CN117250603B - Multichannel entropy detection method for underwater weak target signal - Google Patents
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Abstract
The invention discloses a multichannel entropy detection method of an underwater weak target signal, which belongs to the technical field of signal detection and comprises the following steps: according to multichannel data of underwater weak target signals, carrying out coarse graining treatment with the same time scale, and carrying out phase space reconstruction to obtain embedding dimension ofMCalculating to obtain the average probability of the phase space; changing the embedding dimension of the phase space to obtain the average probability of the phase space with the changed embedding dimension, and calculating the entropy value IMMSE of the multiple channels according to different average probabilities; changing the coarse-grained time scale to obtain different coarse-grained time scale entropy values IMMSE, analyzing the entropy values, and finishing detection of underwater weak target signals. The invention can detect weak target signals in water in high and low signal to noise ratio environment, effectively extract the correlation information among channels, and can keep the stability of algorithm when processing actual complex data, thereby having higher accuracy and stability.
Description
Technical Field
The invention belongs to the field of signal detection, and particularly relates to a multichannel entropy detection method for an underwater weak target signal.
Background
The ocean environment is extremely complex, and signal detection is usually carried out in an environment with low signal-to-noise ratio; the traditional detection method has lower accuracy in detection under the environment with low signal-to-noise; in recent years, detection methods based on nonlinear theory such as chaotic oscillator, stochastic resonance and the like can theoretically meet the detection requirement of weak signals in a low signal-to-noise ratio environment, but the detection result in actual application is not satisfactory; therefore, there is an urgent need for a method for detecting underwater acoustic signals that can accurately find a target under a low signal-to-noise ratio condition.
Entropy is a nonlinear feature quantity and has been widely used in the fields of feature extraction, object classification, identification, and the like. In recent years, with the advent of multi-channel entropy algorithms, the multi-channel entropy algorithms have a good ability to extract correlations between different channel data. Background noise within individual channels is generally considered uncorrelated in the underwater acoustic array signal processing. The target signals in different channels are usually only different by a certain time delay, and have strong correlation. Therefore, the invention utilizes the capability of extracting the correlation information among channels by utilizing the multi-channel entropy algorithm to realize the detection of the underwater weak target signal.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the multichannel entropy detection method for the underwater weak target signal, which can effectively extract the correlation information among channels, can keep the stability of an algorithm when processing actual complex data, and has higher accuracy.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a multichannel entropy detection method of underwater weak target signals comprises the following steps:
step S1: according to the inclusionCarrying out coarse graining treatment on the data of each channel with the same time scale to obtain coarse graining multichannel data;
step S2: according to the coarsened multichannel data, carrying out phase space reconstruction on the data to obtain an embedding dimension ofMIs a phase space of (2);
step S3: based on embedding dimensionMChebyshev distance of any two vectors in the phase space of (2), and calculating to obtain the embedding dimension asMAverage probability of phase space of (a);
step S4: is thatEach of the channelsThe number of elements of the individual channels is increased by 1, the embedding dimension of the phase space is then from +.>Becomes as followsAnd calculate the embedding dimension to be +.>Average probability of phase space of (a);
step S5: based on embedding dimensionMAverage probability and embedding dimension of phase space of (2)Calculating the average probability of the phase space of the multi-channel to obtain an entropy value IMMSE of the multi-channel;
step S6: changing the coarse-grained time scale, calculating to obtain different coarse-grained time scale entropy values IMMSE, and detecting the underwater weak target signal according to the different coarse-grained time scale entropy values IMMSE.
The beneficial effects of the invention are as follows: the invention provides a multichannel entropy detection method for an underwater weak target signal, which is characterized in that an entropy value IMMSE curve result graph is drawn by utilizing a multichannel multi-scale sample entropy detection method, so that the correlation information among channels is effectively extracted, the stability of an algorithm can be kept when complex data are processed, the accuracy and the stability are higher, and the weak target signal can be detected under the condition of complex environment.
