使用多超音波感測器與限制型卡曼濾波器之目標追蹤

本論文探討三維空間裡坐標定位及單一對象的追蹤，採用超音波範圍測量的原理。由於三維空間的定位應用之要求，本模型包含一個超音波發射器和五個接收器裝置在面板的邊緣。首先從每個通道的時差測距派生，然後計算出目標的坐標。時差測距會為回波形狀不一樣而有所影響，原因是傳送過程中所造成的衰減，同時也因為目標類型、大小、位置、方向。本研究提出一個時差測距估計方法解決上述的問題。這種方法提供正確且穩定的時差測距估計，使用Newton-Raphson演算法和雙指數模型，找出合理的反射區間。擴展卡爾曼濾波器的設計是為了估測目標位置，確認時差測距的干擾和異常的問題，有效的降低對目標位置的干擾程度。本系統性質是通過幾種方案的實驗結果，包含了以麥克筆固定位置、人類手指的靜止、人類手指移動的追蹤。關機詞：範圍差距、目標位置、傳感器陣列、擴展卡爾曼濾波。

關鍵字

超音波感測器與限制型卡曼濾波器之目標追蹤

並列摘要

This thesis deals with coordinate localization and tracking of single object in the three-dimensional space based on the principle of ultrasonic range measurement. Due to the requirement of localization application in three-dimensional space, our model consists of 1 ultrasonic transmitter and 5 receivers equipped on the edge of the panel. The time-of-flight (TOF) from each channel is firstly derived and then the target coordinate is obtained. TOF estimation is inaccurate due to shape distortion of echoes waveform, which is caused by attenuation during propagation and also varies with target type, size, location, and orientation. A method of TOF estimation for the above problem is presented. This method provides an accurate and steady TOF estimation by fitting the double exponential model on the reasonable region of envelope using Newton-Raphson optimization. A Constrained Extended Kalman Filter is also designed to estimate the target position inherently accounting the interference and outlier issue of the derived TOF, and effectively reduces the disturbance to target localization in critical measurement condition. The system performance was assessed through experimental evaluation of several scenarios, including localization of stationary marker pen, stationary human’s finger, and tracking on moving human’s finger. Index terms: Range-difference, Localization, Sensor array, Constrained Extended Kalman Filter.

並列關鍵字

Ultrasonic sensor, constrained kalman filter, target tracking, localization

參考文獻

[1] Juan M. Mauricio Villanueva, Sebastian Y.C. Catunda, Raimundo Carlos S. Freire, "Wind Speed Measurement based on Time-of-Flight Estimation using Extended Kalman Filter"

[2] L. Angrisani, A. Baccigalupi, and R. S. L. Moriello, "A measurement method based on Kalman filtering for ultrasonic time-of-flight estimation," IEEE Trans. Instrum. Meas., vol. 55, pp. 442-448, 2006.

[3] A. Hammad, A. Hafez, and M. T. Elewa, "A LabVIEW Based Experimental Platform for Ultrasonic Range Measurements," DSP Journal, vol. 6, pp. 1-8, 2007.

[7] D. Simon and T. Chia. Kalman filtering with state equality constraints. IEEE transactions on Aerospace and Electronic Systems, 38(1):128–136, 2002.

[9] W. Wen and H. Durrant-Whyte. Model-based multi-sensor data fusion. In IEEE conference on Robotics and Automation, pages 1720–1726, Nice, France, 1992

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使用多超音波感測器與限制型卡曼濾波器之目標追蹤

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