The advantages and disadvantages of the existing two-dimensional direction of arrival (DOA) estimation algorithm for frequency hopping signals are analyzed. A two-dimensional DOA estimation algorithm for frequency hopping signals based on sparse Bayesian learning is proposed. The algorithm uses the characteristics of the L-shaped array to convert the three-dimensional information of azimuth, elevation and hopping frequency into one-dimensional spatial frequency information, which reduces the length of redundant dictionary and the difficulty of sparse solution. Then, after singular value decomposition, the matrix operation dimension is reduced, and the algorithm complexity is reduced. The spatial frequency and the hopping frequency are estimated by the sparse bayes algorithm and the fast fourier transform. The spatial frequency and the hopping frequency are correctly paired by the capon spatial frequency matching algorithm to calculate the spatial angle. Finally, the azimuth and elevation angles are calculated according to the spatial angular relationship. The simulation results show that the DOA estimation performance of the algorithm is good under low signal noise ratio or low fast beat, and it is not easy to be affected by the spatial frequency interval and the coherence of the hopping signal source.
LI Hongguang, GUO Ying, SUI Ping, CAI Bin, SU Linghua
. Two-Dimensional DOA Estimation for
Multi-Hopping Signals Based on Sparse Bayes[J]. Journal of Shanghai Jiaotong University, 2020
, 54(4)
: 359
-368
.
DOI: 10.16183/j.cnki.jsjtu.2020.04.004
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