CN111800357A - Method and system for distinguishing modulation types based on cyclic spectrum - Google Patents
Method and system for distinguishing modulation types based on cyclic spectrum Download PDFInfo
- Publication number
- CN111800357A CN111800357A CN202010636330.5A CN202010636330A CN111800357A CN 111800357 A CN111800357 A CN 111800357A CN 202010636330 A CN202010636330 A CN 202010636330A CN 111800357 A CN111800357 A CN 111800357A
- Authority
- CN
- China
- Prior art keywords
- signal
- modulation
- section
- spectrum
- modulation mode
- 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.)
- Pending
Links
- 238000001228 spectrum Methods 0.000 title claims abstract description 148
- 125000004122 cyclic group Chemical group 0.000 title claims abstract description 91
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000001514 detection method Methods 0.000 claims abstract description 13
- 238000007621 cluster analysis Methods 0.000 claims abstract description 12
- 230000015654 memory Effects 0.000 claims description 38
- 238000005259 measurement Methods 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000004422 calculation algorithm Methods 0.000 claims description 4
- 238000003064 k means clustering Methods 0.000 claims description 2
- 238000000605 extraction Methods 0.000 abstract description 3
- 238000004891 communication Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000002401 inhibitory effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000007476 Maximum Likelihood Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000005314 correlation function Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000010363 phase shift Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
Images
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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Digital Transmission Methods That Use Modulated Carrier Waves (AREA)
Abstract
The invention discloses a method and a system for distinguishing modulation types based on a cyclic spectrum, wherein the method comprises the following steps: acquiring a signal of a predicted modulation mode; performing cyclic spectrum modulation on a signal with a preset modulation mode to obtain a spectrum of a cyclic spectrum three-dimensional graph, and acquiring a spectrum section and peak coordinates of the spectrum section; classifying and storing the map cross section and the peak coordinates of the map cross section according to different modulation modes; and performing cyclic spectrum modulation on the received signal of the unknown modulation mode to generate an atlas section of the signal of the unknown modulation mode and peak coordinates of the atlas section, performing cluster analysis on the peak coordinates of the atlas sections of the signal of the unknown modulation mode and the signal of the predicted modulation mode, and combining sub-peak detection of the atlas section to obtain the modulation type of the signal of the unknown modulation mode. The invention provides a method for distinguishing modulation signal types by taking the cyclic spectrum characteristics as a sample for characteristic extraction, and the method has the advantages of higher identification accuracy under the condition of low signal-to-noise ratio and wide application.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to a method and a system for distinguishing modulation types based on a cyclic spectrum.
Background
The signal modulation type identification technology is divided into manual identification and automatic identification, the manual identification mode contains strong subjective factors, the identification type is limited, the identification rate is low, the automatic identification uses various technologies to convert signals to achieve the purpose of identifying different modulation types, various difficulties can be overcome, and the identification performance is better. Currently, the first category describes the communication system using a statistical model and employs a modulation detection decision scheme based on the maximum likelihood criterion; the second category is pattern recognition based on feature parameter extraction. In comparison, the second type of research has wide application and higher accuracy, and at the present stage, a deep neural network is mostly adopted to extract the characteristic parameters and classify the modulation types.
Due to the fact that the wireless communication channel environment is complex and difficult to predict, under the condition of strong noise interference, ideal accuracy cannot be achieved by a plurality of parameter estimation and modulation mode identification methods. Based on a modulation identification algorithm of wavelet transformation, the modulation types of signals are identified by utilizing envelopes of the signals after wavelet transformation, but the influence of wavelet scale factors on identification results is large; the modulation identification algorithm based on the spectrum analysis utilizes the square spectrum or the fourth power spectrum of the signal to perform modulation identification on the signal, but the applicable signal is less. The prior art has the defects of small application range of the identification method and low identification accuracy under the condition of low signal-to-noise ratio.
Disclosure of Invention
Therefore, the method and the system for distinguishing the modulation types based on the cyclic spectrum, provided by the invention, overcome the problems that the application range of the identification method in the prior art is small, and the identification accuracy rate is low under the condition of low signal-to-noise ratio.
