Recognition of ultrasonic multi-echo sequences for autonomous symbolic indoor tracking

This paper presents an autonomous symbolic indoor tracking system for ubiquitous computing applications. The proposed approach is based upon the assumption that topologically discriminable information can be assigned explicitly to different spaces of a given indoor environment. On that assumption, continuous time-of-flight (ToF) measurements of echo-bursts obtained from four orthogonally and coplanarly mounted ultrasonic transducer are used to learn a stochastic room model. While the individual acoustic representation of space is captured using Gaussian mixture densities, the stochastic variabilities in the moving direction of a person are modeled by hidden-Markov-models (HMMs). Experiments within a six room environment resulted in a room recognition rate of 92.21% and a room sequence recognition rate of 66.00% without any pre-fixed devices.