CN106646012B - method for extracting emission spectrum envelope of frequency equipment - Google Patents
method for extracting emission spectrum envelope of frequency equipment Download PDFInfo
- Publication number
- CN106646012B CN106646012B CN201610812759.9A CN201610812759A CN106646012B CN 106646012 B CN106646012 B CN 106646012B CN 201610812759 A CN201610812759 A CN 201610812759A CN 106646012 B CN106646012 B CN 106646012B
- Authority
- CN
- China
- Prior art keywords
- maximum value
- frequency
- spectrum
- spectrum data
- data
- 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
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Complex Calculations (AREA)
Abstract
A method for extracting emission spectrum envelope of frequency equipment includes steps of reducing and of original spectrum data amplitude, solving extremum by difference, firstly, carrying out difference calculation on discrete spectrum data to obtain difference vectors, then extracting maximum values in the spectrum data to form times of maximum value spectrum data as extracted maximum value point data, 4) carrying out endpoint processing on the maximum value point data after reduction processing to generate maximum value spectrum data, 5) carrying out curve fitting on the generated maximum value spectrum data by adopting a spline interpolation mode to obtain spectrum envelope curve after interpolation processing.
Description
Technical Field
The invention belongs to the field of electromagnetic compatibility tests, and particularly relates to an frequency equipment emission spectrum envelope extraction method which can form frequency equipment emission spectrum envelopes by processing local maxima and end points of discrete spectrum data.
Background
The emission spectrum of the frequency equipment is represented by a frequency domain of the emission characteristic of the frequency equipment, and is calculated according to frequency deviation △ f in frequency equipment frequency spectrum compatibility analysis and electromagnetic compatibility analysis.
In order to more conveniently apply the frequency-using equipment emission spectrum to frequency-using equipment frequency spectrum compatibility analysis and electromagnetic compatibility analysis, generally adopts the frequency spectrum envelope rather than the original frequency spectrum test data, and the adoption of the frequency spectrum envelope has the advantages of 1) being capable of reflecting the frequency spectrum characteristics of the frequency-using equipment more truly, especially when the receiving bandwidth of sensitive equipment is larger than the frequency spectrum test bandwidth, 2) reducing the data storage capacity, and the frequency spectrum envelope only needs to access key characteristic points, and 3) being convenient for the conversion of emission spectra of different frequency points.
The data envelope extraction algorithm described in the literature at present mainly includes a Hilbert transform method, a spectrum template method, and the like, and the two methods have the following main problems:
(1) the Hilbert transform method is suitable for time-domain envelope extraction (namely, time information is contained in data), is low-pass equivalent of a signal, and can realize separation of a modulation signal and a carrier signal. Fig. 1 shows the result of extracting the AM modulation signal envelope using the Hilbert transform, from which Hilbert can extract the envelope for data containing time information. The frequency-equipped emission spectrum data has no corresponding time information, so the method cannot be used for extracting the frequency-equipped emission spectrum envelope.
(2) The spectrum template method forms the (linear) envelope of the spectrum by several lines (monotone decreasing), which has the advantages of greatly reduced amount of stored data and the disadvantages of being over conservative and not accurate enough. Fig. 2 is a schematic diagram of extracting the emission spectrum envelope of the frequency equipment by adopting a spectrum template method, and the advantages and the disadvantages of the method can be clearly seen from the results.
Disclosure of Invention
The invention aims to solve the technical problems that a traditional spectrum template method is too rough in spectrum extraction, spectrum data has no time information, and Hilbert transformation cannot be used for extracting envelopes, and the like, and frequency equipment emission spectrum envelope extraction methods are provided, wherein the frequency equipment emission spectrum envelope extraction method is based on local maximum extraction and can be applied to precise modeling of frequency equipment emission spectra in spectrum compatibility analysis and electromagnetic compatibility analysis.