In order to achieve the purpose, the invention provides the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for distinguishing modulation types based on a cyclic spectrum, including:
acquiring a signal of a predicted modulation mode;
performing cyclic spectrum modulation on a signal with a preset modulation mode to obtain a spectrum of a cyclic spectrum three-dimensional graph, and acquiring a spectrum section and peak coordinates of the spectrum section;
classifying and storing the map cross section and the peak coordinates of the map cross section according to different modulation modes;
and performing cyclic spectrum modulation on the received signal of the unknown modulation mode to generate an atlas section of the signal of the unknown modulation mode and peak coordinates of the atlas section, performing cluster analysis on the peak coordinates of the atlas sections of the signal of the unknown modulation mode and the signal of the predicted modulation mode, and combining sub-peak detection of the atlas section to obtain the modulation type of the signal of the unknown modulation mode.
In an embodiment, the predetermined modulation scheme includes: 2FSK, 2ASK, BPSK.
In an embodiment, the step of performing cyclic spectrum modulation on the signal with the predetermined modulation mode to obtain a spectrum of a cyclic spectrum three-dimensional graph, and acquiring a spectrum section and a peak coordinate of the spectrum section includes:
performing cyclic spectrum modulation on a signal with a preset modulation mode to obtain a spectrum of a cyclic spectrum three-dimensional graph, and reducing the dimension to a two-dimensional spectrum;
intercepting a section of a two-dimensional atlas preset numerical value circulation frequency of a signal with a preset modulation mode to obtain an atlas section;
the peak coordinates of the cross-section are obtained by measurement.
In an embodiment, the step of performing cyclic spectrum modulation on the received signal of the unknown modulation scheme to generate a profile cross section of the signal of the unknown modulation scheme and peak coordinates of the profile cross section, performing cluster analysis on the peak coordinates of the profile cross sections of the signal of the unknown modulation scheme and the signal of the predicted modulation scheme, and obtaining the modulation type of the signal of the unknown modulation scheme by combining sub-peak detection of the profile cross section includes:
carrying out cyclic spectrum modulation on the received signal in the unknown modulation mode to obtain a spectrum of a cyclic spectrum three-dimensional graph of the signal in the unknown modulation mode, and reducing the dimension to a two-dimensional spectrum;
intercepting a section of a two-dimensional map preset numerical value cycle frequency of an unknown modulation mode signal to obtain a map section;
obtaining the peak coordinates of the section of the unknown modulation mode signal two-dimensional map through measurement;
carrying out clustering analysis on peak coordinates of the spectrum section of the two-dimensional spectrum of the unknown modulation mode signal and the stored peak coordinates of the spectrum section of the signal of the pre-known modulation mode to distinguish whether the unknown modulation mode signal is a BPSK modulation type;
if the modulation type of the unknown modulation mode signal is not the BPSK modulation type, the unknown modulation mode signal belongs to a 2ASK or 2FSK modulation type, whether a secondary peak exists in the map section of the unknown modulation mode signal is detected based on the characteristic that the secondary peak exists in the map of the 2FSK signal, and the unknown modulation mode signal is distinguished to be the 2ASK or 2FSK modulation type according to whether the secondary peak exists in the map section of the unknown modulation mode signal.
In one embodiment, a section with a cycle frequency α of a cycle spectrum of a signal with a predetermined modulation scheme and a signal with an unknown modulation scheme is cut off as 0.
In an embodiment, when detecting that a sub-peak exists in a spectrum section of an unknown modulation mode signal, the number of peaks is 2, and when not, the number of peaks is 1.
In one embodiment, the peak coordinates of the spectrum cross sections of the unknown modulation mode signal and the predicted modulation mode signal are subjected to clustering analysis by adopting a K-means clustering algorithm.
