A method, apparatus, medium, and device for generating an electromagnetic spectrum posture map
Patent Information
- Application Number
- CN202610894376.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-22
- Publication Date
- 2026-11-03
AI Technical Summary
Existing methods for constructing electromagnetic spectrum situation maps lack stability in complex electromagnetic environments, are costly, and cannot achieve rapid, large-area coverage and real-time updates.
By mapping the relative signal strength data and direction finding data onto a map grid, a rough situation map is generated. An electromagnetic spectrum situation map is then generated using two-dimensional Gaussian fitting and a mixture function. By integrating the characteristics of multi-dimensional data, the situation map is updated in real time.
It enables the rapid and dynamic construction of real-time spectrum situation maps in complex electromagnetic environments, improving the smoothness and guidance of the situation maps while reducing costs.
Smart Images
Figure CN123029277A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of radio monitoring technology, specifically to a method, apparatus, medium, and device for generating an electromagnetic spectrum situation map. Background Technology
[0002] Electromagnetic spectrum situation maps are tools for abstractly representing the radio environment, providing a clear and comprehensive view of the electromagnetic environment within a specific area. Existing methods for constructing electromagnetic spectrum situation maps utilize one dimension of signal information in space, such as methods based on the Power of Atmosphere (POA) and those based on the Optical Area of Atmosphere (AOA).
[0003] Existing methods are not effective for two reasons. First, data from a single dimension is not stable enough in complex electromagnetic environments. Second, most traditional methods require deploying sensors at multiple locations to receive data or using mobile devices to collect data at multiple locations before constructing a situation map. This results in high costs, makes it impossible to quickly cover a large area of the sensing spectrum, and makes it impossible to obtain a situation map in real time. Summary of the Invention
[0004] In summary, the main objective of this application is to provide a method, apparatus, medium, and device for generating an electromagnetic spectrum situation map, aiming to solve the problem that the existing methods for constructing electromagnetic spectrum situation maps are of poor quality.
[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows: In a first aspect, embodiments of this application provide a method for generating an electromagnetic spectrum situation map, comprising the following steps: The relative signal strength data and direction finding data are mapped onto the map grid to obtain a rough signal strength situation map and a rough direction finding situation map. Using the grid latitude and longitude coordinates in the coarse signal strength situation map and the coarse orientation situation map as observation points and the situation values as observation data, two-dimensional Gaussian fitting is performed to obtain the two-dimensional Gaussian function of signal strength and the two-dimensional Gaussian function of orientation. By mixing the two-dimensional Gaussian function of signal intensity and the two-dimensional Gaussian function of directionality, the target two-dimensional Gaussian function is obtained; The latitude and longitude of each grid point in the map grid are used as input to the target two-dimensional Gaussian function, and the output is the situation value of each grid, thus obtaining an electromagnetic spectrum situation map.
[0006] In one possible implementation of the first aspect, the relative signal strength data and direction finding data are mapped onto a map grid to obtain a coarse signal strength situation map and a coarse direction finding situation map, including: The relative signal strength data is mapped onto the map grid using interpolation to obtain a rough signal strength situation map.
[0007] In one possible implementation of the first aspect, the relative signal strength data is mapped to a map grid using interpolation to obtain a coarse signal strength situation map, including: The relative signal intensity data is mapped onto the map grid using different interpolation methods to obtain multiple interpolation results; By combining multiple interpolation results, a rough picture of the signal strength is obtained.
[0008] In one possible implementation of the first aspect, the relative signal strength data and direction finding data are mapped onto a map grid to obtain a coarse signal strength situation map and a coarse direction finding situation map, including: The direction finding and orientation data are mapped to a map grid using a voting scoring method to obtain a rough signal strength situation map and a rough orientation situation map.
[0009] In one possible implementation of the first aspect, the grid latitude and longitude coordinates in the coarse signal strength situation map and the coarse orientation situation map are used as observation points, and the situation values are used as observation data. Two-dimensional Gaussian fitting is then performed on each to obtain the two-dimensional Gaussian function for signal strength and the two-dimensional Gaussian function for orientation, including: Using the grid latitude and longitude coordinates in the coarse signal strength situation map and the coarse orientation situation map as observation points and the situation values as observation data, two-dimensional Gaussian fitting is performed based on the covariance matrix and the mean vector to obtain the two-dimensional Gaussian function of signal strength and the two-dimensional Gaussian function of orientation.
