CN110376436B - Multi-scale noise power spectral line spectrum detection method - Google Patents
Multi-scale noise power spectral line spectrum detection method Download PDFInfo
- Publication number
- CN110376436B CN110376436B CN201910565539.4A CN201910565539A CN110376436B CN 110376436 B CN110376436 B CN 110376436B CN 201910565539 A CN201910565539 A CN 201910565539A CN 110376436 B CN110376436 B CN 110376436B
- Authority
- CN
- China
- Prior art keywords
- data
- line spectrum
- spectrum
- maximum value
- power spectrum
- 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
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/16—Spectrum analysis; Fourier analysis
Landscapes
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- General Physics & Mathematics (AREA)
- Monitoring And Testing Of Transmission In General (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention discloses a multi-scale noise power spectral line spectrum detection method, which comprises the following steps: the first step is as follows: acquiring power spectrum data of a noise signal to be detected; the second step is that: initializing parameters, and setting a plurality of groups of data windows and threshold parameters; the third step: searching for maxima in the power spectrum data; the fourth step: under the condition of each group of parameters, carrying out statistic calculation on window data corresponding to each maximum value point, and preliminarily judging a line spectrum; the fifth step: and comprehensively judging whether each maximum value point is a line spectrum point. The detection method of the invention utilizes the local statistical characteristics of the power spectrum line spectrum to carry out comprehensive judgment under the conditions of a plurality of data windows and threshold parameters, has the characteristics of simple algorithm, high detection probability and strong anti-interference performance, and is suitable for carrying out fast and efficient line spectrum extraction on the noise signal power spectrum.
Description
Technical Field
The invention belongs to the technical field of signal processing, and particularly relates to a multi-scale noise power spectral line spectrum detection method.
Background
The power spectrum line spectrum is the representation of the single-frequency narrow-band component in the signal in the power spectrum. Power spectral line spectrum extraction is one of the important means for obtaining the characteristics of noise signals. The conventional line spectrum extraction method has two types, one is to perform background equalization through a power spectrum signal, estimate a line spectrum detection threshold through fluctuation statistical characteristics in the power spectrum signal and further perform line spectrum detection, and the method utilizes global data, has better robustness, but has poorer performance in detecting signals with larger fluctuation statistical characteristics; the other line spectrum detection method is to judge an abnormal value according to local data points of a power spectrum so as to detect the line spectrum, and the method has better adaptability to power spectrum data with uneven statistical characteristics, but the window length and the threshold of the selected local data directly influence the detection performance, and are difficult to unify the standard, and when the local data has larger fluctuation or a plurality of line spectrums exist locally, the missed detection and the wrong judgment of the line spectrum are easily brought.
Disclosure of Invention
The purpose of the invention is as follows: the method comprises the steps of setting multiple groups of independent windows and threshold parameters, utilizing the difference between a line spectrum value and a local background value in a power spectrum, sequentially detecting each maximum value point by using each group of parameters, carrying out equalization processing and statistic calculation on data in the window of the maximum value point under each group of parameter conditions with different window length scales, judging whether the maximum value point meets the line spectrum condition or not under the group of parameters, and finally giving judgment whether the maximum value point is the line spectrum or not by integrating judgment information obtained under all the group of parameter conditions.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the following technical scheme: a multi-scale noise power spectral line spectrum detection method comprises the following steps:
(1) acquiring power spectrum data s (N) of a noise signal to be detected, wherein N is 0,1, and N-1, wherein N is a power spectrum data serial number, and N is the number of the power spectrum data;
(2) initializing parameters, setting M groups of data windows and threshold parameters, wherein M is a natural number greater than 0, setting a power spectrum sequence number index i to be 0, and setting a line spectrum set to be E;
(3) executing i-i +1 operation, if i is equal to N-1, ending the line spectrum detection work, and at this time, each element in the line spectrum set E is the detected line spectrum parameter; if i is less than N-1, further judging whether the power spectrum data s (i) is a maximum value point, if s (i) is the maximum value point, executing the step (4), and if s (i) is not the maximum value point, repeatedly executing the step;
(4) under each group of parameter conditions, calculating statistic corresponding to the maximum value point s (i), and preliminarily judging whether the maximum value point meets the line spectrum point condition under each group of parameter conditions;
(5) comprehensively judging whether the s (i) is a line spectrum point, and if so, adding the line spectrum point into the set E;
(6) if i is less than N-1, returning to the step (3) to continue the execution, otherwise, ending the method.
