CN105207965A - Automatic VHF/UHF frequency range modulation identification method - Google Patents
Automatic VHF/UHF frequency range modulation identification method Download PDFInfo
- Publication number
- CN105207965A CN105207965A CN201510498093.XA CN201510498093A CN105207965A CN 105207965 A CN105207965 A CN 105207965A CN 201510498093 A CN201510498093 A CN 201510498093A CN 105207965 A CN105207965 A CN 105207965A
- Authority
- CN
- China
- Prior art keywords
- signal
- modulation
- frequency
- vhf
- symbol rate
- 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.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/0012—Modulated-carrier systems arrangements for identifying the type of modulation
Abstract
The invention discloses an automatic VHF/UHF frequency range modulation identification method. The method comprises steps that, 1, data is introduced in order; 2, time domain abrupt detection the on introduced data is carried out; 3, frequency domain segmentation for the introduced data is carried out; 4, amplitude modulation signals are distinguished; 5, phase modulation signals are distinguished; 6, frequency modulation signals are distinguished; 7, unknown signals and noise are distinguished through instantaneous envelope statistics of parametric fluctuation; and 8, majority decision on multiple identification results is carried out, combined decision is carried out by employing multiple identification results, relations among multiple identification results are comprehensively considered, and modulation modes of the identified signals and other satellite information are mastered. Compared with the prior art, distinguish identification on the modulation signals can be effectively realized, the modulation identification scheme is simple to operate, a classifier is reasonable in design, and classification performance is excellent.
Description
Technical field
The present invention relates to a kind of signal modulate field, particularly relate to a kind of Automatic modulation classification method of VHF/UHF frequency range.
Background technology
In the today of modulating and protocol mode is comparatively complicated, Modulation Recognition of Communication Signal becomes a challenging job, especially the blind recognition of modulation system.This recognition methods, mainly to the large class identification of signal madulation, also can identify between the class of partial modulation mode meanwhile.Signal modulate module, is exactly under the prerequisite of unknown modulation intelligence content, judges the modulation system of signal of communication; And some parameters of estimated signal, for next step treatment and analysis provides reliable basis.
Along with the development of the communication technology, signal madulation mode presents varied; This just requires Automatic modulation classification, and the method for Automatic modulation classification is more, and at present, the grader being applied to Modulation Identification mainly comprises three kinds: decision tree, artificial neural net (ANN) and SVMs (SVM).Be need to arrange suitable decision threshold and how to select optimal judgement order for one of traditional decision-tree difficult point, and ANN and SVM does not need to arrange thresholding, but need to train grader.Consider the concrete condition of parameter kind and the identification of extracting at present, employing traditional decision-tree is carried out by the sorting technique of this recognition methods.
Traditional decision-tree is simply direct, is highly suitable for line application.The decision rule of this algorithm utilizes a certain characteristic parameter exactly, according to decision threshold, the set-partition be identified is become two nonoverlapping subclass, and then utilizes another feature parameter to split subset again.In decision tree, first be necessary for each characteristic value and select suitable threshold value, the selected of threshold value can obtain according to theory analysis, but after certain conversion process is carried out to signal, the theoretical value analyzing its characteristic parameter is again difficult, so be adopt test data statistics to obtain mostly.In traditional decision-tree, although adopt identical characteristic value, in sorting algorithm, apply these characteristic values by different order just can obtain many different algorithms.Under same signal to noise ratio, these algorithms but have different classification accuracy rates, and therefore the characteristic value time sequencing of these class methods is also very important.
Summary of the invention
Object of the present invention is just to provide one to solve the problem, and is applied to the Automatic modulation classification method of VHF/UHF frequency range.
To achieve these goals, the technical solution used in the present invention is: a kind of Automatic modulation classification method of VHF/UHF frequency range, method step is as follows
Step one, import data in order;
Step 2, to import data carry out the sudden detection of time domain;
Step 3, to import data carry out frequency domain segmentation;
Step 4, carry out am signals differentiation, namely by discrete spectral line quantity and distribution situation Division identification ASK, AM, CW modulation signal within the scope of signal bandwidth;
Step 5, carry out phase modulated signal differentiation, i.e. and instantaneous frequency kurtosis discontinuous by phase place, phase-modulation and frequency modulated mode are distinguished, identify between phase modulated signal class and the planisphere of blind demodulation will be adopted to judge;
Step 6, carry out frequency modulated signal differentiation, for frequency modulated signal, waveform after frequency discrimination is carried out spectrum analysis, if frequency spectrum is mainly distributed in low-frequency range, be just judged to FSK modulation, if instead frequency spectrum is mainly concentrated in band, then can be considered that FM modulates, FSK modulates between class and identifies and will utilize the statistical property of instantaneous frequency and realize;
Step 7, unknown signaling and noise are distinguished, and are realized by the fluctuation of instantaneous envelope statistical parameter;
The majority decision of step 8, repeatedly recognition result, adopts the result cascading judgement repeatedly identified, and considers the relation between each recognition result, be finally clearly identified modulation system and other satellite informations of signal.
