Complex electromagnetic environment frequency spectrum sensing method and system based on multi-dimensional feature domain

Abstract

The invention discloses a complex electromagnetic environment spectrum sensing method and system based on a multi-dimensional feature domain, and the method comprises the steps: dividing an azimuth dimension 360-degree airspace into 24 regions, and respectively receiving electromagnetic wave signals in different directions; calculating time-frequency instantaneous energy of the electromagnetic wave signal of each area; effective signals in each airspace are detected, and the time-frequency amplitude is calculated; all signals with time-frequency amplitudes exceeding a preset threshold value are screened out, the instantaneous frequency of the signals is calculated by adopting an accumulated phase difference method, a spatial domain multi-angle time-frequency two-dimensional characteristic matrix is obtained, and an unoccupied spectrum range, namely a spectrum hole, is identified; and planning a communication link according to the number of the spectrum holes and the available spectrum range thereof. According to the method, the real-time performance and the accuracy of electromagnetic spectrum situation sensing of electromagnetic spectrum cognitive equipment can be effectively improved through multi-dimensional domain characteristic analysis of the spectrum in a complex electromagnetic environment, so that the frequency utilization strategy of frequency utilization equipment is guided.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic spectrum sensing, and in particular to a method and system for sensing the spectrum of complex electromagnetic environments based on multidimensional feature domains. Background Technology

[0002] With the development of science and technology, electromagnetic waves have been widely used in broadcasting, communications, radar, navigation, and other fields. With the popularization and application of electronic devices, the electromagnetic environment is becoming increasingly complex. Various radar, navigation, communication, IFF (Identification Friend or Foe) signals, electronic jamming signals, etc., have high density, high energy, and diverse modulation patterns. How to effectively perceive the electromagnetic spectrum situation and improve the control capability of the electromagnetic environment in complex electromagnetic environments is a current research hotspot. Electronic countermeasures are a power-defense game conducted in the electromagnetic space. Gaining control of the electromagnetic spectrum means being able to freely use the electromagnetic spectrum within a controllable frequency range, free from electromagnetic threats from the opponent; at the same time, it allows for the restriction of the opponent's use of the electromagnetic spectrum.

[0003] Accurate and real-time electromagnetic spectrum sensing capability is a prerequisite for gaining control of the electromagnetic spectrum. Real-time monitoring of the electromagnetic spectrum situation and understanding of its occupancy can provide guidance for frequency use and enable precise interference with other users' frequency bands, effectively improving electromagnetic spectrum control capabilities. Summary of the Invention

[0004] The main objective of this invention is to provide a method and system for sensing the spectrum of complex electromagnetic environments based on a multi-dimensional feature domain, which can effectively improve the real-time performance and accuracy of electromagnetic spectrum situational awareness of electromagnetic spectrum cognition devices, thereby guiding the frequency usage strategies of frequency-using devices.

[0005] The technical solution adopted in this invention is: A method for sensing the spectrum of complex electromagnetic environments based on a multidimensional feature domain is provided, comprising the following steps: Step 1: Divide the 360° azimuth space into 24 regions and receive electromagnetic wave signals from different directions. Step 2: Calculate the time-frequency instantaneous energy of the electromagnetic wave signal in each region; Step 3: Use the cumulative energy difference detection method to detect the arrival and termination times of valid signals in each airspace; Step 4: Calculate the time-frequency amplitude of the effective signal in each spatial domain using the windowed short-time Fourier analysis method; Step 5: Filter out all signals whose time-frequency amplitude exceeds the preset threshold, and use the cumulative phase difference method to calculate their instantaneous frequency to obtain a two-dimensional time-frequency feature matrix in the spatial domain from multiple angles. Identify the unoccupied spectrum range, which is recorded as a spectrum hole. Step 6: Plan communication links based on the number of spectrum holes and their usable spectrum range.

[0006] Following the above technical solution, in step 1, the airspace is divided with due north as 0°, and then divided into regions 1 to 24 in a clockwise direction, with each region having a range of 15°.

[0007] Following the above technical solution, in step 2, the time-frequency instantaneous energy of the electromagnetic wave signal is calculated as follows:

[0008] in, yes The Middle Total before and after point The intercepted signal consists of a sampled signal. To extract the length, for The The sampled signal at each point; For the sampled signals of each region, The real part of the sampled signal, For the imaginary part of the sampled signal, For area code, , Sampling time, , For cumulative length, This is the frequency corresponding to the time-frequency transformation.