The technical scheme adopted by the invention for solving the technical problems is as follows:
A method for extracting the emission spectrum envelope of a user equipment, comprising the following steps:
(1) performing amplitude classification , namely traversing the whole spectrum data of the original spectrum data, finding out the maximum value of the spectrum, and performing difference between each spectrum data and the maximum value of the spectrum, namely classifying the amplitude into dB;
(2) frequency is classified , namely, the reference frequency value f corresponding to the maximum value of the frequency spectrum is used as the reference frequency value f on the basis of the amplitude classification 0On the basis of each frequency value and f0Making difference, namely, the frequency is normalized to Hz;
(3) times of maximum value calculation, namely, carrying out differential calculation on discrete frequency spectrum data by adopting differential solution extremum to obtain differential vectors, wherein if the signs of adjacent elements in the differential vectors are opposite, the corresponding points are maximum value points, if the first differential vector elements are positive, the maximum value is corresponding to the situation that the back differential vector elements are negative, then the maximum value in the frequency spectrum data is extracted to form times of maximum value frequency spectrum data as the extracted maximum value point data;
(4) and (3) end point processing, namely setting the two end point values of the spectrum data subjected to the regression processing as P '(1) and P' (n), respectively setting the two end point values of the maximum point data extracted in the step (3) as env (2) and env (n-1), and respectively setting the calculation formulas of the maximum point spectrum data end point values env (1) and env (n) generated after the end point processing as follows:
env(1)＝P′(1)+0.5×|P′(1)-env(2)|
env(n)＝P′(n)+0.5×|P′(n)-env(n-1)|
(5) interpolation: and (4) performing curve fitting on the generated maximum value spectrum data by using the frequency indexes in the maximum value spectrum data and the original spectrum data generated in the step (4) and adopting a spline interpolation mode to obtain a spectrum envelope curve after interpolation processing.
According to the scheme, the step (3) further comprises a secondary maximum value calculation step, wherein for frequency utilization equipment with violent frequency spectrum change, secondary extreme value extraction is carried out according to times of envelope extraction conditions, if the smoothness of the envelope is poor, specifically, difference calculation is carried out again on the basis of times of maximum value frequency spectrum data, maximum values in the maximum value frequency spectrum data are extracted, and secondary maximum value frequency spectrum data are formed to serve as the extracted maximum value point data.
According to the scheme, the spline interpolation mode in the step (5) comprises a cubic interpolation method and a spline cubic spline function interpolation method.
Compared with the prior art, the invention has the following beneficial effects:
1. the problems that a traditional spectrum template method is too rough in spectrum extraction, spectrum data have no time information and Hilbert transform cannot be used for extracting envelopes and the like are solved, according to the characteristics of specific equipment spectrum data, -time extremum or secondary extremum is adopted to extract the spectrum envelopes, -degree spectrum envelope data are generated through amplitude-degree classification and frequency-degree classification , and the spectrum envelope data are smoothed through spline interpolation;
2. the invention has simple and clear calculation principle and strong engineering practicability.
Drawings
FIG. 1 is a prior art schematic diagram of extracting an AM modulated signal envelope using a Hilbert transform;
FIG. 2 is a diagram illustrating a prior art method for extracting a spectral envelope by using a spectral template;
FIG. 3 is a diagram of raw spectral data according to an embodiment of the present invention;
FIG. 4 is a schematic illustration of the raw spectral data of FIG. 3 after quantization ;
FIG. 5 is a schematic diagram of an embodiment of the present invention classifying the spectral data and extracted end points unprocessed sub-extreme points;
FIG. 6 is a schematic illustration of normalized spectrum data and endpoint processed maximum spectrum data generated in accordance with an embodiment of the present invention;
FIG. 7 is a schematic diagram of an interpolated spectral envelope obtained by cubic interpolation according to an embodiment of the present invention;
fig. 8 is a schematic diagram of an interpolated spectral envelope obtained by spline interpolation according to an embodiment of the present invention.
Detailed Description
The present invention will now be described in further detail with reference to specific embodiments and with reference to the accompanying drawings.
The frequency equipment used in the embodiment of the invention is a certain high-power transmitting device, the data of the transmitting spectrum of the device is shown in figure 3, wherein the horizontal axis is frequency, and the vertical axis is spectrum energy, and the method for extracting the envelope of the transmitting spectrum of the frequency equipment used in the embodiment of the invention comprises the following steps:
(1) amplitude regression into
For the original frequency spectrum data, traversing the whole frequency spectrum data to find out the maximum value P of the frequency spectrummaxOn the basis, each frequency spectrum data is compared with the maximum value P of the frequency spectrummaxMaking a difference:
Pi′＝Pi-Pmax
wherein, Pi' is the processed (frequency normalized ) spectral amplitude value, PiI is more than or equal to 1 and less than or equal to N, and N is the total number of the frequency spectrum data;
by this step the spectral amplitude is normalized to 0 dB;
