Automated classification of oncoming ground vehicles using acoustic echolocation and supervised machine learning

Abstract

In order to perform useful tasks, robots must have the ability to notice, recognize, and respond to objects in their environment. This requires the acquisition and synthesis of information from a variety of sensors. Here we focus on acoustic echolocation measurements of approaching vehicles, where an acoustic parametric array propagates an audible signal to the oncoming target and the reflected backscattered signal is recorded using the Microsoft Kinect microphone array. Although useful information about the target is hidden inside the noisy time domain measurements, the Dynamic Wavelet Fingerprint process (DWFP) is used to create a time-frequency representation of the data. Intelligent feature selection allows the creation of a small-dimensional feature vector that best differentiates between vehicle types for use in statistical pattern classification routines. Using experimentally measured data from real vehicles at 50 m, this process is able to correctly classify vehicles into one of five known classes with 94% accuracy. Fully three-dimensional simulations allow us to study the nonlinear beam propagation and interaction with real-world targets to improve classification results.