Bayesian learning scheme for sparse DOA estimation based on maximum-a-posteriori of hyperparameters

In this paper, the problem of direction of arrival estimation is addressed by employing Bayesian learning technique in sparse domain. This paper deals with the inference of sparse Bayesian learning (SBL) for both single measurement vector (SMV) and multiple measurement vector (MMV) and its applicability to estimate the arriving signal’s direction at the receiving antenna array; particularly considered to be a uniform linear array. We also derive the hyperparameter updating equations by maximizing the posterior of hyperparameters and exhibit the results for nonzero hyperprior scalars. The results presented in this paper, shows that the resolution and speed of the proposed algorithm is comparatively improved with almost zero failure rate and minimum mean square error of signal’s direction estimate.

Sensitivity Analysis of a Beamforming Technique for Acoustic Measurements

Beamforming is a technique that is used to determine the location of an acoustic source and the sound level spectrum of the signal produced by the source. This technique involves an array of microphones which record acoustic signals at multiple locations. A detailed analysis of the beamforming technique was carried out for three different array geometries: a uniform linear array, a uniform planar array, and a random array. The effect of various parameters, such as the number of microphones in an array, on the applicability of the technique was examined using both simulations and experiments. The simulation results established that the source localization capability of a uniform linear array is limited to an acoustic source lying in the plane of the array. In contrast, a planar array (either uniform or random) does not suffer the above limitation. The results also showed that a random array (eg., a spiral array) is the best of all the array geometries. The experimental results demonstrated the robustness of the beamforming technique in localizing an acoustic source and also confirmed the superiority of a uniform planar array over a uniform linear array.

Sensitivity Analysis of a Beamforming Technique for Acoustic Measurements

Beamforming is a technique that is used to determine the location of an acoustic source and the sound level spectrum of the signal produced by the source. This technique involves an array of microphones which record acoustic signals at multiple locations. A detailed analysis of the beamforming technique was carried out for three different array geometries: a uniform linear array, a uniform planar array, and a random array. The effect of various parameters, such as the number of microphones in an array, on the applicability of the technique was examined using both simulations and experiments. The simulation results established that the source localization capability of a uniform linear array is limited to an acoustic source lying in the plane of the array. In contrast, a planar array (either uniform or random) does not suffer the above limitation. The results also showed that a random array (eg., a spiral array) is the best of all the array geometries. The experimental results demonstrated the robustness of the beamforming technique in localizing an acoustic source and also confirmed the superiority of a uniform planar array over a uniform linear array.

A novel T-shaped sensor cluster for acoustic source localization

This study proposes a new sensor cluster configuration for localizing an acoustic source in a plate using uniform linear array beamforming and T-shaped sensor clusters. This technique requires neither the properties of the plate material nor a dense array of sensors to find the direction of arrival of the acoustic source. It functions by placing four sensors in a cluster in the shape of the letter “‘T” over a small region of the plate. Uniform linear array beamforming-based source localization is carried out by the constructive interference of different sensor signals. However, this approach has the disadvantage of a very low resolution when the direction of arrival approaches certain values. The L-shaped sensor clusters use the information from the time difference of arrival between the sensors to estimate the direction of arrival, which has a high resolution in all directions except for the direction that is very close to vertical to the cluster. In this study, we numerically and experimentally demonstrate that the proposed T-shaped sensor cluster can accurately localize the acoustic source with no blind area. We also detail its superior performance compared to both uniform linear array beamforming and L-shaped clusters. In the experimental investigation, the maximum deviation of impact source localization was reduced significantly, from 54° to 4° for an aluminum plate, and from 42° to 3° for a composite plate. Furthermore, this novel combined sensor array layout requires only a few sensors, which can significantly reduce the cost of structural health monitoring practice.