Interference identification method and system based on intelligent electromagnetic spectrum sensing

Abstract

The invention discloses an interference identification method and system based on intelligent electromagnetic spectrum sensing. The method comprises the following steps: generating a plurality of interference signals under different systems; performing data preprocessing on the generated interference signal to obtain a normalized signal; performing feature extraction on the obtained normalized signal to obtain a feature value; designing a three-layer full-connection BP neural network according to the obtained feature values and training the three-layer full-connection BP neural network; different noise signals are classified by using the designed neural network, a complex classification method is not needed, and signal feature value extraction is simple and effective.

Description

Background Art

[0002] Interference identification is an important part of anti-interference, and therefore has become a research hotspot in the field of communication anti-interference. With the rapid development of wireless communication technology, the communication environment has become increasingly complex, and various interference sources have emerged in an endless stream, bringing huge challenges to the normal operation of the communication system. Malicious interference in the communication system is mainly divided into three categories: deceptive interference, suppression interference, and suppression / deceptive combination interference. Among them, deceptive interference refers to the misleading of the receiver to receive incorrect signals by forging interference signals similar to useful communication signals, and making wrong judgments on relevant information; suppression interference refers to the use of interference signals with a transmission power far greater than that of useful signals, so that the useful signals are submerged in the interference signals, causing the receiver to be unable to effectively receive the transmitted signals, resulting in the satellite communication system being unable to work properly. Due to the diversity of interference methods, there is no universal anti-interference mode that can fully guarantee the security of the communication system, and it is necessary to suppress different interference signals in a targeted manner. Therefore, in order to ensure the safety and reliability of communication, it is necessary to effectively detect the interference in the received signal in real time, understand the characteristics of various interference modes, accurately identify the types of interference, and then make corresponding anti-interference strategies for different types of interference, so as to improve the overall anti-interference performance of the communication system. Therefore, the research on interference signal detection technology and interference signal identification technology is an indispensable component of wireless communication systems and a prerequisite for ensuring the security and reliability of communication systems.

[0003] Therefore, the research and application of interference identification technology is particularly important. Communication interference classification and identification generally requires solving two key problems, namely, signal feature extraction and the selection and design of classification and identification algorithms. Feature extraction is to extract low-order statistics or high-order cumulants of signals from dimensions such as time domain, frequency domain, spatial domain, and power domain to obtain their features.

[0004] The feature extraction process of the interference identification algorithm currently studied based on eigenvalue extraction has a large amount of computational complexity and requires a more efficient classification algorithm. As the number of signal features increases, the dimension of the signal feature vector continues to increase, and the requirements for the computational complexity of the classification algorithm become more stringent. Different types of interference will appear alternately in the same scene and may switch randomly. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide an interference identification method and system based on intelligent electromagnetic spectrum perception in response to the above-mentioned deficiencies in the prior art. Taking electromagnetic spectrum perception as the starting point, based on the different signal characteristics of different interference signals in the time domain and frequency domain, the characteristic values ​​of the interference signals are extracted, and then the purpose of interference identification is achieved through machine learning. The identification of interference signals in complex electromagnetic environments is completed, which is used to solve the technical problem of large amount of computation in the feature extraction process of the current interference identification method, without the need to use complex classification algorithms, and the signal feature value extraction is simple and effective.

[0006] The present invention adopts the following technical solutions: An interference identification method based on intelligent electromagnetic spectrum perception comprises the following steps: Generate multiple interference signals under different standards; Performing data preprocessing on the generated interference signal to obtain a normalized signal; Perform feature extraction on the obtained normalized signal to obtain a feature value; Design a three-layer fully connected BP neural network based on the obtained eigenvalues ​​and train it; The designed neural network is used to classify different noise signals.

[0007] Preferably, generating multiple interference signals under different standards is specifically as follows: Single-tone interference, multi-tone interference, partial-band noise interference and linear swept-frequency interference are generated in shortwave, ultra-shortwave, mobile communication and satellite communication formats respectively.

