Single microphone sound source localisation in partially known environments using sequential spatial sampling

Sound source localization is a fundamental capability for environmental awareness in a wide range of applications, including automotive or automated vehicles. Microphone-array-based signal processing techniques are widely used for this task. However, achieving sufficient localization accuracy often requires a large number of microphones and wide array apertures, which can be incompatible with limited installation space and cost constraints. Moreover, standard array-processing methods often rely on free-field transfer functions. In environments with reflections, diffraction, and scattering, particularly under non-line-of-sight conditions, this mismatch can degrade both accuracy and interpretability. This paper presents a methodology for sound source localization in partially known environments that addresses these challenges by combining two ideas. First, the method reduces sensor requirements by exploiting sequential pressure measurements acquired at different spatial locations along a moving receiver trajectory. Second, environmental effects are incorporated through an approximate acoustic model derived from rough geometric cues assumed to be retrievable from visual sensing modalities. Geometric and acoustic parameters are treated as unknowns and estimated jointly with the source location, reducing the need for precise prior environmental knowledge. Numerical simulations validate the approach in two representative scenarios: (i) a single source in the presence of a wall with unknown absorbing properties and unknown distance, and (ii) a T-junction configuration where the source is not in direct line of sight. The case studies establish proof-of-concept feasibility and highlight the potential of jointly leveraging single or dual sequential measurements and approximate environmental information while maintaining low modeling and computational complexity.