Further, in the step (1), the power spectrum data s (n) of the noise signal to be detected is obtained by the following method: acquiring data received from the sensor and carrying out power spectrum analysis to obtain s (n); or reading the power spectrum data s (n) of the noise signal to be detected from the memory.
Further, in the step (2), parameter initialization is carried out, and M groups of independent windows with different lengths are setThe m-th group of parameters includes the left window length LmLength of right window RmNumber of large value exclusion UmNumber of decimal exclusions DmAmplitude threshold GmWherein, M is 1,2m，Rm，Um，DmAre all natural numbers greater than 0, GmAnd setting a comprehensive threshold slice for a real number greater than 0, setting a power spectrum sequence number index i to be 0 and setting a line spectrum set E to be an empty set, wherein H is a natural number greater than 0.
Further, in the step (3), the specific steps are as follows:
(3-1) performing an i ═ i +1 operation;
(3-2) if i is equal to N-1, finishing the line spectrum detection work, wherein each element in the set E is the detected line spectrum parameter; and if i is less than N-1, further judging whether the power spectrum data s (i) is a maximum value point, namely whether the conditions s (i) is more than or equal to s (i-1) and s (i) is more than s (i +1) or not, if s (i) is the maximum value point, executing the step (4), and if s (i) is not the maximum value point, repeatedly executing the step (3).
Further, in step (4), under each set of parameter conditions, calculating statistics corresponding to a maximum value point s (i), preliminarily determining whether the maximum value point satisfies a line spectrum condition under each set of parameter conditions, and when the mth set of parameters is selected, the steps are as follows:
(4-1) for the maximum value point s (i), intercepting corresponding data from s (n) according to the mth group of parameters set in the step (2), and taking the data length Km＝min(N-1，i+Rm)-max(0，i-Lm) +1, max () and min () denote maximum and minimum values, respectively, and the data truncated using the m-th set of parameters is gm(j)＝s(j+max(0，i-Lm))，j＝0，1，...，Km-1；
(4-2) calculation of gm(j) Mean value of (a)0；
(4-3) calculation of the sequence bm(j) The formula is as follows:
(4-4) vs. sequence gm(j) Performing background equalization to obtain
The formula is as follows:
wherein, the operation is an inner product operation;
(4-6) data sequence
Form a data set
(4-7) in the data set PmIn finding out the front UmA maximum value element and from the data set PmRemoving;
(4-8) in the data set PmIn, find out the front DmA minimum value element and from the data set PmRemoving;
(4-9) calculating the data set P after (4-7) and (4-8) processingmMean value e ofmAnd standard deviation σm；
(4-10) when s (i) > a is satisfiedo+em+Gm×σmThen, the m-th group of line spectrum flags L are setmIf not, the line spectrum flag L of the mth group is setm＝0；
(4-11) repeating the above process until all M sets of parameters are calculated.
Further, in step (5), comprehensively judging whether each maximum value point is a line spectrum point, the judging method is as follows: each group of line spectrum marks satisfies
Then, the s (i) point in the power spectrum data is finally decided as a line spectrum, and the (i, s (i)) at this time is added to the set E as an element.
Has the advantages that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
the detection method of the invention utilizes the local statistical characteristics of the power spectrum line spectrum to carry out comprehensive judgment under the conditions of data windows of multiple scales and threshold parameters, has the characteristics of simple algorithm, high detection probability and strong anti-interference performance, and is suitable for carrying out fast and efficient line spectrum extraction on the noise signal power spectrum.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a power spectrum sequence chart of example 1;
FIG. 3 shows the line spectrum detection results of example 1.
