CN113591310A - Ship radiation noise line spectrum accurate extraction method based on statistic analysis - Google Patents

Ship radiation noise line spectrum accurate extraction method based on statistic analysis Download PDF

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CN113591310A
CN113591310A CN202110876663.XA CN202110876663A CN113591310A CN 113591310 A CN113591310 A CN 113591310A CN 202110876663 A CN202110876663 A CN 202110876663A CN 113591310 A CN113591310 A CN 113591310A
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杨宏晖
郑凯锋
李俊豪
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Ningbo Research Institute of Northwestern Polytechnical University
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Abstract

The invention provides a ship radiation noise line spectrum accurate extraction method based on statistic analysis, which is characterized in that a maximum value, a minimum value, an average value, a median, a range and a variance are used as evaluation indexes, a method for removing a continuous spectrum trend is used for extracting suspected line spectrums possibly containing pseudo line spectrums, then the energy statistics of the line spectrums in the time period is obtained, the statistics are ranked, assigned and weighted, the pseudo line spectrums in the suspected line spectrums can be effectively removed, and line spectrum components in a Lofar image are accurately extracted.

Description

Ship radiation noise line spectrum accurate extraction method based on statistic analysis
Technical Field
The invention relates to the technical field of ship radiation noise extraction, in particular to a ship radiation noise line spectrum accurate extraction method based on statistic analysis.
Background
The ship radiation noise comprises mechanical noise, propeller noise and hydrodynamic noise, and abundant line spectrum components exist on a Lofar graph obtained through short-time Fourier transform, so that the ship radiation noise is relatively stable characteristic information. The accurate extraction of the line spectrum components has important engineering significance for detecting and identifying underwater targets.
According to a common ship radiation noise line spectrum extraction method, a Lofar image is obtained through signal preprocessing and short-time Fourier transform, and then a line spectrum is extracted manually or by designing a threshold value. However, the marine environment is too complex, and interference of many other noises, such as noise of other ship targets, marine environment noise, marine animal cry, etc., can cause that line spectra in the Lofar image are dark and bright, break points occur, and even cross overlapping occurs. Due to the factors, a method for extracting a line spectrum manually or by designing a threshold value is weak in generalization, a pseudo line spectrum may exist, the subsequent underwater acoustic target detection or identification task is not facilitated, and the accuracy of the underwater acoustic target detection or identification is seriously influenced.
The energy statistics of different frequency positions of the Lofar graph, such as energy maximum value, energy minimum value, energy average value, energy median, energy range, energy variance and the like, can help researchers to screen out valuable information and remove pseudo-line spectrum components mixed in a real line spectrum. In a ship radiation noise Lofar graph, a line spectrum shows that the frequency position has a higher energy value in the whole time period, the total energy is larger after accumulation in time, and the line spectrum has a larger total energy after accumulation in time due to the fact that a certain or a plurality of time points have a special value with particularly large energy at the frequency position. The line spectrum and the pseudo-line spectrum have higher energy after time accumulation, and the line spectrum and the pseudo-line spectrum cannot be distinguished by manually designing a threshold or manually extracting the line spectrum. But since the pseudo-line spectrum is "outlier", the energy distribution of the line spectrum is more stable over the entire time period than the pseudo-line spectrum.
Disclosure of Invention
