Sensors, Vol. 20, Pages 1177: Acoustic Indoor Localization Augmentation by Self-Calibration and Machine Learning (Sensors)

An acoustic transmitter can be located by having multiple static microphones. Thesemicrophones are synchronized and measure the time differences of arrival (TDoA). Usually,the positions of the microphones are assumed to be known in advance. However, in practice,this means they have to be manually measured, which is a cumbersome job and is prone to errors.In this paper, we present two novel approaches which do not require manual measurement of thereceiver positions. The first method uses an inertial measurement unit (IMU), in addition to theacoustic transmitter, to estimate the positions of the receivers. By using an IMU as an additional sourceof information, the non-convex optimizers are less likely to fall into local minima. Consequently,the success rate is increased and measurements with large errors have less influence on the finalestimation. The second method we present in this paper consists of using machine learning to learnthe TDoA signatures of certain regions of the localization area. By doing this, the target can be locatedwithout knowing where the microphones are and whether the received signals are in line-of-sight ornot. We use an artificial neural network and random forest classification for this purpose.Keywords: self-calibration; localization; ultrasound; machine learning; indoor localization;