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

Abstract

The present invention discloses a complex electromagnetic environment spectrum sensing method and system based on a multi-dimensional feature domain, wherein the method includes the steps of: dividing the azimuth dimension 360° airspace into 24 regions, respectively receiving electromagnetic wave signals in different directions; calculating the time-frequency instantaneous energy of the electromagnetic wave signal in each region; detecting effective signals in each airspace and calculating the time-frequency amplitude; screening out all signals whose time-frequency amplitude exceeds a preset threshold, and using the cumulative phase difference method to calculate their instantaneous frequencies, obtaining a spatial multi-angle time-frequency two-dimensional feature matrix, identifying unoccupied spectrum ranges, i.e., spectrum holes; planning communication links according to the number of spectrum holes and their available spectrum ranges. The present invention can effectively improve the real-time and accuracy of electromagnetic spectrum cognitive equipment in terms of electromagnetic spectrum situational awareness through multi-dimensional domain feature analysis of spectrum in complex electromagnetic environments, thereby guiding the frequency utilization strategy of frequency utilization equipment.

Description

Technical Field

[0001] The present invention relates to the field of electromagnetic spectrum perception, and in particular to a complex electromagnetic environment spectrum sensing method and system based on a multi-dimensional feature domain. Background Art

[0002] With the development of science and technology, electromagnetic waves have been widely used in broadcasting, communication, radar, navigation and other fields. With the popularization and application of electronic equipment, the electromagnetic environment is becoming increasingly complex. The signals of various radars, navigation, communication, friend-or-foe identification, electronic interference, etc. are dense, energetic, and have variable modulation styles. How to effectively perceive the electromagnetic spectrum situation in a complex electromagnetic environment and improve the control ability of the electromagnetic environment is a hot topic in today's research. Electronic countermeasures are a power-defense game in electromagnetic space. Gaining control of the electromagnetic spectrum means that the electromagnetic spectrum can be used freely within the controllable frequency range without being threatened by the other party's electromagnetic spectrum; at the same time, the other party's use of the electromagnetic spectrum can be restricted.

[0003] Accurate and real-time electromagnetic spectrum perception capability is a prerequisite for obtaining control of the electromagnetic spectrum. Real-time monitoring of the electromagnetic spectrum situation and understanding of the occupancy of the electromagnetic spectrum can provide guidance for frequency use and can accurately interfere with the frequency bands used by others, effectively improving the ability to control the electromagnetic spectrum. Summary of the invention

[0004] The main purpose of the present invention is to provide a complex electromagnetic environment spectrum sensing method and system based on a multi-dimensional feature domain, which can effectively improve the real-time and accuracy of electromagnetic spectrum situation perception of electromagnetic spectrum cognitive equipment, thereby guiding the frequency usage strategy of frequency-using equipment.

[0005] The technical solution adopted by the present invention is:

[0006] A complex electromagnetic environment spectrum sensing method based on a multi-dimensional feature domain is provided, comprising the following steps:

[0007] Step 1: Divide the 360° azimuth space into 24 regions to receive electromagnetic wave signals from different directions respectively;

[0008] Step 2, calculating the time-frequency instantaneous energy of the electromagnetic wave signal in each area;

[0009] Step 3: Using the cumulative energy difference detection method to detect the arrival and end time of the effective signal in each airspace;

[0010] Step 4: Calculate the time-frequency amplitude of the effective signal in each spatial domain by using the windowed short-time Fourier analysis method;

[0011] 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;

[0012] Step 6: Plan the communication links according to the number of spectrum holes and the available spectrum range.

[0013] Following the above technical solution, in step 1, the airspace is divided into areas 1 to 24 in the instantaneous clockwise direction with the north direction as 0°, and each area has a range of 15°.

[0014] Following the above technical solution, in step 2, the time-frequency instantaneous energy of the electromagnetic wave signal is calculated as follows:

[0015]

[0016] 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.

[0017] 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 the electromagnetic wave exceeds the set detection threshold. ,Right now

[0018]

[0019] in, The value is adjusted according to the real-time electromagnetic environment.

[0020] Following the above technical solution, in step 4, the time-frequency domain amplitude The calculation method is:

[0021]

[0022] 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 .

[0023] Following the above technical solution, 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:

[0024] , ;

[0025] , ;

[0026] , ;

[0027] Instantaneous frequency: , is the sampling rate.

[0028] Following the above technical solution, in step 6, it is assumed that the spectrum sensing spectrum hole has , 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:

[0029] min ;

[0030] 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.