[0008] Preferably, the mathematical models of single-tone interference, multi-tone interference, partial-band noise interference and linear swept-frequency interference are , , and They are as follows:

[0009]

[0010]

[0011]

[0012] in, represents the power of single-tone interference, represents the interference frequency, represents a random phase, represents the power of linear frequency modulation interference, represents the total power of multi-tone interference, Indicates multi-tone interference The frequency of the interfering component, represents a random phase, Indicates the sweep center frequency, represents the linear sweep frequency, Show obedience The uniformly distributed random phase on The mean is 0 and the variance is Band-limited Gaussian noise, is the number of audio interferences in multi-tone interference, is a continuously changing time variable.

[0013] Preferably, the data preprocessing of the generated interference signal is specifically performed as follows: First, perform A / D conversion on the received signal. Suppose the digital intermediate frequency signal is expressed as , first normalize the power in the time domain, and get , the power normalized signal Perform Fourier transform to obtain its spectrum, and then perform frequency domain normalization.

[0014] Preferably, the power is normalized to obtain as follows:

[0015] in, is the number of sampling points of the signal.

[0016] Preferably, feature extraction is performed on the obtained normalized signal as follows: On the basis of the normalized signal, the target signal is feature extracted to obtain feature parameters including time domain and frequency domain features, which are used as the input of the BP neural network to detect the type of interference signal through machine learning.

[0017] Preferably, the characteristic parameters include the ratio of the maximum value to the second maximum value of the signal spectrum amplitude, the single-frequency energy concentration, the average frequency domain flatness coefficient, the time domain kurtosis, the power spectrum skewness, the power spectrum kurtosis, the ratio of the spectrum variance to the mean square, the normalized spectrum peak-to-average ratio and the 3dB bandwidth of the normalized spectrum.

[0018] Preferably, the ratio of the maximum value to the second maximum value of the signal spectrum amplitude is for:

[0019] Single frequency energy concentration :

[0020] in, is an integer, is the maximum value of the frequency index; Average frequency domain flatness coefficient :

[0021] in, is the total number of frequency points, For the k The power spectral density at each frequency point is is the average value of the signal power spectrum; Time Domain Kurtosis :

[0022] in, for The mean of yes The standard deviation of Power spectrum skewness :

[0023] in, for The mean of yes The standard deviation of is the function of power spectral density changing with frequency; Power spectrum kurtosis :

[0024] Ratio of the spectrum variance to the square of the mean :

[0025] in, yes The standard deviation of for The mean of Normalized spectrum peak-to-average ratio :

[0026] Normalized spectrum 3dB bandwidth :

[0027] Among them, card{} is the number of elements in the set. for The result of normalization using the mean is: is the frequency amplitude threshold.

[0028] Preferably, after obtaining 9 feature parameters, the first-level recognition module is designed as a three-layer fully connected BP neural network, with 9 input nodes, corresponding to the 9 extracted feature parameters; 12 hidden layer nodes; 8 output nodes, corresponding to 8 classification labels H0~H7; and the BP neural network of the first-level recognition module is trained using the 9 feature parameters.

[0029] In a second aspect, an embodiment of the present invention provides an interference identification system based on intelligent electromagnetic spectrum perception, including: Generation module, generating multiple interference signals under different standards; A preprocessing module performs data preprocessing on the generated interference signal to obtain a normalized signal; An extraction module performs feature extraction on the obtained normalized signal to obtain a feature value; The training module designs a three-layer fully connected BP neural network based on the obtained eigenvalues ​​and trains it; The classification module uses the designed neural network to classify different noise signals.

[0030] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned interference identification method based on intelligent electromagnetic spectrum sensing when executing the computer program.

[0031] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned interference identification method based on intelligent electromagnetic spectrum sensing.

[0032] Compared with the prior art, the present invention has at least the following beneficial effects: An interference identification method based on intelligent electromagnetic spectrum perception, the intelligent electromagnetic spectrum perception interference identification method of the present invention generates interference signals of different formats, performs data preprocessing, extracts characteristic values, and then uses BP neural network for classification. The advantage of this method is that it effectively extracts interference characteristic values ​​based on signal characteristics in the time domain and frequency domain, uses machine learning to simplify the classification process, reduces complex algorithm dependence, and improves recognition efficiency.