(2) frequency regression into
Based on amplitude normalization , the maximum value P of the frequency spectrum is usedmaxCorresponding reference frequency value f0On the basis of each frequency value and f0Making a difference:
fi′＝fi-f0
wherein f isi' is the processed frequency value, fiI is more than or equal to 1 and less than or equal to N, and N is the total number of the frequency spectrum data;
the frequency is normalized to Hz by the step, and the spectrum data after being normalized to is shown in FIG. 4;
(3) times maximum calculation
Because the spectrum data is discrete data, the difference is adopted to solve the extreme value instead of the differential in the specific calculation, and the differential calculation is firstly carried out on the discrete spectrum data, so that differential vectors A are obtained:
A＝diff(P′)
if the signs of adjacent elements in the differential vector A are opposite, the corresponding points are extreme points, and if the first differential vector elements are positive, the maximum value is corresponding to the situation that the last elements are negative;
then extracting the maximum value in the spectrum data to form times maximum value spectrum data, as shown in fig. 5;
carrying out secondary extreme value extraction on frequency-using equipment with violent frequency spectrum change according to the characteristics of the frequency spectrum data of the specific equipment and according to the times of envelope extraction conditions, if the envelope smoothness is poor, carrying out differential calculation again on the basis of times of maximum value frequency spectrum data, extracting the maximum value in the frequency-using equipment, and forming secondary maximum value frequency spectrum data as extracted maximum value point data;
(4) endpoint processing
Setting the two endpoint values of the spectrum data subjected to the regression processing as P '(1) and P' (n), respectively setting the two endpoint values of the maximum point data extracted in the step (3) as env (2) and env (n-1), and respectively setting the calculation formulas of the maximum point spectrum data env (1) and env (n) generated after the endpoint processing as:
env(1)＝P′(1)+0.5×|P′(1)-env(2)|
env(n)＝P′(n)+0.5×|P′(n)-env(n-1)|
the maximum spectral data generated after endpoint processing is shown in fig. 6;
(5) interpolation
In order to make the envelope of the generated maximum value spectrum data have a smoothing effect, the maximum value spectrum data generated in step (4) and the frequency index in the original spectrum data are used, a spline interpolation method is adopted, and a cubic (cubic interpolation) and spline (cubic spline interpolation) method is adopted in to perform curve fitting on the generated maximum value spectrum data to obtain a spectrum envelope after interpolation processing, the spectrum envelope after the cubic interpolation processing is shown in fig. 7, and the spectrum envelope after the spline interpolation processing is shown in fig. 8.
It should be understood that the above examples are only for clearly illustrating the present invention and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And such obvious variations or modifications which fall within the spirit of the invention are intended to be covered by the scope of the present invention.
Claims (2)
1, A method for extracting emission spectrum envelope by frequency equipment, which is characterized by comprising the following steps:
(1) performing amplitude classification , namely traversing the whole spectrum data of the original spectrum data, finding out the maximum value of the spectrum, and performing difference between each spectrum data and the maximum value of the spectrum, namely classifying the amplitude into dB;
(2) frequency is classified , namely, the reference frequency value f corresponding to the maximum value of the frequency spectrum is used as the reference frequency value f on the basis of the amplitude classification 0On the basis of each frequency value and f0Making difference, namely, the frequency is normalized to Hz;
(3) times of maximum value calculation, namely, carrying out differential calculation on discrete frequency spectrum data by adopting differential solution extremum to obtain differential vectors, wherein if the signs of adjacent elements in the differential vectors are opposite, the corresponding points are maximum value points, if the first differential vector elements are positive, the maximum value is corresponding to the situation that the back differential vector elements are negative, then the maximum value in the frequency spectrum data is extracted to form times of maximum value frequency spectrum data as the extracted maximum value point data;
for frequency-using equipment with violent frequency spectrum change, according to the times of envelope extraction conditions, if the envelope smoothness is poor, carrying out secondary extreme value extraction, specifically, carrying out differential calculation again on the basis of times of maximum value frequency spectrum data, extracting the maximum value therein, and forming secondary maximum value frequency spectrum data as extracted maximum value point data;
(4) and (3) end point processing, namely setting the two end point values of the spectrum data subjected to the regression processing as P '(1) and P' (n), respectively setting the two end point values of the maximum point data extracted in the step (3) as env (2) and env (n-1), and respectively setting the calculation formulas of the maximum point spectrum data end point values env (1) and env (n) generated after the end point processing as follows:
env(1)＝P′(1)+0.5×|P′(1)-env(2)|
env(n)＝P′(n)+0.5×|P′(n)-env(n-1)|
(5) interpolation: and (4) performing curve fitting on the generated maximum value spectrum data by using the frequency indexes in the maximum value spectrum data and the original spectrum data generated in the step (4) and adopting a spline interpolation mode to obtain a spectrum envelope curve after interpolation processing.
