PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

State Space-Based Method for the DOA Estimation by the Forward-Backward Data Matrix Using Small Snapshots

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this presentation, a new low computational burden method for the direction of arrival (DOA) estimation from noisy signal using small snapshots is presented. The approach introduces State Space-based Method (SSM) to represent the received array signal, and uses small snapshots directly to form the Hankel data matrix. Those Hankel data matrices are then utilized to construct forward-backward data matrix that is used to estimate the state space model parameters from which the DOA of the incident signals can be extracted. In contrast to existing methods, such as MUSIC, Root-MUSIC that use the covariance data matrix to estimate the DOA and the sparse representation (SR) based DOA which is obtained by solving the sparsest representation of the snapshots, the SSM algorithm employs forward-backward data matrix formed only using small snapshots and doesn't need additional spatial smoothing method to process coherent signals. Three numerical experiments are employed to compare the performance among the SSM, Root-MUSIC and SR-based method as well as Cramér–Rao bound (CRB). The simulation results demonstrate that when a small number of snapshots, even a single one, are used, the SSM always performs better than the other two method no matter under the circumstance of uncorrelated or correlated signal. The simulation results also show that the computational burden is reduced significantly and the number of antenna elements is saved greatly.
Twórcy
autor
  • Department of Electronic Engineering and Information Science, University of Science and Technology of China, China
Bibliografia
  • [1] Ye Tian, Xiaoying Sun, “DOA estimation in the presence of unknown non-uniform noise without knowing the number of sources,” International Journal of Electronics Letters, vol.44, no.1, pp.108-116, 2014. doi: 10.1080/21681724.2014.966771
  • [2] P. Charge, Yide Wang, and J. Saillard, “An extended cyclic MUSIC algorithm,” IEEE Trans. on Signal Processing, vol.51, no.7, pp.1695-1701, 2003. doi:10.1109/TSP.2003.812834
  • [3] A. Barabell, “Improving the resolution performance of eigenstructure-based direction-finding algorithms,” Proc. of ICASSP, vol.8, pp.336-339, 1983. doi: 10.1109/ICASSP.1983.1172124
  • [4] R. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Trans. on Antennas and Propagation, vol.34, no.3, pp.276-280, 1986. doi: 10.1109/TAP.1986.1143830
  • [5] Cheng Qian, Lei Huang, and H. C. So, “Improved unitary Root-MUSIC for DOA estimation based on pseudo-noise resampling,” IEEE Signal Processing Letters, vol.21, no.2, pp.140-144, 2014. doi: 10.1109/LSP.2013.2294676
  • [6] Shu Changgan, Liu Yumin, “An improved forward/backward spatial smoothing Root-MUSIC algorithm based signal decorrelation,” Proc. of IEEE WARTIA, pp. 1252-1255, 2014. doi: 10.1109/LSP.2013.2294676
  • [7] M. D. Zoltowski, S. D. Silverstein, and C. P. Mathews, “Beam-space root-MUSIC for minimum redundancy linear arrays,” IEEE Trans. on Signal Processing, vol.41, no.7, pp.2502-2507, 1993. doi: 10.1109/78.224260
  • [8] Wei Wang, Xianpeng Wang, Xin Li, and Hongru Song, “DOA estimation for monostatic MIMO radar based on unitary root-MUSIC,” International Journal of Electronics, vol.100, no.11, pp.1499-1509, 2013. doi :10.1080/00207217.2012.751319
  • [9] Xiaofei Zhang, Dazhuan Xu, “Improved coherent DOA estimation algorithm for uniform linear arrays,” International Journal of Electronics, vol.96, no.2, 213-222, 2009. doi: 10.1080/00207210802526810
  • [10] A. L. Swindlehurst, T. Kailath, “A performance analysis of subspace-based methods in the presence of model errors, Part I: The MUSIC algorithm,” IEEE Trans. on Signal Processing, vol.40, no.7, pp.1758-1774, 1992. doi : 10.1109/78.143447
