P. Liu, Q. Wan, " Robust beamformer based on total variation minimisation and sparse constraint," Electronic Letters, vol.46, no.25, 2010, pp.1697-1699. 10.1049/el:20080415Y. Zhang, B.P. Ng and Q. Wan, Sidelobe suppression for adaptive beamforming with sparse constraint on beam pattern, Electronics Letters, vol.44, no.10, pp.615-616. 10.1109/tap.1986.1143830R. O. Schmidt, "Multiple emitter location and signal parameter estimation," IEEE Trans. Antennas Propag., vol. 34, no. 3, Mar. 1986, pp.276-280. 10.1109/78.651165M. D. Sacchi, T. J. Ulrych, and C. J. Walker, "Interpolation and extrapolation using a high-resolution discrete fourier transform," IEEE Trans. Signal Process., vol. 46, no. 1, Jan. 1998, pp.31-38. 10.1109/icassp.1998.681832B. D. Jeffs, "Sparse inverse solution methods for signal and image processing applications," Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., vol. 3, 1998, pp.1885-1888. 10.1109/78.558475I. F. Gorodnitsky, and B. D. Rao, "Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum norm algorithm," IEEE Trans. Signal Process., vol. 45, no. 3, Mar. 1997, pp.600-616. 10.1109/78.912914J. J. Fuchs, "On the application of the global matched filter to DOA estimation with uniform circular arrays," IEEE Trans. Signal Process., Vol. 49, No. 4, Apr. 2001, pp.702-709. 10.1109/tsp.2005.850882D. M. Malioutov, M. Cetin, and A. S. Willsky, "A sparse signal reconstruction perspective for source localization with sensor arrays," IEEE Trans. Signal Process., vol. 53, no. 8, Aug. 2005, pp.3010-3022. 10.1007/s00041-008-9045-xE. J. Cands, M. B. Wakin, and S. P. Boyd, "Enhancing sparsity by reweighted L1 minimization," Journal of Fourier Analysis and Applications, vol. 14, no. 5, Dec. 2008, pp.877-905.