A Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) Determined from Phased Microphone Arrays

Abstract

Current processing of acoustic array data is burdened with considerable uncertainty. This study reports an original methodology that serves to demystify array results, reduce misinterpretation, and accurately quantify position and strength of acoustic sources. Traditional array results represent noise sources that are convolved with array beamform response functions, which depend on array geometry, size (with respect to source position and distributions), and frequency. The Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) method removes beamforming characteristics from output presentations. A unique linear system of equations accounts for reciprocal influence at different locations over the array survey region. It makes no assumption beyond the traditional processing assumption of statistically independent noise sources. The full rank equations are solved with a new robust iterative method. DAMAS is quantitatively validated using archival data from a variety of prior high-lift airframe component noise studies, including flap edge/cove, trailing edge, leading edge, slat, and calibration sources. Presentations are explicit and straightforward, as the noise radiated from a region of interest is determined by simply summing the mean-squared values over that region. DAMAS can fully replace existing array processing and presentations methodology in most applications. It appears to dramatically increase the value of arrays to the field of experimental acoustics.

Signal-source trackers on Infrared-based Dedicated Short-Range Communication

“…Time of Arrival or Time difference of Arrival (TOA/TDOA), and Angle or Direction of arrival (AOA/DOA) are techniques to measure the position of a signal source by analyzing the received signal, and are widely used in acoustics [13][14][15], radar [16], ultra-wideband (UWB) technology [17][18][19][20][21][22][23], and wireless ad hoc/sensor networks [24][25][26]. Since the communication range of IR-DSRC is in tens of meters and the wavelength of IR rays used in DSRC is about 1 lm, the corresponding electromagnetic frequency is at 300 THz.…”

Inverse problem with beamforming regularization matrix applied to sound source localization in closed wind-tunnel using microphone array

“…For the sake of conciseness, the DAMAS algorithm is briefly presented. For more information, the reader should refer to [3]. The aim of the DAMAS algorithm is to solve the inverse problemÂX ¼Ŷ where andX stand for the propagation matrix and noise source level, respectively, at each grid point.…”

“…The optimal solution of this minimization problem is obtained by setting the derivative with respect to q of the cost function equation (3) to zero [16,17] q λβ ¼ ðG H G þ λ 2 L H LÞ À 1 G H βp; (5) where ðÁÞ H is the Hermitian transpose. The solution of the minimization problem can be rewritten as…”

“…One of them is a deconvolution technique called DAMAS [3] (that has been extended to DAMAS2 [4] and DAMAS-C [5]). The aim of DAMAS is to extract a source distribution from a beamforming source map by iteratively deconvolving the map.…”

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

“…Although this method can yield higher resolution maps than the conventional focussed beamformer, so is useful for source localisation, it cannot yield information about source strength and can be very sensitive to noise and other signal errors. Some algorithms, such as DAMAS [9], use deconvolution techniques to improve the discrimination between adjacent sources and to accurately determine the position of those sources. The DAMAS algorithm seeks to answer the question: what combination of N uncorrelated monopoles sited at N beamformer map grid positions would yield the given beamformer output map?…”