  • [11] Schell, S.V, “Performance analysis of the cyclic MUSIC method of direction estimation for cyclostationary signals,” IEEE Trans. on Signal Processing, vol.42, no.11, pp.3043-3050, 1994. doi : 10.1109/78.330364
  • [12] Pascal Vallet, Xavier Mestre, and Philippe Loubaton, “Performance analysis of an improved MUSIC DOA estimator,” IEEE Trans. on Signal Processing, vol.63, no.23, pp.6407-6422, 2015. doi : 10.1109/TSP.2015.2465302
  • [13] BHASKAR D.RAO, K.V.S.Hari, “Performance analysis of Root-Music,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.37, no.12, pp.1939-1949, 1989. doi : 10.1109/29.45540
  • [14] I. F. Gorodnitsky, B. D. Rao, “Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum norm algorithm,” IEEE Trans. on Signal Processing, vol.45, no.3, pp.600-616, 1997. doi : 10.1109/78.558475
  • [15] D. Malioutov, M. Cetin, and A. S. Willsky, “A sparse signal reconstruction perspective for source localization with sensor arrays,” IEEE Trans. on Signal Processing, vol.53, no.8, pp.3010–3022, 2005 doi : 10.1109/TSP.2005.850882
  • Xu Xu, Xiaohan Wei, and Zhongfu Ye, “DOA estimation based on sparse signal recovery utilizing weighted-norm penalty,” IEEE signal processing letters, vol.19, no.3, pp.155-158, 2012. doi : 10.1109/LSP.2012.2183592
  • [16] D. L. Donoho, M. Elad, & V. Temlyakvo, “Stable recovery of sparse overcomplete representations in the presence of noise,” IEEE Trans. Inf. Theory, vol.52, pp.6–18, 2006. doi : 10.1109/TIT.2005.860430
  • [17] BHASKAR D. RAO, “Model based processing of Signals: A state space approach,” Proc. of the IEEE, vol.80, no.2, pp.283-309, 1992. doi :10.1109/5.123298
  • [18] BHASKAR D. RAO,. “Relationship between matrix pencil and state space based harmonic retrieval methods,” IEEE Trans. on Acoust., Speech, Signal processing, vol.38, no.1, pp.177-179, 1990. doi: 10.1109/29.45568
  • [19] S. Y. Kung, K. S. Arun, D. V. Bhaskar Rao, “State space and SVD based approximation methods for the harmonic retrieval problem,” Optical Society of America, vol.73, no.12, pp.1799-1811, 1983. doi: 10.1364/JOSA.73.001799
  • [20] B. D. Rao, “Sensitivity considerations in state space model-based harmonic retrieval methods,” IEEE Trans. on Acoust., Speech, Signal Processing, vol.37, no.11, pp.1789-1794,1989. doi : 10.1109/29.46567
  • [21] Hua Chen, “State-space based approximation methods for the harmonic retrieval problem in the presence of known signal poles,” Proc. of ICASSP’96, vol.5, pp.2924-2927, 1996. doi : 10.1109/ICASSP.1996.550166
  • [22] F. Desbouvries, “Unitary Hessenberg and state-space model based methods for the harmonic retrieval problem,” Proc. of IEEE Radar, Sonar and navigation, vol.143, no.6, pp.346-348, 1996. doi :10.1049/ip-rsn:19960853
  • [23] M. Wax, T. Kailath., “Detection of signals by information theoretic criteria,” IEEE Trans. Acoust., Speech. Signal Processing, vol.33, no.2, pp. 387-392, 1985. doi : 10.1109/TASSP.1985.1164557
  • [24] A. Satish, “Maximum likelihood estimation and Cramer-Rao bounds for direction of arrival parameters of a large sensor array,”IEEE Transactions on Antennas and Propagation, vol.44, no.4, pp.478-491, 1986. doi :10.1109/8.489299
  • [25] P. Stoica., “MUSIC, Maximum Likelihood, and Cramer-Rao Bound,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.37, no.5, pp.720-741, 1989. doi:10.1109/ICASSP.1988.197097
  • [26] Joel A. Tropp, Anna C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Transactions on Information Theory, vol.53, no.12, pp.4655-4666, 2007. doi:10.1109/TIT.2007.909108
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d10ce932-56ef-4524-8da2-0bc0287f51a9
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.