Further: the expression of the coarse graining treatment in the step S1 is as follows:
wherein,for coarsened multichannel data +.>Data points of the data of each channel->Multichannel data for underwater weak target signals, < +.>Is the label of each vector in the phase space, +.>Is->Reference numerals when summing the elements in +.>For the time scale>Is->The%>The elements.
The above-mentioned further beneficial effect does: through coarse graining of the time scale, the data can be effectively analyzed from different time scales, so that the data analysis is more perfect, and a more comprehensive analysis result can be obtained when complex signals are analyzed.
Further: the expression of the phase space in the step S2 is as follows:
wherein,for the phase space>For embedding dimension vectors>Is a time delay vector, +.>Are respectively->Embedding dimension corresponding to each channel, < >>Are respectively->Time delay corresponding to each channel,/->Is the label of each vector in the phase space, +.>For the number of channels>To embed dimension vector->Elements of the individual channels->Is the +.>Elements of the individual channels->Is->No. H of the individual channels>Element(s)>Is->No. H of the individual channels>Element(s)>Is->No. H of the individual channels>The elements.
The above-mentioned further beneficial effect does: the phase space reconstruction is carried out on the multi-channel data after coarse graining to obtain Gao Weixiang space, and the analysis and research on complex data can be carried out more conveniently by researching the data of the reconstructed phase space.
Further: the specific steps of the step S3 are as follows:
step S31: based on embedding dimensionMThe Chebyshev distance of any two vectors in the phase space is calculated to obtain the Chebyshev distance between a certain vector in the phase space and the vector except the current vector;
step S32: counting to obtain the number of vectors, the Chebyshev distance between a certain vector and the vectors except the current vector in the phase space of which is smaller than a preset threshold value;
step S33: according to the number of the vectors obtained by statistics, calculating the probability that the Chebyshev distance between a certain vector and the vector except the current vector in the phase space is smaller than a preset threshold value;
step S34: traversing all vectors in a phase space to obtain the probability corresponding to each vector;
step S35: according to the probability corresponding to each vector, calculating to obtain the embedding dimension asMIs a mean probability of phase space of (a).
The above-mentioned further beneficial effect does: and the average probability of the phase space is calculated, so that the subsequent calculation of the phase space entropy value IMMSE is facilitated.
Further: the expression of chebyshev distance in step S31 is as follows:
wherein,and->Two vectors in phase space, +.>Is vector quantityAnd->Chebyshev distance of->Is natural number (i.e.)>And->Are all the labels of the vectors in the phase space, < >>For vector->Middle->Element(s)>For vector->Middle->Element max []To take the maximum value.
The above-mentioned further beneficial effect does: the similarity between any two vectors in the phase space can be calculated by a Chebyshev distance calculation mode.
Further: the probability in step S33 is expressed as follows:
wherein,for the probability of a vector in phase space having a Chebyshev distance from a vector other than the current vector smaller than a preset threshold value, +.>For the number of vectors in the phase space, +.>The number of vectors is that the Chebyshev distance between a certain vector and the vector except the current vector in the phase space is smaller than a preset threshold value.
The above furtherThe beneficial effects of (a) are as follows: the distance between the preset vector and the rest vectors in the phase space can be calculated to be smaller than a preset value by the expressionThe probability of (2) is +.>The distance between all vectors in the subsequent calculated phase space is convenient to be smaller than a preset threshold value +.>Is a probability of (2).
Further: the expression of the average probability in step S35 is as follows:
wherein,for embedding dimension +.>Average probability of phase space of>As the number of vectors in the phase space,the probability of a vector in the phase space having a chebyshev distance from a vector other than the current vector less than a preset threshold.
The above-mentioned further beneficial effect does:for embedding all vectors in the phase space with dimension M, the inter-Chebyshev distance is smaller than a preset threshold +.>By characterizing the similarity between vectors in phase space.
