scholarly journals Research on Wavelet Threshold Denoising Method for UWB Tunnel Personnel Motion Location

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Ning Liu ◽  
Ranqiao Zhang ◽  
Zhong Su ◽  
Guodong Fu ◽  
Jingang He
Keyword(s):  
Low Frequency ◽  
Threshold Function ◽  
Personal Safety ◽  
Threshold Estimation ◽  
Denoising Method ◽  
Positioning Accuracy ◽  
Motion Signal ◽  
Soft Threshold ◽  
Special Environment ◽  
Positioning Algorithm

In the process of tunnel construction, the problems of strong sealing, inconvenient communication, and harsh environment pose a serious threat to the personal safety of construction workers. Therefore, personnel positioning technology has important application value in tunnel safety construction. A special environment for tunnel personnel positioning and the ultrawideband (UWB) positioning system are affected by personnel movement, which leads to the problem of lowering positioning accuracy. A wavelet threshold denoising method for motion positioning of people in tunnels is proposed. The positioning algorithm of the method adopts a three-sided positioning algorithm based on symmetric double-sided two-way ranging. The wavelet analysis is used to decompose the motion signal of the personnel in the tunnel, and the low frequency coefficient and high frequency coefficient of the signal are decomposed to determine the influence of the motion noise of the personnel on the UWB positioning. The soft threshold function and the hard threshold function are, respectively, selected to perform wavelet threshold denoising on the motion positioning result in the tunnel. According to the denoising effect, the db5 wavelet 5-layer decomposition, under the heuristic threshold estimation criterion, the soft threshold function denoising is the best denoising method. The verification by the positioning experiment shows that the method is suitable for tunnel personnel positioning. The wavelet threshold denoising method can weaken the influence of outliers in the motion positioning of UWB personnel and improve the positioning accuracy.

scholarly journals Signal Denoising Method Based on Improved Wavelet Threshold Function for Microchip Electrophoresis C4D Equipment

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yaonan Tong ◽  
Jingui Li ◽  
Yaohui Xu ◽  
Lichen Cao
Keyword(s):  
Microchip Electrophoresis ◽  
Threshold Function ◽  
Signal Denoising ◽  
Threshold Method ◽  
Decomposition Level ◽  
Denoising Method ◽  
Capacitively Coupled ◽  
Signal Peak ◽  
Soft Threshold ◽  
Simulation Signal

A signal denoising method using improved wavelet threshold function is presented for microchip electrophoresis based on capacitively coupled contactless conductivity detection (ME-C4D) device. The evaluation results of denoising effect for the ME-C4D simulation signal show that using Daubechies 5 (db5) wavelet at a decomposition level 4 can produce the best performance. Furthermore, the denoising effect is compared with, as well as proved to be superior to, the existing techniques, such as Savitzky–Golay, Fast Fourier Transform, and soft threshold method. This method has been successfully applied to the self-developed ME-C4D equipment. After executing this method, the noise is cleanly removed, and the signal peak shape and peak area are well maintained.

scholarly journals DENOISING OF LIDAR ECHO SIGNAL BASED ON WAVELET ADAPTIVE THRESHOLD METHOD

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 215-220
Author(s):  
S. H. Long ◽  
G. Q. Zhou ◽  
H. Y. Wang ◽  
X. Zhou ◽  
J. L. Chen ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Adaptive Threshold ◽  
Threshold Function ◽  
Threshold Method ◽  
Denoising Method ◽  
Adaptive Wavelet ◽  
Signal Characteristics ◽  
Soft Threshold ◽  
Two Parameters ◽  
Unbiased Risk

Abstract. The wavelet threshold method is widely used in signal denoising. However, traditional hard threshold method or soft threshold method is deficient for depending on fixed threshold and instability. In order to achieve efficient denoising of echo signals, an adaptive wavelet threshold denoising method, absorbing the advantages of the hard threshold and the soft threshold, is proposed. Based on the advantages of traditional threshold method, new threshold function is continuous, steerable and flexibly changeable by adjusting two parameters. The threshold function is flexibly changed between the hard threshold and the soft threshold function by two parameter adjustments. According to the Stein unbiased risk estimate (SURE), this new method can determine thresholds adaptively. Adopting different thresholds adaptively at different scales, this method can automatically track noise, which can effectively remove the noise on each scale. Therefore, the problems of noise misjudgement and incomplete denoising can be solved, to some extent, in the process of signal processing. The simulation results of MATLAB show that compared with hard threshold method and soft threshold method, the signal-to-noise ratio (SNR) of the proposed de-noising method is increased by nearly 2dB, and 4dB respectively. It is safely to conclude that, when background noise eliminated, the new wavelet adaptive threshold method preserves signal details effectively and enhances the separability of signal characteristics.

Wavelet Image Denoising Based on the New Threshold Function

2013 ◽  
Vol 347-350 ◽  
pp. 2231-2235
Author(s):  
Hui Tang ◽  
Zeng Li Liu ◽  
Lin Chen ◽  
Zai Yu Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Threshold Function ◽  
Decomposition Level ◽  
Denoising Method ◽  
Global Threshold ◽  
Derivative Discontinuity ◽  
Soft Threshold ◽  
Wavelet Image ◽  
Constant Deviation

A new threshold function was proposed to overcome that hard threshold function is not continuous, soft threshold function has constant deviation and derivative discontinuity defects. It will be applied to using different thresholds denoising method with different decomposition level based on the D.J global threshold. Experimental results shows that the denoising result of new threshold function is superior to the traditional soft and hard threshold function in minimum mean square error (MSE) and peak signal to noise ratio (PSNR).

An Improvement Based on Wavelet Transform Speech Denoising Threshold Function

2014 ◽  
Vol 998-999 ◽  
pp. 828-832
Author(s):  
Wen Long Xing ◽  
Bo Hu Zhang
Keyword(s):  
Wavelet Coefficient ◽  
Smooth Curve ◽  
Threshold Function ◽  
Denoising Method ◽  
Absolute Value ◽  
Advantages And Disadvantages ◽  
Speech Denoising ◽  
Soft Threshold ◽  
Simulation Results ◽  
Constant Deviation

The wavelet threshold denoising method is widely researched and used, the hard threshold and soft threshold which exist different advantages and disadvantages are two kinds commonly used threshold function; the threshold function was improved between hard threshold and soft threshold by using the exponential function. The smooth curve is monotonically increasing, no discontinuity and thus will not produce oscillation. Overcoming shortcomings that the hard threshold function and soft threshold function to be zero in wavelet coefficient is small and soft threshold exist constant deviation, and when the absolute value is greater than the threshold coefficient, soft threshold function is disposed with the same contraction method. After the simulation results prove that compared with traditional speech enhancement algorithms, the new threshold function has been improved to a certain extent.

The Research of a Critical Threshold Denoising Method and its Parameters Selection

2013 ◽  
Vol 475-476 ◽  
pp. 263-267
Author(s):  
Qian Xiao ◽  
Yan Hui Jiang ◽  
Bin Wang ◽  
Mei Jia Liu ◽  
Mei Xia Song
Keyword(s):  
Threshold Function ◽  
Critical Threshold ◽  
Threshold Method ◽  
Simulation Experiments ◽  
Denoising Method ◽  
Parameters Selection ◽  
Soft Threshold ◽  
Selection For ◽  
Constant Deviation ◽  
Selection Of

For soft threshold function are likely to cause a constant deviation with the original signal, hard threshold function can not fully remove noise and the selection of semi threshold function parameters is complex, we presented a critical threshold function, and analyzed the parameter selection for the new threshold. The simulation experiments prove that the denoising of critical threshold method is much better, and it also can make up for the deficiencies of traditional threshold.

scholarly journals An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Min Wang ◽  
Zhen Li ◽  
Xiangjun Duan ◽  
Wei Li
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Singular Value ◽  
Denoising Method ◽  
Diagonal Part ◽  
Value Decomposition ◽  
Image Part