[0009] Following the above technical solution, in step 3, the accumulated energy difference Defined as The cumulative energy difference detection method is used to determine whether the real-time cumulative energy of electromagnetic waves exceeds the set detection threshold. ,Right now

[0010] in, The value is adjusted according to the real-time electromagnetic environment.

[0011] Following the above technical solution, in step 4, the time-frequency domain amplitude... The calculation method is as follows:

[0012] in, For window functions, For the length of the window, For window number, For the sampled signals of each region, For area code, , Sampling time .

[0013] Following the above technical solution, in step 5, the instantaneous frequency is calculated by first calculating the cumulative phase difference result. , To accumulate the number of times, Then, the defuzzification operation is performed, specifically as follows: , ; , ; , ; Instantaneous frequency: , The sampling rate.

[0014] Following the above technical solution, in step 6, assume that the spectrum sensing spectrum holes have... The, the The usable range of each spectrum is Define the granularity of spectrum planning based on specific frequency usage requirements and equipment capabilities. Then the spectrum resources are Assuming there is Each node needs to use a frequency, and the node set is represented as... Define a vector The vector This represents the communication frequency usage strategy of each link node, and the communication link frequency usage strategy is a vector. , The number of spectral granularities between the two frequency usage strategies. The path loss factor is the number of linked nodes in the communication link network. The set of nodes that participate in the communication link is used The frequency usage strategy optimization problem can be expressed as: min ; Calculate the frequency allocation strategy for node communication links within the available frequency band, and ensure that the spectrum spacing between the frequency used for communication links and the access network frequency used by the nodes participating in the communication links is greater than [a certain value]. To eliminate co-location interference, while the planned frequency bandwidth requirements for communication links meet the minimum communication link bandwidth requirements.

[0015] Following the above technical solution, a high-speed parallel acquisition and processing method across the entire airspace is specifically adopted to receive electromagnetic wave signals from different directions.

[0016] The present invention also provides a complex electromagnetic environment spectrum sensing system based on a multidimensional feature domain, comprising: The region division module is used to divide the 360° azimuth space into 24 regions, each receiving electromagnetic wave signals from different directions. The instantaneous energy calculation module is used to calculate the time-frequency instantaneous energy of the electromagnetic wave signal in each region; The effective signal detection module is used to detect the arrival and termination times of effective signals in each airspace using the cumulative energy difference detection method. The time-frequency amplitude calculation module is used to calculate the time-frequency amplitude of the effective signal in each spatial domain using the windowed short-time Fourier analysis method; The spectrum hole finding module is used to filter out all signals whose time-frequency amplitude exceeds a preset threshold, and calculates their instantaneous frequency using the cumulative phase difference method to obtain a two-dimensional time-frequency feature matrix in the spatial domain from multiple angles, and identifies the unoccupied spectrum range, which is denoted as a spectrum hole. The planning module is used to plan communication links based on the number of spectrum holes and their available spectrum range.

[0017] The present invention also provides a computer storage medium storing a computer program executable by a processor, the computer program executing the complex electromagnetic environment spectrum sensing method based on multidimensional feature domain described in the above technical solution.

[0018] The beneficial effects of this invention are as follows: By dividing the airspace, this invention monitors the changes in signal energy over time in different directions across the entire airspace in real time. It then integrates multi-dimensional signal characteristics from the time, frequency, spatial, and energy domains to achieve a comprehensive analysis of the complex electromagnetic environment spectrum situation across the entire airspace at a given location, determining spectrum occupancy and guiding the frequency usage strategies of frequency-using equipment. This invention enables continuous monitoring of the electromagnetic spectrum in complex electromagnetic environments, providing frequency references for equipment using electromagnetic frequencies. It effectively improves the real-time performance and accuracy of electromagnetic spectrum recognition equipment and possesses advantages such as excellent sensing capabilities, versatility, and strong engineering feasibility.

[0019] Furthermore, by dividing the airspace and using parallel processing for different airspaces, high-precision and high-efficiency real-time monitoring can be achieved.

[0020] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a complex electromagnetic environment spectrum sensing method based on a multi-dimensional feature domain according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the airspace division and radiation source distribution according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the time-frequency patterns of various radiation sources in an embodiment of the present invention; Figure 4 These are time-domain diagrams of received signals in different spatial domains according to embodiments of the present invention; Figure 5 This is a time-space-energy domain diagram of an embodiment of the present invention; Figure 6 This is a graphical representation of the two-dimensional time-frequency domain feature matrix of a signal according to an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0024] It should be noted that the illustrations provided in the embodiments of the present invention are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0025] In this invention, it should also be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first" and "second" are used only for descriptive and distinguishing purposes and should not be construed as indicating or implying relative importance.