Further: the expression of the entropy value IMMSE in step S5 is as follows:
wherein,is entropy value (L)>To embed dimension +.>Is a time delay vector, +.>For a preset threshold value->For the length of the multichannel data, +.>Is natural logarithmic and is->For embedding dimension +.>Average probability of phase space of>For embedding the average probability of the phase space of dimension M,pis the number of channels.
The above-mentioned further beneficial effect does: and calculating entropy values IMMSE of phase spaces with different dimensions by using an improved multi-channel entropy IMMSE algorithm, and detecting the underwater weak target signal by judging whether the entropy values IMMSE are wholly reduced.
Further: the specific step of detecting the underwater weak target in the step S6 is as follows:
a1: drawing to obtain an entropy value IMMSE curve result graph by taking a time scale as an abscissa and a coarse-grained time scale entropy value IMMSE as an ordinate;
a2: calculating and obtaining an entropy value IMMSE curve of pure noise according to the entropy value IMMSE curve result diagram;
a3: calculating and acquiring an entropy value IMMSE curve of the underwater weak target signal according to the entropy value IMMSE curve result graph;
a4: judging whether the entropy value IMMSE curve of the underwater weak target signal has integral descending condition compared with the entropy value IMMSE curve of pure noise, if so, indicating that the underwater weak target signal exists, otherwise, the underwater weak target signal does not exist.
The above-mentioned further beneficial effect does: by drawing the IMMSE curve result graph, the change condition of the entropy value IMMSE of the multi-channel data can be visually represented, and the weak target signal can be further judged and detected.
Drawings
FIG. 1 is a flow chart of a multi-channel entropy detection method of underwater weak target signals;
FIG. 2 is a graph of the result of entropy IMMSE extraction of inter-channel correlation information;
fig. 3 is a graph showing the results of calculating a target signal with high signal-to-noise ratio, a target signal with low signal-to-noise ratio, and three different data types by using the entropy value IMMSE.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, the invention provides a multichannel entropy detection method of an underwater weak target signal, which comprises the following steps:
step S1: according to the inclusionCarrying out coarse graining treatment on the data of each channel with the same time scale to obtain coarse graining multichannel data;
step S2: according to the coarsened multichannel data, carrying out phase space reconstruction on the data to obtain an embedding dimension ofMIs a phase space of (2);
step S3: based on embedding dimensionMChebyshev distance of any two vectors in the phase space of (2), and calculating to obtain the embedding dimension asMAverage probability of phase space of (a);
step S4: is thatThe number of elements per channel in the number of channels is increased by 1, the embedding dimension of the phase space is from +.>Becomes as followsAnd calculate the embedding dimension to be +.>Average probability of phase space of (a);
step S5: based on embedding dimensionMAverage probability and embedding dimension of phase space of (2)Calculating the average probability of the phase space of the multi-channel to obtain an entropy value IMMSE of the multi-channel;
step S6: changing the coarse-grained time scale, calculating to obtain different coarse-grained time scale entropy values IMMSE, and detecting the underwater weak target signal according to the different coarse-grained time scale entropy values IMMSE.
Step S1 of multichannel data of weak underwater target signals，/>，/>WhereinNFor the data length of each channel,pis the number of channels; the coarsened multichannel data of step S1 +.>，，/>Wherein->Is->Whole-down integer, at the same time +.>For the coarsened data length, n is the time scale.
The expression of the coarse graining treatment in step S1 is as follows:
wherein,for coarsened multichannel data +.>Data points of the data of each channel->Multichannel data for underwater weak target signals, < +.>Is the label of each vector in the phase space, +.>Is->Reference numerals when summing the elements in +.>For the time scale>Is->The%>The elements.
According to the expression, the multi-dimensional time sequence is subjected to coarse graining treatment, and data can be effectively analyzed from different time scales through coarse graining of different time scales, so that data analysis is more perfect, and a better analysis result can be obtained when complex signals are analyzed.