[0033] Furthermore, a variety of interference signals under different standards are generated, covering shortwave, ultra-shortwave, mobile communication and satellite communication, etc., to ensure that the constructed interference identification system has wide adaptability. By simulating typical interference in different communication environments, the generalization ability of the system is enhanced, which helps to improve the recognition rate of actual interference signals.

[0034] Furthermore, the generated interference signal is preprocessed, especially the power normalization and frequency domain normalization of the time domain signal. This step ensures that the influence of signals with different strengths is weakened and the influence of noise is eliminated, thereby improving the accuracy of subsequent feature extraction.

[0035] Furthermore, feature extraction is performed on the normalized signal to obtain time domain and frequency domain feature parameters. Through feature extraction, the original signal is converted into feature parameters that are easy to classify, so that the neural network can more efficiently identify the signal type and simplify the complexity of interference identification.

[0036] Furthermore, characteristic parameters such as the ratio of the maximum to the second maximum value of the signal spectrum amplitude and the single-frequency energy concentration can effectively describe the spectral characteristics and time domain characteristics of the signal. The setting of these parameters helps to distinguish different types of interference signals, and through normalization processing, the robustness of the model in different environments is guaranteed.

[0037] Furthermore, using 9 characteristic parameters to train the first-level BP neural network helps to fully extract the multi-dimensional features of the interference signal. Through a three-layer fully connected neural network, different interference signals are classified into 8 categories. This design simplifies the classification algorithm and reduces the computational complexity.

[0038] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0039] In summary, the present invention takes electromagnetic spectrum perception as the entry point, extracts the characteristic values ​​of interference signals based on the different signal characteristics of different interference signals in the time domain and frequency domain, and then achieves the purpose of interference identification through machine learning. There is no need to use complex classification algorithms, and the signal characteristic value extraction is simple and effective.

[0040] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings to be used in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 Block diagram for interference identification; Figure 2 To test the linearity between the predicted value and the target value; Figure 3 Demonstration diagram for interference signal identification; Figure 4A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 5 The present invention is a block diagram of an electronic device provided according to an embodiment. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0045] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0046] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0047] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0048] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0049] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0050] The present invention provides an interference identification method based on intelligent electromagnetic spectrum perception, which is mainly designed to complete two tasks, namely interference generation and interference identification. Interference generation uses four types of interference sources in this project, including single-tone interference, multi-tone interference, broadband noise interference, and linear frequency sweep interference. Interference identification uses a neural network to extract and classify the interference signal.

[0051] See also Figure 1 The invention discloses an interference identification method based on intelligent electromagnetic spectrum perception, which identifies a series of interference signals, such as single-tone interference, comb spectrum interference, linear frequency modulation interference and broadband interference; firstly, the received signal is subjected to time domain and frequency domain feature extraction, including single-frequency energy concentration, average frequency domain flatness coefficient, time domain kurtosis, power spectrum skewness and other features; after obtaining the characteristic parameters of the signal, a neural network is designed, and the neural network of the recognition module is trained using the above features; finally, the electromagnetic interference signal is identified and classified using the neural network; the specific steps are as follows: S1. Generate multiple interference signals under different standards; Single-tone interference, multi-tone interference, partial-band noise interference and linear swept-frequency interference are generated in shortwave, ultra-shortwave, mobile communication and satellite communication formats respectively.

[0052] The characteristics of single-tone interference are that it is continuous in time domain but single in frequency domain. Its mathematical model is as follows: (1) in, represents the power of single-tone interference, represents the interference frequency, represents a random phase, subject to Uniform distribution on .

[0053] The characteristics of multi-tone interference are continuity in the time domain and multi-tone in the frequency domain. Its interference model is as follows: (2) in, represents the total power of multi-tone interference, Indicates multi-tone interference The frequency of the interfering component, represents a random phase, which obeys Uniform distribution on .

[0054] Partial-band noise interference is a broadband interference that interferes with the communication system by covering a large range of the communication signal bandwidth or even the full band. It presents the characteristics of band-limited Gaussian white noise. Its mathematical model is expressed as: (3) in, The mean is 0 and the variance is The corresponding bandwidth is the interference bandwidth.

