Multi-target DOA Estimation Using Beam-Doppler Unitary ESPRIT
WEN Cai① WU Jianxin② WANG Tong② ZHOU Yan① PENG Jinye ①
①(School of Information Science and Technology, Northwest University, Xi’an 710069, China)
②(National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China)
Abstract：High-resolution Direction Of Arrival (DOA) estimation is a critical issue for mainbeam multi-target tracking in ground-based or airborne early warning radar system. A Beam-Doppler Unitary ESPRIT (BD- UESPRIT) algorithm is proposed to deal with this problem. Firstly, multiple snapshots without spatial aperture loss are obtained using the technique of time-smoothing. Then the conjugate centrosymmetric Discrete Fourier Transform (DFT) matrix is used to transform the extracted data into beam-Doppler domain. Finally, the rotational invariance property of the space-time beam is exploited to estimate DOA. Since the proposed algorithm takes full advantage of temporal information and is implemented in low-dimensional beamspace, the DOA estimation accuracy can be improved greatly with dramatically reduced computational complexity. Numerical examples are given to verify the effectiveness of the proposed algorithm.
SKOLNIK M I. Radar Handbook[M]. 3rd Edition, New York: McGraw-Hill, 2008: Chap. 4.
[2]
BELLILI F, ELGUET C, AMOR S B, et al. Code-aided DOA estimation from turbo-coded QAM transmissions: Analytical CRLBs and maximum likelihood estimator[J]. IEEE Transactions on Wireless Communications, 2017, 16(5): 2850-2865. doi: 10.1109/TWC.2017.2669026.
[3]
LIU G, CHEN H, SUN X, et al. Modified MUSIC algorithm for DOA estimation with Nyström approximation[J]. IEEE Sensors Journal, 2016, 16(12): 4673-4674. doi: 10.1109/JSEN. 2016.2557488.
[4]
LIN J, MA X, YAN S, et al. Time-frequency multi-invariance ESPRIT for DOA estimation[J]. IEEE Antennas and Wireless Propagation Letters, 2016, 15: 770-773. doi: 10.1109 /LAWP.2015.2473664.
[5]
WU J, WANG T, and BAO Z. Fast realization of maximum likelihood angle estimation with small adaptive uniform linear array[J]. IEEE Transactions on Antennas and Propagation, 2010, 58(12): 3951-3960. doi: 10.1109/TAP.2010. 2078447.
[6]
QIAN C, HUANG L, and SO H C. Improved unitary root- MUSIC for DOA estimation based on pseudo-noise resampling[J]. IEEE Signal Processing Letters, 2014, 21(2): 140-144. doi: 10.1109/LSP.2013.2294676.
[7]
HAARDT M and NOSSEK J A. Unitary ESPRIT: How to obtain increased estimation accuracy with a reduced computational burden[J]. IEEE Transactions on Signal Processing, 1995, 43(5): 1232-1242. doi: 10.1109/78.382406.
[8]
KAY S. Fundamentals of Statistical Signal Processing: Estimation Theory[M]. Englewood Cliffs, NJ: Prentice-Hall, 1993: 157-214.
[9]
ATHLEY F. Asymptotically decoupled angle-frequency estimation with sensor arrays[C]. Proceedings of the 33rd Asilomar Conference on Signals, Systems, and Computers, PacificGrove, USA, 1999: 1098-1102.
[10]
ZHANG Xiaofei, ZHOU Ming, CHEN Han, et al. Two- dimensional DOA estimation for acoustic vector-sensor array using a successive MUSIC[J]. Multidimensional Systems and Signal Processing, 2014, 25(3): 583-600. doi: https://doi.org/ 10.1007/s11045-012-0219-y.
[11]
LEMMA A N, VANDERVEEN A J, and DEPRETTERE E F. Analysis of joint angle-frequency estimation using ESPRIT[J]. IEEE Transactions on Signal Processing, 2003, 51(5): 1264-1283. doi: 10.1109/TSP.2003.810306.
[12]
LIU F, WANG J, and DU R. Unitary-JAFE algorithm for joint angle-frequency estimation based on Frame-Newton method[J]. Signal Processing, 2010, 90(3): 809-820. doi: https://doi.org/10.1016/j.sigpro.2009.08.013.
[13]
DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109 /TIT.2006.871582.
[14]
ENDER J H G. On compressive sensing applied to radar[J]. Signal Processing, 2010, 90(5): 1402-1414. doi: https://doi. org/10.1016/j.sigpro.2009.11.009.
[15]
KNUDSON K, SAAB R, and WARD R. One-bit compressive sensing with norm estimation[J]. IEEE Transactions on Information Theory, 2016, 62(5): 2748-2758. doi: 10.1109/ TIT.2016.2527637.
[16]
MALIOUTOV D, CETIN M, and WILLSKY A S. A sparse signal reconstruction perspective for source localization with sensor arrays[J]. IEEE Transactions on Signal Processing, 2005, 53(8): 3010-3022. doi: 10.1109/TSP.2005.850882.
[17]
CARLIN M, ROCCA P, OLIVERI G, et al. Directions- of-arrival estimation through Bayesian compressive sensing strategies[J]. IEEE Transactions on Antennas and Propagation, 2013, 61(7): 3828-3838. doi: 10.1109/TAP.2013. 2256093.
[18]
SHEN Q, LIU W, CUI W, et al. Focused compressive sensing for underdetermined wideband DOA estimation exploiting high-order difference coarrays[J]. IEEE Signal Processing Letters, 2017, 24(1): 86-90. doi: 10.1109/LSP.2016.2638880.
[19]
ROCCA P, HANNAN M A, SALUCCI M, et al. Single- snapshot DOA estimation in array antennas with mutual coupling through a multiscaling BCS strategy[J]. IEEE Transactions on Antennas and Propagation, 2017, 65(6): 3203-3213. doi: 10.1109/TAP.2017.2684137.
[20]
ZOLTOWSKI M D, HAARDT M, and MATHEWS C P. Closed-form 2-D angle estimation with rectangular arrays in element space or beamspace via unitary ESPRIT[J]. IEEE Transactions on Signal Processing, 1996, 44(2): 316-328. doi: 10.1109/78.485927.
[21]
PESAVENTO M, GERSHMAN A B, and HAARDT M. Unitary root-MUSIC with a real-valued eigendecomposition: A theoretical and experimental performance study[J]. IEEE Transactions on Signal Processing, 2000, 48(5): 1306-1314. doi: 10.1109/78.839978.
[22]
WAX M and KAILATH T. Detection of signals by information theoretic criteria[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1985, 33(2): 387-392. doi: 10.1109/TASSP.1985.1164557.
[23]
RISSANEN J. A universal prior for integers and estimation by minimum description length[J]. The Annals of Statistics, 1983, 11(2): 431-466. doi: 10.1214/aos/1176346150.
