CN114593815B - Line spectrum extraction technology based on self-noise data - Google Patents
Line spectrum extraction technology based on self-noise data Download PDFInfo
- Publication number
- CN114593815B CN114593815B CN202210073273.3A CN202210073273A CN114593815B CN 114593815 B CN114593815 B CN 114593815B CN 202210073273 A CN202210073273 A CN 202210073273A CN 114593815 B CN114593815 B CN 114593815B
- Authority
- CN
- China
- Prior art keywords
- line spectrum
- spectrum
- self
- signal
- noise
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001228 spectrum Methods 0.000 title claims abstract description 78
- 238000000605 extraction Methods 0.000 title claims abstract description 26
- 238000000034 method Methods 0.000 claims abstract description 20
- 238000001514 detection method Methods 0.000 claims abstract description 13
- 238000005070 sampling Methods 0.000 claims abstract description 9
- 238000009499 grossing Methods 0.000 claims abstract description 8
- 238000010183 spectrum analysis Methods 0.000 claims abstract description 7
- 238000012216 screening Methods 0.000 claims abstract description 6
- 238000004088 simulation Methods 0.000 claims abstract description 6
- 238000010606 normalization Methods 0.000 claims abstract description 5
- 238000012935 Averaging Methods 0.000 claims description 3
- 239000012634 fragment Substances 0.000 claims description 3
- 238000009825 accumulation Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 abstract description 3
- 238000004458 analytical method Methods 0.000 description 13
- 238000011160 research Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012731 temporal analysis Methods 0.000 description 1
- 238000000700 time series analysis Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
- G06F17/142—Fast Fourier transforms, e.g. using a Cooley-Tukey type algorithm
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Engineering & Computer Science (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Discrete Mathematics (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention discloses a line spectrum extraction technology based on self-noise data, which comprises the following steps: acquiring an acquisition signal of marine self-noise data of a ship or generating a sampling signal by simulation through acquisition equipment, dividing the acquisition signal into a plurality of sections, performing centering treatment on each section of signal, performing power spectrum analysis to obtain a power spectrum of the signal, subtracting a continuous spectrum in the power spectrum of the noise signal by using a three-point smoothing method to obtain a straightened line spectrum, performing normalization treatment, reserving a maximum value, setting a peak value as 1, accumulating, setting 25% screening, multiplying the position corresponding to the accumulation result of the maximum value, extracting a characteristic line spectrum, and drawing a line spectrum time-frequency diagram. The invention establishes a set of complete ship self-noise signal line spectrum detection and extraction model, reduces the influence of incoherent peak values through sub-package reading of self-noise data and accumulation of correlation results of each package of data, and can effectively improve the detection capability of the ship self-noise line spectrum.
Description
Technical Field
The invention belongs to the field of underwater sound engineering, and relates to a line spectrum extraction technology based on self-noise data.
Background
In the processing analysis and recognition of acoustic signals, the detection and extraction of line spectra is of great importance. By extracting the characteristic line spectrum, the motion parameters of the target can be estimated. Therefore, the detection and extraction of the ship line spectrum are always the key points of research at home and abroad. The self-noise data of the ship have a large amount of low-frequency line spectrums, and analysis and research of line spectrum extraction are carried out on the self-noise data, so that technical support can be provided for deep knowledge of the noise characteristics of the ship platform and reduction of the self-noise of the ship to improve tactical maneuver decision level.
