Intelligent system modelling of a three-dimensional ultrasonic positioning system using neural networks and genetic algorithms

Abstract

This paper presents a neural network model for a three-dimensional ultrasonic position estimation system that uses the difference in the time of arrivals of waves from a transmitter to various receivers. Even though a linearized analytical model for the three-dimensional system exists and is currently being used to estimate the position of the transmitter, its accuracy is highly dependent on complex and time consuming signal conditioning. A neural network approach is developed to train the system based on unconditioned training sets obtained directly from the receivers. It is proposed to use the final trained system to estimate the three-dimensional position in real time using these raw signals, thereby simplifying the hardware and the computational software as well as increasing the update rate. The weights of the neural network are obtained from a traditional back-progation method and by using genetic algorithms. Results for one-, two- and three-dimensional systems are presented as proof of concept. The performance of the neural network model using the raw signals is shown to be comparable to the analytical model using conditioned signals. Further, it is shown that the neural network model is extremely robust in terms of providing accurate position estimates, even after loss of information from multiple receivers. This work has significant applications in robotics, autonomous systems, virtual reality and image-guided surgery.