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Title: Robust multi-source localisation by FOA using normalised intensity vectors: technologies, policies, and management for climate-resilient acoustic systems

Authors: Yijie Wang; Maoshen Jia

Addresses: School of Information Science and Technology, Beijing University of Technology, Beijing City,100124, China ' School of Information Science and Technology, Beijing University of Technology, Beijing City,100124, China

Abstract: First-order ambisonics (FOA) microphones have become a core hardware solution for multi-source localisation due to their compact structure and full 3D sound field capture capability. However, their localisation performance degrades in complex acoustic environments with strong interference and mixed sound sources of unequal intensity. An improved minima controlled recursive averaging (IMCRA) algorithm was introduced for adaptive noise suppression. In addition, two-dimensional Gaussian kernel density estimation (KDE) was applied to map discrete angular observations into a continuous spatial probability density surface, which improved localisation accuracy and stability. The proposed division-normalised intensity vector method exhibited strong interference resistance, compact hardware compatibility, and high localisation accuracy. The study provides technical support for advancing multi-source localisation in complex acoustic environments and offers a basis for technology selection, policy formulation, and system management in audio localisation applications.

Keywords: first-order ambisonics; FOA; acoustic environment; microphone; multi-source; intensity vector; system management; policy formulation.

DOI: 10.1504/IJETM.2026.155737

International Journal of Environmental Technology and Management, 2026 Vol.29 No.7, pp.86 - 110

Received: 02 Apr 2026
Accepted: 30 Jun 2026
Published online: 11 Aug 2026 *