[0010] In one possible implementation of the first aspect, a target two-dimensional Gaussian function is obtained by mixing a two-dimensional Gaussian function of signal intensity and a two-dimensional Gaussian function of orientation, including: Based on the two-dimensional Gaussian function of signal strength and the two-dimensional Gaussian function of orientation, the corresponding covariance matrix and mean vector are obtained. Based on the covariance matrix and mean vector corresponding to the two-dimensional Gaussian function of signal strength and the two-dimensional Gaussian function of directionality, a mixed covariance matrix and a mixed mean vector are constructed. The target two-dimensional Gaussian function is obtained based on the mixture covariance matrix and the mixture mean vector.
[0011] In one possible implementation of the first aspect, before mapping the relative signal strength data and direction finding data to a map grid to obtain a coarse signal strength situation map and a coarse direction finding situation map, the method further includes: A map grid is generated based on the preset situation map boundaries and map resolution.
[0012] Secondly, embodiments of this application provide an electromagnetic spectrum situation map generation device, comprising: The mapping module is used to map the relative signal strength data and direction finding data to the map grid respectively, to obtain a rough signal strength situation map and a rough direction finding situation map; The fitting module is used to take the grid latitude and longitude of the signal strength coarse situation map and the orientation coarse situation map as observation points and the situation value as observation data, and perform two-dimensional Gaussian fitting to obtain the signal strength two-dimensional Gaussian function and the orientation two-dimensional Gaussian function respectively. The mixing module is used to mix the signal strength two-dimensional Gaussian function and the directionality two-dimensional Gaussian function to obtain the target two-dimensional Gaussian function; The acquisition module is used to take the latitude and longitude of each grid point in the map grid as input to the target two-dimensional Gaussian function, and output the situation value of each grid to obtain the electromagnetic spectrum situation map.
[0013] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the electromagnetic spectrum situation map generation method provided in any of the first aspects above.
[0014] Fourthly, embodiments of this application provide an electronic device, including a processor and a memory, wherein, Memory is used to store computer programs; The processor is used to load and execute a computer program to cause the electronic device to perform the electromagnetic spectrum situation map generation method provided in any of the first aspects above.
[0015] Compared with the prior art, the beneficial effects of this application are: This application proposes an electromagnetic spectrum situation map generation method, apparatus, medium, and device. The method includes: mapping relative signal strength data and direction finding data onto a map grid to obtain a coarse signal strength situation map and a coarse direction finding situation map; using the grid latitude and longitude coordinates in the coarse signal strength situation map and the direction finding situation map as observation points and situation values as observation data, performing two-dimensional Gaussian fitting on each to obtain a two-dimensional Gaussian function for signal strength and a two-dimensional Gaussian function for direction finding; mixing the two-dimensional Gaussian function for signal strength and the two-dimensional Gaussian function for direction finding to obtain a target two-dimensional Gaussian function; using the latitude and longitude coordinates of each grid point in the map grid as input to the target two-dimensional Gaussian function, and outputting the situation value of each grid to obtain an electromagnetic spectrum situation map. This application overcomes the shortcomings of single-dimensional data by leveraging the characteristics of direction finding (DST) being insensitive to changes in signal source distance, while signal relative strength is less affected by multipath effects and obstruction compared to DST. First, coarse DSTs under different data are obtained through separate mapping. However, these DSTs lack smoothness and are accompanied by noise. Therefore, a two-dimensional Gaussian fitting method is used to convert them into different two-dimensional Gaussian functions. Then, the two Gaussian functions are mixed, incorporating the characteristics of the multidimensional data to achieve mutual complementarity and cross-validation. Finally, a spectral DST map is generated using grid points as the basis and DST values as the representation. This map not only includes information on relative signal strength but also integrates spatial information from DST and signal level values, giving the DST a certain degree of directional guidance towards the signal source. By updating signal relative strength data and direction finding DST data in real time, a real-time spectral DST map can be rapidly and dynamically constructed, improving the effectiveness of the construction method. Attached Figure Description
[0016] Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application; Figure 2 A flowchart illustrating the electromagnetic spectrum situation map generation method provided in this application embodiment; Figure 3 A schematic diagram of the distribution of raw direction finding data in the electromagnetic spectrum situation map generation method provided in this application embodiment; Figure 4 A schematic diagram of the distribution of preprocessed raw direction finding data in the electromagnetic spectrum situation map generation method provided in this application embodiment; Figure 5 A flowchart illustrating one implementation of the electromagnetic spectrum situation map generation method provided in this application embodiment; Figure 6 A schematic diagram of the distribution of a two-dimensional Gaussian function for the electromagnetic spectrum situation map generation method provided in this application embodiment; Figure 7A schematic diagram illustrating the distribution of a two-dimensional Gaussian function of signal strength in the electromagnetic spectrum situation map generation method provided in this application embodiment; Figure 8 A schematic diagram of an electromagnetic spectrum situation map provided in the electromagnetic spectrum situation map generation method of this application embodiment; Figure 9 A schematic diagram of the module for generating an electromagnetic spectrum situation map provided in the embodiments of this application; The diagram is labeled as follows: 101-Processor, 102-Communication bus, 103-Network interface, 104-User interface, 105-Memory. Detailed Implementation
[0017] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.