In a second aspect, an embodiment of the present invention provides a system for distinguishing modulation types based on a cyclic spectrum, including:
the predictive signal acquisition module is used for acquiring a signal of a predictive modulation mode;
the cyclic spectrum modulation module is used for carrying out cyclic spectrum modulation on the signal with the predicted modulation mode to obtain a spectrum of a cyclic spectrum three-dimensional graph and obtain a spectrum section and peak coordinates of the spectrum section;
the database storage module is used for classifying and storing the atlas section and the crest coordinate of the atlas section according to different modulation modes;
and the signal distinguishing module is used for performing cyclic spectrum modulation on the received signal in the unknown modulation mode, generating the atlas cross section of the signal in the unknown modulation mode and the peak coordinates of the atlas cross section, performing cluster analysis on the peak coordinates of the atlas cross section of the signal in the unknown modulation mode and the signal in the predicted modulation mode, and combining sub-peak detection of the atlas cross section to obtain the modulation type of the signal in the unknown modulation mode.
In a third aspect, an embodiment of the present invention provides a terminal, including: the apparatus includes at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executable by the at least one processor to cause the at least one processor to perform a method for distinguishing modulation types based on a cyclic spectrum according to a first aspect of the embodiments of the present invention.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are configured to cause the computer to execute the method for distinguishing modulation types based on a cyclic spectrum according to the first aspect of the embodiment of the present invention.
The technical scheme of the invention has the following advantages:
the method and the system for distinguishing the modulation types based on the cyclic spectrum extract the signal characteristics in the cyclic spectrum by utilizing the obvious distinguishing characteristics of the cyclic spectrum of different modulation signals, distinguish the modulation types of the signals as basic characteristics, carry out the cyclic spectrum modulation on the signals, measure the peak coordinates on a cross section diagram, carry out cluster analysis by utilizing stored data and unknown information data, and carry out secondary peak detection, realize the distinguishing of the different modulation signal types, have higher identification accuracy under the condition of low signal-to-noise ratio, and have wide application.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of an example of a method for distinguishing modulation types based on a cyclic spectrum according to an embodiment of the present invention;
fig. 2 is a flowchart of a specific example of a method for distinguishing modulation types based on a cyclic spectrum according to an embodiment of the present invention;
FIG. 3 is a block diagram of a system for discriminating modulation types based on a cyclic spectrum according to an embodiment of the present invention;
fig. 4 is a composition diagram of a specific example of a terminal according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
The method for distinguishing modulation types based on the cyclic spectrum provided by the embodiment of the invention, as shown in fig. 1, comprises the following steps:
step S1: and acquiring a signal of a predicted modulation mode.
In an embodiment of the present invention, the predictive modulation method includes: 2FSK, 2ASK, BPSK. In practice, 2FSK is binary frequency shift keying, 2ASK is binary amplitude keying, and BPSK is phase shift keying, the above three modulation modes are used as examples in this embodiment, but not limited thereto, and signals with different cyclic spectrum characteristics are all applicable.
Step S2: and performing cyclic spectrum modulation on the signal with the preset modulation mode to obtain a spectrum of a cyclic spectrum three-dimensional graph, and acquiring a spectrum section and peak coordinates of the spectrum section.
In the embodiment of the invention, the spectral correlation function of the modulation signal has a larger value at the position where the cycle frequency is not zero, but the stationary noise is almost zero or has a small value at the position where the cycle frequency is not zero, which is not enough to influence the effective signal, and when the signal-to-noise ratio is low, the signal can be distinguished through the cycle spectrum, so that the signal of the predicted modulation mode is subjected to cycle spectrum modulation. If there is an interference signal in the received signal of the predicted modulation method and the key parameter of the interference signal is different from the effective signal, the cyclic spectrogram is completely different, so that the signals can be distinguished. Therefore, the cyclic spectrogram is used as a sample for characteristic extraction, so that the method has an inhibiting effect on stable noise and interference, can reflect more characteristics of a modulation signal, and can obtain high identification accuracy in a low signal-to-noise ratio transmission environment.