As preferably, in step one, data import the comparatively systemic circulation buffer area that inside modules opens in an orderly manner, recognizer by circular buffer district progressively, piecemeal carry out Classification and Identification, data importing adopts different threads to realize with identifying.
As preferably, in step 2, less section segmentation is carried out to the envelope importing data, by two sections, front and back energy jump, judge that signal time domain continuity or burst detect; Meanwhile, the signal section that Time Domain Piecewise detects is used for frequency domain carriers and bandwidth estimation and follow-up identifying processing.
As preferably, in step 3, using the value between frequency spectrum transition band and signal as noise gate, be applied to bandwidth sum center frequency estimation; Using the energy within the scope of the signal bandwidth estimated and with the ratio of the energy summation within the scope of narrow band filter as arrowband signal to noise ratio.
As preferably, in step 4, after completing frequency domain segmentation, the whole valid data of this section need be utilized to calculate High-Resolution Spectral, discrete spectral line distribution situation in retrieval band, and adopt whether certain SNR criterion detection is discrete spectral line, if the discrete spectral line of frequency spectrum existence anduniquess, tentatively determines whether AM, CW, ASK signal, then according to signal, whether there is symbol rate information and therefrom distinguish ASK, according to spectral bandwidth with about carrier frequency symmetry, AM is distinguished again, otherwise be considered as CW signal; If preliminary judgement is AM modulation, also needing to utilize the kurtosis value of its envelope to judge further is AM speech, or AM2FSK Multi-modulation signal, and for the AM2FSK signal of secondary modulation, its digital modulation symbol rate adopts general 2FSK symbol rate to estimate mode.
As preferably, in step 5, phase modulated signal is single-carrier signal, its instantaneous frequency kurtosis value is comparatively large, utilizes envelope frequency spectrum, according to bandwidth value according to certain limit detected symbol rate information, if there is symbol rate information, then tentatively be considered as phase modulated signal, the identification between phase modulated signal MPSK and MQAM, adopt the planisphere of blind demodulation mode to judge.
As preferably, in step 6, for frequency modulated signal, waveform after frequency discrimination is carried out spectrum analysis, if frequency spectrum is mainly distributed in low-frequency range, be considered as FSK modulation, if instead frequency spectrum is mainly concentrated in certain band, then can be considered that FM modulates, this tentatively realizes the differentiation of analog-modulated and digital modulation.
As preferably, if FM modulates, whether the baseband signal after then also needing the waveform after with regard to demodulation again to utilize waveform kurtosis value and frequency discrimination has symbol rate information to confirm the Multi-modulation signal into FM (2FSK), and the symbol rate of FM (2FSK) is estimated to estimate that mode is similar with 2FSK symbol rate; If continuous fsk modulated signal, symbol rate is estimated to utilize instantaneous frequency discrete spectrum line features to estimate; If burst FSK, because of the symbol quantity of one section of bursty data does not reach requirement, utilize aforesaid way to be difficult to realize, symbol rate estimation need be realized by each code element number of samples according to a preliminary estimate.
As preferably, demonstrate,prove as 2FSK modulation, and modulation index is close to 0.5 if sentenced, then whether recycle the discrete spectrum line features of the quadratic power spectrum of primary signal, sentencing card is MSK modulation signal.
As preferably, in step 7, to in frequency spectrum not existence anduniquess discrete spectral line and without the signal of symbol rate information, distinguish unknown signaling and noise by the fluctuation of instantaneous envelope, because part distinctive signal direct estimation or the symbol rate information of extracting signal exist certain difficulty; Need to start other mode subregion identification distinctive signals; Need for distinctive signal the automatic identification module recognition detection starting these distinctive signals, it mainly adopts the modes such as power side's spectrogram of signal or blind demodulation to carry out recognition detection.