[0055] Linear frequency sweep interference is a time-varying suppression interference, characterized by the linear change of instantaneous frequency over time. Its interference model can be expressed as: (4) in, represents the power of linear frequency modulation interference, Indicates the sweep center frequency, represents the linear sweep frequency, Show obedience A uniformly distributed random phase on .

[0056] S2, preprocessing the data for interference noise; First, perform A / D conversion on the received signal. Suppose the digital intermediate frequency signal is expressed as In order to avoid unnecessary interference of signals with different strengths on recognition, the power is normalized in the time domain to obtain , the power normalized signal Perform Fourier transform to obtain its spectrum , and then perform frequency domain normalization.

[0057] (5) (6) S3. Perform feature extraction on the normalized signal in step S2.

[0058] On the basis of the normalized signal, the target signal is feature extracted, including nine features in the time domain and frequency domain, which are used as the input of the BP neural network. The interference signal type is detected through machine learning to achieve the function of interference signal identification. The extracted feature parameters are as follows: 1) The ratio of the maximum value to the second maximum value of the signal spectrum amplitude: (7) 2) Single frequency energy concentration: (8) in, is a small integer, set in simulation .

[0059] 3) Average frequency domain flatness coefficient: In the frequency domain, the square of the signal spectrum can be regarded as the power of the signal, that is: (9) Normalizing it, the normalized power (frequency domain) is: (10) in, express The mean of .

[0060] right The impulse part of is extracted by subtracting the sliding average of the normalized power from itself, and we get: (11) The second item is The normalized smoothed power spectrum after sliding average, is the length of the sliding average window. ,like If it is not an odd number, then take To be closest An odd number.

[0061] Finally, the average spectrum flatness coefficient is obtained: (12) 4) Time domain kurtosis: (13) in, for The mean of yes The standard deviation of .

[0062] 5) Power spectrum skewness: (14) in, for The mean of yes The standard deviation of .

[0063] 6) Power spectrum kurtosis: (15) 7) Ratio of spectrum variance to mean square: (16) in, yes The standard deviation of for The mean of .

[0064] 8) Normalized spectrum peak-to-average ratio: (17) 9) 3dB bandwidth of normalized spectrum: (18) Among them, card{} represents the number of elements in the set. for The result of normalization using the mean is: .

[0065] S4. Design and train a neural network based on the feature values ​​extracted in step S3. After obtaining 9 feature parameters, design the first-level recognition module as a three-layer fully connected BP neural network. The number of input nodes is 9, corresponding to the 9 extracted feature parameters; the number of hidden layer nodes is 12; the number of output nodes is 8, corresponding to 8 classification labels, i.e., H0~H7; use the above 9 features to train the BP neural network of the first-level recognition module; S5. Use the neural network designed in step S4 to classify different noise signals.

[0066] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0067] In yet another embodiment of the present invention, an interference identification system based on intelligent electromagnetic spectrum sensing is provided. The system can be used to implement the above-mentioned interference identification method based on intelligent electromagnetic spectrum sensing. Specifically, the interference identification system based on intelligent electromagnetic spectrum sensing includes a generation module, a preprocessing module, an extraction module, a training module and a classification module.

[0068] Among them, the generation module generates multiple interference signals under different standards; A preprocessing module performs data preprocessing on the generated interference signal to obtain a normalized signal; An extraction module performs feature extraction on the obtained normalized signal to obtain a feature value; The training module designs a three-layer fully connected BP neural network based on the obtained eigenvalues ​​and trains it; The classification module uses the designed neural network to classify different noise signals.

[0069] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the interference identification method based on intelligent electromagnetic spectrum perception, including: Generate a variety of interference signals under different formats; perform data preprocessing on the generated interference signals to obtain normalized signals; perform feature extraction on the obtained normalized signals to obtain eigenvalues; design a three-layer fully connected BP neural network based on the obtained eigenvalues ​​and train it; use the designed neural network to classify different noise signals.

