Sensor Physical Interpretation, Signal and Artificial Intelligence Processing
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Ping Chen, Xingliang He, Wei Song, "Parameter Recognition of ModeConverted Wave in SingleSource Ultrasound Using Gabor Transform for Bolt Axial Stress Evaluation", Journal of Sensors, vol. 2020, Article ID 8883845, 11 pages, 2020. https://doi.org/10.1155/2020/8883845
Parameter Recognition of ModeConverted Wave in SingleSource Ultrasound Using Gabor Transform for Bolt Axial Stress Evaluation
Abstract
In this paper, a novel parameter recognition method of modeconverted wave in singlesource ultrasound is put forward and applied to the estimation of axial stresses in bolts. To overcome the distortion and aliasing of the modeconverted wave in singlesource ultrasonic signal, a timefrequency parameter recognition method based on the Gabor transform is introduced to recognize the accurate time of flight (TOF) of the modeconverted wave. Based on the mode conversion of the singlesource longitudinal ultrasound, a new nonlinear evaluation model based on acoustoelastic equation is derived to determine the axial stress of bolt. The performance of the proposed method is evaluated by comparing it with the commonly used TOF ratio method of longitudinal wave and shear wave (LS). The experiment result shows that the proposed method is more effective in detecting the connection status of bolted joints than the traditional LS method.
1. Introduction
Loosening is one kind of failure that can markedly impact the stiffness and reliability of bolted joints. Insufficient preload of bolt will significantly increase the risk of overall mechanical structural disintegration. One way to address the critical issues in maintaining the strength of the structure and ensuring the safety of engineers is to apply appropriate pretightening force to the bolted joints and conduct longterm online monitoring of their axial stress. The most frequently used nondestructive estimation methods of stress include the photoelasticity method [1], resistance strain gauge method [2], piezoresistance method [3], active sensing method [4, 5], and ultrasonic testing method [6–9]. Among all these methods, the ultrasonic testing method is considered as a potential alternative technique due to its convenience to measure bolt tension with better accuracy.
Among the ultrasonic testing technologies, the acoustoelastic method by using TOF [6–12] is a typical stress measurement method. In the method, the TOF difference before and after bolt loading is calculated and multiplied with an acousticstress factor to achieve the axial stress. Whereas one shortcoming of this method is that it cannot be utilized to measure the stress of a fastened bolt. Yasui et al. [13] proposed the LS method based on multisource ultrasound to solve this problem, in which the stress is measured according to the TOF ratio of longitudinal wave (Lwave) and shear wave (Swave) by two separate transducers mounted on the two ends of the bolt with great care and precision. And the TOF ratio was proved to be independent of the original length of the bolt or the initial TOF. However, the coupling conditions (e.g., installation position, pressure, and coupling agent concentration) of the Lwave and Swave transducers must be repeatable to ensure accuracy. Hence, this method is difficult to achieve reliable measurement results under practical conditions. Kim and Hong [14] proposed a stress measurement method using mode conversion of longitudinal waves to reduce the experimental error caused by different contact condition. However, compared with the multisource method, owing to aliasing and distortion in the modeconverted wave signal used in Kim’s method, it’s difficult to obtain accurate TOF directly. They used a special designed probe with an acoustic lens to generate the modeconverted wave with high SNR (signalnoise ratio). In addition, it is necessary to provide enough space to install the specially designed probe, and the length of the workpiece cannot exceed the focal length of the lens. Therefore, a new parameter recognition method of the modeconverted wave in singlesource ultrasound must be studied to promote the measurement efficiency of the bolt.
In this paper, a novel axial stress evaluation method using the modeconverted wave generated by singlesource ultrasound based on the Gabor transform is proposed. Firstly, the distortion and aliasing of modeconverted process in singlesource ultrasonic longitudinal signal are analyzed. Then, the parameter recognition method based on the Gabor transform and Gaussian empirical model to obtain the accurate TOF of the modeconverted signal is introduced and its effectiveness is proved by simulation. Next, a new nonlinear model for evaluating the axial stress of bolt is derived based on the acoustoelastic equation. Finally, the performance of the proposed method is evaluated by comparing it with the commonly used TOF ratio method of longitudinal wave and shear wave. And the experiment result shows that the proposed method is more effective in detecting the connection status of bolted joints than the traditional LS method.