In the embodiment of the present invention, the process of step S2 is executed, which specifically includes: performing cyclic spectrum modulation on a signal with a preset modulation mode to obtain a spectrum of a cyclic spectrum three-dimensional graph, and reducing the dimension of the three-dimensional graph to a two-dimensional graph in order to simplify the complexity of calculation; intercepting a section of a two-dimensional atlas preset numerical value circulation frequency of a signal with a preset modulation mode to obtain an atlas section; the peak coordinates of the cross-section are obtained by measurement. After the dimension of the atlas of the three-dimensional atlas is reduced to the two-dimensional atlas, the section of the atlas is cut off, the crest coordinate is measured on the section of the atlas, the section of the atlas is 0 (the section of the atlas is 0), the circulation spectrogram is symmetrical by taking a zero longitudinal axis as a symmetry axis, unilateral coordinates can be measured in a unified mode during measurement, and the positive axis coordinates can be measured in a unified mode.
Step S3: and classifying and storing the spectrum section and the peak coordinates of the spectrum section according to different modulation modes.
In the embodiment of the invention, a large number of acquired signals with a preset modulation mode are subjected to cyclic spectrum modulation to obtain a spectrum of a cyclic spectrum three-dimensional graph, the cross section of the spectrum and the peak coordinates of the cross section of the spectrum are acquired, enough peak coordinates are collected, the cross section graph and the peak coordinates are classified and stored in a database according to different modulation modes and serve as basic data for modulation type identification, and the signal types of the database established in the embodiment are known and can be directly classified.
Step S4: and performing cyclic spectrum modulation on the received signal of the unknown modulation mode to generate an atlas section of the signal of the unknown modulation mode and peak coordinates of the atlas section, performing cluster analysis on the peak coordinates of the atlas sections of the signal of the unknown modulation mode and the signal of the predicted modulation mode, and combining sub-peak detection of the atlas section to obtain the modulation type of the signal of the unknown modulation mode.
In the embodiment of the present invention, the process of step S4 is executed, which specifically includes: carrying out cyclic spectrum modulation on the received signal in the unknown modulation mode to obtain a spectrum of a cyclic spectrum three-dimensional map of the signal in the unknown modulation mode, and reducing the dimension of the three-dimensional map to a two-dimensional map in order to simplify the complexity of calculation; intercepting a section of a two-dimensional map preset numerical value cycle frequency of an unknown modulation mode signal to obtain a map section; obtaining the peak coordinates of the section of the unknown modulation mode signal two-dimensional map through measurement; carrying out clustering analysis on peak coordinates of the spectrum section of the two-dimensional spectrum of the unknown modulation mode signal and peak coordinates of the spectrum section of the signal of the pre-known modulation mode stored in a classified manner, wherein a K-mean clustering mode can be selected to distinguish whether the unknown modulation mode signal is a BPSK modulation type; if the modulation type of the unknown modulation mode signal is not the BPSK modulation type, the unknown modulation mode signal belongs to a 2ASK or 2FSK modulation type, whether a secondary peak exists in the map section of the unknown modulation mode signal is detected based on the characteristic that the secondary peak exists in the map of the 2FSK signal, and the unknown modulation mode signal is distinguished to be the 2ASK or 2FSK modulation type according to whether the secondary peak exists in the map section of the unknown modulation mode signal, so that the three modulation signal types are distinguished, and the higher identification accuracy rate is achieved under the condition of low signal-to-noise ratio.
