Data Collection and Analysis Scripts for "Experimental Tracking of an Ultrasonic Source with Unknown Dynamics Using a Stereo Sensor"

Data Description: This data was collected using two Dodotronic Momimic microphones and a Teensy 4.0 development board. The raw data recorded are samples of incoming ultrasonic signals output by a modified senscomp ultrasonic transducer and the measured time between recorded samples. This data is used to create a measurement matrix of the estimated bearing of the ultrasonic source and the measured time between samples. These measurements are used in a linear minimum mean square error algorithm for estimating the distance of the ultrasonic source to the sensor, allowing tracking of the source. Abstract of related Resource: Sound source localization (SSL) is the ability to successfully estimate the bearing and distance of a sound in space relative to the sensing position and pose. SSL as a topic of interest for engineers often revolves around the ability of robots to track other robots, human voices, or other acoustic objects. Common approaches to this goal frequently use large arrays, computationally intensive and complex machine learning methods, or require known dynamic models of a system which may not always be available. In this work we seek to experimentally verify a solution to SSL using a minimal amount of inexpensive equipment on a two microphone, i.e. stereo, sensing platform. A previously developed Bayesian estimator allows for localization of an emitter using easily available a priori information and timing data received from the sensor platform. Our results show that our approach is accurate for the tested paths and that the estimator can correct itself when dynamic assumptions are broken for short times due to hardware and software limitations.数据说明：本数据集采用两支Dodotronic Momimic麦克风及一块Teensy 4.0开发板采集得到。所记录的原始数据包含经改装的senscomp超声换能器输出的入射超声信号采样，以及记录采样间的时间间隔测量值。本数据集用于构建超声源估计方位与采样间测量时间间隔的测量矩阵。这些测量值可应用于线性最小均方误差算法，以估算超声源与传感器间的距离，从而实现对声源的追踪。 相关资源摘要：声源定位（Sound Source Localization, SSL）是指基于感知位置与位姿，准确估算空间中声源的方位与距离的能力。作为工程师关注的研究方向，声源定位通常围绕机器人追踪其他机器人、人类语音或其他声学目标的能力展开。实现该目标的常见方案往往需要使用大型阵列、计算量庞大且复杂度较高的机器学习方法，或是依赖系统已知的动态模型，但这类模型并非总能获取。本研究旨在基于双麦克风（即立体声）感知平台，使用少量低成本设备，对声源定位解决方案进行实验验证。此前开发的贝叶斯估计器可利用易于获取的先验信息与从感知平台接收的时间数据，实现发射源的定位。实验结果表明，本方法在测试路径下具备较高精度，且当因软硬件限制导致动态假设短期内失效时，估计器可自行完成修正。