2. TOF Recognition Method in SingleSource Ultrasound
2.1. ModeConverted Wave in Ultrasonic Testing
The velocities and polarization directions of the Swave and the Lwave are related to the elastic properties, anisotropy, and current stress state of materials. Both the Lwave and the Swave can be transmitted in solid medium, while only the Lwave can be transmitted in fluid medium. When a longitudinal wave beam enters the solid medium from the fluid medium, it will not only be reflected at the interface but will also derive an orthogonal bipolarized transverse wave and some other waveforms near the interface, such as the Rayleigh wave and the Love wave. Similarly, when there exist ultrasound incidents from the solid medium to the fluid medium, reflection wave and orthogonal bipolarized transverse wave will occur. This phenomenon is called mode conversion. Figure 1 shows a typical mode conversion process in a monostatic pulseecho experiment, in which the red beam represents a longitudinal wave and the green beam represents a shear wave.
In Figure 1, L_{inc} represents the incident singlesource longitudinal wave generated by the transducer. At the liquidsolid interface, L_{inc} derives a reflection wave L_{refl}, a transmission Lwave (LL), and a transmission shear wave (LT). Suppose that the reflection angle of the longitudinal wave is and the angle of shear wave is , the relationship between them can be described by Snell’s law:
In equation (1), and refer to the acoustical velocities of Lwave and Swave in the solid medium, respectively. When LL and LT reach the other end of the mounted solid, the mode conversion occurs again. The LL generates the LLL wave and the LLT wave, and the LT generates the LTL wave and the LTT wave. At last, all the waves undergo mode conversion again after arriving at the left solid interface, and the part transmitted into the coupling layer will all be converted to longitudinal waves and captured by the transducer. It should be noted that the propagation distance of the LLT and the LTL is exactly the same theoretically.
The transit time of acoustical wave can be rephrased as TOF, and and denote the transit time of Lwave and Swave, respectively, in this paper. The relationship between the TOF and the sound velocity can be expressed as where is the length of the solid medium. And the TOF of the converted waves (, , , and ) in solid can be expressed as
When the incident direction is vertical to the interface, the angles and are approximately equal to zero. So, their relationship can be described as
Equations (3) and (4) can determine the positions of the converted waves in a timedomain signal approximately. In general, the LTT wave has serious attenuation in amplitude and falls in the vicinity of the second echo of LLL (because the travel speed of the Lwave is nearly two times than that of the Swave). Therefore, the LTT wave is not suitable for stress evaluation. Owing to the relatively low attenuation, it is possible for the remaining LLT wave and the LTL wave to be adopted in stress evaluation. However, their waveforms are completely overlapped in the time domain because the LLT and LTL have exactly the same TOF in theory (here we use LC to represent the overlapped modeconverted waveforms). Moreover, LC often exhibits varying degrees of nonlinear distortion and aliasing caused by coupling conditions and multiple mode conversion. As a result, the direct acquisition of the LC TOF through the original signal is almost infeasible.
2.2. Parameter Recognition Method Using the Gabor Transform
The crosscorrelation method is the most commonly used parameter recognition method to obtain the TOF of an ultrasonic signal, which can achieve high estimation accuracy under the condition of high signaltonoise ratio (SNR). However, due to the nonlinear distortion and aliasing in LC, applying the crosscorrelation method to this situation may cause extra error [15]. This paper introduces an ultrasonic signal processing method based on an empirical model [15, 16] and the Gabor transform [17] to recognize the parameter of the LC. The Gabor transform is a timefrequency transform method and widely used in signal parameter estimation for the ability of revealing the local frequency distribution of a signal or an image. From the perspective of timefrequency analysis, this paper applies the Gabor transform to estimate the parameter of the empirical model of LC. The authors use to express the signal of LC in time domain, and can be rephrased as a twodimensional timefrequency expansion by applying the Gabor transform:
In equation (5), and represent the time and frequency coordinates of the twodimensional plane, and are the width of the time and frequency grid of the plane, respectively, is the Gabor expansion coefficient, and is the Gabor generating function. What is more, is also defined as the Gabor transform (GT) of :
According to the physical characteristics of the ultrasonic transducer commonly used in bolt stress detection technology, the Gauss attenuation signal [18] is selected as the empirical model to describe LC:
In equation (7), is the amplitude and is the shape factor. Since the Gaussian function is still a Gaussian kernel function after Fourier transformation, its energy distribution is concentrated in both the frequency domain and the time domain, which can clearly reveal the local details of the signal. Considering that is a Gaussian model, an analysis window function with a Gaussian kernel is selected as the Gabor generating function:
To ensure the energy uniformity of the window function, let
Then, the Euclidean distance of Gabor generating function and the empirical model is adopted to characterize the similarity between and , which can be expressed as
In fact, the purpose of finding the maximum similarity between the Gabor generating function and the empirical model is equivalent to obtaining the minimum value of equation (10). Since and are both nonnegative and unrelated to and , the minimum value of equation (10) is transformed into the maximum value of . And can be further written as
