Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 70, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2020.3047501
Keywords
Acoustic source localization; beamforming; Kullback-Leibler (KL) divergence; nonsynchronous array measurement (NAM); student-t priors; variational Bayesian (VB) inference
Funding
- National Natural Science Foundation of China [61701440, 11704248]
- State Key Laboratory of Mechanical System and Vibration [MSV202001]
- Science and Technology on Sonar Laboratory [6142109KF201901]
- State Key Laboratory of Compressor Technology [SKLYSL201812, SKL-YSJ201903]
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This article introduces the VB-NAM method for fast and robust localization of low-frequency acoustic sources, using VB inference and KL divergence optimization to solve the inverse problem of NAM power propagation model. The adaptive parameter evaluation reduces the impact of interferences in NAM, showing validation through simulations and experimental results.
This article proposes a variational Bayesian (VB) inference based on the nonsynchronous array measurement (NAM) (VB-NAM) method in order to obtain the fast and robust localization of low-frequency acoustic sources. To enlarge the aperture size compared with the prototype array, the NAM is performed to measure the acoustic pressures with low-frequency based on the forward power propagation model. The implementation of the NAM can be reformulated into a cross-spectral matrix (CSM) completion problem. Then, to solve the inverse problem of the NAM power propagation model, the VB inference based on the Student-t priors and Kullback-Leibler (KL) divergence optimization is proposed. The advantages of the proposed VB-NAM benefit from the optimization of matrix inversion and adaptive estimation of regularization parameters. The contribution of the adaptive parameter evaluation is to reduce the impact of multiple interferences (such as additive noise and the matrix completion error) in NAM. Finally, both simulations at 800 Hz and experimental results at 1000 Hz are presented to show the validation of the proposed VB-NAM method, even under the anisotropic Gaussian noise conditions. Algorithm performance and iteration process are analyzed to demonstrate the efficiency and robustness.
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