Detailed Description
The invention is further described with reference to the following figures and examples:
as shown in fig. 1, the present invention provides a method for detecting a multi-scale noise power spectrum, which comprises the following steps:
(1) acquiring power spectrum data s (N) of a noise signal to be detected, wherein N is 0,1, and N-1, wherein N is a power spectrum data serial number, and N is the number of the power spectrum data;
(2) initializing parameters, setting M groups of data windows and threshold parameters, wherein M is a natural number greater than 0, setting a power spectrum sequence number index i to be 0, and setting a line spectrum set to be E;
(3) executing i-i +1 operation, if i is equal to N-1, ending the line spectrum detection work, and at this time, each element in the line spectrum set E is the detected line spectrum parameter; if i is less than N-1, further judging whether the power spectrum data s (i) is a maximum value point, if s (i) is the maximum value point, executing the step (4), and if s (i) is not the maximum value point, repeatedly executing the step;
(4) under each group of parameter conditions, calculating statistic corresponding to the maximum value point s (i), and preliminarily judging whether the maximum value point meets the line spectrum point condition under each group of parameter conditions;
(5) comprehensively judging whether the s (i) is a line spectrum point, and if so, adding the line spectrum point into the set E;
(6) if i is less than N-1, returning to the step (3) to continue execution; otherwise, the method ends.
Further, in the step (1), the power spectrum data s (n) of the noise signal to be detected is obtained by the following method: acquiring data received from the sensor and carrying out power spectrum analysis to obtain s (n); or reading the power spectrum data s (n) of the noise signal to be detected from the memory.
Further, in step (2), parameter initialization is performed, M groups of independent parameters with different window length scales are set, and the M group of parameters includes the left window length LmLength of right window RmNumber of large value exclusion UmNumber of decimal exclusions DmAmplitude threshold GmWherein, M is 1,2m，Rm，Um，DmAre all natural numbers greater than 0, GmAnd setting a comprehensive threshold H for a real number larger than 0, setting a power spectrum sequence number index i to be 0, and setting a line spectrum set E as a null set, wherein H is a natural number larger than 0.
Further, in the step (3), the specific steps are as follows:
(3-1) performing an i ═ i +1 operation;
(3-2) if i is equal to N-1, ending the line spectrum detection work, and determining each element in the set E as the detected line spectrum parameter; and if i is less than N-1, further judging whether the power spectrum data s (i) is a maximum value point, namely whether the conditions s (i) is more than or equal to s (i-1) and s (i) is more than s (i +1) or not, if s (i) is the maximum value point, executing the step (4), and if s (i) is not the maximum value point, repeatedly executing the step (3).
Further, in step (4), under each set of parameter conditions, calculating statistics corresponding to a maximum value point s (i), preliminarily determining whether the maximum value point satisfies a line spectrum condition under each set of parameter conditions, and when the mth set of parameters is selected, the steps are as follows:
(4-1) for the maximum value point s (i), intercepting corresponding data from s (n) according to the mth group of parameters set in the step (2), and taking the data length Km＝min(N-1，i+Rm)-max(0，i-Lm) +1, max () and min () denote taking the maximum and minimum values, respectively, andthe data intercepted with the mth set of parameters is gm(j)＝s(j+max(0，i-Lm))，j＝0，1，...，Km-1；
(4-2) calculation of gm(j) Mean value of (a)0；
(4-3) calculation of the sequence bm(j) The formula is as follows:
(4-4) vs. sequence gm(j) Performing background equalization to obtain
The formula is as follows:
wherein, the operation is an inner product operation;
(4-6) data sequence
Form a data set
(4-7) in the data set PmIn finding out the front UmA maximum value element and from the data set PmRemoving;
(4-8) in the data set PmIn, find out the front DmA minimum value element and from the data set PmRemoving;
(4-9) calculating the data set P after (4-7) and (4-8) processingmMean value e ofmAnd standard deviation σm；
(4-10) when s (i) > a is satisfied0+em+Gm×σmThen, the m-th group of line spectrum flags L are setmIf not, the line spectrum flag L of the mth group is setm＝0；
(4-11) repeating the above process until all M sets of parameters are calculated.