In conclusion, the parameter recognition of the empirical model of LC can be described as
2.3. Parameter Recognition Using Particle Filter
Absolutely, the solution of equation (12) is a typical twodimensional optimization problem. Taking both estimation accuracy and convergence speed into account, the particle filter method is selected as the solver of equation (12). The basic idea of the particle filter algorithm is the Monte Carlo method, which approximates and replaces the probability density function of the system through a large number of random samples and selects a subset of particles for sequential importance sampling according to the posterior probability in order to shrink the scope of estimation [19–22]. Assume the target vector :
The parameter recognition process of the empirical model of LC based on the particle filter method is as follows: (1)Suppose the lengths of the time and the frequency axials of the Gabor expansion plane is and , respectively. Establish the initial particle set according to the number of particles :
In equation (14), refers to the target vector of each particle in the initial set and is the probability of each particle in the initial set. They can be expressed as
To ensure generality, the uniform distribution is selected as the initial importance distribution: (2)Substituting the value of and into equation (11) for each particle in the set, then the expression of can be derived. Next, we can calculate the state transition probability according to the likelihood between and . The normalized weight corresponding to the state transition probability of each particle is given by(3)Perform sequential importance resample according to the elimination threshold which equals to the average of . The particles with low weight are eliminated and those with high weight are retained and copied. After resample, the new particle set can be derived as(4)Repeat procedures (1) to (3) until the values of every particle are exactly the same as each other, and the target vector of the last set is expressed as
and are corresponding to the TOF and the central frequency of modeconverted wave LC.
2.4. Simulation
To evaluate the performance of the proposed method, a group of double Gaussian echo signals with different TOF and central frequency are introduced to simulate the modeconverted wave LC. The general expression of the signals is where and represent the amplitude of the echo envelope, and are the TOF, and are the central frequencies, and and are the shape factors. In each signal, , , , and . The values of the TOF and the central frequency of these signals are shown in Table 1. The initial set is made up of 10000 random chosen particles within the range of and ±0.5 MHz around the preset values. After 10 dB~10 dB Gaussian white noise is applied to the signals to simulate different SNR conditions, the TOFs and the central frequencies of No. 1~No. 8 signals are recognized and each signal is calculated over 100 times. The No. 1 signal with 5 dB noise is shown in Figure 2(a), and its Gabor expansion is shown in Figure 2(b). It can be observed in Figure 2(a) that the timedomain signal is severely distorted and the echoes are submerged in clutter, while the echoes can be clearly recognized in the diagram of Gabor expansion (Figure 2(b)) and the recognized results are shown in Table 2.

(a) Simulation signal
(b) Gabor expansion result

The TOF difference caused by the acoustoelastic effect in an actual measurement is generally about 1%, while for the proposed method the measurement error of TOF is about 0.5% in average under the condition of low SNR, as shown in Table 2. Therefore, the parameter recognition using the Gabor transform is efficient and can be applied to the TOF recognition of modeconverted wave in singlesource ultrasound.
3. Nonlinear Evaluation Model of Bolt Axial Stress
3.1. Nonlinear Evaluation Model Based on ModeConverted Ultrasound
The existing evaluation models [6–14] of bolt axial stress are simplified and linear. And it is inevitable for them to induce extra fitting error. In this part, a new nonlinear evaluation model of bolt axial stress is proposed, its nonlinear feature is much better to meet the characteristic of acoustic elastic effect in theory. According to the theory of elasticity and nonlinear acoustic [23], where and are the velocities of longitudinal wave and shear wave in a solid medium under stress, respectively; and are their velocities in the stressfree state; and are the Lame coefficients; and are the thirdorder elastic constants and is the density of the solid. Equation (21) can be rewritten as where
The propagation speed of elastic waves will change along with the stress status of the medium on the basis of equation (22), while in practical measurement, because the change of sound velocity is too weak to detect, TOF is often frequently used instead of velocity. Figure 3 shows a typical bolt connection structure, in which and are the equivalent stress length and overall length of the bolt, respectively. Since the stress distribution in a loaded bolt is inhomogeneous, the bolt can be simplified to an axisymmetric cylinder with uniform stress distribution, in which represents the length of the cylinder. Then, the TOF can be expressed as where and are the TOF of Lwave and Swave, respectively. Squaring the quotient of by , we can obtain
Let
Then, equation (25) can be simplified as
Since the TOF of LC can be expressed as
the TOF ratio of LC wave to LLL wave (LCL method) is derived as
Finally, the axial stress can be calculated by the following nonlinear model:
Based on the derivations above, as long as the coefficients related to material properties and the TOF ratio are known, the axial stress of the bolt can be determined.