[0018] See attached document Figure 1 , attached Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. The communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 104 may also include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface). The memory 105 may be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as at least one disk storage device. The processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component.
[0019] Those skilled in the art will understand that the appendix Figure 1 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0020] As attached Figure 1As shown, the memory 105, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and an electromagnetic spectrum situation map generation device.
[0021] In the appendix Figure 1 In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in this application can be set in the electronic device. The electronic device calls the electromagnetic spectrum situation map generation device stored in the memory 105 through the processor 101 and executes the electromagnetic spectrum situation map generation method provided in the embodiment of this application.
[0022] See attached document Figure 2 Based on the hardware device of the foregoing embodiments, embodiments of this application provide an electromagnetic spectrum situation map generation method, applied to a head-up display, comprising the following steps: S10: Map the relative signal strength data and direction finding data onto the map grid to obtain a rough signal strength situation map and a rough direction finding situation map.
[0023] In practical implementation, relative signal strength refers to a numerical value that directly or indirectly reflects the strength changes of the same signal at different times or locations, including but not limited to received signal power, level value, signal IQ amplitude, spectral amplitude, signal-to-noise ratio, etc., and can be used as... express, Indicates the time sequence number. Direction finding accuracy refers to the direction finding result relative to the current point obtained by the equipment performing direction finding on the signal source at the current point, expressed as... express, This represents the time sequence number. The above data is then mapped onto a map grid to create a preliminary situational map, providing a foundation for subsequent data fusion.
[0024] In one embodiment, before mapping the relative signal strength data and direction finding data to a map grid to obtain a coarse signal strength situation map and a coarse direction finding situation map, the method further includes: A map grid is generated based on the preset situation map boundaries and map resolution.
[0025] In the specific implementation process, the boundaries and resolution of the situation map are set. Resolution refers to the size of each grid cell into which the map is divided. The size of the situation map is characterized by, but is not limited to, the minimum and maximum latitude and longitude, or the center latitude and longitude and radius or diameter. The maximum latitude and longitude is used. , and minimum latitude and longitude , The boundaries of the situation map are represented by the number of horizontal grids (columns). And the number of vertical grid lines (rows). The map's resolution is represented by the following formula:
[0026]
[0027] in, , These represent the indexes of the grid rows and columns, respectively. Longitude Latitude The combination of latitude and longitude can be directly used to characterize the location of a point; the latitude and longitude of the current location of the equipment are represented by... express, This indicates the time sequence number, which includes the longitude and latitude of the equipment's location.
[0028] A grid point in a map grid can be represented as:
[0029] in, Indicates the first Line 1 The grid points in the column contain the latitude and longitude of each grid point, as well as its situational value. The initial value of the situation value is 0; then the map grid can be represented as
[0030] It should be noted that the relative signal strength must correspond one-to-one with the latitude and longitude of the device, that is... ,at this time The latitude and longitude obtained when receiving signal strength can be expressed as In addition, there exists , A signal relative strength data can be represented as ; The direction finding coordinates must correspond one-to-one with the equipment's latitude and longitude, that is... Similarly, the latitude and longitude obtained when the device acquires orientation can be expressed as... The same exists. , Then a direction finding direction finding data can be represented as: ; The relative signal strength and direction finding indices are both independent data sources and do not need to be matched one-to-one; that is, they exist. .
[0031] Different data are mapped to the map grid using different methods. For relative signal strength data, the methods include mapping relative signal strength data and direction finding data to the map grid separately to obtain a coarse signal strength situation map and a coarse direction finding situation map, including: The relative signal strength data is mapped onto the map grid using interpolation to obtain a rough signal strength situation map.