The expression of the phase space in step S2 is as follows:
wherein,for the phase space>For embedding dimension vectors>Is a time delay vector, +.>Are respectively->Embedding dimension corresponding to each channel, < >>Are respectively->Time delay corresponding to each channel,/->Is the label of each vector in the phase space, +.>For the number of channels>To embed dimension vector->Elements of the individual channels->Is the +.>Elements of the individual channels->Is->No. H of the individual channels>Element(s)>Is->No. H of the individual channels>Element(s)>Is->No. H of the individual channels>The elements.
For multi-channel data after coarse grainingReconstructing the phase space to obtain an embedding dimension ofMIs a phase space of (a)By studying the reconstructed phase space data, complex data can be better analyzed and studied.
The specific steps of the step S3 are as follows:
step S31: based on embedding dimensionMThe Chebyshev distance of any two vectors in the phase space is calculated to obtain the Chebyshev distance between a certain vector in the phase space and the vector except the current vector;
step S32: counting to obtain the number of vectors, the Chebyshev distance between a certain vector and the vectors except the current vector in the phase space of which is smaller than a preset threshold value;
step S33: according to the number of the vectors obtained by statistics, calculating the probability that the Chebyshev distance between a certain vector and the vector except the current vector in the phase space is smaller than a preset threshold value;
step S34: traversing all vectors in a phase space to obtain the probability corresponding to each vector;
step S35: according to the probability corresponding to each vector, calculating to obtain the embedding dimension asMIs a mean probability of phase space of (a).
The expression of chebyshev distance in step S31 is as follows:
wherein,and->Two vectors in phase space, +.>Is vector quantityAnd->Chebyshev distance of->Is natural number (i.e.)>And->Are all the labels of the vectors in the phase space, < >>For vector->Middle->Element(s)>For vector->Middle->Element max []To take the maximum value.
The similarity between any two vectors in the phase space can be calculated through the Chebyshev distance calculation expression, and the Chebyshev distance of any two vectors is calculatedSelect phase space +.>Counting to obtain that the Chebyshev distance between the vector and other vectors is smaller than a preset threshold valueRVector number +.>Further calculate +.>The occurrence probability is->；
The expression of the probability in step S33 is as follows:
wherein,is a certain one in the phase spaceProbability of a vector being less than a preset threshold from the chebyshev distance of the vector other than the current vector, +>For the number of vectors in the phase space, +.>The number of vectors is that the Chebyshev distance between a certain vector and the vector except the current vector in the phase space is smaller than a preset threshold value.
The expression of the average probability in step S35 is as follows:
wherein,for embedding dimension +.>Average probability of phase space of>As the number of vectors in the phase space,the probability of a vector in the phase space having a chebyshev distance from a vector other than the current vector less than a preset threshold.
The expression of the entropy value IMMSE in step S5 is as follows:
wherein,is entropy value (L)>To embed dimension +.>Is a time delay vector, +.>For a preset threshold value->For the length of the multichannel data, +.>Is natural logarithmic and is->For embedding dimension +.>Average probability of phase space of>For embedding the average probability of the phase space of dimension M,pis the number of channels.
The specific steps for detecting the underwater weak target in the step S6 are as follows:
a1: drawing to obtain an entropy value IMMSE curve result graph by taking a time scale as an abscissa and a coarse-grained time scale entropy value IMMSE as an ordinate;
a2: calculating and obtaining an entropy value IMMSE curve of pure noise according to the entropy value IMMSE curve result diagram;
a3: calculating and acquiring an entropy value IMMSE curve of the underwater weak target signal according to the entropy value IMMSE curve result graph;
a4: judging whether the entropy value IMMSE curve of the underwater weak target signal is reduced compared with the entropy value IMMSE curve of the pure noise, if so, indicating that the underwater weak target signal exists, otherwise, not exists.