For the extraction of the low-frequency line spectrum, a plurality of methods exist at home and abroad, and the main analysis methods thereof include a modern spectrum analysis method, a high-order spectrum analysis method, a self-adaptive technology analysis method, a time-frequency technology analysis method and the like. Since the high-order cumulant method can automatically suppress the interference of gaussian noise, the line spectrum analysis method for the high-order cumulant is paid attention to in the analysis research of line spectrum extraction. Scholars Zhang Xianda studied the algorithmic properties of the higher order cumulant method and the obvious advantages of the higher order cumulant method over the power spectrum in detail in a book of time series analysis, higher order statistics method (university of Qinghai Press, 4 th 1996). The scholars Zhang Xiaoyun (application of high-order statistics in the feature extraction of the mine target, the university of Harbin engineering, the university of Shuoshi, 2 nd year 2008) apply the analysis method of the high-order statistics to the extraction of the feature information of the underwater target, and obtain good effects. For a time-frequency technical analysis method for converting a time domain signal into a time-frequency combined domain, hou Tieshuang et al (time-frequency analysis of ship noise passing characteristics, national institute of industrial and university of northwest, 12 months 2003) apply the time-frequency technical analysis method to the extraction of a ship low-frequency characteristic line spectrum, and apply the analysis method to the processing of experimental data, so that the change condition of the ship characteristic line spectrum frequency with time can be seen clearly from the obtained result. Since the experimental conditions of classical time-frequency analysis require a high signal-to-noise ratio and do not touch the processing and application in the presence of strong background noise interference of signal data, scholars have proposed a high-order cumulant time-frequency analysis method in order to effectively solve this problem.
Summarizing research results of related line spectrum detection and line spectrum extraction methods at home and abroad in recent years, in the line spectrum extraction process, the research results are interfered by incoherent peaks in background noise, the accuracy and reliability of characteristic line spectrum extraction meeting requirements are insufficient, and the effect is influenced in ship self-noise line spectrum detection.
Disclosure of Invention
Aiming at the problems, the invention provides a line spectrum extraction technology based on self-noise data by a method for reducing the influence of incoherent peaks, and the characteristic line spectrum detection capability of ship self-noise signals is effectively improved.
The technical scheme of the invention is as follows:
Step one: dividing the acquired signal into P sections, and carrying out centering treatment on a sampling sample x p (i) of the noise signal of the P section to ensure that the average value of the sampling sample is zero:
and then carrying out power spectrum analysis to obtain a power spectrum of the signal:
y'p(i)＝FFT[yp(i)]；
Step two: smoothing the power spectrum: subtracting a continuous spectrum in the noise signal power spectrum by using a three-point smoothing method to obtain a straightened line spectrum;
step three: normalization: normalizing the straightened line spectrum;
Step four: peak detection: averaging the normalized signal Y p', and discarding points smaller than the average value, namely setting Flag (i) for each point, and discarding the point with Flag (i) of-1;
Wherein the method comprises the steps of
Then the turning points are reserved, i.e. a new Flag (i) is set for each point, and the points where Flag (i) is 1 are reserved:
finally, setting the minimum value in the turning point to 0, wherein the rest point is the spectrum peak value; the specific method is to set a new Flag (i) for each point, and zero the point with Flag (i) of-1:
Wherein:
step five: setting the part greater than 0 in the result obtained in the step four as 1, accumulating the corresponding spectrum peak value part, setting 25% screening limit, namely, the accumulated result is less than 25% of the number of packets, and setting the influence of incoherent peak value at the position as 0;
Step six: and accumulating and summing the results of the step four of all the fragments, multiplying the results of the step five, and outputting X (i) as a characteristic line spectrum:
step seven: and (3) drawing a line spectrum time-frequency chart according to the characteristic line spectrum obtained in the step (six).
The invention has the advantages that: the invention establishes a set of complete ship self-noise signal line spectrum detection and extraction model, reduces the influence of incoherent peak values through sub-package reading of self-noise data and accumulation of correlation results of each package of data, and can effectively improve the detection capability of the ship self-noise line spectrum.
Drawings
Fig. 1 is a flow chart of a line spectrum extraction technique based on self-noise data.
Fig. 2 is a low frequency line spectrum peak and line spectrum time-frequency diagram extracted from simulation data.
Detailed Description
In order to facilitate understanding by those skilled in the art, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific examples. It will be apparent that the embodiments described below are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
as shown in fig. 1, a flow chart of the present invention is shown. The acquisition signal can be acquired from marine self-noise data of the ship through acquisition equipment or generated through simulation.