Further, in step (5), comprehensively judging whether each maximum value point is a line spectrum point, the judging method is as follows: each group of line spectrum marks satisfies
Then, the s (i) point in the power spectrum data is finally decided as a line spectrum, and the (i, s (i)) at this time is added to the set E as an element.
Example 1
Simulating a colored power spectrum signal s (n), wherein n is 0,1,., 1000, and the number of power spectrum data is 1001, and each data point in the power spectrum signal contains random noise, and s (100), s (150), s (500), s (508), and s (700) in the power spectrum signal contain line spectrum components with the amplitudes of 6, 9, 10, 5, 4, respectively, and the power spectrum signal s (n) is shown in fig. 2;
according to the step (2), initializing parameters, and setting 4 groups of data windows and threshold parameters, wherein the parameters of each group are set as follows: group 1 parameters have left window length L110, right window length R110, the number of large exclusion U1Number of exclusion of Small value D ═ 313, amplitude threshold G18; group 2 parameters have left window length L215, right window length R215, large exclusion number U2Number of decimal exclusions D424, amplitude threshold G28; group 3 parameters have left window length L320, right window length R320, large exclusion number U3Number of exclusion of decimal fraction D ═ 535, amplitude threshold G37; group 4 parameters have left window length L430, right window length R430, large exclusion number U4Number of exclusion of decimal fraction D ═ 848, amplitude threshold G47; and setting the power spectrum sequence number index i to be 0 and setting the line spectrum set E as an empty set when the comprehensive threshold H is 2.
According to the step (3), i +1 operation is executed, if i is equal to N-1, the line spectrum detection operation is finished, and at this time, each element in the line spectrum set E is the detected line spectrum parameter, E { (100,186.70), (150,187.97), (500,181.21), (508, 177.96), (700,161.95) }, as shown in fig. 3, wherein the line spectrum is marked with a ×; if i is less than N-1, further judging whether the power spectrum data s (i) is a maximum value point, if s (i) is the maximum value point, executing the step (4), and if s (i) is not the maximum value point, repeatedly executing the step;
(4) under each group of parameter conditions, calculating statistic corresponding to the maximum value point s (i), and preliminarily judging whether the maximum value point meets the line spectrum point condition under each group of parameter conditions;
(5) comprehensively judging whether the s (i) is a line spectrum point, and if so, adding the line spectrum point into the set E;
(6) if i is less than N-1, returning to the step (3) to continue execution; otherwise, the method flow ends.
Claims (3)
1. A multi-scale noise power spectral line spectrum detection method is characterized by comprising the following steps:
(1) acquiring power spectrum data s (N) of a noise signal to be detected, wherein N is 0,1, … and N-1, wherein N is a power spectrum data serial number, and N is the number of the power spectrum data;
(2) initializing parameters, and setting M groups of data windows and threshold parameters, wherein M is a natural number greater than 0, a power spectrum sequence number index i is set to be 0, and a line spectrum set is set to be E;
(3) executing i-i +1 operation, if i is equal to N-1, ending the line spectrum detection work, and at this time, each element in the line spectrum set E is the detected line spectrum parameter; if i < N-1, further judging whether the power spectrum data s (i) is a maximum value point, if s (i) is the maximum value point, executing the step (4), and if s (i) is not the maximum value point, repeatedly executing the step (3);
(4) under each group of parameter conditions, calculating statistic corresponding to the maximum value point s (i), and preliminarily judging whether the maximum value point meets the line spectrum point condition under each group of parameter conditions;
(5) comprehensively judging whether the s (i) is a line spectrum point, and if so, adding the line spectrum point into the set E;
(6) if i < N-1, returning to the step (3) to continue execution; otherwise, the method ends;
