Object Localization and Tracking System Using Multiple Ultrasonic Sensors with Newton–Raphson Optimization and Kalman Filtering Techniques

Abstract

In this paper, an object localization and tracking system is implemented with an ultrasonic sensing technique and improved algorithms. The system is composed of one ultrasonic transmitter and five receivers, which uses the principle of ultrasonic ranging measurement to locate the target object. This system has several stages of locating and tracking the target object. First, a simple voice activity detection (VAD) algorithm is used to detect the ultrasonic echo signal of each receiving channel, and then a demodulation method with a low-pass filter is used to extract the signal envelope. The time-of-flight (TOF) estimation algorithm is then applied to the signal envelope for range measurement. Due to the variations of position, direction, material, size, and other factors of the detected object and the signal attenuation during the ultrasonic propagation process, the shape of the echo waveform is easily distorted, and TOF estimation is often inaccurate and unstable. In order to improve the accuracy and stability of TOF estimation, a new method of TOF estimation by fitting the general (GN) model and the double exponential (DE) model on the suitable envelope region using Newton–Raphson (NR) optimization with Levenberg–Marquardt (LM) modification (NRLM) is proposed. The final stage is the object localization and tracking. An extended Kalman filter (EKF) is designed, which inherently considers the interference and outlier problems of range measurement, and effectively reduces the interference to target localization under critical measurement conditions. The performance of the proposed system is evaluated by the experimental evaluation of conditions, such as stationary pen localization, stationary finger localization, and moving finger tracking. The experimental results verify the performance of the system and show that the system has a considerable degree of accuracy and stability for object localization and tracking.