Based on the reasons, a large number of experimental analyses are combined, and aiming at the problem that a pseudo line spectrum exists in a manually designed threshold or manually extracted line spectrum, the invention provides a ship radiation noise line spectrum accurate extraction method based on statistic analysis.
The technical scheme of the invention is as follows:
the ship radiation noise line spectrum accurate extraction method based on statistic analysis comprises the following steps:
step 1: acquiring a radiation noise signal x emitted by a ship, and carrying out mean value normalization processing on the radiation noise signal x emitted by the ship:
Figure BDA0003190532740000021
normalizing the mean value of the processed signal
Figure BDA0003190532740000022
Performing Short Time Fourier Transform (STFT) to obtain a data matrix corresponding to the signal Lofar map:
Figure BDA0003190532740000023
the X dimension is M multiplied by N; x has a time span of T (unit: s) and a frequency span of f (unit: Hz);
carrying out maximum and minimum normalization processing on the data matrix X of the Lofar graph:
Figure BDA0003190532740000024
step 2: to pair
Figure BDA0003190532740000025
And (3) performing time accumulation to obtain the sum of energies of different frequency points in the T time period, and recording the sum as a vector p, wherein the jth element in the vector p is as follows:
Figure BDA0003190532740000026
and (3) performing maximum and minimum normalization processing on the energy vectors p corresponding to different frequency points:
Figure BDA0003190532740000027
and step 3: vector taking
Figure BDA0003190532740000028
Minimum value point p ofminAnd its corresponding frequency point position fminUsing fminAnd pminFitting a continuous spectrum trend curve to obtain a function phi: p is a radical ofmin＝Φ(fmin) (ii) a Substituting the obtained function phi into the vector
Figure BDA0003190532740000029
Corresponding frequency position f, obtaining the corresponding energy value of each frequency point pair of the continuous spectrum, and recording the energy value as
Figure BDA00031905327400000210
Figure BDA00031905327400000211
And 4, step 4: will vector
Figure BDA00031905327400000212
Is subtracted from each element of
Figure BDA00031905327400000213
The energy value corresponding to each frequency point after the trend of the continuous spectrum is removed is obtained and is marked as pd：
Figure BDA00031905327400000214
The energy vector p after removing the continuous spectrum trenddCarrying out maximum and minimum normalization processing:
Figure BDA00031905327400000215
and 5: will be provided with
Figure BDA00031905327400000216
The middle elements are arranged from large to small to form a new vector p'dIs taken to be p'dThe element of the top-middle 1/2 is taken as 2 times of the average value as the extracted line spectrum threshold s:
Figure BDA00031905327400000217
INT (N/2) is to round N/2;
step 6: will be provided with
Figure BDA00031905327400000218
Elements smaller than the threshold s are zeroed:
Figure BDA00031905327400000219
Figure BDA00031905327400000220
the frequency corresponding to the element position without zero setting is the frequency f corresponding to the proposed line spectrump：fp＝(fp1,fp2,…,fpn) N is the number of extracted line spectra;
and 7: calculating the data matrix corresponding to the Lofar graph
Figure BDA0003190532740000031
The maximum value for each column, denoted as vector a: a ═ a1,a2,…,aN) Wherein
Figure BDA0003190532740000032
Calculating the data matrix corresponding to the Lofar graph
Figure BDA0003190532740000033
The minimum value for each column, denoted as vector b: b ═ b1,b2,…,bN) Wherein
Figure BDA0003190532740000034
Calculating the data matrix corresponding to the Lofar graph
Figure BDA0003190532740000035
The average for each column, denoted as vector c: c ═ c1,c2,…,cN) Wherein
Figure BDA0003190532740000036
Calculating the data matrix corresponding to the Lofar graph
Figure BDA0003190532740000037
Median of each column, denoted vector d: d ═ d (d)1,d2,…,dN) Wherein
Figure BDA0003190532740000038
X' is
Figure BDA0003190532740000039
Each row of elements in the matrix are arranged from large to small;
calculating the data matrix corresponding to the Lofar graph
Figure BDA00031905327400000317
Range for each column, denoted as vector g: g is a-b;
calculating the data matrix corresponding to the Lofar graph
