|
|
|
| The Two-dimensional DOA Estimation Base on the L-shaped Array |
| ZHANG Zhi-wei1,TAO Jian-wu2,SUI Yi-xiang1 |
1. Air Force 93032, Yanji, Jilin 133000, China
2. Department of Flight Vehicle Control,Aviation University of Air Force, Changchun, Jilin 130022, China |
|
|
|
|
Abstract The traditional two-dimentional DOA estimation can estimate the less number of signals, and have high computational complexity, so based on the L-sparse array, the two-dimensional DOA estimation method was proposed. The method is that co-even array (CEA) placed on the L-shaped array form the virtual array to increase the array freedom; By dividing the virtual array into several equally spaced sparse subarrays, to compress the search range of angle and reduce the computational complexity consequently. By MUSIC searching, spectral peaks were obtained, and employing the maximum-likelihood criterion, the elevation and azimuth of incident signals were chosen, and through maximum-likelihood angle pairing method to pair angle between elevation and azimuth lastly. The algorithm enable estimate the number of signals more than the number of actual physical matrix, what’s more, it can increase the estimation precision and reduce the computational complexity. The results of MATLAB simulation verified the validity of this method.
|
|
Received: 31 October 2018
Published: 01 September 2019
|
|
|
|
|
|
［1］Zoltowski M D, Haardt M, Mathews C P. Closed-form 2-D angle estimation with rectangular arrays in element space or beamspace via unitary ESPRIT［J］. Signal Processing IEEE Transactions on, 1996, 44(2):316-328.
［2］张建国, 徐科军, 董帅,等. 基于希尔伯特变换的科氏质量流量计信号处理方法研究与实现［J］. 计量学报, 2017, 38(3):309-314.
Zhang J D, Xu K J, Dong S, et al. Study and Implementation of Signal Processing Method for Coriolis Mass Flowmeter Based on Hilbert Transform［J］. ACTA Metrologica Sinica, 2017, 38(3):309-314.
［3］Qin S, Zhang Y, M G Amin. Generalized Coprime Array Configurations for Direction-of-Arrival Estimation［J］. IEEE Trans Signal Processing, 2015, 63(6):1377-1390.
［4］Hu N, Ye Z, Xu X, Bao M. DOA Estimation for Sparse Array via Sparse Signal Reconstruction［J］. IEEE Trans. Aerospace and Electronic System, 2013, 9(2): 760-773.
［5］Yan F G, Liu S, Wang J, et al. Fast DOA Estimation Using Co-prime Array［J］. Electronics Letters, 2018,54(7):409-410.
［6］ Gu J F, Wei P. Joint SVD of Two Cross-Correlation Matrices to Achieve Automatic Pairing in 2-D Angle Estimation Problems［J］. IEEE Antennas & Wireless Propagation Letters, 2007, 6:553-556.
［7］Hu Y, Lu J, Qiu X. Direction of arrival estimation of multiple acoustic sources using a maximum likelihood method in the spherical harmonic domain［J］. Applied Acoustics, 2018, 135:85-90.
［8］del Rio J E F, Catedra-Perez M F. The matrix pencil method for two-dimensional direction of arrival estimation employing an L-shaped array［J］. IEEE Transactions on Antennas & Propagation, 1997, 45(11):1693-1694.
［9］Wang G, Xin J, Zheng N, et al. Computationally Efficient Subspace-Based Method for Two-Dimensional Direction Estimation With L-Shaped Array［J］. IEEE Transactions on Signal Processing, 2011, 59(7):3197-3212.
［10］Liu C L, Vaidyanathan P P. Super Nested Arrays: Linear Sparse Arrays with Reduced Mutual Coupling—Part I: Fundamentals［J］. IEEE Trans Signal Processing, 2016, 64(15): 3997-4012.
［11］Liu C L, Vaidyanathan P P. Remarks on The Spatial Smoothing Step in Coarray MUSIC［J］. IEEE Signal Processing Letters, 2015, 22(9): 1438-1442.
［12］Stoica P, Nehorai A. MUSIC, Maximum likelihood, Cramer-Rao Bound［J］. IEEE TransAcoust, Speech, Signal Process, 1989, 37(5): 720-741. |
|
|
|
2019, Vol. 40 