Compared with prior art, the invention has the advantages that: the present invention effectively can carry out Modulation Identification to VHF/UHF frequency band signals, identify that embodiment is simple, classifier design is reasonable, and classification performance is also superior.
Accompanying drawing explanation
Fig. 1 is principle of the invention block diagram;
Fig. 2 is Time Domain Piecewise effect schematic diagram of the present invention;
Fig. 3 is frequency domain stepwise schematic views of the present invention;
Fig. 4 is certain 8PSK Modulation Identification process planisphere.
Embodiment
Embodiment: be described further to the Automatic modulation classification method of a kind of VHF/UHF frequency range of the present invention below.Recognizer main body of the present invention is applied in VHF/UHF frequency range, and the signal to noise ratio of this signal can not be too low, and the modulation system of signal mainly considers that serial is modulated, and do not carry out the automatic identification of parallel modulation signal.And this frequency range AM and FM secondary modulation more, obviously sudden.Therefore, the main signal kind of identification includes:
Analog-modulated: FM broadcast/speech, AM, CW
Digital modulation: ASK, MPSK, 2PSK, 4PSK, 8PSK, MQAM, 8QAM, 16QAM, 32QAM, 64QAM, MSK, MFSK, 2FSK, 4FSK etc.
The whole bandwidth of consideration sampled signal is a signal, and sampling rate is signal bandwidth 4 ~ 8 times, and this contributes to automated manner frequency measurement and tests the speed and relevant judgement process.
Concrete grammar of the present invention is as follows, as Fig. 1:
Step one, import data in an orderly manner; Data be import in an orderly manner inside modules open comparatively systemic circulation buffer area, recognizer by circular buffer district progressively, piecemeal carry out Classification and Identification, data importing with identify adopt different threads to realize.Because ultrashort wave frequency band burst is more, for including useful signal section in identifying processing, realizing signal burst information simultaneously and estimating, so data importing and identification utilize different threads to realize.System supports I/Q complex signal and real signal two kinds of data importings.If real signal, inside modules is realized according to relevant parameters to I/Q complex signal continuously without the conversion interrupted by the step such as mixing, filtering.
Step 2, to import data carry out the sudden detection of time domain; Carrying out less section segmentation (such as: 1 millisecond, or fixing number of samples 128) to importing the envelope of data, by two sections, front and back energy jump, judging signal time domain continuity or burst detection; Meanwhile, the signal section detected by Time Domain Piecewise is used for frequency domain carriers and bandwidth estimation and follow-up identifying processing, and Time Domain Piecewise effect schematic diagram as shown in Figure 2.
Step 3, to import data carry out frequency domain segmentation, based on the ratio of sample rate and signal bandwidth at zone of reasonableness, the non-weak signal segment data of regular length is carried out to the FFT Power estimation (as: 1024) of less sampling point, can obtain meet the demands, frequency domain resolution is higher, stable frequency spectrum after multi-frame mean, this contributes to frequency and bandwidth estimation.Using the value between frequency spectrum transition band and signal as noise gate, be applied to bandwidth sum center frequency estimation.Using the energy within the scope of the signal bandwidth estimated and with the ratio of the energy summation within the scope of narrow band filter as arrowband signal to noise ratio, frequency domain stepwise schematic views is as shown in Figure 3.
Step 4, carry out am signals differentiation, namely the detection of discrete spectral line quantity and position within the scope of signal bandwidth is passed through, Division identification ASK and AM, CW signal, after completing frequency domain segmentation, the whole valid data of this section need be utilized to calculate High-Resolution Spectral, discrete spectral line distribution situation in retrieval band, and adopt whether certain SNR criterion detection is discrete spectral line, if the discrete spectral line of frequency spectrum existence anduniquess, tentatively determine whether AM, CW, ASK signal, then according to signal, whether there is symbol rate information and therefrom distinguish ASK, whether AM and CW is distinguished about carrier frequency symmetry again according to frequency spectrum.
Step 5, carry out phase modulated signal difference, namely by phase step and instantaneous frequency kurtosis, and the discrete spectral line produced by symbol rate in envelope spectrum, phase place and frequency modulated mode are distinguished; Identification between phase modulated signal, judges adopting the planisphere of blind demodulation mode.Phase modulated signal is single-carrier signal, its instantaneous frequency kurtosis value is larger, utilize envelope frequency spectrum, according to bandwidth value according to certain limit detected symbol rate information, if there is symbol rate information, be then tentatively considered as phase modulated signal, the identification between phase modulated signal MPSK and MQAM, adopt the planisphere of blind demodulation mode to judge, if Fig. 4 is certain 8PSK Modulation Identification process planisphere.