[0031] Following the above technical solution, a full-space high-speed parallel acquisition and processing method is specifically adopted to receive electromagnetic wave signals in different directions respectively.

[0032] The present invention also provides a complex electromagnetic environment spectrum sensing system based on a multi-dimensional feature domain, comprising:

[0033] 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;

[0034] An instantaneous energy calculation module is used to calculate the time-frequency instantaneous energy of the electromagnetic wave signal in each area;

[0035] 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;

[0036] 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;

[0037] 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;

[0038] The planning module is used to plan the communication link according to the number of spectrum holes and the range of available spectrum.

[0039] The present invention also provides a computer storage medium, which stores a computer program that can be executed by a processor, and the computer program executes the complex electromagnetic environment spectrum sensing method based on a multi-dimensional feature domain described in the above technical solution.

[0040] The beneficial effects of the present invention are as follows: the present invention divides the airspace into different airspaces to monitor in real time the changes in signal energy in different directions within the entire airspace over time, and then integrates multi-dimensional signal characteristics such as time domain, frequency domain, airspace, and energy domain to realize the spectrum situation analysis of the complex electromagnetic environment of the entire airspace at the location, determine the spectrum occupancy, and guide the frequency usage strategy of frequency-using equipment; the present invention can realize continuous monitoring of the electromagnetic spectrum in a complex electromagnetic environment, provide a frequency reference for electromagnetic frequency-using equipment, and effectively improve the real-time and accuracy of electromagnetic spectrum recognition equipment, and has the advantages of excellent perception ability, versatility, and strong engineering feasibility.

[0041] Furthermore, by dividing the airspace and adopting parallel processing methods for different airspaces, high-precision and high-efficiency real-time monitoring can be achieved.

[0042] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is a flow chart of a complex electromagnetic environment spectrum sensing method based on a multi-dimensional feature domain according to an embodiment of the present invention;

[0045] Figure 2 is a schematic diagram of airspace division and radiation source distribution according to an embodiment of the present invention;

[0046] Figure 3 is a schematic diagram of the time-frequency graphs of various radiation sources in an embodiment of the present invention;

[0047] Figure 4 is a time domain diagram of signals received in different spatial domains according to an embodiment of the present invention;

[0048] Figure 5 is a time-space-energy domain diagram of an embodiment of the present invention;

[0049] Figure 6 It is a two-dimensional characteristic matrix graph of the signal in the time-frequency domain according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0051] It should be noted that the illustrations provided in the embodiments of the present invention are only used to illustrate the basic concept of the present invention in a schematic manner. Therefore, the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout type may also be more complicated.

[0052] In the present invention, it is also necessary to explain that, if the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, if the terms "first" and "second" appear, they are only used for description and distinction purposes, and cannot be understood as indicating or implying relative importance.

[0053] like Figure 1 As shown, the complex electromagnetic environment spectrum sensing method based on the multi-dimensional feature domain in the embodiment of the present invention includes the following steps:

[0054] S1. Divide the 360° azimuth space into 24 areas to receive electromagnetic wave signals from different directions respectively;

[0055] S2, calculating the time-frequency instantaneous energy of the electromagnetic wave signal in each area;

[0056] S3, using the cumulative energy difference detection method to detect the arrival and end time of the effective signal in each airspace;

[0057] S4, using the windowed short-time Fourier analysis method to calculate the time-frequency amplitude of the effective signal in each spatial domain;

[0058] S5, screening out all signals whose time-frequency amplitude exceeds a preset threshold, and calculating their instantaneous frequencies using a cumulative phase difference method, obtaining a spatial multi-angle time-frequency two-dimensional feature matrix, and identifying unoccupied spectrum ranges, recorded as spectrum holes;

[0059] S6. Plan the communication links according to the number of spectrum holes and the range of available spectrum.

[0060] In step S1, the 360° airspace of the azimuth dimension is divided into 24 regions. The electromagnetic wave signals in different directions can be received respectively by using a high-speed parallel acquisition and processing method for the entire airspace. The airspace division can effectively evaluate the distribution of electromagnetic signals in different directions.

[0061] In step S2, the time-frequency instantaneous energy of the electromagnetic wave signal in each area can be calculated through spectrum splicing, spectrum extraction, spectrum analysis and other processing. By dividing the airspace, high-precision and high-efficiency parallel processing is used for different airspaces to monitor the change of signal energy in different directions in the entire airspace over time in real time.