As shown in fig. 2, the abscissa is the time scale of different coarse grains, the ordinate is the entropy value IMMSE, and fig. 2 is a result data curve of respectively calculating correlated gaussian white noise between channels and uncorrelated gaussian white noise between channels by using the entropy value IMMSE; and independently and repeatedly generating 10 times of noise data, calculating a corresponding IMMSE entropy value, calculating to obtain the mean value and the variance of the 10 times of entropy value results, and drawing an IMMSE result graph. When correlation exists between channels, the overall structure of the IMMSE curve is not changed greatly, and the IMMSE curve has a monotonically decreasing trend. As shown in fig. 2, the entropy value of the IMMSE curve of the inter-channel correlated gaussian white noise is reduced over all scales, i.e., the inter-channel correlation information can be effectively extracted using the IMMSE algorithm, compared to the IMMSE curve of the inter-channel uncorrelated gaussian white noise.
As shown in fig. 3, the abscissa is the different coarse-grain time scales, the ordinate is the entropy value IMMSE, and fig. 3 is the result curves of three different types of data, namely, the target signal with pure gaussian white noise and high signal-to-noise ratio and the target signal with low signal-to-noise ratio are respectively calculated by using the entropy value IMMSE; in the target signal with high signal-to-noise ratio, the IMMSE curve of the underwater weak target signal is obviously reduced as a whole, meanwhile, the integral structure of the curve is changed compared with pure Gaussian white noise, and the pure noise is separated from the underwater weak target signal, namely, the underwater weak target signal can be effectively detected under the condition of high signal-to-noise ratio by using an IMMSE algorithm; in the target signal with low signal-to-noise ratio, the underwater weak target signal is completely submerged in noise when being observed from a time domain, but the target signal curve with low signal-to-noise ratio is observed from an IMMSE result curve, and is still obviously reduced compared with the IMMSE result curve of pure Gaussian white noise. Because the result of the IMMSE is quite stable as a whole, the variance is smaller, and the mean value of the IMMSE result curve under the conditions of pure Gaussian white noise and low signal-to-noise ratio is relatively close, two different signals can be still effectively distinguished, and the signal detection of an underwater weak target is realized.
The beneficial effects of the invention are as follows: the invention provides a multichannel entropy detection method for underwater weak target signals, which can detect the weak target signals in water in a high-low signal-to-noise ratio environment by detecting entropy of multichannel data, effectively extract correlation information among channels, and can keep stability of an algorithm when complex signal data are processed, and has higher accuracy and stability.
Claims (8)
1. A multichannel entropy detection method of an underwater weak target signal is characterized by comprising the following steps:
step S1: according to the inclusionCarrying out coarse graining treatment on the data of each channel with the same time scale to obtain coarse graining multichannel data;
step S2: according to the coarsened multichannel data, carrying out phase space reconstruction on the data to obtain an embedding dimension ofMIs a phase space of (2);
step S3: based on embedding dimensionMChebyshev distance of any two vectors in the phase space of (2), and calculating to obtain the embedding dimension asMAverage probability of phase space of (a);
step S4: is thatThe number of elements per channel in the number of channels is increased by 1, the embedding dimension of the phase space is from +.>Become->And calculate the embedding dimension to be +.>Average probability of phase space of (a);
step S5: based on embedding dimensionMAverage probability and embedding dimension of phase space of (2)Calculating the average probability of the phase space of the multi-channel to obtain an entropy value IMMSE of the multi-channel;
step S6: changing the coarse-grained time scale, calculating to obtain different coarse-grained time scale entropy values IMMSE, and detecting the underwater weak target signal according to the different coarse-grained time scale entropy values IMMSE;
the specific step of detecting the underwater weak target in the step S6 is as follows:
a1: drawing to obtain an entropy value IMMSE curve result graph by taking a time scale as an abscissa and a coarse-grained time scale entropy value IMMSE as an ordinate;
a2: calculating and obtaining an entropy value IMMSE curve of pure noise according to the entropy value IMMSE curve result diagram;
a3: calculating and acquiring an entropy value IMMSE curve of the underwater weak target signal according to the entropy value IMMSE curve result graph;
a4: judging whether the entropy value IMMSE curve of the underwater weak target signal is reduced compared with the entropy value IMMSE curve of the pure noise, if so, indicating that the underwater weak target signal exists, otherwise, not exists.