In the embodiment of the present invention, a specific process of obtaining a modulation type of an unknown modulation mode signal is performed, as shown in fig. 2, a cyclic spectrum modulation is performed on a received external signal to obtain a spectrum of a cyclic spectrum three-dimensional graph, the received external signal is a signal of a predicted modulation mode, and a graph α ═ 0 spectrum section and a peak coordinate of the spectrum section are obtained; classifying and storing the map cross section and the peak coordinates of the map cross section according to different modulation modes, and collecting a large number of signals as a database; performing cyclic spectrum modulation on a received unknown signal to obtain a cyclic spectrum alpha of the unknown modulation mode signal, wherein the cyclic spectrum alpha is 0 section and peak coordinate, performing cluster analysis on the peak coordinate of the unknown modulation mode signal and the peak coordinate obtained by each modulation mode stored in a database, wherein the peak coordinate of the BPSK modulation mode is obviously different from other two types, and directly distinguishing whether the signal is a BPSK modulation type, if the unknown coordinate is mixed in 2ASK and 2FSK types, performing secondary peak detection on the cyclic spectrogram section diagram of the unknown modulation mode signal, if the secondary peak exists in the spectrogram section of the unknown modulation mode signal, the number of the peaks is 2, if no secondary peak exists, the number of the peaks is 1, if the secondary peak is detected by the unknown modulation mode signal, the signal is a 2FSK modulation signal, otherwise, the signal is a 2ASK signal. The three modulation signal types are distinguished through the process, the method can be applied to actual transmission environments, the cyclic spectrum has an inhibiting effect on stable noise and interference, the characteristics of the modulation signals can be reflected more, and the identification accuracy rate is higher under the condition of low signal to noise ratio.
The embodiment of the invention provides a method for distinguishing modulation types based on a cyclic spectrum, which is characterized in that the method utilizes the obvious distinguishing characteristics of the cyclic spectrum of different modulation signals to extract the signal characteristics in the cyclic spectrum to be used as the basic characteristics to distinguish the modulation types of the signals, carries out cyclic spectrum modulation on the signals, measures the peak coordinates on a sectional diagram, carries out cluster analysis by utilizing stored data and unknown information data, and carries out secondary peak detection, thereby realizing the distinguishing of different modulation signal types, having higher identification accuracy rate under the condition of low signal-to-noise ratio and having wide application.
Example 2
An embodiment of the present invention provides a system for distinguishing modulation types based on a cyclic spectrum, as shown in fig. 3, including:
a predicted signal obtaining module 1, configured to obtain a signal of a predicted modulation scheme; this module executes the method described in step S1 in embodiment 1, and is not described herein again.
The cyclic spectrum modulation module 2 is used for carrying out cyclic spectrum modulation on the signal with the preset modulation mode to obtain a spectrum of a cyclic spectrum three-dimensional graph and obtain a spectrum section and peak coordinates of the spectrum section; this module executes the method described in step S2 in embodiment 1, and is not described herein again.
The database storage module 3 is used for storing the atlas section and the crest coordinate of the atlas section in a classified manner according to different modulation modes; this module executes the method described in step S3 in embodiment 1, and is not described herein again.
The signal distinguishing module 4 is used for performing cyclic spectrum modulation on the received signal in the unknown modulation mode, generating an atlas section of the signal in the unknown modulation mode and peak coordinates of the atlas section, performing cluster analysis on the peak coordinates of the atlas sections of the signal in the unknown modulation mode and the signal in the predicted modulation mode, and combining sub-peak detection of the atlas section to obtain the modulation type of the signal in the unknown modulation mode; this module executes the method described in step S4 in embodiment 1, and is not described herein again.
The embodiment of the invention provides a system for distinguishing modulation types based on a cyclic spectrum, which utilizes the obvious distinguishing characteristics of the cyclic spectrum of different modulation signals to extract the signal characteristics in the cyclic spectrum, and uses the signal characteristics as the basic characteristics to distinguish the modulation types of the signals.
Example 3
An embodiment of the present invention provides a terminal, as shown in fig. 4, including: at least one processor 401, such as a CPU (Central Processing Unit), at least one communication interface 403, memory 404, and at least one communication bus 402. Wherein a communication bus 402 is used to enable connective communication between these components. The communication interface 403 may include a Display (Display) and a Keyboard (Keyboard), and the optional communication interface 403 may also include a standard wired interface and a standard wireless interface. The Memory 404 may be a high-speed RAM Memory (Random Access Memory) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The memory 404 may optionally be at least one memory device located remotely from the processor 401. Wherein the processor 401 may execute the method of distinguishing modulation types based on the cyclic spectrum in embodiment 1. A set of program codes is stored in the memory 404, and the processor 401 calls the program codes stored in the memory 404 for executing the method of discriminating modulation types based on the cyclic spectrum in embodiment 1. The communication bus 402 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 402 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one line is shown in FIG. 4, but it is not intended that there be only one bus or one type of bus. The memory 404 may include a volatile memory (RAM), such as a random-access memory (RAM); the memory may also include a non-volatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviated: HDD) or a solid-state drive (english: SSD); the memory 404 may also comprise a combination of memories of the kind described above. The processor 401 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of a CPU and an NP.
