An experimental comparison of the focused beamformer and the inverse method for the characterisation of acoustic sources in ideal and non-ideal acoustic environments

摘要

The current availability and affordability of multi-microphone array systems has awakened a strong interest in sound source location and characterisation in many fields of experimental acoustics and noise control. Although the theory behind the design of such arrays has been known for some time, the algorithms used to process the microphone signals are the subject of on-going research and development. The beamformer algorithm is well-known and relatively simple to implement. It is useful for 'scanning' an area to find the position of sound sources but has limited ability to characterise fully the spatial distribution of the strength of an acoustic source. The inverse method, on the other hand, requires prior information regarding the position of sources or an appropriate discretisation of the source strength distribution. The method is more difficult to implement but can yield more useful source characterisation data. This paper presents a comparison between the two methods based on experimental data. The results show that, in the presence of more than one source, the beamformer cannot yield reliable estimates of the source strength of individual sources since the output from the beamformer is shown to be dependent upon the degree of correlation between multiple sources as well as the source strengths themselves. The inverse method, on the other hand, is shown to yield reliable estimates of source strength when more than one source is present, regardless of the correlation between the sources, although the results presented here are restricted to the case of a relatively small number of sources. It is clearly demonstrated, both theoretically and through carefully controlled experiments, that either method can be used effectively under reverberant conditions through the use of measured Green functions in place of the simple geometrically-derived free-space values of the Green function. This greatly improves the possibility for the successful use of these methods in many important industrial applications.