[0032] For direction finding and orientation data, the following steps are taken: Relative signal strength data and direction finding and orientation data are mapped onto a map grid to obtain a coarse signal strength situation map and a coarse orientation data situation map, including: The direction finding and orientation data are mapped to a map grid using a voting scoring method to obtain a rough signal strength situation map and a rough orientation situation map.
[0033] In practical implementation, the method of this application is applicable to various types of equipment, such as fixed station equipment, mobile equipment, and aerial equipment. Whether using mobile equipment to obtain corresponding data by moving the equipment to different locations, or deploying multiple fixed devices at multiple different locations to collect data, there is a problem that the data is relatively small compared to the entire map, the observation points are sparse, and the amount of data is small. However, situation maps require calculating the probability of each grid in the entire map being a signal source. In addition, according to the principle of signal propagation, the stronger the signal, the greater the probability that the point is a signal source, but the directionality cannot directly characterize the location of the signal source. Therefore, processing is required to expand the data and transform the data.
[0034] For relative signal intensity data, interpolation can be used to map the data onto a grid. Interpolation methods include, but are not limited to, inverse distance weighted interpolation, kriging interpolation, and thin-plate spline interpolation. This can be represented as:
[0035] Interpolation can be performed using at least one interpolation method; that is... , , ,in Indicates different interpolation methods; This represents map grids calculated using different interpolation methods; map grids calculated using different interpolation methods have the same latitude and longitude, i.e.:
[0036] And grid value For the same latitude and longitude, different interpolation methods may yield the same or different results. If multiple interpolation methods are used, the results are combined to obtain a rough map of signal strength. Line 1 When the grid values of the columns are mixed,
[0037] After mixing, the rough trend of signal strength can be represented as follows: That is, the relative signal strength data is mapped onto a map grid using interpolation to obtain a rough signal strength situation map, including: The relative signal intensity data is mapped onto the map grid using different interpolation methods to obtain multiple interpolation results; By combining multiple interpolation results, a rough picture of the signal strength is obtained.
[0038] For direction finding data, including but not limited to voting scoring and AOA methods, the direction finding is mapped onto a map grid to obtain a coarse situation map of the direction finding. This application uses the voting scoring method as an example. The voting scoring method involves voting on map grids within the area pointed to by the direction finding. The votes from multiple direction finding values for the same grid are accumulated, and the accumulated score is the grid value for that grid. The specific voting method is as follows: Based on the current latitude and longitude of the device and the latitude and longitude of the grid, the orientation of the grid relative to the device can be calculated:
[0039] in, , for The latitude and longitude of the location of the time-tracking device. , For grid latitude and longitude; if ,in The orientation range can be determined by empirical values or the orientation accuracy of the orientation-finding equipment. Generally, You can get a score. , ,otherwise, The grid value for a given grid can be obtained by accumulating scores. Finally, a voting score is assigned to each grid to obtain a coarse situation map with orientation. .
[0040] In one embodiment, before mapping the relative signal strength data and direction finding data to a map grid to obtain a coarse signal strength situation map and a coarse direction finding situation map, the method further includes: The original signal relative intensity data and original direction finding direction data are preprocessed to extract effective data and obtain signal relative intensity data and direction finding direction data.
[0041] In the specific implementation process, the data is preprocessed, and the processed valid data is accumulated and saved. When the equipment receives data, multiple signal relative strength data or direction finding data can be obtained at the same location. Therefore, bad and redundant data must be removed to ensure reliability and practicality in dynamic and complex electromagnetic environments. After data removal, to ensure data coherence and better understanding of subsequent processing, the processed data can be reordered. Specifically: Preprocessing for relative signal strength can include cleaning and analysis. Cleaning refers to deleting any missing data in a relative signal strength dataset. Analysis involves analyzing data with the same latitude and longitude but different signal strengths over a continuous period of time. Unique signal strengths are obtained through methods including but not limited to maximum value, average value, and weighted average value, and redundant data is removed. Subsequently, the remaining valid data are numbered and sorted in chronological order.
[0042] The specific operation method in the analysis stage of signal relative intensity data preprocessing is as follows, taking the maximum value as an example, if:
[0043] Compare the corresponding signal strengths. At this point, delete the remaining data and retain the largest data as the valid data for that period; accumulate and save the valid data, and sort the accumulated valid data by number to obtain the signal relative strength data L. After re-sorting... .