[0026] like Figure 1 As shown, the present invention provides a method for sensing the spectrum of complex electromagnetic environments based on a multi-dimensional feature domain, comprising the following steps: S1. Divide the 360° azimuth space into 24 regions to receive electromagnetic wave signals from different directions. S2. Calculate the time-frequency instantaneous energy of the electromagnetic wave signal in each region; S3. The arrival and termination times of effective signals in each airspace are detected using the cumulative energy difference detection method. S4. The windowed short-time Fourier analysis method is used to calculate the time-frequency amplitude of the effective signal in each spatial domain; S5. Filter out all signals whose time-frequency amplitude exceeds the preset threshold, and use the cumulative phase difference method to calculate their instantaneous frequency to obtain a two-dimensional time-frequency feature matrix in the spatial domain from multiple angles. Identify the unoccupied spectrum range, which is denoted as a spectrum hole. S6. Plan communication links based on the number of spectrum holes and their usable spectrum range.

[0027] In step S1, the 360° azimuth space is divided into 24 regions. Electromagnetic wave signals from different directions can be received by using a high-speed parallel acquisition and processing method across the entire space. The spatial division can effectively assess the distribution of electromagnetic signals in different directions.

[0028] In step S2, the time-frequency instantaneous energy of the electromagnetic wave signal in each region can be calculated through spectrum splicing, spectrum extraction, spectrum analysis, etc. By dividing the spatial domain, a high-precision and high-efficiency parallel processing method is used to monitor the change of signal energy in different directions over time in the entire spatial domain in real time.

[0029] Step S3 uses the cumulative energy difference detection method to detect the arrival and end times of effective signals in each spatial domain. The cumulative energy difference detection method can effectively determine the presence or absence of a signal. The obtained signal arrival and end times are used to calculate the pulse width of pulse-type signals. In step S5, the cumulative phase difference method is used to calculate the instantaneous frequency of all signals whose time-frequency amplitude exceeds the set threshold. The unoccupied spectrum range, i.e., the spectrum hole, is obtained by using the spatial multi-angle time-frequency two-dimensional feature matrix obtained by signal sorting and identification.

[0030] Preferably, in step S1, such as Figure 2 As shown, the airspace can be divided with true north as 0°, and sequentially into regions 1 to 24 in a clockwise direction, each region ranging from 15°. A high-speed parallel acquisition and processing method across the entire airspace is used to receive electromagnetic wave signals from different directions, resulting in a total of 24 parallel signals being processed. The received signal in each region can be represented as follows: , For area code, , Sampling time, .

[0031] Preferably, in step S2, the time-frequency instantaneous energy of the electromagnetic wave signal is defined as follows:

[0032] in, yes The Middle Total before and after point The intercepted signal consists of a sampled signal. To extract the length, for The The sampled signal at each point; For the sampled signals of each region, The real part of the sampled signal, For the imaginary part of the sampled signal, For area code, , Sampling time, , For cumulative length, This is the frequency corresponding to the time-frequency transformation.

[0033] Preferably, in step S3, the accumulated energy difference Defined as ; The cumulative energy difference detection method determines whether the real-time cumulative energy of electromagnetic waves exceeds a set detection threshold. ,Right now

[0034] in, It can be adjusted according to the real-time electromagnetic environment. Preferably, in step S4, the time-frequency domain amplitude The calculation method is as follows:

[0035] in, For window functions, For the length of the window, For window number, For the sampled signals of each region, For area code, , Sampling time .

[0036] Preferably, in step S5, the instantaneous frequency is calculated by first calculating the cumulative phase difference result. , To accumulate the number of iterations and perform defuzzification, the specific method is as follows: , ; , ; , ; The method for calculating instantaneous frequency is as follows: The spatial multi-angle time-frequency two-dimensional feature matrix is ​​obtained by counting all frequencies exceeding the threshold. The sampling rate.