Figure BDA00031905327400000318
The variance of each column, denoted as vector h: h ═ h (h)1,h2,…,hN) Wherein
Figure BDA00031905327400000310
And 8: vector a is brought at spectral frequency fpThe value of the corresponding column is taken out and recorded as
Figure BDA00031905327400000311
Figure BDA00031905327400000312
The vectors b, c, d, g, h are similarly aligned at the spectral frequency fpThe value of the corresponding column is taken out and recorded as
Figure BDA00031905327400000313
And step 9: will be provided with
Figure BDA00031905327400000314
The minimum k-INT (n/5) elements are sequentially assigned as (k, k-1, …,1) from small to large, and the rest elements are set as 0; in the same way will
Figure BDA00031905327400000315
The minimum k-INT (n/5) elements are sequentially assigned as (k, k-1, …,1) from small to large, and the rest elements are set as 0; will be provided with
Figure BDA00031905327400000316
The largest k-INT (n/5) elements are assigned with values of (k, k-1, …,1) in sequence from large to small;
step 10: generating new vector
Figure BDA0003190532740000041
Weighted and summed to produce a new vector, denoted as l, preferably
Figure BDA0003190532740000042
Designing a threshold value, preferably a threshold value q (k 4 + 2+ k 2) 0.6, and when l (i) is equal to q, the point is a pseudo-wire spectrum; will f ispRemoving the frequency of the medium-false line spectrum to obtain the frequency f corresponding to the actually existing line spectrump'。
Advantageous effects
The invention provides a ship radiation noise line spectrum accurate extraction method based on statistic analysis by using a maximum value, a minimum value, an average value, a median, a range and a variance as evaluation indexes.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1: flow chart of the method of the invention.
FIG. 2: the simulation signal Lofar diagram used in the method of the invention.
FIG. 3: the simulation signal used in the method of the invention is removed from the line spectrogram after the pseudo-line spectrum.
FIG. 4: lofar plot of measured signals for the method of the invention.
FIG. 5: the measured signal used in the method of the invention is the line spectrogram after the pseudo-line spectrum is removed.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The embodiment provides a method for extracting a suspected line spectrum by removing a continuous spectrum trend aiming at the problem of line spectrum extraction of radiation ship noise, and evaluates and analyzes the extracted suspected line spectrum by utilizing a maximum value, a minimum value, a mean value, a median, a range and a variance in statistics so as to achieve the purpose of removing a pseudo line spectrum. The method can more accurately extract the frequency position of the suspected line spectrum in the Lofar image, and evaluate and analyze the energy statistics of the extracted suspected line spectrum, thereby more accurately extracting the ship radiation noise line spectrum.
The signal x used in this embodiment is an analog signal simulated by matlab, and the simulation time duration is 84 s.
(1) Obtaining a radiation noise signal x ═ x1+x2+x3+x4+x5+x6+x7+x8，
Wherein the signal x1＝2*sin(2*π*f1*t)，x2＝10*sin(2*π*f2*t)，x3＝5*cos(2*π*f3*t)，x4＝7*sin(2*π*f4*t)，x5＝sin(2*π*f5*t)，x6＝3*sin(2*π*f6*t)，x7＝4*cos(2*π*f7*t)，x8＝cos(2*π*f8*t)；f1＝100Hz，f2＝200Hz，f3＝30Hz，f4＝400Hz，f5＝45Hz，f6＝254Hz，f7＝85Hz，f8213 Hz. The radiation noise signal x is subjected to noise addition processing, and the signal-to-noise ratio SNR is-30 dB.
(2) And (4) preprocessing a training sample. Carrying out mean normalization processing, short-time Fourier transform and maximum and minimum normalization processing on x in sequence to obtain a data matrix of the Lofar image
Figure BDA0003190532740000051
Figure BDA0003190532740000052
Dimension of (d) is 159 × 500, time span is T-84 s, and frequency span is f-500 Hz.
(3) To pair
Figure BDA0003190532740000053
And performing accumulation in time to obtain that the sum of the energies of different frequency points in 84s is p, and the jth element in the vector p is:
Figure BDA0003190532740000054
and carrying out maximum and minimum normalization processing on p to obtain
Figure BDA0003190532740000055
(4) Vector taking
Figure BDA0003190532740000056