Step 6, carry out frequency modulated signal difference, carry out spectrum analysis by the waveform after frequency discrimination, if frequency spectrum is mainly distributed in low-frequency range, be considered as FSK modulation, if instead frequency spectrum is mainly concentrated in certain band, then can be considered that FM modulates.This tentatively realizes the differentiation of analog-modulated and digital modulation.If be FM modulation, then whether the baseband signal after also needing the waveform after with regard to demodulation again to utilize the kurtosis value of waveform and secondary frequency discrimination has symbol rate information to confirm the signal with secondary modulation for FM (2FSK).The symbol rate of FM (2FSK) is estimated to estimate that mode is identical with 2FSK symbol rate; If conventional fsk modulated signal continuously, symbol rate is estimated to utilize the discrete spectrum line features of instantaneous frequency to estimate; If burst FSK, because its symbol quantity does not reach certain requirement, aforesaid way is utilized to be difficult to realize, need by estimating that each code element number of samples realizes symbol rate and estimates; If sentenced card for 2FSK modulation, and modulation index is close to 0.5, then whether recycle the discrete spectrum line features of the quadratic power spectrum of primary signal, sentencing card is MSK modulation signal.
Step 7, distinguish signal and noise by the fluctuation of instantaneous envelope.Because part distinctive signal direct estimation or the information such as symbol rate of extracting signal exist certain difficulty; Need to start other mode subregion identification distinctive signals; Need for distinctive signal the automatic identification module recognition detection starting these distinctive signals, it mainly adopts the spectrogram of the power side of signal to carry out recognition detection.
The majority decision of step 8, repeatedly recognition result.
Adopt the result cascading judgement that repeatedly identifies, and consider the relation that each recognition result is shown in, be finally clearly identified modulation system and other satellite informations of signal.To the final Modulation Identification conclusion of a signal, rely on certain once result weigh.For improving the accuracy and reliability that identify, need to adopt the result cascading judgement that repeatedly identifies, and consider the relation that each recognition result is shown in, be finally just clearly identified modulation system and other satellite informations of signal.Concrete majority decision can be different according to different situations.
The correlated characteristic of Main Basis signal time-frequency figure of the present invention, frequency spectrum, temporal pattern (original waveform, instantaneous amplitude, instantaneous frequency etc.), and whether certain frequency spectrum of time domain waveform has obvious discrete spectral line and statistical parameter (as: kurtosis), realizes parameter extraction.If by certain conversion or process, the symbol rate or spectrum signature that match can be obtained, is illustrated as corresponding modulating mode.The present invention also will take majority decision criterion, carry out cascading judgement to the result repeatedly identified, be formed being identified more reliably describing of signal madulation mode.
Based on the parameter kind extracted and concrete condition, the present invention adopts traditional decision-tree to carry out, and whole Modulation Identification module is mainly extracted reliable parameter and identified from the relevant performance of the time-frequency figure of signal, spectrogram, instantaneous parameters; The feature mainly comprised: whether certain frequency spectrum of time domain waveform (original waveform, instantaneous amplitude, instantaneous frequency etc.) has some statistical parameter (such as: remove mean normalization variance, mean square deviation and average ratio, kurtosis) of obvious discrete spectral line, time domain waveform.By in the spectrogram of these different aspects of selective analysis signal or Time-domain Statistics in identifying, therefrom extract the information of comparatively reliable reference value, become the reliable basis of Automatic modulation classification.Wherein, comparatively outstanding a bit, by certain conversion or process, can obtain symbol rate information, that just illustrates that signal is digital modulation mode.
Finally, system will take majority decision criterion, carry out cascading judgement to the result repeatedly identified, be formed being identified more reliably describing of signal madulation mode.
The mode identification method that the present invention mainly adopts feature based to extract, comprises preliminary treatment, feature extraction and sorting algorithm.Wherein, pretreated content is relevant with specific algorithm, but mostly comprises carrier frequency, symbol rate, bandwidth, power estimation etc.The Modulation Identification extracted due to feature based is implemented not only simple, and when classifier design is reasonable, its classification performance is also superior.