[0044] Preprocessing of direction finding data can include cleaning, analysis, and aggregation. Cleaning is similar to cleaning relative signal strength data, removing any missing data, which will not be elaborated here. Analysis also involves analyzing data with the same latitude and longitude over a continuous time period. Aggregation involves statistically analyzing the direction finding over this period to obtain the range with the highest probability of direction finding, retaining the direction finding within this range, and removing the direction finding outside this range. For example, if:
[0045] Statistical orientation data:
[0046] Existing angle range:
[0047]
[0048] Compare Size, If it is the maximum value, If the data is invalid, delete that data entry; then sort the remaining valid data to obtain direction finding data B. At this point, unlike the valid signal strength data, the data still contains... .
[0049] Taking direction finding and orientation data as an example, as shown in the attached figure. Figure 3 The image shows a schematic diagram of the distribution of raw direction finding orientation data. Figure 4 This is a schematic diagram of the original direction finding and orientation data after preprocessing, i.e., the direction finding and orientation data. The arrows in the diagram indicate the direction of orientation, and the origin in the upper right corner is the location of the signal source.
[0050] S20: Using the grid latitude and longitude coordinates in the coarse signal strength situation map and the coarse orientation situation map as observation points and the situation values as observation data, perform two-dimensional Gaussian fitting to obtain the two-dimensional Gaussian function of signal strength and the two-dimensional Gaussian function of orientation.
[0051] In practical implementation, the coarse situation map lacks smoothness and is accompanied by some noise, making direct fusion impossible. Therefore, each grid point in the coarse situation map is used as an observation point, and the situation value is used as the observation data for two-dimensional Gaussian fitting. Different coarse situation maps yield different two-dimensional Gaussian functions. Specifically: the grid latitude and longitude of the signal strength coarse situation map and the orientation coarse situation map are used as observation points, and the situation value is used as the observation data. Two-dimensional Gaussian fitting is performed separately to obtain the signal strength two-dimensional Gaussian function and the orientation two-dimensional Gaussian function, including: Using the grid latitude and longitude coordinates in the coarse signal strength situation map and the coarse orientation situation map as observation points and the situation values as observation data, two-dimensional Gaussian fitting is performed based on the covariance matrix and the mean vector to obtain the two-dimensional Gaussian function of signal strength and the two-dimensional Gaussian function of orientation.
[0052] In practical implementation, the probability density of the two-dimensional Gaussian distribution is:
[0053] in, For two-dimensional variables, Let covariance matrix be the variance matrix. The correlation coefficient between two-dimensional variables. , , These are the standard deviations of the grid's longitude and latitude, respectively. It is the mean vector. , These are the means of a two-dimensional Gaussian distribution along the longitude and latitude directions, respectively; superscript. This indicates transpose, using grid points as observation points, i.e. The situation value is used as observation data. A rough trend chart of signal strength. A rough situational map showing the direction. The two-dimensional Gaussian functions with different signal intensities were obtained by fitting the observed data separately. and the two-dimensional Gaussian function of the indicator dimension Fitting methods include the EM algorithm, least squares method, and neural networks.
[0054] S30: Mix the signal intensity two-dimensional Gaussian function and the directionality two-dimensional Gaussian function to obtain the target two-dimensional Gaussian function.
[0055] In the specific implementation process, the different two-dimensional Gaussian functions obtained in the aforementioned steps are Gaussian mixed to achieve the fusion of the situation map, obtaining the fused two-dimensional Gaussian mixture function, i.e., the target two-dimensional Gaussian function. Specifically, the target two-dimensional Gaussian function is obtained by mixing the signal intensity two-dimensional Gaussian function and the directionality two-dimensional Gaussian function, including: Based on the two-dimensional Gaussian function of signal strength and the two-dimensional Gaussian function of orientation, the corresponding covariance matrix and mean vector are obtained. Based on the covariance matrix and mean vector corresponding to the two-dimensional Gaussian function of signal strength and the two-dimensional Gaussian function of directionality, a mixed covariance matrix and a mixed mean vector are constructed. The target two-dimensional Gaussian function is obtained based on the mixture covariance matrix and the mixture mean vector.
