scholarly journals A hybrid displacement estimation method for ultrasonic elasticity imaging

2010 ◽  
Vol 57 (4) ◽  
pp. 866-882 ◽  
Author(s):  
Lujie Chen ◽  
R James Housden ◽  
Graham M Treece ◽  
Andrew H Gee ◽  
Richard W Prager
Keyword(s):  
Estimation Method ◽  
Elasticity Imaging ◽  
Displacement Estimation ◽  
Ultrasonic Elasticity

scholarly journals Research on the estimation method of the point-of-interest (POI) displacement for ultra-precision flexible motion system based on functional optical fiber sensor

2021 ◽  
Vol 22 ◽  
pp. 48
Author(s):  
Yujie Li ◽  
Ming Zhang ◽  
Yu Zhu
Keyword(s):  
Optical Fiber ◽  
Optical Fiber Sensor ◽  
Estimation Method ◽  
Fiber Optic Sensor ◽  
Fiber Sensor ◽  
Optic Fiber ◽  
Estimation Model ◽  
Fiber Sensors ◽  
Optimal Position ◽  
Displacement Estimation

This paper proposes a POI displacement estimation method based on the functional optical fiber sensor and the phase modulation principle to improve the POI displacement estimation accuracy. First, the relation between the object deformation and the optic fiber lightwave phase is explained; the measurement principle of functional optical fiber sensor based on the heterodyne interference principle and its layout optimization method is proposed, and a POI displacement estimation model is presented based on the data approach. Secondly, a beam is taken as the simulation object, the optimal position and length of the optical fiber sensor are determined based on its simulation data. Finally, the experimental device is designed to verify the effectiveness of the POI displacement estimation method based on the optic fiber sensors. The frequency-domain plot of the signals shows that the optical fiber sensors can express the flexible deformation of the analyzed object well. The POI displacement estimation model with the fiber optic sensor signals as one of the inputs is constructed. Through estimating the test data, the error using the optical fiber sensor-based POI displacement estimation method proposed in this paper reduces by more than 61% compared to the rigid body-based assumption estimation method.

scholarly journals Dynamic Displacement Estimation for Long-Span Bridges Using Acceleration and Heuristically Enhanced Displacement Measurements of Real-Time Kinematic Global Navigation System

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5092
Author(s):  
Kiyoung Kim ◽  
Hoon Sohn
Keyword(s):  
Kalman Filter ◽  
Large Scale ◽  
Reduction Method ◽  
Estimation Method ◽  
Sampling Frequency ◽  
Displacement Measurement ◽  
Dynamic Displacement ◽  
Displacement Estimation ◽  
Gnss Receiver ◽  
Civil Infrastructures

In this paper, we propose a dynamic displacement estimation method for large-scale civil infrastructures based on a two-stage Kalman filter and modified heuristic drift reduction method. When measuring displacement at large-scale infrastructures, a non-contact displacement sensor is placed on a limited number of spots such as foundations of the structures, and the sensor must have a very long measurement distance (typically longer than 100 m). RTK-GNSS, therefore, has been widely used in displacement measurement on civil infrastructures. However, RTK-GNSS has a low sampling frequency of 10–20 Hz and often suffers from its low stability due to the number of satellites and the surrounding environment. The proposed method combines data from an RTK-GNSS receiver and an accelerometer to estimate the dynamic displacement of the structure with higher precision and accuracy than those of RTK-GNSS and 100 Hz sampling frequency. In the proposed method, a heuristic drift reduction method estimates displacement with better accuracy employing a low-pass-filtered acceleration measurement by an accelerometer and a displacement measurement by an RTK-GNSS receiver. Then, the displacement estimated by the heuristic drift reduction method, the velocity measured by a single GNSS receiver, and the acceleration measured by the accelerometer are combined in a two-stage Kalman filter to estimate the dynamic displacement. The effectiveness of the proposed dynamic displacement estimation method was validated through three field application tests at Yeongjong Grand Bridge in Korea, San Francisco–Oakland Bay Bridge in California, and Qingfeng Bridge in China. In the field tests, the root-mean-square error of RTK-GNSS displacement measurement reduces by 55–78 percent after applying the proposed method.

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