The memory 404 may include a volatile memory (RAM), such as a random-access memory (RAM); the memory may also include a non-volatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviation: HDD), or a solid-state drive (english: SSD); the memory 404 may also comprise a combination of memories of the kind described above.
The processor 401 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of a CPU and an NP.
The processor 401 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The aforementioned PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.
Optionally, the memory 404 is also used to store program instructions. The processor 401 may call program instructions to implement the method of distinguishing modulation types based on the cyclic spectrum as in embodiment 1.
An embodiment of the present invention further provides a computer-readable storage medium, where computer-executable instructions are stored on the computer-readable storage medium, and the computer-executable instructions may execute the method for distinguishing modulation types based on a cyclic spectrum in embodiment 1. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard disk (Hard disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. 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 obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.
Claims (10)
1. A method for distinguishing modulation types based on a cyclic spectrum, comprising:
acquiring a signal of a predicted modulation mode;
performing cyclic spectrum modulation on a signal with a preset modulation mode to obtain a spectrum of a cyclic spectrum three-dimensional graph, and acquiring a spectrum section and peak coordinates of the spectrum section;
classifying and storing the map cross section and the peak coordinates of the map cross section according to different modulation modes;
and performing cyclic spectrum modulation on the received signal of the unknown modulation mode to generate an atlas section of the signal of the unknown modulation mode and peak coordinates of the atlas section, performing cluster analysis on the peak coordinates of the atlas sections of the signal of the unknown modulation mode and the signal of the predicted modulation mode, and combining sub-peak detection of the atlas section to obtain the modulation type of the signal of the unknown modulation mode.
2. The method according to claim 1, wherein the predicting the modulation scheme comprises: 2FSK, 2ASK, BPSK.
3. The method according to claim 1, wherein the step of performing cyclic spectrum modulation on the signal of the predetermined modulation mode to obtain a spectrum of a cyclic spectrum three-dimensional graph and obtaining a spectrum section and a peak coordinate of the spectrum section comprises:
performing cyclic spectrum modulation on a signal with a preset modulation mode to obtain a spectrum of a cyclic spectrum three-dimensional graph, and reducing the dimension to a two-dimensional spectrum;
intercepting a section of a two-dimensional atlas preset numerical value circulation frequency of a signal with a preset modulation mode to obtain an atlas section;
the peak coordinates of the cross-section are obtained by measurement.
4. The method according to claim 1, wherein the step of performing cyclic spectrum modulation on the received unknown modulation mode signal to generate an atlas cross section of the unknown modulation mode signal and peak coordinates of the atlas cross section, performing cluster analysis on the peak coordinates of the atlas cross sections of the unknown modulation mode signal and the predicted modulation mode signal, and combining sub-peak detection of the atlas cross section to obtain the modulation type of the unknown modulation mode signal comprises:
carrying out cyclic spectrum modulation on the received signal in the unknown modulation mode to obtain a spectrum of a cyclic spectrum three-dimensional graph of the signal in the unknown modulation mode, and reducing the dimension to a two-dimensional spectrum;
intercepting a section of a two-dimensional map preset numerical value cycle frequency of an unknown modulation mode signal to obtain a map section;
obtaining the peak coordinates of the section of the unknown modulation mode signal two-dimensional map through measurement;
carrying out clustering analysis on peak coordinates of the spectrum section of the two-dimensional spectrum of the unknown modulation mode signal and the stored peak coordinates of the spectrum section of the signal of the pre-known modulation mode to distinguish whether the unknown modulation mode signal is a BPSK modulation type;
if the modulation type of the unknown modulation mode signal is not the BPSK modulation type, the unknown modulation mode signal belongs to a 2ASK or 2FSK modulation type, whether a secondary peak exists in the map section of the unknown modulation mode signal is detected based on the characteristic that the secondary peak exists in the map of the 2FSK signal, and the unknown modulation mode signal is distinguished to be the 2ASK or 2FSK modulation type according to whether the secondary peak exists in the map section of the unknown modulation mode signal.