Step one: simulation generates a sampling signal x (i), wherein the input signal is a pulse signal with f1=1 kHz, f2=2 kHz and f3=3 kHz, the pulse width t=25 ms, the sampling rate fs=40 kHz, the interference input is random noise, and the signal-to-noise ratio is-3 dB. The sampled signal is divided into P segments, one segment at a time being processed. First, the sampled sample x p (i) of the p-th noise signal is centered, so that the average value of the sampled sample is zero, and the centering of the noise signal can be expressed as:
Then, carrying out power spectrum analysis to obtain a power spectrum of the noise signal:
y'p(i)＝FFT[yp(i)]；
step two: smoothing the power spectrum: subtracting a continuous spectrum in the noise signal power spectrum by using a three-point smoothing method to obtain a straightened line spectrum diagram;
step three: normalization: normalization processing is carried out on the straightened line spectrum graph, and the formula is expressed as follows:
Step four: peak detection: averaging the normalized signal Y p', and discarding points smaller than the average value, namely setting Flag (i) for each point, and discarding the point with Flag (i) of-1;
Wherein the method comprises the steps of
Then the turning points are reserved, i.e. a new Flag (i) is set for each point, and the points where Flag (i) is 1 are reserved:
And finally, setting the minimum value in the turning point to 0, wherein the rest points are spectrum peaks. The specific method is to set a new Flag (i) for each point, and zero the point with Flag (i) of-1:
Wherein:
step five: setting the part greater than 0 in the result obtained in the step four as 1, accumulating the corresponding spectrum peak value part, setting 25% screening limit, namely, the accumulated result is less than 25% of the number of packets, and setting the influence of incoherent peak value at the position as 0;
Step six: and accumulating and summing the results of the step four of all the fragments, multiplying the results of the step five, and outputting X (i) as a characteristic line spectrum:
step seven: and (3) drawing a line spectrum time-frequency chart according to the characteristic line spectrum obtained in the step (six).
As shown in FIG. 2, the ship self-noise is simulated by using the sampling signals, and the line spectrum extraction process is carried out, and the simulation results can find that the line spectrum peaks of 1k, 2k and 3k are clear, and no obvious incoherent peak interference exists near the line spectrum peaks. Therefore, the line spectrum extraction model effectively reduces the influence of incoherent peak values, realizes the effective detection and extraction of the line spectrum of self-noise, and has higher reliability.
Alterations, modifications, substitutions and variations of the embodiments herein will be apparent to those of ordinary skill in the art in light of the teachings of the present invention without departing from the spirit and principles of the invention.
Claims (4)
1. A line spectrum extraction method based on self-noise data is characterized by comprising the following steps:
Step one: dividing the acquired signal into P sections, and carrying out centering treatment on a sampling sample x p (i) of the noise signal of the P section to ensure that the average value of the sampling sample is zero:
and then carrying out power spectrum analysis to obtain a power spectrum of the signal:
y'p(i)＝FFT[yp(i)]；
step two: smoothing the power spectrum to obtain a straightened line spectrum;
step three: normalization: normalizing the straightened line spectrum;
Step four: peak detection: averaging the normalized signal Y' p, and discarding points smaller than the average value, namely setting Flag (i) for each point, and discarding the point with Flag (i) of-1;
Wherein the method comprises the steps of
Then the turning points are reserved, i.e. a new Flag (i) is set for each point, and the points where Flag (i) is 1 are reserved:
finally, setting the minimum value in the turning point to 0, wherein the rest point is the spectrum peak value; the specific method is to set a new Flag (i) for each point, and zero the point with Flag (i) of-1:
Wherein:
Step five: setting the part greater than 0 in the result obtained in the step four as 1, and then accumulating the corresponding spectrum peak value part; setting a screening limit value, namely setting the accumulated result to be 0 when the accumulated result is smaller than the screening limit value;
Step six: and accumulating and summing the results of the step four of all the fragments, multiplying the results of the step five, and outputting X (i) as a characteristic line spectrum:
step seven: and (3) drawing a line spectrum time-frequency chart according to the characteristic line spectrum obtained in the step (six).