[0062] Step S3 uses the cumulative energy difference detection method to detect the arrival and end time of the effective signal in each airspace. The cumulative energy difference detection method can effectively determine the presence or absence of the signal. The obtained signal arrival and end time is used to calculate the pulse width of the pulse signal;

[0063] 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, and the unoccupied spectrum range, namely, the spectrum hole, is obtained by obtaining the spatial multi-angle time-frequency two-dimensional feature matrix obtained by signal sorting and identification.

[0064] Preferably, in step S1, Figure 2 As shown in the figure, the airspace can be divided into areas 1 to 24 according to the instantaneous clockwise direction, with the north direction being 0°. Each area has a range of 15°. The full airspace high-speed parallel acquisition and processing method is used to receive electromagnetic wave signals in different directions. A total of 24 parallel signals are processed. The received signals in each area can be expressed as , is the area number, , is the sampling time, .

[0065] Preferably, in step S2, the time-frequency instantaneous energy of the electromagnetic wave signal is defined as follows:

[0066]

[0067] 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.

[0068] Preferably, in step S3, the accumulated energy difference Defined as ;

[0069] The cumulative energy difference detection method is to determine whether the real-time cumulative energy of the electromagnetic wave exceeds the set detection threshold. ,Right now

[0070]

[0071] in, Can be adjusted according to the real-time electromagnetic environment;

[0072] Preferably, in step S4, the time-frequency domain amplitude The calculation method is:

[0073]

[0074] 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 .

[0075] Preferably, in step S5, the instantaneous frequency is calculated by first calculating the cumulative phase difference result , is the cumulative number of times, and performs defuzzification operation. The specific method is:

[0076] , ;

[0077] , ;

[0078] , ;

[0079] The method to calculate the instantaneous frequency is: , count all the frequency values ​​exceeding the threshold, and obtain the spatial multi-angle time-frequency two-dimensional feature matrix; is the sampling rate.

[0080] In step S6, the spectrum occupancy can be evaluated according to the frequency distribution analysis results, a spectrum planning model can be constructed, and the optimal frequency utilization strategy can be selected. The specific method for constructing the spectrum planning model is as follows: , The available spectrum range is , define the spectrum planning granularity based on specific frequency requirements and equipment capabilities , then the spectrum resources are Assume that nodes need to use the frequency, and the node set can be expressed as , define a Vector To represent 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 can be expressed as:

[0081] min

[0082] Calculate the vector of the frequency strategy that satisfies node communication within the available frequency band , that is, the spectrum interval between the frequency used by the communication link and the access network frequency used by the liaison nodes participating in the communication link is greater than the set spectrum width (The width between two frequencies can be obtained based on experience or simulation results), which eliminates co-site interference. At the same time, the planned communication link bandwidth requirement meets the minimum communication link bandwidth requirement.

[0083] In one embodiment of the present invention, it is assumed that there are four radiation sources distributed in regions 5, 8 and 18, one in each of regions 5 and 8, and two in region 18. Figure 3 As shown in the figure, the signal frequency domain waveforms of each radiation source signal are shown; among them, radiation source 1 is a single-frequency pulse signal, radiation source 2 is a linear frequency modulation signal, radiation source 3 is a nonlinear frequency modulation signal, and radiation source 4 is a frequency hopping signal. Figure 4 As shown, the figure shows the time domain waveform of the signal received in each area, wherein the amplitude of the signal waveform displayed in area 5, area 8 and area 18 is higher than that in other areas, indicating that the radiation source signal is received in this area.

[0084] like Figure 5 As shown in the time-space-energy domain diagram, each effective spatial domain signal is subjected to time-frequency analysis, such as 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 limitedly evaluated, and spectrum holes can be obtained to solve the optimal frequency planning result.

[0085] In order to implement the above method embodiment, the present invention also provides a complex electromagnetic environment spectrum sensing system based on a multi-dimensional feature domain, which specifically includes:

[0086] 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;

[0087] An instantaneous energy calculation module is used to calculate the time-frequency instantaneous energy of the electromagnetic wave signal in each area;

[0088] 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;

[0089] 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;

[0090] 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;

[0091] The planning module is used to plan the communication link according to the number of spectrum holes and the range of available spectrum.

[0092] Each module is mainly used to implement each step of the above method embodiment, which will not be described one by one here.

[0093] The present application also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, and the program realizes corresponding functions when executed by a processor. When the computer-readable storage medium of this embodiment is executed by a processor, the complex electromagnetic environment spectrum sensing method based on a multidimensional feature domain of the method embodiment is realized.

[0094] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0095] The order of execution of each step in the above embodiment does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0096] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all these improvements and changes should fall within the scope of protection of the appended claims of the present invention.

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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