Resolution and quantification accuracy enhancement of functional delay and sum beamforming for three-dimensional acoustic source identification with solid spherical arrays. (1st May 2017)

• Record Type: Journal Article
• Authors: Chu, Zhigang; Yang, Yang; Shen, Linbang
• Is Part Of: Mechanical systems and signal processing. Volume 88(2017)
• Page Start: 274
• Page End: 289
• Publication Date: 2017-05-01
• Subjects: Solid spherical arrays -- Three-dimensional acoustic source identification -- Functional delay and sum -- Beamforming -- Ridge detection -- Deconvolution; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.11.027
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Functional delay and sum (FDAS) is a novel beamforming algorithm introduced for the three-dimensional (3D) acoustic source identification with solid spherical microphone arrays. Being capable of offering significantly attenuated sidelobes with a fast speed, the algorithm promises to play an important role in interior acoustic source identification. However, it presents some intrinsic imperfections, specifically poor spatial resolution and low quantification accuracy. This paper focuses on conquering these imperfections by ridge detection (RD) and deconvolution approach for the mapping of acoustic sources (DAMAS). The suggested methods are referred to as FDAS+RD and FDAS+RD+DAMAS. Both computer simulations and experiments are utilized to validate their effects. Several interesting conclusions have emerged: (1) FDAS+RD and FDAS+RD+DAMAS both can dramatically ameliorate FDAS's spatial resolution and at the same time inherit its advantages. (2) Compared to the conventional DAMAS, FDAS+RD+DAMAS enjoys the same super spatial resolution, stronger sidelobe attenuation capability and more than two hundred times faster speed. (3) FDAS+RD+DAMAS can effectively conquer FDAS's low quantification accuracy. Whether the focus distance is equal to the distance from the source to the array center or not, it can quantify the source average pressure contribution accurately. This study will be of great significance to the accurate and quick localization and quantification of acoustic sources in cabin environments. Highlights: Ridge detection and deconvolution are used to enhance FDAS's source identification capability. Without sacrificing its advantages, FDAS's resolution is dramatically ameliorated. FDAS+RD+DAMAS also can effectively compensate for FDAS's quantification deviation. FDAS+RD+DAMAS is more than two hundred times faster than conventional DAMAS. Sources in enclosed environments can be located and quantified both definitely and quickly.