Minimum value point p ofminAnd its corresponding frequency point position fminUsing fminAnd pminFitting a continuous spectrum trend curve to obtain a function phi: p is a radical ofmin＝Φ(fmin)。
(5) Substituting the obtained function phi into the vector
Figure BDA0003190532740000057
Corresponding to the frequency position f, obtaining the corresponding energy value of each frequency point of the continuous spectrum, and recording the corresponding energy value as
Figure BDA0003190532740000058
Figure BDA0003190532740000059
(6) Will vector
Figure BDA00031905327400000510
Is subtracted from each element of
Figure BDA00031905327400000511
The energy value corresponding to each frequency point after the trend of the continuous spectrum is removed is obtained and is marked as pd：
Figure BDA00031905327400000512
And to pdPerforming maximum and minimum normalization processing to obtain
Figure BDA00031905327400000513
(7) Will be provided with
Figure BDA00031905327400000514
The middle elements are arranged from large to small to form a new vector p'dIs taken to be p'dThe element of the top-middle 1/2 is taken as 2 times of the average value as the extracted line spectrum threshold s:
Figure BDA0003190532740000061
(8) will be provided with
Figure BDA0003190532740000062
Elements smaller than the threshold s are set to 0:
Figure BDA0003190532740000063
Figure BDA0003190532740000064
the frequencies corresponding to the positions of elements with no 0's in them are the proposed frequencies that may include a pseudo-line spectrumFrequency f corresponding to suspected line spectrump：fpAt (30,45,85,100,200,213,254,400,461) Hz, the number of line spectra n extracted is 9.
(9) Calculating the data matrix corresponding to the Lofar graph
Figure BDA0003190532740000065
Maximum value a for each column: a ═ a1,a2,…,a500) Wherein a isj＝max(X1j,X2j,…,X159j),j＝1,2,…,500。
(10) Calculating the data matrix corresponding to the Lofar graph
Figure BDA0003190532740000066
Minimum value b of each column: b ═ b1,b2,…,b500) Wherein b isj＝min(X1j,X2j,L,X159j),j＝1,2,…,N。
(11) Calculating the data matrix corresponding to the Lofar graph
Figure BDA0003190532740000067
Average value c for each column: c ═ c1,c2,…,c500) Wherein
Figure BDA0003190532740000068
(12) Calculating the data matrix corresponding to the Lofar graph
Figure BDA0003190532740000069
Median of each column, denoted vector d: d ═ d (d)1,d2,…,d500) Wherein d isj＝X'79jX' is
Figure BDA00031905327400000610
Wherein each column of elements is arranged from small to large in the matrix.
(13) Calculating the data matrix corresponding to the Lofar graph
Figure BDA00031905327400000611
Each column beingIs recorded as vector g: g-a-b.
(14) Calculating the data matrix corresponding to the Lofar graph
Figure BDA00031905327400000612
The variance of each column, denoted as vector h: h ═ h (h)1,h2,…,h500) Wherein
Figure BDA00031905327400000613
(15) Extracting line spectrum frequency f from vector apThe value of the corresponding column is taken out and recorded as
Figure BDA00031905327400000614
Figure BDA00031905327400000615
The line spectrum frequency f extracted from the vectors b, c, d, g and h is extracted in the same waypThe value of the corresponding column is taken out and recorded as
Figure BDA00031905327400000616
(16) Will be provided with
Figure BDA00031905327400000617
The smallest 2 elements are sequentially assigned with values (2,1) from small to large, and the rest elements are set to be 0; in the same way will
Figure BDA00031905327400000618
The smallest 2 elements are sequentially assigned with values (2,1) from small to large, and the rest elements are set to be 0; will be provided with
Figure BDA00031905327400000619
The largest 2 elements are sequentially assigned with values (2,1) from large to small, and the rest elements are set to be 0.
(17) Generating new vector
Figure BDA0003190532740000071
Weighting and adding to obtain a new vector, and recording the new vector as l:
Figure BDA0003190532740000072
the design threshold q is 12, when l (i) is greater than 12, then this is the pseudowire spectrum.
(18) Will f ispRemoving the frequency 461Hz of the medium-false line spectrum to obtain the frequency f corresponding to the actually existing line spectrump'＝(30,45,85,100,200,213,254,400)Hz。
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.

Claims (2)

1. A ship radiation noise line spectrum accurate extraction method based on statistic analysis is characterized by comprising the following steps: the method comprises the following steps:
step 1: acquiring a radiation noise signal x emitted by a ship, and carrying out mean value normalization processing on the radiation noise signal x emitted by the ship:
Figure FDA0003190532730000011
carrying out short-time Fourier transform on the signal x after the mean value normalization processing to obtain a data matrix corresponding to a signal Lofar diagram:
Figure FDA0003190532730000012
the X dimension is M multiplied by N;
carrying out maximum and minimum normalization processing on the data matrix X of the Lofar graph:
Figure FDA0003190532730000013
step 2: to pair
Figure FDA0003190532730000014
The accumulation over the time is carried out,obtaining the sum of energies of different frequency points in a time span T time period, and recording the sum as a vector p, wherein the jth element in the vector p is:
Figure FDA0003190532730000015
and (3) performing maximum and minimum normalization processing on the energy vectors p corresponding to different frequency points:
Figure FDA0003190532730000016
and step 3: vector taking
Figure FDA0003190532730000017
Minimum value point p ofminAnd its corresponding frequency point position fminUsing fminAnd pminFitting a continuous spectrum trend curve to obtain a function phi: p is a radical ofmin＝Φ(fmin) (ii) a Substituting the obtained function phi into the vector
Figure FDA0003190532730000018
Corresponding frequency position f, obtaining the corresponding energy value of each frequency point pair of the continuous spectrum, and recording the energy value as
Figure FDA0003190532730000019
Figure FDA00031905327300000110
And 4, step 4: will vector
Figure FDA00031905327300000111
Is subtracted from each element of
Figure FDA00031905327300000112
The energy value corresponding to each frequency point after the trend of the continuous spectrum is removed is obtained and is marked as pd：
Figure FDA00031905327300000113
The energy vector p after removing the continuous spectrum trenddCarrying out maximum and minimum normalization processing:
Figure FDA00031905327300000114
and 5: will be provided with
Figure FDA00031905327300000115
The middle elements are arranged from large to small to form a new vector p'dIs taken to be p'dThe element of the top-middle 1/2 is taken as 2 times of the average value as the extracted line spectrum threshold s:
Figure FDA00031905327300000116
INT (N/2) is to round N/2;
step 6: will be provided with
Figure FDA00031905327300000117
Elements smaller than the threshold s are zeroed:
Figure FDA00031905327300000118
Figure FDA00031905327300000119
the frequency corresponding to the element position without zero setting is the frequency f corresponding to the proposed line spectrump：fp＝(fp1,fp2,…,fpn) N is the number of extracted line spectra;
and 7: calculating the data matrix corresponding to the Lofar graph
Figure FDA0003190532730000021
The maximum value for each column, denoted as vector a: a ═ a1,a2,…,aN) Wherein
Figure FDA0003190532730000022
Calculating the minimum value of each row of the data matrix X corresponding to the Lofar graph, and recording the minimum value as a vector b:b＝(b1,b2,…,bN) Wherein
Figure FDA0003190532730000023
Calculating the data matrix corresponding to the Lofar graph
Figure FDA0003190532730000024
The average for each column, denoted as vector c: c ═ c1,c2,…,cN) Wherein
Figure FDA0003190532730000025
Calculating the data matrix corresponding to the Lofar graph
Figure FDA0003190532730000026
Median of each column, denoted vector d: d ═ d (d)1,d2,…,dN) Wherein
Figure FDA0003190532730000027
X' is
Figure FDA0003190532730000028
Each row of elements in the matrix are arranged from large to small;
calculating the data matrix corresponding to the Lofar graph
Figure FDA0003190532730000029
Range for each column, denoted as vector g: g is a-b;
calculating the data matrix corresponding to the Lofar graph
Figure FDA00031905327300000210
The variance of each column, denoted as vector h: h ═ h (h)1,h2,…,hN) Wherein
Figure FDA00031905327300000211
And 8: vector a is brought at spectral frequency fpThe value of the corresponding column is taken out and recorded as
Figure FDA00031905327300000212
Figure FDA00031905327300000213
The vectors b, c, d, g, h are similarly aligned at the spectral frequency fpThe value of the corresponding column is taken out and recorded as
Figure FDA00031905327300000214
And step 9: will be provided with
Figure FDA00031905327300000215
The minimum k-INT (n/5) elements are sequentially assigned as (k, k-1, …,1) from small to large, and the rest elements are set as 0; in the same way will
Figure FDA00031905327300000216
The minimum k-INT (n/5) elements are sequentially assigned as (k, k-1, …,1) from small to large, and the rest elements are set as 0; will be provided with
Figure FDA00031905327300000217
Figure FDA00031905327300000218
The largest k-INT (n/5) elements are assigned with values of (k, k-1, …,1) in sequence from large to small;
step 10: generating new vector
Figure FDA00031905327300000219
Weighting and adding to obtain a new vector, marking as l, and designing a threshold value q, wherein when l (i) is more than or equal to q, the position is a pseudo-line spectrum; will f ispRemoving the frequencies of the medium-false line spectrum to obtain the frequency f 'corresponding to the actually existing line spectrum'p。
2. The vessel radiation noise line spectrum accurate extraction method based on statistic analysis as claimed in claim 1, wherein: in step 10
Figure FDA0003190532730000031
The threshold q is (k 4 × 2+ k × 2) × 0.6.
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Citations (2)

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Publication number Priority date Publication date Assignee Title
CN109655148A (en) * 2018-12-19 2019-04-19 南京世海声学科技有限公司 A kind of autonomous extracting method of ship noise non-stationary low frequency spectrum lines
CN112560949A (en) * 2020-12-15 2021-03-26 南京航空航天大学 Hyperspectral classification method based on multilevel statistical feature extraction

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Publication number Priority date Publication date Assignee Title
CN109655148A (en) * 2018-12-19 2019-04-19 南京世海声学科技有限公司 A kind of autonomous extracting method of ship noise non-stationary low frequency spectrum lines
CN112560949A (en) * 2020-12-15 2021-03-26 南京航空航天大学 Hyperspectral classification method based on multilevel statistical feature extraction

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