[0037] In step S6, the spectrum occupancy can be assessed based on the frequency distribution analysis results, a spectrum planning model can be constructed, and the optimal frequency usage strategy can be selected. Specifically, the method for constructing the spectrum planning model is as follows: assuming there are spectrum holes... The, the The usable range of each spectrum is Define the granularity of spectrum planning based on specific frequency usage requirements and equipment capabilities. Then the spectrum resources are Assume there is Each node needs to use a frequency, and the set of nodes can be represented as follows: Define a vector The frequency usage strategy of each link node is represented by a vector. , The number of spectral granularities between the two frequency usage strategies. The path loss factor is the number of linked nodes in the communication link network. The set of nodes that participate in the communication link is used The frequency usage strategy optimization problem can be expressed as: min

[0038] Calculate the vector of frequency usage strategies that satisfy node communication within the available frequency band. This ensures that the spectral spacing between the frequency used in the communication link and the access network frequency used by the nodes participating in the communication link is greater than the set spectral width. (The bandwidth between the two frequencies can be obtained based on experience or simulation results), which eliminates co-location interference, while the planned communication link bandwidth requirements meet the minimum communication link bandwidth requirements.

[0039] In one embodiment of the present invention, it is assumed that there are four radiation sources distributed in regions 5, 8, and 18, one each in regions 5 and 8, and two in region 18. Figure 3 As shown in the figure, the frequency domain waveforms of the signals from each radiation source are displayed; among them, radiation source 1 is a single-frequency pulse signal, radiation source 2 is a linear frequency modulated signal, radiation source 3 is a nonlinear frequency modulated signal, and radiation source 4 is a frequency-hopping signal. Figure 4 As shown in the figure, the time-domain waveforms of the signals received in each region are displayed. The signal waveforms in regions 5, 8, and 18 have higher amplitudes than those in other regions, indicating that these regions receive signals from radiation sources.

[0040] like Figure 5 As shown, this is a time-space-energy domain diagram. Time-frequency analysis is performed on each effective spatial domain signal, as follows: Figure 6 As shown, the time-frequency domain two-dimensional feature matrix of the signal is plotted, and the change of the signal spectrum in the spatial domain over time is displayed in real time. By calculating the instantaneous amplitude, phase and frequency of each signal, the current spectrum occupancy can be evaluated to a limited extent, and spectrum holes can be obtained to solve the optimal frequency planning result.

[0041] To implement the above method embodiments, the present invention also provides a complex electromagnetic environment spectrum sensing system based on a multi-dimensional feature domain, specifically including: The region division module is used to divide the 360° azimuth space into 24 regions, each receiving electromagnetic wave signals from different directions. The instantaneous energy calculation module is used to calculate the time-frequency instantaneous energy of the electromagnetic wave signal in each region; The effective signal detection module is used to detect the arrival and termination times of effective signals in each airspace using the cumulative energy difference detection method. The time-frequency amplitude calculation module is used to calculate the time-frequency amplitude of the effective signal in each spatial domain using the windowed short-time Fourier analysis method; The spectrum hole finding module is used to filter out all signals whose time-frequency amplitude exceeds a preset threshold, and calculates their instantaneous frequency using the cumulative phase difference method to obtain a two-dimensional time-frequency feature matrix in the spatial domain from multiple angles, and identifies the unoccupied spectrum range, which is denoted as a spectrum hole. The planning module is used to plan communication links based on the number of spectrum holes and their available spectrum range.

[0042] Each module is mainly used to implement the various steps of the above method embodiments, which will not be described in detail here.

[0043] This application also provides a computer-readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, App application store, etc., which stores a computer program, and the program implements corresponding functions when executed by a processor. This embodiment describes a method for sensing the spectrum of complex electromagnetic environments based on a multi-dimensional feature domain when the computer-readable storage medium is executed by a processor.

[0044] It should be noted that, depending on the implementation needs, the various steps / components described in this application can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.

[0045] The order of the steps in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0046] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A complex electromagnetic environment spectrum sensing method based on multi-dimensional feature domain, characterized in that: The following steps are involved: Step 1: Divide the 360° azimuth space into 24 regions to receive electromagnetic wave signals from different directions respectively; Step 2, calculating the time-frequency instantaneous energy of the electromagnetic wave signal in each area; Step 3: Using the cumulative energy difference detection method to detect the arrival and end time of the effective signal in each airspace; Step 4: Calculate the time-frequency amplitude of the effective signal in each spatial domain by using the windowed short-time Fourier analysis method; Step 5: Filter out all signals whose time-frequency amplitude exceeds a preset threshold, and use the cumulative phase difference method to calculate their instantaneous frequency, obtain a spatial multi-angle time-frequency two-dimensional feature matrix, and identify the unoccupied spectrum range, which is recorded as a spectrum hole; Step 6: Plan the communication links according to the number of spectrum holes and the available spectrum range.