5. The method for distinguishing the modulation types based on the cyclic spectrum according to claim 3 or 4, wherein the cyclic frequency α of the cyclic spectrum of the signal with the predicted modulation mode and the signal with the unknown modulation mode is cut off as 0 section.
6. The method according to claim 4, wherein the number of peaks is 2 when detecting that there is a sub-peak in the profile cross-section of the unknown modulation mode signal, and the number of peaks is 1 when not detecting.
7. The method according to claim 1, wherein the peak coordinates of the profile cross-sections of the unknown modulation mode signal and the predicted modulation mode signal are clustered using a K-means clustering algorithm.
8. A system for discriminating modulation types based on a cyclic spectrum, comprising:
the predictive signal acquisition module is used for acquiring a signal of a predictive modulation mode;
the cyclic spectrum modulation module is used for carrying out cyclic spectrum modulation on the signal with the predicted modulation mode to obtain a spectrum of a cyclic spectrum three-dimensional graph and obtain a spectrum section and peak coordinates of the spectrum section;
the database storage module is used for classifying and storing the atlas section and the crest coordinate of the atlas section according to different modulation modes;
and the signal distinguishing module is used for performing cyclic spectrum modulation on the received signal in the unknown modulation mode, generating the atlas cross section of the signal in the unknown modulation mode and the peak coordinates of the atlas cross section, performing cluster analysis on the peak coordinates of the atlas cross section of the signal in the unknown modulation mode and the signal in the predicted modulation mode, and combining sub-peak detection of the atlas cross section to obtain the modulation type of the signal in the unknown modulation mode.
9. A terminal, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method for discriminating modulation types based on cyclic spectrum of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing a computer to perform the method for discriminating modulation types based on a cyclic spectrum according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010636330.5A CN111800357A (en) | 2020-07-03 | 2020-07-03 | Method and system for distinguishing modulation types based on cyclic spectrum |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010636330.5A CN111800357A (en) | 2020-07-03 | 2020-07-03 | Method and system for distinguishing modulation types based on cyclic spectrum |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111800357A true CN111800357A (en) | 2020-10-20 |
Family
ID=72810169
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010636330.5A Pending CN111800357A (en) | 2020-07-03 | 2020-07-03 | Method and system for distinguishing modulation types based on cyclic spectrum |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111800357A (en) |
Citations (9)
| 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 |
| CN101977091A (en) * | 2010-10-19 | 2011-02-16 | 中国人民解放军信息工程大学 | Method and system for monitoring electromagnetic spectrum |
| CN105721371A (en) * | 2016-02-19 | 2016-06-29 | 徐州坤泰电子科技有限公司 | Method for identifying common digital modulation signal based on cyclic spectrum correlation |
| CN106130942A (en) * | 2016-07-05 | 2016-11-16 | 东南大学 | A kind of wireless communication signals Modulation Identification based on Cyclic Spectrum and method for parameter estimation |
| CN106789788A (en) * | 2016-12-26 | 2017-05-31 | 北京邮电大学 | A kind of wireless digital signal Modulation Mode Recognition method and device |
| CN108600135A (en) * | 2018-04-27 | 2018-09-28 | 中国科学院计算技术研究所 | A kind of recognition methods of signal modulation mode |
| CN109818892A (en) * | 2019-01-18 | 2019-05-28 | 华中科技大学 | Construct Cyclic Spectrum characteristic parameter extraction model and signal modulation mode recognition methods |
| CN110321953A (en) * | 2019-07-03 | 2019-10-11 | 中山大学 | Deep learning intelligent modulation recognition methods based on circulation Power estimation |
