Estimating ultrasonic time-of-flight through echo signal envelope and modified Gauss Newton method

Lu, Zhenkun; Yang, Cui; Qin, Donghong; Luo, Yuling; Momayez, Moe

doi:10.1016/j.measurement.2016.08.013

Cited by 28 publications

(10 citation statements)

References 16 publications

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“…For assessing the performance of the D-net in TOF estimation, the separated signals from S-net are used by comparing them with the model-based estimation method (ME) [ 18 ]. A total of 500 ultrasonic echo signals are acquired in the testing set under different SNRs, which contain 15 dB, 10 dB, 5 dB for TOF estimation.…”

Section: Tests and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In terms of the single ultrasonic echo signal, research on TOF has been carried out by researchers around the world who have created plenty of methods to tackle the issue. All existing methods can be roughly divided into two categories corresponding to the mathematical model including model-based estimation of ultrasonic echoes [16]; Kalman filtering for TOF Estimation [17]; echo signal envelope [18], etc., and to energy distribution including peak-value; information criteria [19,20]; higher-order statistics method [21]; Teager-Kaiser energy operator (TKEO) [22], etc. With the recent surge in developments of deep learning, audio signal processing has adopted this method to extensively tackle problems that were difficult to solve in the past, such as source separation, audio enhancement, and generative models for speech, sound, and music synthesis [23].…”

Section: Introductionmentioning

confidence: 99%

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A Study on Determining Time-Of-Flight Difference of Overlapping Ultrasonic Signal: Wave-Transform Network

Zhang

et al. 2020

Sensors

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An ultrasonic sensors system is commonly used to measure the wall thickness of buried pipelines in the transportation of oil and gas. The key of the system is to precisely measure time-of-flight difference (TOFD) produced by the reflection of ultrasonic on the inner and outer surfaces of the pipelines. In this paper, based on deep learning, a novel method termed Wave-Transform Network is proposed to tackle the issues. The network consists of two parts: part 1 is designed to separate the potential overlapping ultrasonic echo signals generated from two surfaces, and part 2 is utilized to divide the sample points of each signal into two types corresponding to before and after the arrival time of ultrasonic echo, which can determine the time-of-flight (TOF) of each signal and calculate the thickness of pipelines. Numerical simulation and actual experiments are carried out, and the results show satisfactory performances.

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Section: Tests and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Study on Determining Time-Of-Flight Difference of Overlapping Ultrasonic Signal: Wave-Transform Network

Zhang

et al. 2020

Sensors

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“…Estimating the TOF through parametric approximation through envelope extraction [38,40,41] has been widely used in ultrasound ranging and nondestructive testing. The received signal is usually modelled as a delayed Gaussian echo signal, and the TOF can be estimated after its envelope is extracted [42].…”

Section: Tof Estimationmentioning

confidence: 99%

Online Time-Resolved Reconstruction Method for Acoustic Tomography System

Bao

Jia

2020

IEEE Trans. Instrum. Meas.

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“…It is simple, but has low precision due to noise interference. The random signal processing method is used to estimate ultrasonic TOF in some works in the literature [ 18 , 19 ], considering environmental noise, but these algorithms are too complex for UIPSs based on WSNs. The ultrasonic signals are encoded into various digital codes, such as binary frequency shift-keyed and code division multiple access codes [ 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

A Robust High-Accuracy Ultrasound Indoor Positioning System Based on a Wireless Sensor Network

Liu

2017

Sensors

105

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This paper describes the development and implementation of a robust high-accuracy ultrasonic indoor positioning system (UIPS). The UIPS consists of several wireless ultrasonic beacons in the indoor environment. Each of them has a fixed and known position coordinate and can collect all the transmissions from the target node or emit ultrasonic signals. Every wireless sensor network (WSN) node has two communication modules: one is WiFi, that transmits the data to the server, and the other is the radio frequency (RF) module, which is only used for time synchronization between different nodes, with accuracy up to 1 μs. The distance between the beacon and the target node is calculated by measuring the time-of-flight (TOF) for the ultrasonic signal, and then the position of the target is computed by some distances and the coordinate of the beacons. TOF estimation is the most important technique in the UIPS. A new time domain method to extract the envelope of the ultrasonic signals is presented in order to estimate the TOF. This method, with the envelope detection filter, estimates the value with the sampled values on both sides based on the least squares method (LSM). The simulation results show that the method can achieve envelope detection with a good filtering effect by means of the LSM. The highest precision and variance can reach 0.61 mm and 0.23 mm, respectively, in pseudo-range measurements with UIPS. A maximum location error of 10.2 mm is achieved in the positioning experiments for a moving robot, when UIPS works on the line-of-sight (LOS) signal.

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Estimating ultrasonic time-of-flight through echo signal envelope and modified Gauss Newton method

Cited by 28 publications

References 16 publications

A Study on Determining Time-Of-Flight Difference of Overlapping Ultrasonic Signal: Wave-Transform Network

A Study on Determining Time-Of-Flight Difference of Overlapping Ultrasonic Signal: Wave-Transform Network

Online Time-Resolved Reconstruction Method for Acoustic Tomography System

A Robust High-Accuracy Ultrasound Indoor Positioning System Based on a Wireless Sensor Network

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