in step (2), the parameter initialization method is as follows: setting M groups of independent parameters with different window length scales, wherein the M group of parameters comprises a left window length LmLength of right window RmNumber of large value exclusion UmNumber of decimal exclusions DmAmplitude threshold GmWherein M is 1,2, …, M, Lm,Rm,Um,DmAre all natural numbers greater than 0, GmSetting a comprehensive threshold H for a real number larger than 0, setting a power spectrum sequence number index i to be 0 and setting a line spectrum set E as a null set, wherein H is a natural number larger than 0;
in the step (3), the concrete steps are as follows:
(3-1) performing an i ═ i +1 operation;
(3-2) if i is equal to N-1, finishing the line spectrum detection work, and collecting each element in the E, namely the detected line spectrum parameter; if i < N-1, further judging whether the power spectrum data s (i) is a maximum value point, namely whether the conditions s (i) ≧ s (i-1) and s (i) > s (i +1) are met, if s (i) is the maximum value point, executing the step (4), and if s (i) is not the maximum value point, repeatedly executing the step (3);
in step (4), under each group of parameter conditions, calculating statistics corresponding to the maximum value point s (i), preliminarily judging whether the maximum value point meets the line spectrum condition under each group of parameter conditions, and when the mth group of parameters is selected, the steps are as follows:
(4-1) aiming at the maximum value point s (i), intercepting corresponding data from s (n) according to the selected mth group of parameters, and fetching data length Km＝min(N-1,i+Rm)-max(0,i-Lm) +1, max () and min () denote maximum and minimum values, respectively, and the data truncated using the m-th set of parameters is gm(j)＝s(j+max(0,i-Lm))，j＝0,1,…,Km-1；
(4-2) calculation of gm(j) Mean value of (a)0；
(4-3) calculation of the sequence bm(j) The formula is as follows:
(4-4) vs. sequence gm(j) Performing background equalization to obtain
The formula is as follows:
wherein, the operation is an inner product operation;
(4-6) data sequence
Form a data set
(4-7) in the data set PmIn finding out the front UmA maximum value element and from the data set PmRemoving;
(4-8) in the data set PmIn, find out the front DmA minimum value element and from the data set PmRemoving;
(4-9) calculating the data set P after (4-7) and (4-8) processingmMean value e ofmAnd standard deviation σm；
(4-10) when s (i) is satisfied>a0+em+Gm×σmThen, the m-th group of line spectrum flags L are setmIf not, the line spectrum flag L of the mth group is setm＝0；
(4-11) repeating the above process until all M sets of parameters are calculated.
2. The method for detecting the power spectrum of multi-scale noise according to claim 1, wherein in the step (1), the power spectrum data s (n) of the noise signal to be detected is obtained by the following method: receiving collected data from a sensor and carrying out power spectrum analysis to obtain s (n); or reading the power spectrum data s (n) of the noise signal to be detected from the memory.
3. The method for detecting the spectrum of the multi-scale noise power spectrum according to claim 1 or 2, wherein in the step (5), whether each maximum point is a line spectrum point is comprehensively judged, and the judgment method is as follows: each group of line spectrum marks satisfies
Then, the s (i) point in the power spectrum data is finally decided as a line spectrum, and the (i, s (i)) at this time is added to the set E as an element.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910565539.4A CN110376436B (en) | 2019-06-27 | 2019-06-27 | Multi-scale noise power spectral line spectrum detection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910565539.4A CN110376436B (en) | 2019-06-27 | 2019-06-27 | Multi-scale noise power spectral line spectrum detection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110376436A CN110376436A (en) | 2019-10-25 |
| CN110376436B true CN110376436B (en) | 2021-06-01 |
Family
ID=68250717
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910565539.4A Active CN110376436B (en) | 2019-06-27 | 2019-06-27 | Multi-scale noise power spectral line spectrum detection method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110376436B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112379357B (en) * | 2020-11-16 | 2023-10-27 | 东南大学 | Signal discrimination method based on secondary processing of pulse signal estimated time parameter |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002148329A (en) * | 2000-11-10 | 2002-05-22 | Taiyo Musen Co Ltd | Disturbance wave removing method |