| CN110798419A (en) * | 2019-10-28 | 2020-02-14 | 北京邮电大学 | Modulation mode identification method and device |
-
2020
- 2020-07-03 CN CN202010636330.5A patent/CN111800357A/en active Pending
Patent Citations (9)
| 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 |
| CN101977091A (en) * | 2010-10-19 | 2011-02-16 | 中国人民解放军信息工程大学 | Method and system for monitoring electromagnetic spectrum |
| CN105721371A (en) * | 2016-02-19 | 2016-06-29 | 徐州坤泰电子科技有限公司 | Method for identifying common digital modulation signal based on cyclic spectrum correlation |
| CN106130942A (en) * | 2016-07-05 | 2016-11-16 | 东南大学 | A kind of wireless communication signals Modulation Identification based on Cyclic Spectrum and method for parameter estimation |
| CN106789788A (en) * | 2016-12-26 | 2017-05-31 | 北京邮电大学 | A kind of wireless digital signal Modulation Mode Recognition method and device |
| CN108600135A (en) * | 2018-04-27 | 2018-09-28 | 中国科学院计算技术研究所 | A kind of recognition methods of signal modulation mode |
| CN109818892A (en) * | 2019-01-18 | 2019-05-28 | 华中科技大学 | Construct Cyclic Spectrum characteristic parameter extraction model and signal modulation mode recognition methods |
| CN110321953A (en) * | 2019-07-03 | 2019-10-11 | 中山大学 | Deep learning intelligent modulation recognition methods based on circulation Power estimation |
| CN110798419A (en) * | 2019-10-28 | 2020-02-14 | 北京邮电大学 | Modulation mode identification method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2018019194A1 (en) | Image recognition method, terminal, and nonvolatile storage medium | |
| CN108616900B (en) | Method for distinguishing indoor and outdoor measurement reports and network equipment | |
| CN107733541A (en) | Method, apparatus, equipment and the computer-readable recording medium of frequency spectrum perception | |
| US20220269996A1 (en) | Information processing apparatus, information processing method, and storage medium | |
| CN111626360B (en) | Method, apparatus, device and storage medium for detecting boiler fault type | |
| CN117435939B (en) | IGBT health state evaluation method based on big data | |
| CN115170571A (en) | Method and device for identifying pathological images of hydrothorax and ascites cells and medium | |
| CN116975672B (en) | Temperature monitoring method and system for coal mine belt conveying motor | |
| CN114332012A (en) | Defect detection method, device, equipment and computer readable storage medium | |
| JPWO2018055673A1 (en) | Interference identification apparatus and interference identification method | |
| Hamid et al. | Spectrum sensing through spectrum discriminator and maximum minimum eigenvalue detector: A comparative study | |
| CN111800357A (en) | Method and system for distinguishing modulation types based on cyclic spectrum | |
| CN106452627B (en) | A kind of noise power estimation method and device for broader frequency spectrum perception | |
| CN105357154B (en) | A kind of signal detecting method of joint Power double threshold and delay correlation matrix | |
| CN116520068A (en) | Diagnostic method, device, equipment and storage medium for electric power data | |
| CN110749443A (en) | Rolling bearing fault diagnosis method and system based on high-order origin moment | |
| US9904660B1 (en) | Nonparametric method for measuring clustered level of time rank in binary data | |
| EP3328112A1 (en) | Determining coverage efficiency of an access point in a wireless network | |
| Alammar et al. | Automatic extraction of signal areas from radio spectrograms based on the Hough transform | |
| CN111726861A (en) | Indoor positioning method, device and system for heterogeneous equipment and storage medium | |
| CN114759991B (en) | Cyclostationary signal detection and modulation identification method based on visibility graph | |
| CN117135679B (en) | Terminal GSM and Wi-Fi coexistence test method, system and medium | |
| CN111329420A (en) | Data processing method, device and equipment of dish washing machine | |
| CN113820333B (en) | Battery pole piece abnormality detection method, device, upper computer and detection system | |
| CN116318444B (en) | Two-dimensional spectrum sensing method, device, electronic equipment and storage medium |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201020 |