TY - JOUR
T1 - Off-grid DOA estimation through variational Bayesian inference in colored noise environment
AU - Zhang, Yahao
AU - Yang, Yixin
AU - Yang, Long
N1 - Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/4
Y1 - 2021/4
N2 - This paper provides a direction-of-arrival (DOA) estimation method based on sparse Bayesian learning for a colored noise environment. In this method, the harmonic noise model is absorbed into the covariance matrix model to express the noise objectively. As such, the covariance matrix is parameterized with the signal powers and noise parameters. Given that the existing Bayesian models cannot be directly used for this covariance matrix model, this paper establishes a new probabilistic model. Different priors are assigned for signal power vector and noise parameter vector since signal power vector is sparse but noise parameter vector is not. Based on this probabilistic model, the variational Bayesian inference is applied to estimate signal powers and noise parameters. Moreover, first-order Taylor series expansion is applied to approximate the virtual steering vector as a function of the grid error between the true DOA and the closest grid point. Grid error is estimated in the Bayesian framework and applied to modify the grid, thus alleviating basis mismatch. Simulation results prove that the proposed method achieves high estimation accuracy with a mild computational complexity in a colored noise environment.
AB - This paper provides a direction-of-arrival (DOA) estimation method based on sparse Bayesian learning for a colored noise environment. In this method, the harmonic noise model is absorbed into the covariance matrix model to express the noise objectively. As such, the covariance matrix is parameterized with the signal powers and noise parameters. Given that the existing Bayesian models cannot be directly used for this covariance matrix model, this paper establishes a new probabilistic model. Different priors are assigned for signal power vector and noise parameter vector since signal power vector is sparse but noise parameter vector is not. Based on this probabilistic model, the variational Bayesian inference is applied to estimate signal powers and noise parameters. Moreover, first-order Taylor series expansion is applied to approximate the virtual steering vector as a function of the grid error between the true DOA and the closest grid point. Grid error is estimated in the Bayesian framework and applied to modify the grid, thus alleviating basis mismatch. Simulation results prove that the proposed method achieves high estimation accuracy with a mild computational complexity in a colored noise environment.
KW - Colored noise
KW - First-order Taylor series expansion
KW - Harmonic noise model
KW - Off-grid direction-of-arrival estimation
KW - Variational Bayesian inference
UR - http://www.scopus.com/inward/record.url?scp=85099817000&partnerID=8YFLogxK
U2 - 10.1016/j.dsp.2021.102967
DO - 10.1016/j.dsp.2021.102967
M3 - 文章
AN - SCOPUS:85099817000
SN - 1051-2004
VL - 111
JO - Digital Signal Processing: A Review Journal
JF - Digital Signal Processing: A Review Journal
M1 - 102967
ER -