2. The method for extracting an emission spectrum envelope by using a frequency equipment as claimed in claim 1, wherein the spline interpolation mode of the step (5) comprises two methods of cubic interpolation and spline cubic spline function interpolation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610812759.9A CN106646012B (en) | 2016-09-09 | 2016-09-09 | method for extracting emission spectrum envelope of frequency equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610812759.9A CN106646012B (en) | 2016-09-09 | 2016-09-09 | method for extracting emission spectrum envelope of frequency equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106646012A CN106646012A (en) | 2017-05-10 |
| CN106646012B true CN106646012B (en) | 2020-01-31 |
Family
ID=58853050
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610812759.9A Active CN106646012B (en) | 2016-09-09 | 2016-09-09 | method for extracting emission spectrum envelope of frequency equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106646012B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107769866B (en) * | 2017-09-22 | 2020-12-01 | 中国舰船研究设计中心 | Method for predicting coupling power of receiver |
| CN107576848B (en) * | 2017-09-27 | 2019-07-26 | 中国电子科技集团公司第五十四研究所 | A kind of template setting and template detection method based on spectrum analysis |
| CN108196237B (en) * | 2017-12-26 | 2021-06-25 | 中南大学 | Method for inhibiting parasitic amplitude modulation in FMCW radar echo signal |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005338558A (en) * | 2004-05-28 | 2005-12-08 | Toshiba Corp | Receiving device |
| CN1875887A (en) * | 2005-06-10 | 2006-12-13 | 深圳迈瑞生物医疗电子股份有限公司 | Method for extracting envelope curve of sound spectrogram |
| CN103178806A (en) * | 2011-12-23 | 2013-06-26 | 中国科学院声学研究所 | Envelope extraction method and system for one-dimensional data |
| CN203984361U (en) * | 2014-08-22 | 2014-12-03 | 哈尔滨同为电气股份有限公司 | A kind of low frequency signal envelope of practicality extracts circuit |
| CN104504181A (en) * | 2014-12-10 | 2015-04-08 | 宁波大学 | Signal envelope line extracting method based on sparse recovery |
-
2016
- 2016-09-09 CN CN201610812759.9A patent/CN106646012B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005338558A (en) * | 2004-05-28 | 2005-12-08 | Toshiba Corp | Receiving device |
| CN1875887A (en) * | 2005-06-10 | 2006-12-13 | 深圳迈瑞生物医疗电子股份有限公司 | Method for extracting envelope curve of sound spectrogram |
| CN103178806A (en) * | 2011-12-23 | 2013-06-26 | 中国科学院声学研究所 | Envelope extraction method and system for one-dimensional data |
| CN203984361U (en) * | 2014-08-22 | 2014-12-03 | 哈尔滨同为电气股份有限公司 | A kind of low frequency signal envelope of practicality extracts circuit |
| CN104504181A (en) * | 2014-12-10 | 2015-04-08 | 宁波大学 | Signal envelope line extracting method based on sparse recovery |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106646012A (en) | 2017-05-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107124381B (en) | Automatic identification method for digital communication signal modulation mode | |
| CN110728360B (en) | Micro-energy device energy identification method based on BP neural network | |
| CN106646012B (en) | method for extracting emission spectrum envelope of frequency equipment | |
| CN108764077B (en) | Digital signal modulation classification method based on convolutional neural network | |
| CN106685478B (en) | Frequency hopping signal parameter estimation method based on signal time-frequency image information extraction | |
| CN112749633B (en) | Separate and reconstructed individual radiation source identification method | |
| CN106330805B (en) | A kind of signal modulation mode automatic identifying method and system | |
| CN104850859A (en) | Multi-scale analysis based image feature bag constructing method | |
| CN113225282A (en) | Communication signal modulation identification method based on BP neural network | |
| CN110176250A (en) | It is a kind of based on the robust acoustics scene recognition method locally learnt | |
| CN113095162B (en) | Spectrum sensing method based on semi-supervised deep learning | |
| EP4012578A1 (en) | Face retrieval method and device | |
| CN109067678A (en) | Based on Higher Order Cumulants WFRFT signal cascade Modulation Identification method, wireless communication system | |
| CN116708108A (en) | Communication signal modulation recognition system and method | |
| CN110086554B (en) | Signal identification method based on spectrum sensing | |
| CN116506273A (en) | Novel MPSK modulation signal identification and classification method | |
| CN115147895B (en) | Face fake identifying method and device | |
| Bammer et al. | Invariance and stability of Gabor scattering for music signals | |
| CN116383719A (en) | MGF radio frequency fingerprint identification method for LFM radar | |
| CN106815314A (en) | Image search method based on amplitude phase hybrid modeling | |
| CN106448676A (en) | Robot speech recognition system based on natural language processing | |
| CN108229487B (en) | Significance detection method combining space domain and frequency domain | |
| CN113657138A (en) | Radiation source individual identification method based on equipotential planet chart | |
| Gros et al. | Joint use of bivariate empirical mode decomposition and convolutional neural networks for automatic modulation recognition | |
| KR20200046281A (en) | System and Method for Retrieving Image Based Content Using Color Descriptor and Discrete Wavelet Transform |
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 |