[0070] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0071] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0072] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0073] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the interference identification method based on intelligent electromagnetic spectrum sensing in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows: Generate a variety of interference signals under different formats; perform data preprocessing on the generated interference signals to obtain normalized signals; perform feature extraction on the obtained normalized signals to obtain eigenvalues; design a three-layer fully connected BP neural network based on the obtained eigenvalues ​​and train it; use the designed neural network to classify different noise signals.

[0074] See also Figure 4 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the interference identification method based on intelligent electromagnetic spectrum sensing in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the interference identification system based on intelligent electromagnetic spectrum sensing in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0075] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 4 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0076] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0077] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0078] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0079] Any reference to a memory, database or other medium used in the embodiments provided in the present application may include at least one of a non-volatile and a volatile memory. Non-volatile memory may include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical memory, a high-density embedded non-volatile memory, a resistive random access memory (ReRAM), a magnetic random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. Volatile memory may include a random access memory (RAM) or an external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc.

[0080] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0081] See also Figure 5The terminal device is an electronic device 600, and the electronic device is in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0082] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 executes the steps of various exemplary embodiments of the present invention described in the above method part of this specification; for example, the processing unit 610 can execute the following steps: Figure 1 Follow the steps shown in .

[0083] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0084] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0085] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0086] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0087] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0088] In practical applications, the present invention verifies the interference identification effect in complex electromagnetic environments through simulation. By constructing interference scenarios of different formats such as shortwave, ultra-shortwave, mobile communication and satellite communication, typical interference signals such as single tone, multi-tone, linear frequency sweep and band-limited noise are simulated and generated, and the interference identification method proposed in this article is used for testing. Experimental data show that after 9 feature parameter extractions and BP neural network training, the interference identification accuracy of the present invention exceeds 95% under high signal-to-noise ratio conditions, and can also maintain a recognition rate of more than 85% under low signal-to-noise ratio conditions. This shows that the present invention has good anti-interference performance in different electromagnetic environments and is suitable for complex interference scenarios in military and communication systems.

[0089] See also Figure 2 , the figure shows the linear fitting results of the predicted value and the actual target value of the interference signal. It can be seen from the figure that the correlation between the predicted value and the target value is high, and the fitting curve is close to the 45-degree oblique line, indicating that the BP neural network of the present invention is fully trained and has a high recognition accuracy. From this result, it can be concluded that the feature extraction method and neural network model adopted by the present invention can effectively distinguish different types of interference signals, significantly reduce the recognition error, and improve the accuracy of signal classification. This conclusion further proves that the method of the present invention can realize the accurate identification of various complex interferences in actual electromagnetic spectrum perception applications.

[0090] See also Figure 3, the figure shows the distribution diagram of the recognition results of different types of interference signals, including single tone, multi-tone, band-limited noise and linear frequency sweep interference. It can be seen from the figure that various types of interference signals are accurately classified, the classification boundaries are clear, and there is less confusion between interference signals. The figure shows that the present invention can perform accurate classification and recognition when facing interference signals of various standards, avoiding misjudgment between different types of interference. At the same time, through the learning and training of feature parameters by BP neural network, the present invention can effectively extract useful features in complex background noise to ensure the accuracy and stability of classification.

[0091] In summary, the interference identification method and system based on intelligent electromagnetic spectrum perception of the present invention has efficient feature extraction capability, combines time domain and frequency domain features to accurately identify a variety of interference signals, and simplifies the classification process by adopting BP neural network, reduces computational complexity and improves classification accuracy. Experiments have verified the wide adaptability and strong generalization ability of this method under various communication standards, can maintain a high recognition accuracy in complex electromagnetic environments, and exhibits good anti-interference performance, which is suitable for interference identification and protection tasks in modern complex communication environments.

[0092] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0093] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0094] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0095] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0096] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0097] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0098] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0099] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0102] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. An interference identification method based on intelligent electromagnetic spectrum perception, characterized in that: The following steps are involved: Generate multiple interference signals under different standards; Performing data preprocessing on the generated interference signal to obtain a normalized signal; Perform feature extraction on the obtained normalized signal to obtain a feature value; Design a three-layer fully connected BP neural network based on the obtained eigenvalues ​​and train it; The designed neural network is used to classify different noise signals.