Above exhaustive presentation is carried out to a kind of Automatic modulation classification method provided by the present invention, apply specific case herein to set forth principle of the present invention and execution mode, the explanation of above embodiment just understands method of the present invention and core concept thereof for helping; Simultaneously, for one of ordinary skill in the art, according to thought of the present invention, all will change in specific embodiments and applications, to change of the present invention and improve will be possible, and design and the scope of accessory claim defined can not be exceeded, in sum, this description should not be construed as limitation of the present invention.
Claims (10)
1. an Automatic modulation classification method for VHF/UHF frequency range, is characterized in that: method step is as follows
Step one, import data in order;
Step 2, to import data carry out the sudden detection of time domain;
Step 3, to import data carry out frequency domain segmentation;
Step 4, carry out am signals differentiation, namely by discrete spectral line quantity and distribution situation Division identification ASK, AM, CW modulation signal within the scope of signal bandwidth;
Step 5, carry out phase modulated signal differentiation, i.e. and instantaneous frequency kurtosis discontinuous by phase place, phase-modulation and frequency modulated mode are distinguished, identify between phase modulated signal class and the planisphere of blind demodulation will be adopted to judge;
Step 6, carry out frequency modulated signal differentiation, for frequency modulated signal, waveform after frequency discrimination is carried out spectrum analysis, if frequency spectrum is mainly distributed in low-frequency range, be just judged to FSK modulation, if instead frequency spectrum is mainly concentrated in band, then can be considered that FM modulates, FSK modulates between class and identifies and will utilize the statistical property of instantaneous frequency and realize;
Step 7, unknown signaling and noise are distinguished, and are realized by the fluctuation of instantaneous envelope statistical parameter;
The majority decision of step 8, repeatedly recognition result, adopts the result cascading judgement repeatedly identified, and considers the relation between each recognition result, be finally clearly identified modulation system and other satellite informations of signal.
2. the Automatic modulation classification method of a kind of VHF/UHF frequency range according to claim 1, it is characterized in that: in step one, data are the comparatively systemic circulation buffer areas importing inside modules unlatching in an orderly manner, recognizer by circular buffer district progressively, piecemeal carry out Classification and Identification, data importing with identify adopt different threads to realize.
3. the Automatic modulation classification method of a kind of VHF/UHF frequency range according to claim 1, is characterized in that: in step 2, carries out less section segmentation to the envelope importing data, by two sections, front and back energy jump, judges signal time domain continuity or burst detection; Meanwhile, the signal section that Time Domain Piecewise detects is used for frequency domain carriers and bandwidth estimation and follow-up identifying processing.
4. the Automatic modulation classification method of a kind of VHF/UHF frequency range according to claim 3, is characterized in that: in step 3, using the value between frequency spectrum transition band and signal as noise gate, is applied to bandwidth sum center frequency estimation; Using the energy within the scope of the signal bandwidth estimated and with the ratio of the energy summation within the scope of narrow band filter as arrowband signal to noise ratio.
5. the Automatic modulation classification method of a kind of VHF/UHF frequency range according to claim 1, it is characterized in that: in step 4, after completing frequency domain segmentation, the whole valid data of this section need be utilized to calculate High-Resolution Spectral, discrete spectral line distribution situation in retrieval band, and adopt whether certain SNR criterion detection is discrete spectral line, if the discrete spectral line of frequency spectrum existence anduniquess, tentatively determine whether AM, CW, ASK signal, then according to signal, whether there is symbol rate information and therefrom distinguish ASK, according to spectral bandwidth with about carrier frequency symmetry, AM is distinguished again, otherwise be considered as CW signal, if preliminary judgement is AM modulation, also needing to utilize the kurtosis value of its envelope to judge further is AM speech, or AM2FSK Multi-modulation signal, and for the AM2FSK signal of secondary modulation, its digital modulation symbol rate adopts general 2FSK symbol rate to estimate mode.
6. the Automatic modulation classification method of a kind of VHF/UHF frequency range according to claim 1, it is characterized in that: in step 5, phase modulated signal is single-carrier signal, its instantaneous frequency kurtosis value is comparatively large, utilizes envelope frequency spectrum, according to bandwidth value according to certain limit detected symbol rate information, if there is symbol rate information, then tentatively be considered as phase modulated signal, the identification between phase modulated signal MPSK and MQAM, adopt the planisphere of blind demodulation mode to judge.