3.2. Flowchart of Stress Evaluation
The process of the measurement and calibration methods of bolt axial stress using singlesource modeconverted ultrasound is shown in Figure 4. (1)Mill the head of the tested bolt flat and mount a longitudinal transducer on it properly to ensure stable coupling status(2)Obtain the original signal by conducting a monostatic pulseecho experiment, and then resample the signal for at least 100 times to get the statistical data of the signal(3)Extract waveform of LLL () and LC () of each signal by a rectangular window (4)Utilize the Gabor transform to get the Gabor expansion of and (5)Calculate the TOF of LC and LLL by the particle filter optimization method(6)Obtain the value of of each signal by dividing the TOF of LC to the TOF of LLL(7)Average the statistical and get the best estimate value to reduce the random errors(8)Change the load of the tested bolt and repeat operations 2–6 for at least 10 times to complete the calibration process of the tested bolt(9)Correlate the stress and to get the calibration data. Then, nonlinear fitting is conducted to estimate the value of , , and in equation (30) to get the evaluation model of axial stress of the bolt(10)Evaluate the axial stress by the nonlinear evaluation model
4. Experiment
4.1. Setup
The JSR PRC50 ultrasonic transceiving card with gain range from 14 dB to 60 dB is selected as the actuator for the ultrasonic transducers. And the ADlink PCIe9852 oscilloscope with sampling rates upper to 200 MHz is selected as the data acquisition hardware. They are integrated into a portable industrial computer, as shown in Figure 5. Then, the ultrasonic calibration/measurement software to implement excitation, acquisition, and processing of ultrasonic signals is programmed based on LABVIEW. The Dakota PT7 magnetic longitudinal ultrasonic transducer is chosen as the Lwave source and an Olympus V156RM transverse ultrasonic transducer is chosen as the Swave source. The central frequency of both probes is 5 MHz. Two types of bolts with different specifications and materials are selected as the test samples. The specification of the specimens is listed in Table 3. The CTM2200S tensile testing machine is utilized to simulate the loading condition with the maximum load 2000 kg and load accuracy ±0.1%.

4.2. TOF Estimation
Figure 6 shows the ultrasonic signal collected in sample excited by the PT7 Lwave transducer. According to equations (3) and (4), the waveforms of LLL and LC can be recognized obviously, as shown in Figure 6. And the TOF of LLL can almost be obtained directly because its waveform is clearly a single sharp pulse. While the waveform of LC is consisted of several peaks with similar amplitudes and different phases, its TOF cannot be directly determined. Figure 7 shows the waveform of LC intercepted from the original signal and its Gabor expansion. From Figure 7(b), it can be observed that there is a sharp peak in the diagram of Gabor expansion, and the TOF of the intercepted waveform can be obtained by the proposed particle filter method.
(a) LC waveform
(b) Gabor expansion of LC
In order to eliminate the influence of temperature on the sound velocity, the whole experiment is carried out under constant temperature (20°C). The loading range is 0190 MPa and the loading step is 10 MPa. The elongation of TOF caused by the acoustoelastic effect is around 1 nanosecond per MPa, but the minimum sampling period of PCIe9852 is 5 ns. There is a certain probability that the change of the signal cannot be collected intact, so the onedimensional fast Fourier interpolation method is introduced to enhance the sample rate. After being processed by tentime upsampling, the time resolution of the system is upgraded to 0.5 ns. The estimated TOFs under different stress status using the proposed method are shown in Tables 4 and 5, in which is the time ratio of longitudinal wave (L) to shear wave (S) and is the time ratio of converted wave (LC) to longitudinal wave.