| CN104217616A (en) * | 2014-08-19 | 2014-12-17 | 中国电子科技集团公司第二十三研究所 | Inland waterway traffic monitoring implementation method based on optical fiber acoustic sensors |
| CN109061591A (en) * | 2018-07-23 | 2018-12-21 | 东南大学 | A kind of time-frequency line-spectrum detection method based on sequential cluster |
| CN109285561A (en) * | 2018-09-06 | 2019-01-29 | 东南大学 | A kind of ship propeller cavitation noise Modulation Spectral Feature fidelity Enhancement Method based on adaptive window length |
| 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 |
-
2019
- 2019-06-27 CN CN201910565539.4A patent/CN110376436B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002148329A (en) * | 2000-11-10 | 2002-05-22 | Taiyo Musen Co Ltd | Disturbance wave removing method |
| CN104217616A (en) * | 2014-08-19 | 2014-12-17 | 中国电子科技集团公司第二十三研究所 | Inland waterway traffic monitoring implementation method based on optical fiber acoustic sensors |
| CN109061591A (en) * | 2018-07-23 | 2018-12-21 | 东南大学 | A kind of time-frequency line-spectrum detection method based on sequential cluster |
| CN109285561A (en) * | 2018-09-06 | 2019-01-29 | 东南大学 | A kind of ship propeller cavitation noise Modulation Spectral Feature fidelity Enhancement Method based on adaptive window length |
| 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 |
Non-Patent Citations (1)
| Title |
|---|
| "舰船噪声包络的高阶统计量特征研究";周清平;《中国优秀硕士学位论文全文数据库（工程科技II辑）》;20040315(第3期);C036-24 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110376436A (en) | 2019-10-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11057788B2 (en) | Method and system for abnormal value detection in LTE network | |
| CN113838054B (en) | Mechanical part surface damage detection method based on artificial intelligence | |
| CN111562108A (en) | Rolling bearing intelligent fault diagnosis method based on CNN and FCMC | |
| CA2931624A1 (en) | Systems and methods for event detection and diagnosis | |
| CN115063409A (en) | Method and system for detecting surface material of mechanical cutter | |
| CN110619296A (en) | Signal noise reduction method based on singular decomposition | |
| CN112380992B (en) | Method and device for evaluating and optimizing accuracy of monitoring data in machining process | |
| CN112614109B (en) | Image quality evaluation method, apparatus and computer readable storage medium | |
| CN113569756B (en) | Abnormal behavior detection and positioning method, system, terminal equipment and readable storage medium | |
| CN111967535A (en) | Fault diagnosis method and device for temperature sensor in grain storage management scene | |
| CN113128567A (en) | Abnormal electricity consumption behavior identification method based on electricity consumption data | |
| CN114972339B (en) | Data enhancement system for bulldozer structural member production abnormity detection | |
| CN110376436B (en) | Multi-scale noise power spectral line spectrum detection method | |
| CN117076941A (en) | Optical cable bird damage monitoring method, system, electronic equipment and readable storage medium | |
| CN114002334A (en) | Structural damage acoustic emission signal identification method and device and storage medium | |
| CN117665935A (en) | Monitoring data processing method for broken rock mass supporting construction process | |
| CN110569764A (en) | mobile phone model identification method based on convolutional neural network | |
| CN104977602A (en) | Control method and apparatus for earthquake data acquisition construction | |
| CN111901268A (en) | BPSK/QPSK signal modulation identification method based on frequency spectrum rearrangement and Gumbel distribution fitting test | |
| CN116977834A (en) | Method for identifying internal and external images distributed under open condition | |
| Cope et al. | Classifying plant leaves from their margins using dynamic time warping | |
| CN112014821B (en) | Unknown vehicle target identification method based on radar broadband characteristics | |
| CN111830488B (en) | Echo signal data processing method and system based on GM-APD laser ranging system | |
| CN112016539B (en) | Signal identification method and device, electronic equipment and storage medium | |
| CN109945075B (en) | Method and device for detecting leakage degree of water supply pipeline |
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 | ||
| GR01 | Patent grant |