This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD), with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts), with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with image rotations and the enhancement of the directional features: to filter the diagonal part, one needs first to rotate it 45 degrees and rotate it back after filtering. Finally, reconstruct the image from the low-frequency part and the filtered high-frequency parts by the inverse wavelet transform to get the final denoising image. Experiments show the effectiveness of this method, compared with relevant methods.

scholarly journals Factor Graph-Assisted Distributed Cooperative Positioning Algorithm in the GNSS System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3748 ◽  
Author(s):  
Chengkai Tang ◽  
Lingling Zhang ◽  
Yi Zhang ◽  
Houbing Song
Keyword(s):  
Smart Cities ◽  
Factor Graph ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Cooperative Positioning ◽  
Measured Range ◽  
Ranging Error ◽  
Positioning Algorithm ◽  
Range Error ◽  
Cooperative Nodes

The development of smart cities calls for improved accuracy in navigation and positioning services; due to the effects of satellite orbit error, ionospheric error, poor quality of navigation signals and so on, it is difficult for existing navigation technology to achieve further improvements in positioning accuracy. Distributed cooperative positioning technology can further improve the accuracy of navigation and positioning with existing GNSS (Global Navigation Satellite System) systems. However, the measured range error and the positioning error of the cooperative nodes exhibit larger reductions in positioning accuracy. In response to this question, this paper proposed a factor graph-aided distributed cooperative positioning algorithm. It establishes the confidence function of factor graphs theory with the ranging error and the positioning error of the coordinated nodes and then fuses the positioning information of the coordinated nodes by the confidence function. It can avoid the influence of positioning error and ranging error and improve the positioning accuracy of cooperative nodes. In the simulation part, the proposed algorithm is compared with a mainly coordinated positioning algorithm from four aspects: the measured range error, positioning error, convergence speed, and mutation error. The simulation results show that the proposed algorithm leads to a 30–60% improvement in positioning accuracy compared with other algorithms under the same measured range error and positioning error. The convergence rate and mutation error elimination times are only 1 / 5 to 1 / 3 of the other algorithms.

A Localization Algorithm Research to Eliminate the Indoor NLOS Error

2014 ◽  
Vol 989-994 ◽  
pp. 2232-2236 ◽  
Author(s):  
Jia Zhi Dong ◽  
Yu Wen Wang ◽  
Feng Wei ◽  
Jiang Yu
Keyword(s):  
Indoor Environment ◽  
Arrival Time ◽  
Channel Model ◽  
Indoor Positioning ◽  
Superior Performance ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Software Simulation ◽  
Positioning Algorithm

Currently, there is an urgent need for indoor positioning technology. Considering the complexity of indoor environment, this paper proposes a new positioning algorithm (N-CHAN) via the analysis of the error of arrival time positioning (TOA) and the channels of S-V model. It overcomes an obvious shortcoming that the accuracy of traditional CHAN algorithm effected by no-line-of-sight (NLOS). Finally, though MATLAB software simulation, we prove that N-CHAN’s superior performance in NLOS in the S-V channel model, which has a positioning accuracy of centimeter-level and can effectively eliminate the influence of NLOS error on positioning accuracy. Moreover, the N-CHAN can effectively improve the positioning accuracy of the system, especially in the conditions of larger NLOS error.

An Improved Wavelet Threshold De-Noising Data Processing Method Research in Deformation Monitoring

2011 ◽  
Vol 90-93 ◽  
pp. 2858-2863
Author(s):  
Wei Li ◽  
Xu Wang
Keyword(s):  
Data Processing ◽  
Deformation Monitoring ◽  
Processing Method ◽  
Threshold Function ◽  
Wavelet Coefficients ◽  
New Approach ◽  
Data Processing Method ◽  
Soft Threshold ◽  
Fixed Bias ◽  
Better Than

Due to the soft and hard threshold function exist shortcomings. This will reduce the performance in wavelet de-noising. in order to solve this problem，This article proposes Modulus square approach. the new approach avoids the discontinuity of the hard threshold function and also decreases the fixed bias between the estimated wavelet coefficients and the wavelet coefficients of the soft-threshold method．Simulation results show that SNR and MSE are better than simply using soft and hard threshold，having good de-noising effect in Deformation Monitoring.

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