4.3. Calibration/Measurement
The axial stress calibration tests of bolts are carried out using the LS method and the LCL method, respectively. Figure 8 shows the calibration curves obtained from nonlinear fitting by LevenbergMarquardt method. It can be clearly observed that the results of the LCL method are significantly better than the results of the LS method from Figure 8. The correlation coefficients between the fitting model and original data of the LCL method are 0.9957 for sample and 0.9994 for sample . The correlation coefficients of the LS method are 0.9487 for sample and 0.94664 for sample .
(a) Sample result of the LS method
(b) Sample result of the LCL method
(c) Sample result of the LS method
(d) Sample result of the LCL method
Another two bolts exactly the same as the samples are chosen to test the validity of the proposed method. After loading by the tensile testing machine at the step of 20 MPa, the TOFs under different stress status using the proposed method are recognized. And then, the LevenbergMarquardt models of calibration results are utilized to evaluate the stress of bolts. The evaluation results are shown in Tables 6 and 7 and Figure 9. In Figure 9, the yellow bar represents the absolute error using the proposed method and the blue bar refers to the absolute error of the LS method. According to the results, the average evaluation errors of the LCL method and the LS method for sample are 1.789% and 4.671%, respectively. And the average errors of sample are 2.582% and 4.634%. As a result, the proposed method has a higher accuracy as it utilizes a single longitudinal ultrasound. However, the accuracy of the LS method is relatively low for the difference of the coupling conditions of Lwave and Swave transducers.


(a) Error of the sample
(b) Error of the sample
5. Conclusions
In order to determine the parameters of modeconverted wave in singlesource ultrasound for bolt stress evaluation, a timefrequency parameter recognition method based on the Gabor transform and a new bolt axial stress evaluation model are proposed in this paper. After analyzing the propagation and the mode conversion process of ultrasonic longitudinal wave in mounted solid, a parameter recognition method based on the Gabor transform to achieve the accurate TOF of the modeconverted wave is introduced to overcome the distortion and aliasing in singlesource ultrasonic signal and its effectiveness is proved by simulation. Based on the mode conversion of singlesource ultrasound and the acoustoelastic effect, a nonlinear evaluation model of the axial stress is put forward. The proposed method is compared with the commonly used LS method by a series of calibrations and experiments. And the experiment results show that the proposed method can effectively detect the bolt axial stress ranging from 20 MPa to 200 MPa with a higher accuracy. As a result, the proposed method is more efficient and applicable in detecting the connection status of bolted joints than the traditional LS method.
Data Availability
The measurement data used to support the findings of this study are included within the article.
Conflicts of Interest
The authors declare no conflict of interest.
Acknowledgments
This research was funded by the National Natural Science Foundation of China (grant number 51374264) and the Science and Technology Major Project of Chongqing (grant number cstc2018jszxcyztzxX0032).
References
 L. U. Peng, Y. U. Le, L. Hua et al., “Research on screw bolt’s pretightening force by coating photoelasticity,” Chinese quarterly of mechanics, vol. 2, pp. 275–278, 2013. View at: Google Scholar
 X. Wang, Q. Sun, F. Zhang, J. Duan, H. Liang, and H. Wang, “Design on static resistance strain gauge of mine roof bolt based on ATMega16L,” Journal of Liaoning Technical University (Natural Science), vol. 2, pp. 288–292, 2011. View at: Google Scholar
 Z. G. Guo and Z. Sun, “Piezoelectric impedance based prestress force monitoring for PSC beam,” Advanced Materials Research, vol. 255260, pp. 742–746, 2011. View at: Publisher Site  Google Scholar
 J. Xu, C. Wang, H. Li, C. Zhang, J. Hao, and S. Fan, “Health monitoring of bolted spherical joint connection based on active sensing technique using piezoceramic transducers,” Sensors, vol. 18, no. 6, p. 1727, 2018. View at: Publisher Site  Google Scholar