2. The line spectrum extraction method based on self-noise data as claimed in claim 1, wherein: the sampling signal in the first step is generated by acquiring self-noise data or simulation of the ship sea through acquisition equipment.
3. The line spectrum extraction method based on self-noise data as claimed in claim 1, wherein: and step two, the power spectrum smoothing is to subtract continuous spectrums in the noise signal power spectrum by adopting a three-point smoothing method to obtain a straightened line spectrum.
4. The line spectrum extraction method based on self-noise data as claimed in claim 1, wherein: the screening limit in step five was 25%.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210073273.3A CN114593815B (en) | 2022-01-21 | 2022-01-21 | Line spectrum extraction technology based on self-noise data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210073273.3A CN114593815B (en) | 2022-01-21 | 2022-01-21 | Line spectrum extraction technology based on self-noise data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114593815A CN114593815A (en) | 2022-06-07 |
| CN114593815B true CN114593815B (en) | 2024-06-07 |
Family
ID=81804827
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210073273.3A Active CN114593815B (en) | 2022-01-21 | 2022-01-21 | Line spectrum extraction technology based on self-noise data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114593815B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH01209385A (en) * | 1988-02-17 | 1989-08-23 | Oki Electric Ind Co Ltd | Line spectrum detector |
| JPH05196717A (en) * | 1992-01-20 | 1993-08-06 | Tech Res & Dev Inst Of Japan Def Agency | Sonar-signal detecting and processing method |
| CN102243302A (en) * | 2011-04-15 | 2011-11-16 | 东南大学 | Method for extracting line spectrum time accumulation characteristics of hydro-acoustic target radiation noise |
| CN105277934A (en) * | 2015-09-24 | 2016-01-27 | 哈尔滨工程大学 | Weak lien spectrum object passive detection method based on array |
| CN105959035A (en) * | 2016-06-14 | 2016-09-21 | 东南大学 | Direct sequence spread spectrum signal interception detection method |
| 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 |
| CN110135316A (en) * | 2019-05-07 | 2019-08-16 | 中国人民解放军海军潜艇学院 | The automatic detection and extracting method of low frequency spectrum lines in a kind of ship-radiated noise |
| CN111708006A (en) * | 2020-05-28 | 2020-09-25 | 哈尔滨工程大学 | Target line spectrum detection method suitable for unmanned platform detection sonar |
| CN113534115A (en) * | 2021-05-31 | 2021-10-22 | 中国船舶重工集团公司第七一五研究所 | Active and passive combined processing sonar target high-precision line spectrum extraction method |
| CN113887450A (en) * | 2021-10-09 | 2022-01-04 | 中国人民解放军91977部队 | Modulation spectrum feature-based underwater target online screening method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8022864B2 (en) * | 2007-11-08 | 2011-09-20 | The United States Of America As Represented By The Secretary Of Commerce | Detection of transient signals in doppler spectra |
-
2022
- 2022-01-21 CN CN202210073273.3A patent/CN114593815B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH01209385A (en) * | 1988-02-17 | 1989-08-23 | Oki Electric Ind Co Ltd | Line spectrum detector |
| JPH05196717A (en) * | 1992-01-20 | 1993-08-06 | Tech Res & Dev Inst Of Japan Def Agency | Sonar-signal detecting and processing method |
| CN102243302A (en) * | 2011-04-15 | 2011-11-16 | 东南大学 | Method for extracting line spectrum time accumulation characteristics of hydro-acoustic target radiation noise |
| CN105277934A (en) * | 2015-09-24 | 2016-01-27 | 哈尔滨工程大学 | Weak lien spectrum object passive detection method based on array |
| CN105959035A (en) * | 2016-06-14 | 2016-09-21 | 东南大学 | Direct sequence spread spectrum signal interception detection method |
| 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 |