[0056] In practical implementation, the formula for the probability density of a two-dimensional Gaussian mixture is similar to that of the aforementioned two-dimensional Gaussian distribution probability density, and can be expressed as:
[0057] As shown in the above formula, the covariance matrix and mean vector in the aforementioned two-dimensional Gaussian distribution probability density formula are replaced with corresponding terms. Wherein, , The mean vector and covariance matrix of the merged Gaussian distribution are given. , These are the two-dimensional Gaussian functions fitted to the coarse signal strength situation map and the two-dimensional Gaussian functions fitted to the coarse directionality situation map, respectively. , , , These are its covariance matrix and mean vector, respectively. From the equation, we can see that... (“ " indicates less than in the positive definite sense, that is If the matrix is positive definite, then the uncertainty is reduced, making the two-dimensional Gaussian distribution more concentrated, thereby reducing the impact of noise on the overall distribution of the situation map.
[0058] S40: Using the latitude and longitude of each grid point in the map grid as input to the target two-dimensional Gaussian function, the output is the situation value of each grid, thus obtaining an electromagnetic spectrum situation map.
[0059] In the specific implementation process, the latitude and longitude of the grid points will be... Substituting into the formula for the probability density of a two-dimensional Gaussian mixture In the process, the situation value of each grid after mixing is obtained. The hybrid situation map is concentrated in locations where multiple sets of evidence do not conflict, strengthening the representation of locations where evidence does not conflict and weakening locations where evidence conflicts, thereby suppressing noise in the situation map, improving the robustness of the algorithm, and integrating directionality and level information to provide a more comprehensive, smoother, and more reliable situation map. In this embodiment, the shortcomings of single-dimensional data are compensated for by leveraging the characteristics of direction finding, which is insensitive to changes in signal source distance, and the fact that signal relative strength is less affected by multipath effects and occlusion compared to direction finding, to generate a situation map. First, coarse situation maps under different data are obtained by mapping them separately. At this point, the situation map lacks smoothness and is accompanied by noise. Therefore, it is converted into different two-dimensional Gaussian functions by two-dimensional Gaussian fitting. Then, the two Gaussian functions are mixed to integrate the characteristics of multi-dimensional data, making them complementary and cross-validated. Finally, a spectral situation map is generated using grid points as the generation basis and situation values as the representation. This map not only includes information on relative signal strength but also integrates spatial information of direction finding and level values, giving the situation map a certain degree of guidance towards the signal source. By updating the signal relative strength data and direction finding data in real time, a real-time spectral situation map can be quickly and dynamically constructed, improving the effectiveness of the construction method.
[0060] As attached Figure 5 As shown in the accompanying drawings, this application will be further described in the following embodiments: First, set the boundary and resolution of the situation map, generate a map grid, and input accumulated data over a period of time, including relative signal strength, directionality, and device latitude and longitude. After preprocessing the data, retain the valid data. Next, map different data to the map grid and fit a two-dimensional Gaussian function to each. Then, perform Gaussian mixing and generate a mixed situation map based on the mixed two-dimensional Gaussian function. Finally, determine whether it converges. If it converges, output the final electromagnetic spectrum situation map. If it does not converge, accumulate new data and return to the mapping step in a loop until it finally converges.
[0061] It should be noted that in the mixed two-dimensional Gaussian distribution, Let be the mean of the Gaussian distribution, which represents the center location of the two-dimensional Gaussian distribution. This is the location with the highest probability density in the situation map, that is, the location where the signal source has the highest probability. At this point, it can also be considered... The positioning result is as follows. If the device's position changes within a continuous period of time, and the positioning result is the same regardless of the situation map calculated using different data, then convergence can be considered achieved at this point.
[0062] This application utilizes a vehicle-mounted direction-finding device to locate the signal source at a distance of approximately 5.5 km. The test frequency is 100.9 MHz. The vehicle-mounted device simultaneously receives the signal level value and performs direction finding on the signal source to obtain the direction indication, as shown in the attached diagram. Figure 6 - Appendix Figure 8 The image shows an electromagnetic spectrum map during signal source location. Dots indicate signal source locations, and irregular curves represent the vehicle-mounted equipment's trajectory. (Attached) Figure 6 The image shows the distribution of the two-dimensional Gaussian function of the dimension obtained by fitting the dimension coarse map before mixing; attached. Figure 7 The distribution map of the two-dimensional Gaussian function of the signal intensity is obtained by fitting a rough map of the signal intensity before mixing; then, the mixing is performed using the method of this application, and the final electromagnetic spectrum situation map is shown in the appendix. Figure 8 As shown in the figure, the information in the figure indicates that it integrates spatial information of directionality and level value. The situation map has a certain guiding function and can guide personnel to approach the signal source.
