Sound field reconstruction is essential in applications such as spatial audio, room acoustics, and noise control. Dynamic measurements—i.e., measurements with a moving microphone or array—offer an alternative to conventional static measurements that can reduce acquisition time and cost in sound field reconstruction problems. However, existing dynamic methods often rely on the decomposition of large matrices, which limits the maximum frequency and area that can be covered by the moving microphone. In this study, we propose a matrix-free time-domain reconstruction framework for signals recorded by moving microphones. We formulate time-domain plane wave and point source expansions that avoid the need to store large matrices. In addition, we analyze the use of smoothing and sparsity-promoting regularization within the proposed matrix-free framework. Results from simulated data show that the proposed approach can extend the frequency range and reconstruction area, offering a promising direction for scalable and efficient sound field reconstruction from dynamic measurements.
Verburg et al. (2025) studied this question.