| CN110135316A (en) * | 2019-05-07 | 2019-08-16 | 中国人民解放军海军潜艇学院 | The automatic detection and extracting method of low frequency spectrum lines in a kind of ship-radiated noise |
| CN111708006A (en) * | 2020-05-28 | 2020-09-25 | 哈尔滨工程大学 | Target line spectrum detection method suitable for unmanned platform detection sonar |
| CN113534115A (en) * | 2021-05-31 | 2021-10-22 | 中国船舶重工集团公司第七一五研究所 | Active and passive combined processing sonar target high-precision line spectrum extraction method |
| CN113887450A (en) * | 2021-10-09 | 2022-01-04 | 中国人民解放军91977部队 | Modulation spectrum feature-based underwater target online screening method |
Non-Patent Citations (4)
| Title |
|---|
| Detection of line-spectrum of radiated noises from underwater target based on chaotic system;WANG H K等;IEEE International Conference on Signal Processing, Communication and Computing;20131231;全文 * |
| 基于序列匹配的螺旋桨轴频自动提取方法;杨日杰;郑晓庆;韩建辉;李大卫;;振动与冲击;20180828(16);全文 * |
| 基于矢量相干积累的舰船特征线谱增强算法;张炜;程锦房;许杰;;探测与控制学报;20130226(01);全文 * |
| 基于矢量线阵的目标低频线谱提取方法;李海涛等;应用声学;20200331;第39卷(第2期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114593815A (en) | 2022-06-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106885971B (en) | Intelligent background noise reduction method for cable fault detection pointing instrument | |
| CN108120976B (en) | Ground clutter spectrum leakage suppression method based on Doppler channel characteristics | |
| CN105785324B (en) | Linear frequency-modulated parameter estimating method based on MGCSTFT | |
| CN111059066B (en) | Centrifugal pump cavitation state discrimination method based on autocorrelation spectrum and balanced square envelope spectrum | |
| CN110135316B (en) | Automatic detection and extraction method for low-frequency line spectrum in ship radiation noise | |
| CN105071830B (en) | A kind of detection recognition method of direct sequence signal | |
| CN112945546B (en) | Precise diagnosis method for complex faults of gearbox | |
| CN111769810B (en) | Fluid mechanical modulation frequency extraction method based on energy kurtosis spectrum | |
| CN113567969B (en) | Illegal sand dredger automatic monitoring method and system based on underwater acoustic signals | |
| CN111398909A (en) | Clutter environment unmanned aerial vehicle detection method based on cepstrum analysis | |
| CN113607415A (en) | Bearing fault diagnosis method based on short-time stochastic resonance under variable rotating speed | |
| CN111680737A (en) | Radar radiation source individual identification method under differential signal-to-noise ratio condition | |
| Le et al. | Weak LFM signal dectection based on wavelet transform modulus maxima denoising and other techniques | |
| CN114593815B (en) | Line spectrum extraction technology based on self-noise data | |
| CN113341378A (en) | Self-adaptive channelized receiving method based on frequency spectrum differential entropy detection | |
| CN115266094B (en) | Rolling bearing fault diagnosis method based on TVD and MED | |
| CN109541549B (en) | Intermittent sampling forwarding interference suppression method based on EMD and sparse signal processing | |
| CN112466322A (en) | Electromechanical device noise signal feature extraction method | |
| CN112788057A (en) | LDoS attack detection method based on FSWT time-frequency distribution | |
| Qin et al. | A new method of parameter estimation of frequency-hopping signal | |
| CN106629929B (en) | Energy-concerving and environment-protective sewage equipment | |
| CN116304584B (en) | Self-adaptive noise filtering method based on envelope spectrum peak value screening | |
| CN113541729B (en) | Time-frequency graph denoising method based on time-frequency matrix | |
| CN114268385B (en) | Weak signal detection method based on maximum vertex feature | |
| Yang et al. | Research on submarine signal detection method based on Welch algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |