CN110956136A - Hydrophone signal denoising method based on ESMD and roughness penalty smoothing technology - Google Patents
Hydrophone signal denoising method based on ESMD and roughness penalty smoothing technology Download PDFInfo
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Abstract
The invention belongs to the field of acoustic measurement, relates to ultrasonic field measurement data processing design, and particularly relates to a hydrophone signal denoising method based on ESMD and roughness penalty smoothing technology, which comprises equipment to be detected, an oscilloscope and an upper computer, and is characterized in that: s1, acquiring acoustic signals sent by the medical ultrasonic equipment by the hydrophone, and uploading the acoustic signals to an upper computer through an oscilloscope for further processing; s2, decomposing the collected signal containing noise into a series of IMF components by using an ESMD method; s3, performing linear correlation analysis on the IMF components obtained by decomposition and the original noise-containing signals, and dividing the IMF components into IMF components with low correlation and IMF components with high correlation; s4, performing further data processing on the IMF component with high correlation through a roughness penalty technology; and S5, reconstructing the processed new IMF component to obtain a new de-noising signal.
Description
Technical Field
The invention belongs to the field of acoustic metering, relates to ultrasonic field measurement data processing design, and particularly relates to a hydrophone signal denoising method based on ESMD and roughness penalty smoothing technology.
Background
With the rapid development of ultrasound technology, ultrasound has become more and more widely used in the medical field in recent years. The application of ultrasound in medicine mainly focuses on two aspects of ultrasound diagnosis and ultrasound therapy, and the ultrasound frequencies used by the two aspects are different. Ultrasonic diagnosis adopts ultrasonic waves with higher frequency, so that the resolution of tissues can be improved, and a clearer acoustic image can be obtained; the ultrasonic treatment adopts the ultrasonic wave with lower frequency, so that the penetration rate to tissues can be increased, and a better treatment effect is achieved. Therefore, before the device is used, the sound field parameters of the medical ultrasonic medical equipment need to be accurately measured, and the parameters of the ultrasonic frequency and the like of the equipment are ensured to be in a range safe to human bodies.
At present, in the measurement of medical ultrasonic equipment, a hydrophone method is a common measurement method. The hydrophone is controlled to scan underwater through the built three-axis motion platform, ultrasonic signals are collected in real time, and then data processing is carried out in the upper computer to obtain all parameters of an ultrasonic sound field. But is disturbed by noise, such as noise in the environment and vibration generated during movement of the hydrophone. The occurrence of these noises can affect the accuracy of the signals collected by the hydrophones, and thus the final measurement results. Therefore, the signals collected by the hydrophones need to be denoised, and the accuracy of sound field parameter measurement is improved.
Disclosure of Invention
The invention aims to provide a hydrophone signal denoising method based on ESMD and roughness penalty smoothing technology aiming at the problem of noise generated in the process of measuring ultrasonic sound field parameters by a hydrophone method so as to obtain more accurate signal data.
The technical scheme adopted by the invention is as follows:
1. a hydrophone signal denoising method based on ESMD and roughness punishment smoothing technology comprises equipment to be detected, an oscilloscope and an upper computer, and is characterized in that: the method comprises the following steps:
s1, the hydrophone collects acoustic signals sent by the medical ultrasonic equipment and uploads the acoustic signals to an upper computer through an oscilloscope to be processed in the next step;
s2, decomposing the collected signal containing noise into a series of IMF components by using an ESMD method;
s3, performing linear correlation analysis on the IMF components obtained by decomposition and the original noise-containing signals, and dividing the IMF components into IMF components with low correlation and IMF components with high correlation;
s4, performing further data processing on the IMF component with high correlation through a roughness penalty technology;
and S5, reconstructing the processed new IMF component to obtain a new de-noising signal.
Further, the step 2 comprises the steps of,
s21, finding all extreme points of the noise-containing signals x (t) received by the hydrophone, including all the extreme points and the minimum points, and recording as S (t) (t is more than or equal to 1 and less than or equal to n);
s22, connecting all adjacent S (t) by line segments, marking the middle points as F (t) (1 ≦ t ≦ n-1), and respectively arranging a middle point F on the left side and the right side0And Fn；
S23, establishing p interpolation curves L by using n +1 middle points1,...,Ln(p.gtoreq.1) and calculating the average value thereof
L*(t)＝(L1+...+Ln)/p
S24 reaction of x (t) -L*(t) the sequence repeats the above steps until | L*(t) | ≦ ε (ε is the allowable error), or when the number of screenings is reached, then the first IMF component IMF is calculated1；
S25, converting the residual sequence x (t) -imf1Repeating the above four steps until the final residual sequence rn(t) is a single signal, or only one extreme remains, resulting in the remaining IMF component IMF2,...,imfn；
S26, then in a limited integer interval Kmin,Kmax]Changing the maximum value K and repeating the above five steps;
s27 calculating x (t) -rn(t) variance value σ2And draw sigma/sigma0And K, where σ0Is the standard deviation of the noisy signal x (t);
s28 in the interval [ K ]min,Kmax]In using sigma/sigma0Is given as the minimum value of0In the use of K0And repeating the first five steps and outputting all the obtained modal components.
Further, the IMF component with high correlation obtained after decomposition in step 4 is further processed by using the following formula;
in the formula: f. ofnIs the nth original signal element value; f. ofn *Is the corresponding noise reduction value; f. of*(x) Representing an estimation function; the penalty factor λ is determined by cross-validation.
The invention has the advantages and positive effects that:
in the invention, the ultrasonic signals collected by the hydrophone are processed by using the ESMD and roughness penalty smoothing technology combined denoising method, so that the noise in the ultrasonic signals is effectively removed, the sound field measurement result is more accurate, and the measurement precision is improved.
In the invention, an ESMD method is adopted in the step 2, and the whole data is optimized by using the least square idea, thereby determining the optimal screening times K0In combination with K0All modal components are output, and the denoising effect is superior to that of a single empirical mode method; in step 3, linear correlation analysis is performed on each basic mode component to distinguish an IMF component with low correlation from an IMF component with high correlation, and then in step 4, the IMF component with high correlation is further processed by applying a roughness penalty smoothing technology, so that the smoothness of a denoised signal can be effectively controlled, the adverse effects of noise suppression and signal distortion are prevented, and a better denoising effect is achieved.
Drawings
FIG. 1 is a diagram of ultrasonic signals collected by a hydrophone;
FIG. 2 is a schematic diagram of the decomposed IMF components;
FIG. 3 is a graph of a linear correlation analysis of IMF components;
FIG. 4 is a diagram of a denoised ultrasound signal.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be illustrative, not limiting and are not intended to limit the scope of the invention.
A hydrophone signal denoising method based on ESMD and roughness punishment smoothing technology comprises equipment to be detected, an oscilloscope and an upper computer, and is characterized in that: the method comprises the following steps:
s1, as shown in figure 1, the hydrophone collects the acoustic signals sent by the medical ultrasonic equipment and uploads the acoustic signals to an upper computer through an oscilloscope to be processed in the next step;
s2, decomposing the collected signal containing noise into a series of IMF components by using an ESMD method, as shown in FIG. 2, including IMF1、imf2、imf3、imf4、imf5Five components;
the step S2 includes the steps of,
s21, finding all extreme points of the noise-containing signals x (t) received by the hydrophone, including all the extreme points and the minimum points, and recording as S (t) (t is more than or equal to 1 and less than or equal to n);
s22, connecting all adjacent S (t) by line segments, marking the middle points as F (t) (1 ≦ t ≦ n-1), and respectively arranging a middle point F on the left side and the right side0And Fn；
S23, establishing p interpolation curves L by using n +1 middle points1,...,Ln(p.gtoreq.1) and calculating the average value thereof
L*(t)＝(L1+...+Ln)/p
S24 reaction of x (t) -L*(t) the sequence repeats the above steps until | L*(t) | ≦ ε (ε is the allowable error), or when the number of passes reaches a predetermined maximum value K, then the first IMF component IMF is calculated1；
S25, converting the residual sequence x (t) -imf1Repeating the above four steps until the final residual sequence rn(t) is a single signal, or only one extreme remains, resulting in the remaining IMF component IMF2,...,imfn；
S26, then in a limited integer interval Kmin,Kmax]Changing the maximum value K and repeating the above five steps;
s27 calculating x (t) -rn(t) variance value σ2And draw sigma/sigma0And K, where σ0Is the standard deviation of the noisy signal x (t);
s28 in the interval [ K ]min,Kmax]In using sigma/sigma0Is given as the minimum value of0In the use of K0And repeating the first five steps and outputting all the obtained modal components.
S3, as shown in fig. 3, performing linear correlation analysis on the 5 IMF components obtained by each decomposition and the original noisy signal, and dividing the IMF components into IMF components with low correlation and IMF components with high correlation;
s4, performing further data processing on the IMF component with high correlation through a roughness penalty technology;
in step S4, the IMF component with high correlation obtained after decomposition is further processed by using the following formula;
in the formula: f. ofnIs the nth original signal element value; f. ofn *Is the corresponding noise reduction value; f. of*(x) Representing an estimation function; the penalty factor λ is determined by cross-validation.
S5, reconstructing the processed new IMF component to obtain a new de-noising signal, as shown in fig. 4.
Claims (3)
1. A hydrophone signal denoising method based on ESMD and roughness punishment smoothing technology comprises equipment to be detected, an oscilloscope and an upper computer, and is characterized in that: the method comprises the following steps:
s1, the hydrophone collects acoustic signals sent by the medical ultrasonic equipment and uploads the acoustic signals to an upper computer through an oscilloscope to be processed in the next step;
s2, decomposing the collected signal containing noise into a series of IMF components by using an ESMD method;
s3, performing linear correlation analysis on the IMF components obtained by decomposition and the original noise-containing signals, and dividing the IMF components into IMF components with low correlation and IMF components with high correlation;
s4, performing further data processing on the IMF component with high correlation through a roughness penalty technology;
and S5, reconstructing the processed new IMF component to obtain a new de-noising signal.
2. The hydrophone signal denoising method based on ESMD and roughness penalty smoothing technology as claimed in claim 1, wherein: the step 2 comprises the steps of, in the step,
s21, finding all extreme points of the noise-containing signals x (t) received by the hydrophone, including all the extreme points and the minimum points, and recording as S (t) (t is more than or equal to 1 and less than or equal to n);
s22, connecting all adjacent S (t) by line segments, marking the middle points as F (t) (1 ≦ t ≦ n-1), and respectively arranging a middle point F on the left side and the right side0And Fn；
S23, establishing p interpolation curves L by using n +1 middle points1,...,Ln(p.gtoreq.1) and calculating the average value thereof
L*(t)＝(L1+...+Ln)/p
S24 reaction of x (t) -L*(t) the sequence repeats the above steps until | L*(t) | ≦ ε (ε is the allowable error), or when the number of passes reaches a predetermined maximum value K, then the first IMF component IMF is calculated1；
S25, converting the residual sequence x (t) -imf1Repeating the above four steps until the final residual sequence rn(t) is a single signal, or only one extreme remains, resulting in the remaining IMF component IMF2,...,imfn；
S26, then in a limited integer interval Kmin,Kmax]Changing the maximum value K and repeating the above five steps;
s27 calculating x (t) -rn(t) variance value σ2And draw sigma/sigma0And K, where σ0Is the standard deviation of the noisy signal x (t);
s28 in the interval [ K ]min,Kmax]In using sigma/sigma0Is given as the minimum value of0In the use of K0Repeating the first five steps and outputting all the obtained modulesA state component.
3. The hydrophone signal denoising method based on ESMD and roughness penalty smoothing technology as claimed in claim 1, wherein: in the step 4, the IMF component with high correlation obtained after decomposition is further processed by using the following formula;
in the formula: f. ofnIs the nth original signal element value;
is the corresponding noise reduction value; f. of*(x) Representing an estimation function; the penalty factor λ is determined by cross-validation.
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| CN114136249A (en) * | 2021-11-30 | 2022-03-04 | 国网上海市电力公司 | Novel denoising method for transformer winding deformation ultrasonic detection signal |
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| CN114136249B (en) * | 2021-11-30 | 2023-08-22 | 国网上海市电力公司 | Transformer winding deformation ultrasonic detection signal denoising method |
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