7. the Automatic modulation classification method of a kind of VHF/UHF frequency range according to claim 1, it is characterized in that: in step 6, for frequency modulated signal, waveform after frequency discrimination is carried out spectrum analysis, if frequency spectrum is mainly distributed in low-frequency range, be considered as FSK modulation, if instead frequency spectrum is mainly concentrated in certain band, then can be considered that FM modulates, this tentatively realizes the differentiation of analog-modulated and digital modulation.
8. the Automatic modulation classification method of a kind of VHF/UHF frequency range according to claim 7, it is characterized in that: if FM modulation, whether the baseband signal after then also needing the waveform after with regard to demodulation again to utilize waveform kurtosis value and frequency discrimination has symbol rate information to confirm the Multi-modulation signal into FM (2FSK), and the symbol rate of FM (2FSK) is estimated to estimate that mode is similar with 2FSK symbol rate; If continuous fsk modulated signal, symbol rate is estimated to utilize instantaneous frequency discrete spectrum line features to estimate; If burst FSK, because of the symbol quantity of one section of bursty data does not reach requirement, utilize aforesaid way to be difficult to realize, symbol rate estimation need be realized by each code element number of samples according to a preliminary estimate.
9. the Automatic modulation classification method of a kind of VHF/UHF frequency range according to claim 8, it is characterized in that: if sentenced card for 2FSK modulation, and modulation index is close to 0.5, then whether recycle the discrete spectrum line features of the quadratic power spectrum of primary signal, sentencing card is MSK modulation signal.
10. the Automatic modulation classification method of a kind of VHF/UHF frequency range according to claim 1, it is characterized in that: in step 7, to in frequency spectrum not existence anduniquess discrete spectral line and without the signal of symbol rate information, unknown signaling and noise is distinguished, because the symbol rate information of part distinctive signal direct estimation or extraction signal exists certain difficulty by the fluctuation of instantaneous envelope; Need to start other mode subregion identification distinctive signals; Need for distinctive signal the automatic identification module recognition detection starting these distinctive signals, it mainly adopts the modes such as power side's spectrogram of signal or blind demodulation to carry out recognition detection.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510498093.XA CN105207965B (en) | 2015-08-14 | 2015-08-14 | A kind of Automatic modulation classification method of VHF/UHF frequency ranges |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510498093.XA CN105207965B (en) | 2015-08-14 | 2015-08-14 | A kind of Automatic modulation classification method of VHF/UHF frequency ranges |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105207965A true CN105207965A (en) | 2015-12-30 |
| CN105207965B CN105207965B (en) | 2018-07-24 |
Family
ID=54955405
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510498093.XA Active CN105207965B (en) | 2015-08-14 | 2015-08-14 | A kind of Automatic modulation classification method of VHF/UHF frequency ranges |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105207965B (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106950544A (en) * | 2017-03-06 | 2017-07-14 | 哈尔滨工程大学 | It is a kind of that method for distinguishing is known based on the big time width signal subsection that DSP is realized |
| CN107507630A (en) * | 2017-07-17 | 2017-12-22 | 嘉兴开泽电子设备有限公司 | A kind of non-cooperation voice communication receives data dead time section recognition methods |
| CN108205079A (en) * | 2016-12-16 | 2018-06-26 | 北京普源精电科技有限公司 | Utilize the method and device of frequency spectrograph detection 2FSK signals |
| CN108599880A (en) * | 2018-03-26 | 2018-09-28 | 西安电子科技大学 | The air-ground intercom system of civil aviaton based on convolutional neural networks interferes method for early warning |
| CN109347775A (en) * | 2018-10-22 | 2019-02-15 | 西南交通大学 | A kind of modulation format recognition methods of combined strength fluctuation and phase fluctuation feature |
| CN109687924A (en) * | 2019-02-15 | 2019-04-26 | 青岛海洋科学与技术国家实验室发展中心 | A kind of shortwave available frequency resources automatic testing method based on measured data |
| CN110048977A (en) * | 2019-03-14 | 2019-07-23 | 中国人民解放军战略支援部队信息工程大学 | The recognition methods of short-wave signal system and device based on the detection of gray level co-occurrence matrixes textural characteristics |
| CN112003803A (en) * | 2020-08-10 | 2020-11-27 | 四川九洲电器集团有限责任公司 | Detection and reception equipment for VHF and UHF band aviation radio station signals |
| CN112332968A (en) * | 2020-11-05 | 2021-02-05 | 中国人民解放军32802部队 | Short wave broadband automatic reconnaissance identification and control method based on multidimensional characteristics |
| CN112737668A (en) * | 2020-12-30 | 2021-04-30 | 广东省电信规划设计院有限公司 | Satellite communication signal high-precision modulation classification method, device and system |
| CN114584227A (en) * | 2022-01-12 | 2022-06-03 | 中国电子科技集团公司第十研究所 | Automatic burst signal detection method |
| CN117201249A (en) * | 2023-11-01 | 2023-12-08 | 中孚安全技术有限公司 | Signal modulation mode identification method, system and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0544991A2 (en) * | 1991-11-29 | 1993-06-09 | Daimler-Benz Aerospace Aktiengesellschaft | Method for the automatic classification of digitally modulated signals, and apparatus to carry out the method |
| CN101834819A (en) * | 2010-05-14 | 2010-09-15 | 哈尔滨工业大学 | Analog-digital mixing modulation recognition device and digital modulation recognition device based on parallel judgment |
| CN101917369A (en) * | 2010-07-30 | 2010-12-15 | 中国人民解放军信息工程大学 | Method for identifying modulation mode of communication signal |
| CN104052703A (en) * | 2014-07-04 | 2014-09-17 | 哈尔滨工程大学 | Method for microsampling data digital modulation recognition |
-
2015
- 2015-08-14 CN CN201510498093.XA patent/CN105207965B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0544991A2 (en) * | 1991-11-29 | 1993-06-09 | Daimler-Benz Aerospace Aktiengesellschaft | Method for the automatic classification of digitally modulated signals, and apparatus to carry out the method |
| CN101834819A (en) * | 2010-05-14 | 2010-09-15 | 哈尔滨工业大学 | Analog-digital mixing modulation recognition device and digital modulation recognition device based on parallel judgment |
| CN101917369A (en) * | 2010-07-30 | 2010-12-15 | 中国人民解放军信息工程大学 | Method for identifying modulation mode of communication signal |
| CN104052703A (en) * | 2014-07-04 | 2014-09-17 | 哈尔滨工程大学 | Method for microsampling data digital modulation recognition |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108205079A (en) * | 2016-12-16 | 2018-06-26 | 北京普源精电科技有限公司 | Utilize the method and device of frequency spectrograph detection 2FSK signals |
| CN106950544A (en) * | 2017-03-06 | 2017-07-14 | 哈尔滨工程大学 | It is a kind of that method for distinguishing is known based on the big time width signal subsection that DSP is realized |
| CN106950544B (en) * | 2017-03-06 | 2020-01-31 | 哈尔滨工程大学 | method for realizing large time-width signal segmentation identification based on DSP |
| CN107507630A (en) * | 2017-07-17 | 2017-12-22 | 嘉兴开泽电子设备有限公司 | A kind of non-cooperation voice communication receives data dead time section recognition methods |
| CN108599880B (en) * | 2018-03-26 | 2020-09-22 | 西安电子科技大学 | Civil aviation ground-air intercom system interference early warning method based on convolutional neural network |
| CN108599880A (en) * | 2018-03-26 | 2018-09-28 | 西安电子科技大学 | The air-ground intercom system of civil aviaton based on convolutional neural networks interferes method for early warning |
| CN109347775A (en) * | 2018-10-22 | 2019-02-15 | 西南交通大学 | A kind of modulation format recognition methods of combined strength fluctuation and phase fluctuation feature |
| CN109347775B (en) * | 2018-10-22 | 2020-12-18 | 西南交通大学 | Modulation format identification method combining intensity fluctuation and phase fluctuation characteristics |
| CN109687924A (en) * | 2019-02-15 | 2019-04-26 | 青岛海洋科学与技术国家实验室发展中心 | A kind of shortwave available frequency resources automatic testing method based on measured data |
| CN109687924B (en) * | 2019-02-15 | 2021-05-07 | 青岛海洋科学与技术国家实验室发展中心 | Short wave available frequency resource automatic detection method based on measured data |
| CN110048977A (en) * | 2019-03-14 | 2019-07-23 | 中国人民解放军战略支援部队信息工程大学 | The recognition methods of short-wave signal system and device based on the detection of gray level co-occurrence matrixes textural characteristics |
| CN110048977B (en) * | 2019-03-14 | 2022-03-01 | 中国人民解放军战略支援部队信息工程大学 | Short wave signal system identification method and device based on gray level co-occurrence matrix texture feature detection |
