Large region acoustic source mapping using movable arrays

Abstract

Mapping environmental noise with high resolution on a large scale (such as a city) is prohibitively expensive with current approaches, which use a large, dense array spanning the entire region of interest, or sequential noise measurements at thousands of locations on a dense grid. We propose instead a new acoustic measurement scheme using a small movable array (for example, mounted on a vehicle driving along the streets of a city) to rapidly acquire measurements at many different locations. A multiple-point sparse constrained deconvolution approach for the mapping of acoustic sources (MPSC-DAMAS) and a multiple-point covariance matrix fitting (MP-CMF) approach are developed to accurately estimate the locations and powers of stationary noise sources across the region of interest. Computer simulations of large region acoustic mapping demonstrate that superior resolution and much lower power estimation errors are achieved by the proposed approaches compared to the state-of-the-art SC-DAMAS approach and CMF approach.