AUTOMATIC MICROPHONE LOCALIZATION INSIDE A CAR CABIN FOR EXPERIMENTAL ACOUSTIC MODAL ANALYSIS

Interior noise comfort requirements of modern vehicles are of ever increasing importance in the automotive industry. Within this context, Acoustic Modal Analysis (AMA) plays a crucial role in the optimisation process of the acoustic parameters of car cavities. An (acoustic) modal analysis test can involve a huge amount of measurement points, particularly when the results are used for the validation and the updating of a finite element (FE) model. An important objective during the measurement setup phase is the creation of an accurate geometrical wireframe model representing the 3D positions of the microphones instrumented inside the car cabin. In current practice these positions are determined by time-consuming manual measurements, which typically yield inaccurate results. In this paper, an automatic microphone localization procedure is presented which provides more accurate 3D positions in a reduced time compared to manual measurements. The microphone localization procedure is based on multilateration, similarly as in the global positioning system. Four or more acoustic sources (anchors) are utilized for measuring the time-of-arrival (TOA) between sources and microphones. The TOA measurements can then be combined to determine the position of each microphone in three-dimensional space. However, due to the complex structure and the obstructions inside a typical car cabin, such localization procedures suffer from non-line-of-sight (NLOS) errors, i.e. situations where the signal arrives at a sensor through reflections instead of through the direct (shortest) path. In this case, some of the measured distances are significantly overestimated, which consequently leads to large 3D position errors. Hence, in this work a robust procedure is developed which detects and prunes these NLOS measurements so that the microphones are localized using the LOS distances only. The procedure will be validated by simulation experiments in a realistic scenario, i.e. a detailed 3D CAE model of an automotive interior.