| CN112003803A (en) * | 2020-08-10 | 2020-11-27 | 四川九洲电器集团有限责任公司 | Detection and reception equipment for VHF and UHF band aviation radio station signals |
| CN112332968A (en) * | 2020-11-05 | 2021-02-05 | 中国人民解放军32802部队 | Short wave broadband automatic reconnaissance identification and control method based on multidimensional characteristics |
| CN112332968B (en) * | 2020-11-05 | 2021-07-20 | 中国人民解放军32802部队 | Short wave broadband automatic reconnaissance identification and control method based on multidimensional characteristics |
| CN112737668A (en) * | 2020-12-30 | 2021-04-30 | 广东省电信规划设计院有限公司 | Satellite communication signal high-precision modulation classification method, device and system |
| CN112737668B (en) * | 2020-12-30 | 2022-10-14 | 广东省电信规划设计院有限公司 | Satellite communication signal high-precision modulation classification method, device and system |
| CN114584227A (en) * | 2022-01-12 | 2022-06-03 | 中国电子科技集团公司第十研究所 | Automatic burst signal detection method |
| CN114584227B (en) * | 2022-01-12 | 2023-08-22 | 中国电子科技集团公司第十研究所 | Automatic burst signal detection method |
| CN117201249A (en) * | 2023-11-01 | 2023-12-08 | 中孚安全技术有限公司 | Signal modulation mode identification method, system and device |
| CN117201249B (en) * | 2023-11-01 | 2024-02-20 | 中孚安全技术有限公司 | Signal modulation mode identification method, system and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105207965B (en) | 2018-07-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105207965A (en) | Automatic VHF/UHF frequency range modulation identification method | |
| CN101834819B (en) | Analog-digital mixing modulation recognition device and digital modulation recognition device based on parallel judgment | |
| EP1083716B1 (en) | Discrimination of modulation type | |
| CN108764077B (en) | Digital signal modulation classification method based on convolutional neural network | |
| CN103199945B (en) | The recognition methods of cognitive radio signal modulation system when a kind of low signal-to-noise ratio | |
| CN104301056A (en) | Frequency spectrum monitoring method based on signal feature analysis | |
| CN103414527A (en) | Signal detection method based on energy detection | |
| CN106330805B (en) | A kind of signal modulation mode automatic identifying method and system | |
| Hassan et al. | Automatic modulation recognition using wavelet transform and neural network | |
| CN112422465B (en) | Signal modulation identification equipment | |
| CN108243130B (en) | Demodulation method, demodulation device, spectrum detector and computer readable storage medium | |
| CN111814777B (en) | Modulation pattern recognition method based on characteristic quantity grading | |
| De Vito et al. | Prototype of an automatic digital modulation classifier embedded in a real-time spectrum analyzer | |
| Triantafyllakis et al. | Phasma: An automatic modulation classification system based on random forest | |
| Dubuc et al. | An automatic modulation recognition algorithm for spectrum monitoring applications | |
| CN108631893B (en) | GMSK channel interference detection method | |
| Zhang | Automatic modulation classification based on statistical features and support vector machine | |
| Laghate et al. | USRP N210 demonstration of wideband sensing and blind hierarchical modulation classification | |
| Zhao et al. | Automatic modulation recognition based on multi-dimensional feature extraction | |
| JP2007336078A (en) | Modulated wave discriminating device, modulated wave discriminating method, and receiving device | |
| CN108270703A (en) | A kind of signal of communication digital modulation type recognition methods | |
| Kumar et al. | Automatic modulation classification by exploiting cyclostationary features in wavelet domain | |
| Bao et al. | Spectrum segmentation for wideband sensing of radio signals | |
| Azza et al. | Implementation of an automatic modulation recognition system on a software defined radio platform | |
| CN106453174B (en) | Cognition wireless network resource allocation methods based on signal modulate |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20220330 Address after: 610000 No. 4, floor 19, unit 1, building 11, No. 188, middle section of Fucheng Avenue, high tech Zone, Chengdu, Sichuan Patentee after: Chengdu Xingxiang Technology Co.,Ltd. Address before: 610000 No. 1, floor 11, building 1, torch era, No. 4, Keyuan Third Road, Gaopeng Avenue, high tech Zone, Chengdu, Sichuan Patentee before: CHENGDU ZHONG'AN SPECTRUM SCIENCE & TECHNOLOGY CO.,LTD. |