Department of Computer Science (IIF), University of Freiburg, 79110 Freiburg, Germany.
Department of Microsystems Engineering (IMTEK), University of Freiburg, 79110 Freiburg, Germany.
Sensors (Basel). 2020 Feb 20;20(4):1177. doi: 10.3390/s20041177.
An acoustic transmitter can be located by having multiple static microphones. These microphones 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 the receiver positions. The first method uses an inertial measurement unit (IMU), in addition to the acoustic transmitter, to estimate the positions of the receivers. By using an IMU as an additional source of 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 final estimation. The second method we present in this paper consists of using machine learning to learn the TDoA signatures of certain regions of the localization area. By doing this, the target can be located without knowing where the microphones are and whether the received signals are in line-of-sight or not. We use an artificial neural network and random forest classification for this purpose.
可以通过使用多个静态麦克风来定位声学发射器。这些麦克风是同步的，并测量到达时间差 (TDoA)。通常，假设麦克风的位置是预先知道的。然而，在实践中，这意味着它们必须手动测量，这是一项繁琐的工作，容易出错。在本文中，我们提出了两种不需要手动测量接收器位置的新方法。第一种方法除了声学发射器外，还使用惯性测量单元 (IMU) 来估计接收器的位置。通过将 IMU 用作附加信息源，非凸优化器不太可能陷入局部最小值。因此，成功率提高了，并且具有较大误差的测量对最终估计的影响较小。我们在本文中提出的第二种方法包括使用机器学习来学习定位区域某些区域的 TDoA 特征。通过这样做，即使不知道麦克风的位置以及接收到的信号是否在视距内，也可以定位目标。为此，我们使用人工神经网络和随机森林分类器。