2. The complex electromagnetic environment spectrum sensing method based on multi-dimensional feature domain according to claim 1 is characterized in that: In step 1, the airspace is divided into areas 1 to 24 in the clockwise direction with due north as 0°, and each area has a range of 15°.

3. The complex electromagnetic environment spectrum sensing method based on multi-dimensional feature domain according to claim 1 is characterized in that: In step 2, the time-frequency instantaneous energy of the electromagnetic wave signal is calculated as follows: in, yes Middle Total before and after The intercepted signal is composed of sampled signals. To cut the length, for No. The sampling signal of each point; is the sampling signal of each area, is the real part of the sampled signal, is the imaginary part of the sampled signal, is the area number, , is the sampling time, , is the cumulative length, is the frequency corresponding to the time-frequency transformation.

4. The complex electromagnetic environment spectrum sensing method based on multi-dimensional feature domain according to claim 3 is characterized in that: In step 3, the accumulated energy difference Defined as The cumulative energy difference detection method is used to determine whether the real-time cumulative energy of the electromagnetic wave exceeds the set detection threshold. ,Right now in, The value is adjusted according to the real-time electromagnetic environment.

5. The complex electromagnetic environment spectrum sensing method based on multi-dimensional feature domain according to claim 1 is characterized in that: In step 4, the time-frequency domain amplitude The calculation method is: in, is the window function, is the window length, is the window number, is the sampling signal of each area, is the area number, , The sampling time .

6. The complex electromagnetic environment spectrum sensing method based on multi-dimensional feature domain according to claim 1 is characterized in that: In step 5, the instantaneous frequency is calculated by first calculating the cumulative phase difference result , is the cumulative number of times, , and then perform the defuzzification operation, specifically: , ； , ； ， ； Instantaneous frequency: , is the sampling rate.

7. The complex electromagnetic environment spectrum sensing method based on multi-dimensional feature domain according to claim 1 is characterized in that: In step 6, assume that the spectrum sensing spectrum hole is , The available spectrum range is , define the spectrum planning granularity based on specific frequency requirements and equipment capabilities , then the spectrum resources are ; Assume there is The nodes need to use the frequency, and the node set is expressed as , define a Vector , the vector Represents the communication frequency strategy of each link node. The communication link frequency strategy is a vector , is the number of spectrum granularities between two frequency strategies, is the path loss factor, and the number of connected nodes in the communication link network is , the set of connected nodes participating in the communication link is The frequency strategy optimization problem is expressed as: min ; Calculate the available frequency band to meet the node communication link frequency strategy, and ensure that the spectrum interval between the communication link frequency and the access network frequency of the connected nodes participating in the communication link is greater than , to eliminate co-site interference, and at the same time the planned communication link frequency bandwidth requirements meet the minimum communication link bandwidth requirements.

8. The complex electromagnetic environment spectrum sensing method based on multi-dimensional feature domain according to claim 1 is characterized in that: Specifically, a high-speed parallel acquisition and processing method is adopted in the entire airspace to receive electromagnetic wave signals in different directions.

9. A complex electromagnetic environment spectrum sensing system based on multi-dimensional feature domain, characterized in that: include: The area division module is used to divide the 360° airspace in the azimuth dimension into 24 areas to receive electromagnetic wave signals in different directions respectively; An instantaneous energy calculation module is used to calculate the time-frequency instantaneous energy of the electromagnetic wave signal in each area; The effective signal detection module is used to detect the arrival and end time of the effective signal in each airspace by using the cumulative energy difference detection method; A time-frequency amplitude calculation module is used to calculate the time-frequency amplitude of the effective signal in each spatial domain by using a windowed short-time Fourier analysis method; The spectrum hole search module is used to filter out all signals whose time-frequency amplitude exceeds the preset threshold, and calculate their instantaneous frequency using the cumulative phase difference method to obtain a spatial multi-angle time-frequency two-dimensional feature matrix, and identify the unoccupied spectrum range, which is recorded as a spectrum hole; The planning module is used to plan the communication link according to the number of spectrum holes and the range of available spectrum.

10. A computer storage medium, characterized in that: A computer program executable by a processor is stored therein, and the computer program executes the complex electromagnetic environment spectrum sensing method based on a multi-dimensional feature domain as described in any one of claims 1 to 8.

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