2. The multi-channel entropy detection method of underwater weak target signals according to claim 1, wherein the expression of the coarse graining process in step S1 is as follows:
wherein,for coarsened multichannel data +.>Data points of the data of each channel->Multichannel data for underwater weak target signals, < +.>Is the label of each vector in the phase space, +.>Is->Reference numerals when summing the elements in +.>For the time scale>Is->The%>The elements.
3. The multi-channel entropy detection method of underwater weak target signals according to claim 1, wherein the expression of the phase space in step S2 is as follows:
wherein,for the phase space>For embedding dimension vectors>Is a time delay vector, +.>Are respectively->Embedding dimension corresponding to each channel, < >>Are respectively->Time delay corresponding to each channel,/->Is the label of each vector in the phase space, +.>For the number of channels>To embed dimension vector->Elements of the individual channels->Is the +.>Elements of the individual channels->Is->No. H of the individual channels>Element(s)>Is->No. H of the individual channels>The number of elements to be added to the composition,is->No. H of the individual channels>The elements.
4. The multi-channel entropy detection method of underwater weak target signals according to claim 1, wherein the specific steps of step S3 are as follows:
step S31: based on embedding dimensionMThe Chebyshev distance of any two vectors in the phase space is calculated to obtain the Chebyshev distance between a certain vector in the phase space and the vector except the current vector;
step S32: counting to obtain the number of vectors, the Chebyshev distance between a certain vector and the vectors except the current vector in the phase space of which is smaller than a preset threshold value;
step S33: according to the number of the vectors obtained by statistics, calculating the probability that the Chebyshev distance between a certain vector and the vector except the current vector in the phase space is smaller than a preset threshold value;
step S34: traversing all vectors in a phase space to obtain the probability corresponding to each vector;
step S35: according to the probability corresponding to each vector, calculating to obtain the embedding dimension asMIs a mean probability of phase space of (a).
5. The multi-channel entropy detection method of underwater weak target signals according to claim 4, wherein the chebyshev distance in step S31 is expressed as follows:
wherein,and->Two vectors in phase space, +.>For vector->Andchebyshev distance of->Is natural number (i.e.)>And->Are all the labels of the vectors in the phase space, < >>For vector->Middle->Element(s)>For vector->Middle->Element max []To take the maximum value.
6. The multi-channel entropy detection method of underwater weak target signals according to claim 4, wherein the probability expression in step S33 is as follows:
wherein,for the probability of a vector in phase space having a Chebyshev distance from a vector other than the current vector smaller than a preset threshold value, +.>For the number of vectors in the phase space, +.>Is a phase spaceThe number of vectors, the distance between a certain vector and the vector Chebyshev other than the current vector, is smaller than a preset threshold value.
7. The multi-channel entropy detection method of underwater weak target signals according to claim 4, wherein the expression of the average probability in step S35 is as follows:
wherein,for embedding dimension +.>Average probability of phase space of>As the number of vectors in the phase space,the probability of a vector in the phase space having a chebyshev distance from a vector other than the current vector less than a preset threshold.
8. The multi-channel entropy detection method of underwater weak target signals according to claim 1, wherein the expression of the entropy value IMMSE in step S5 is as follows:
wherein,is entropy value (L)>To embed dimension +.>Is a time delay vector, +.>For a preset threshold value->For the length of the multichannel data, +.>Is natural logarithmic and is->For embedding dimension +.>Average probability of phase space of>For embedding the average probability of the phase space of dimension M,pis the number of channels.
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| WO2021056727A1 (en) * | 2019-09-27 | 2021-04-01 | 山东科技大学 | Joint noise reduction method based on variational mode decomposition and permutation entropy |
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