2. The interference identification method based on intelligent electromagnetic spectrum perception according to claim 1 is characterized in that: Generate multiple interference signals under different standards: Single-tone interference, multi-tone interference, partial-band noise interference and linear swept-frequency interference are generated in shortwave, ultra-shortwave, mobile communication and satellite communication formats respectively.

3. The interference identification method based on intelligent electromagnetic spectrum perception according to claim 2 is characterized in that: Mathematical models of single-tone interference, multi-tone interference, partial-band noise interference, and linear swept-frequency interference , , and They are as follows: in, represents the power of single-tone interference, represents the interference frequency, represents a random phase, represents the power of linear frequency modulation interference, represents the total power of multi-tone interference, Indicates multi-tone interference The frequency of the interfering component, represents a random phase, Indicates the sweep center frequency, represents the linear sweep frequency, Show obedience The uniformly distributed random phase on The mean is 0 and the variance is Band-limited Gaussian noise, is the number of audio interferences in multi-tone interference, is a continuously changing time variable.

4. The interference identification method based on intelligent electromagnetic spectrum perception according to claim 1 is characterized in that: The data preprocessing of the generated interference signal is specifically as follows: First, perform A / D conversion on the received signal. Suppose the digital intermediate frequency signal is expressed as , first normalize the power in the time domain, and get , the power normalized signal Perform Fourier transform to obtain its spectrum, and then perform frequency domain normalization.

5. The interference identification method based on intelligent electromagnetic spectrum perception according to claim 3 is characterized in that: Power normalization gives as follows: in, is the number of sampling points of the signal.

6. The interference identification method based on intelligent electromagnetic spectrum perception according to claim 1 is characterized in that: The feature extraction of the obtained normalized signal is as follows: On the basis of the normalized signal, the target signal is feature extracted to obtain feature parameters including time domain and frequency domain features, which are used as the input of the BP neural network to detect the type of interference signal through machine learning.

7. The interference identification method based on intelligent electromagnetic spectrum perception according to claim 6 is characterized in that: The characteristic parameters include the ratio of the maximum to the second maximum value of the signal spectrum amplitude, the single-frequency energy concentration, the average frequency domain flatness coefficient, the time domain kurtosis, the power spectrum skewness, the power spectrum kurtosis, the ratio of the spectrum variance to the mean square, the normalized spectrum peak-to-average ratio and the 3dB bandwidth of the normalized spectrum.

8. The interference identification method based on intelligent electromagnetic spectrum perception according to claim 7 is characterized in that: The ratio of the maximum value to the second maximum value of the signal spectrum amplitude for: Single frequency energy concentration : in, is an integer, is the maximum value of the frequency index; Average frequency domain flatness coefficient : in, is the total number of frequency points, For the k The power spectral density at each frequency point is is the average value of the signal power spectrum; Time Domain Kurtosis : in, for The mean of yes The standard deviation of Power spectrum skewness : in, for The mean of yes The standard deviation of is the function of power spectral density changing with frequency; Power spectrum kurtosis : Ratio of the spectrum variance to the square of the mean : in, yes The standard deviation of for The mean of Normalized spectrum peak-to-average ratio : Normalized spectrum 3dB bandwidth : Among them, card{} is the number of elements in the set. for The result of normalization using the mean is: is the frequency amplitude threshold.

9. The interference identification method based on intelligent electromagnetic spectrum perception according to claim 1 is characterized in that: After obtaining 9 characteristic parameters, the first-level recognition module is designed as a three-layer fully connected BP neural network with 9 input nodes corresponding to the 9 extracted characteristic parameters and 12 hidden layer nodes. The number of output nodes is 8, corresponding to 8 classification labels H0~H7; and 9 feature parameters are used to train the BP neural network of the first-level recognition module.

10. An interference identification system based on intelligent electromagnetic spectrum perception, characterized in that: include: Generation module, generating multiple interference signals under different standards; A preprocessing module performs data preprocessing on the generated interference signal to obtain a normalized signal; An extraction module performs feature extraction on the obtained normalized signal to obtain a feature value; The training module designs a three-layer fully connected BP neural network based on the obtained eigenvalues ​​and trains it; The classification module uses the designed neural network to classify different noise signals.

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