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

Version 3 2023-07-06, 16:03
Version 2 2023-03-10, 13:55
Version 1 2023-03-10, 13:45
posted on 2023-07-06, 16:03 authored by Aidan BradleyAidan Bradley, Nicole AbaidNicole Abaid, Masoud Shirazi

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.


CAREER: Collective behavior in multi-agent systems with active sensing

Directorate for Engineering

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University Libraries, Virginia Tech

Corresponding Author Name

Nicole Abaid

Corresponding Author E-mail Address

Files/Folders in Dataset and Description

[algorithmWithData.m]- script which runs the linear minimum mean square error estimation. Outputting estimated distance (variable 'r'), state error covariance history (variable 'Pxxhist'), and the number of data points that are not used in the correction step (variable 'ignorcount'). [dataCollection.m] - script is used to collect data from the hardware and calculated the measurement matrix used in the estimation algorithm. [figureMakingCode.m] - Script which calculates results and creates figures presented in paper. [analysisSteps.mlx] - a supplementary file to help describe how general steps of analysis were performed. [] - This data set includes all the raw data of each trial run of the experiment. The main difference between this and the abridged data set is that this includes multiple variables for sound amplitude data and variables that run the data transfer from the data collection hardware to the hardware running the MATLAB scripts. In Format: 201221runX.mat where X is the trial run number from 1-9 [AbridgedData.mat] - This data set is abridged to be only the data necessary for calculating the results and creating the figures presented in the passive tracking paper. In Format: runXdata.mat where X is the trial run number from 1-9