 F. Wang, Y. J. Xu, L. Wu, L. Li, D. Liu, and L. Zhu, “A PEFKS and CPABEbased distributed security scheme in interestcentric opportunistic networks,” International Journal of Distributed Sensor Networks, vol. 2013, 10 pages, 2013. View at: Publisher Site  Google Scholar
 S. G. Joshi and R. G. Pathare, “Ultrasonic instrument for measuring bolt stress,” Ultrasonics, vol. 22, no. 6, pp. 261–269, 1984. View at: Publisher Site  Google Scholar
 J. S. Heyman and E. Churn, “Ultrasonic measurement of axial stress,” Journal of Testing and Evaluation, vol. 10, no. 5, pp. 202–211, 1992. View at: Google Scholar
 D. I. Crecraft, “The measurement of applied and residual stresses in metals using ultrasonic waves,” Journal of Sound and Vibration, vol. 5, no. 1, pp. 173–192, 1967. View at: Publisher Site  Google Scholar
 N. Y. Nikitina and L. A. Ostrovsky, “An ultrasonic method for measuring stresses in engineering materials,” Ultrasonics, vol. 35, no. 8, pp. 605–610, 1998. View at: Publisher Site  Google Scholar
 A. C. Holt, B. Cunningham, G. C. Johnson, and D. Auslander, “An ultrasonic technique for axial boltstress determination,” Review of Progress in Quantitative Nondestructive Evaluation, pp. 1549–1557, 1987. View at: Publisher Site  Google Scholar
 D. E. Bray, “Application of ultrasonic stress measurement to engineering components,” Journal of the Acoustical Society of America, vol. 102, no. 5, pp. 3082–3082, 1997. View at: Publisher Site  Google Scholar
 H. U. Youbin, X. U. Yong, and Z. Jianmin, “Application of ultrasonic measurement technique for bolt stress to tightening of mill shell,” Mining & Processing Equipment, vol. 8, pp. 112–116, 2012. View at: Google Scholar
 H. Yasui, H. Tanaka, I. Fujii, and K. Kawashima, “Ultrasonic measurement of axial stress in short Bolts with consideration of nonlinear deformation,” JSME International Journal Series A Solid Mechanics and Material Engineering, vol. 42, no. 1, pp. 111–118, 1999. View at: Publisher Site  Google Scholar
 N. Kim and M. Hong, “Measurement of axial stress using modeconverted ultrasound,” NDT & E International, vol. 42, no. 3, pp. 164–169, 2009. View at: Publisher Site  Google Scholar
 R. Demirli and J. Saniie, “Modelbased estimation of ultrasonic echoes part I: analysis and algorithms,” IEEE Transaction Ultrasonic, Ferro Electrics and Frequency Control, vol. 48, no. 3, pp. 787–802, 2001. View at: Publisher Site  Google Scholar
 L. Angrisani, A. Baccigalupi, and R. SchianoLoMoriello, “A measurement method based on Kalman filtering for ultrasonic timeofflight estimation,” IEEE Transactions on Instrumentation and Measurement, vol. 55, no. 2, pp. 442–448, 2006. View at: Publisher Site  Google Scholar
 Z. K. Lu, C. Yang, and J. W. Wang, “Gabor transform based timefrequency estimation of ultrasonic echo signal,” Journal of Electronics & Information Technology, vol. 3, pp. 652–657, 2013. View at: Google Scholar
 D. W. Wang, Z. B. Wang, Y. X. Chen et al., “Ultrasonic echo processing method based on dualGaussian attenuation model,” Acta Physica Sinica, vol. 8, pp. 168–176, 2019. View at: Google Scholar
 H. Huang, W. Li, D. A. Luo, D. W. Qiu, and Y. Gao, “An improved particle filter algorithm for geomagnetic indoor positioning,” Journal of Sensors, vol. 2018, 9 pages, 2018. View at: Publisher Site  Google Scholar
 M. Mishra, J. Odelius, A. Thaduri, A. Nissen, and M. Rantatalo, “Particle filterbased prognostic approach for railway track geometry,” Mechanical systems and signal processing, vol. 96, pp. 226–238, 2017. View at: Publisher Site  Google Scholar
 K. Li and L. Chang, “Robust Gaussian particle filter based on modified likelihood function,” IET Science, Measurement & Technology, vol. 12, no. 1, pp. 132–137, 2018. View at: Publisher Site  Google Scholar
 Y. Kim, W. Chung, and D. Hong, “Indoor parking localization based on dual weighted particle filter,” International Journal of Precision Engineering & Manufacturing, vol. 19, no. 2, pp. 293–298, 2018. View at: Publisher Site  Google Scholar
 J. L. Rose and P. B. Nagy, “Ultrasonic waves in solid media,” Journal of the Acoustical Society of America, vol. 107, no. 4, pp. 18071808, 2000. View at: Publisher Site  Google Scholar
Copyright
Copyright © 2020 Ping Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.