[0063] See attached document Figure 9 Based on the same inventive concept as in the foregoing embodiments, this application also provides an electromagnetic spectrum situation map generation device, comprising: The mapping module is used to map the relative signal strength data and direction finding data to the map grid respectively, to obtain a rough signal strength situation map and a rough direction finding situation map; The fitting module is used to take the grid latitude and longitude of the signal strength coarse situation map and the orientation coarse situation map as observation points and the situation value as observation data, and perform two-dimensional Gaussian fitting to obtain the signal strength two-dimensional Gaussian function and the orientation two-dimensional Gaussian function respectively. The mixing module is used to mix the signal strength two-dimensional Gaussian function and the directionality two-dimensional Gaussian function to obtain the target two-dimensional Gaussian function; The acquisition module is used to take the latitude and longitude of each grid point in the map grid as input to the target two-dimensional Gaussian function, and output the situation value of each grid to obtain the electromagnetic spectrum situation map.
[0064] Those skilled in the art should understand that the division of the various modules in the embodiments is merely a logical functional division. In actual applications, they can be fully or partially integrated into one or more actual carriers. These modules can be implemented entirely in software through processing unit calls, entirely in hardware, or a combination of software and hardware. It should be noted that each module in the electromagnetic spectrum situation map generation device in this embodiment corresponds one-to-one with each step in the electromagnetic spectrum situation map generation method in the aforementioned embodiments. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned electromagnetic spectrum situation map generation method, which will not be repeated here.
[0065] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the electromagnetic spectrum situation map generation method provided in the embodiments of this application.
[0066] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide an electronic device, including a processor and a memory, wherein, Memory is used to store computer programs; The processor is used to load and execute computer programs to enable electronic devices to perform the electromagnetic spectrum situation map generation method provided in the embodiments of this application.
[0067] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a device including one or any combination of the above-mentioned memories. The computer may be a variety of computing devices, including smart terminals and servers.
[0068] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
[0069] As an example, executable instructions may, but do not necessarily, correspond to files in the file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborative files (e.g., a file that stores one or more modules, subroutines, or code sections).
[0070] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.
[0071] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.
[0072] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0073] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a multimedia terminal device (which may be a mobile phone, computer, television receiver, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0074] In summary, this application provides a method, apparatus, medium, and device for generating an electromagnetic spectrum situation map. The method includes: mapping relative signal strength data and direction finding data onto a map grid to obtain a coarse signal strength situation map and a coarse direction finding situation map; using the grid latitude and longitude coordinates in the coarse signal strength situation map and the coarse direction finding situation map as observation points and the situation value as observation data, performing two-dimensional Gaussian fitting on each to obtain a two-dimensional Gaussian function for signal strength and a two-dimensional Gaussian function for direction finding; mixing the two-dimensional Gaussian function for signal strength and the two-dimensional Gaussian function for direction finding to obtain a target two-dimensional Gaussian function; using the latitude and longitude coordinates of each grid point in the map grid as input to the target two-dimensional Gaussian function, and outputting the situation value of each grid to obtain an electromagnetic spectrum situation map. This application overcomes the shortcomings of single-dimensional data by leveraging the characteristics of direction finding (DST) being insensitive to changes in signal source distance, while signal relative strength is less affected by multipath effects and obstruction compared to DST. First, coarse DSTs under different data are obtained through separate mapping. However, these DSTs lack smoothness and are accompanied by noise. Therefore, a two-dimensional Gaussian fitting method is used to convert them into different two-dimensional Gaussian functions. Then, the two Gaussian functions are mixed, incorporating the characteristics of the multidimensional data to achieve mutual complementarity and cross-validation. Finally, a spectral DST map is generated using grid points as the basis and DST values as the representation. This map not only includes information on relative signal strength but also integrates spatial information from DST and signal level values, giving the DST a certain degree of directional guidance towards the signal source. By updating signal relative strength data and direction finding DST data in real time, a real-time spectral DST map can be rapidly and dynamically constructed, improving the effectiveness of the construction method.
[0075] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A method for generating an electromagnetic spectrum situation map, characterized in that, Includes the following steps: The relative signal strength data and direction finding data are mapped onto the map grid to obtain a rough signal strength situation map and a rough direction finding situation map. Using the grid latitude and longitude coordinates in the coarse signal strength situation map and the coarse orientation situation map as observation points and the situation values as observation data, two-dimensional Gaussian fitting is performed on them respectively to obtain the two-dimensional Gaussian function of signal strength and the two-dimensional Gaussian function of orientation. By mixing the signal strength two-dimensional Gaussian function and the orientation two-dimensional Gaussian function, the target two-dimensional Gaussian function is obtained; The latitude and longitude of each grid point in the map grid are used as the input of the target two-dimensional Gaussian function, and the situation value of each grid is output to obtain the electromagnetic spectrum situation map.
2. The electromagnetic spectrum situation map generation method according to claim 1, characterized in that, The step of mapping the relative signal strength data and direction finding data onto a map grid to obtain a coarse signal strength situation map and a coarse direction finding situation map includes: The relative signal strength data is mapped onto the map grid using an interpolation method to obtain a rough signal strength situation map.
3. The electromagnetic spectrum situation map generation method according to claim 2, characterized in that, The step of mapping relative signal strength data onto a map grid using interpolation to obtain a rough signal strength situation map includes: The relative signal intensity data is mapped onto the map grid using different interpolation methods to obtain multiple interpolation results; By combining multiple interpolation results, a rough signal strength situation map is obtained.
4. The electromagnetic spectrum situation map generation method according to claim 1, characterized in that, The step of mapping the relative signal strength data and direction finding data onto a map grid to obtain a coarse signal strength situation map and a coarse direction finding situation map includes: The direction finding and orientation data are mapped to a map grid using a voting scoring method to obtain a rough signal strength situation map and a rough orientation situation map.
5. The electromagnetic spectrum situation map generation method according to claim 1, characterized in that, The step of using the grid latitude and longitude coordinates in the coarse signal strength situation map and the coarse orientation situation map as observation points and the situation values as observation data, and performing two-dimensional Gaussian fitting on them respectively to obtain the two-dimensional Gaussian function of signal strength and the two-dimensional Gaussian function of orientation, includes: Using the grid latitude and longitude coordinates in the coarse signal strength situation map and the coarse orientation situation map as observation points and the situation values as observation data, two-dimensional Gaussian fitting is performed based on the covariance matrix and the mean vector to obtain the two-dimensional Gaussian function of signal strength and the two-dimensional Gaussian function of orientation.
6. The electromagnetic spectrum situation map generation method according to claim 1, characterized in that, The process of mixing the signal strength two-dimensional Gaussian function and the orientation two-dimensional Gaussian function to obtain the target two-dimensional Gaussian function includes: Based on the two-dimensional Gaussian function of signal strength and the two-dimensional Gaussian function of orientation, the corresponding covariance matrix and mean vector are obtained. Based on the covariance matrix and mean vector corresponding to the two-dimensional Gaussian function of signal strength and the two-dimensional Gaussian function of directionality, a mixed covariance matrix and a mixed mean vector are constructed. Based on the mixture covariance matrix and the mixture mean vector, the target two-dimensional Gaussian function is obtained.
7. The electromagnetic spectrum situation map generation method according to claim 1, characterized in that, Before mapping the relative signal strength data and direction finding data to a map grid to obtain a coarse signal strength situation map and a coarse direction finding situation map, the method further includes: The map grid is generated based on the preset situation map boundaries and map resolution.
8. An electromagnetic spectrum situation map generation device, characterized in that, include: The mapping module is used to map the relative signal strength data and direction finding data to the map grid respectively, to obtain a rough signal strength situation map and a rough direction finding situation map; The fitting module is used to take the grid latitude and longitude of the signal strength coarse situation map and the orientation coarse situation map as observation points and the situation value as observation data, and perform two-dimensional Gaussian fitting to obtain the signal strength two-dimensional Gaussian function and the orientation two-dimensional Gaussian function respectively. The mixing module is used to mix the signal strength two-dimensional Gaussian function and the orientation two-dimensional Gaussian function to obtain the target two-dimensional Gaussian function; The acquisition module is used to take the latitude and longitude of each grid point in the map grid as the input of the target two-dimensional Gaussian function, and output the situation value of each grid to obtain an electromagnetic spectrum situation map.
9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it implements the electromagnetic spectrum situation map generation method as described in any one of claims 1-7.
10. An electronic device, characterized in that, Including processor and memory, among which, The memory is used to store computer programs; The processor is used to load and execute the computer program to cause the electronic device to perform the electromagnetic spectrum situation map generation method as described in any one of claims 1-7.