CN115878959A - Situation awareness visualization system for complex electromagnetic spectrum - Google Patents

Abstract

The invention discloses a complex electromagnetic spectrum-oriented situational awareness visualization system, the system includes: a sensing layer, a data layer and a functional layer; wherein, the sensing layer is used to receive spectrum sensing data collected from discrete position points; the The data layer is used to store and manage the acquired spectrum sensing data and map information data, as well as the equipment data and result data of the corresponding acquisition equipment; the functional layer is used to fuse and process the spectrum sensing data of discrete locations to form The regional electromagnetic spectrum situation distribution is presented in combination with map information data, and the location of the radiation source is located. The system of the present invention does not simply display the spectrum situation of the data sampling points for the collected data of the spectrum in the area, but forms the spectrum situation data of the entire target area through interpolation, completion and calculation.

Description

A situational awareness visualization system for complex electromagnetic spectrum

Technical Field

The present invention belongs to the technical field of electromagnetic environment measurement, and in particular relates to a situation awareness visualization system for complex electromagnetic spectrum.

Background Art

In electromagnetic spectrum warfare, the radio spectrum situation in the target area is presented in a visual manner, which is of great significance for frequency analysis, frequency assignment, spectrum situation trend analysis and spectrum resource management decision support in the combat environment.

At present, in the field of radio spectrum management, there are many software or methods for spectrum data collection and processing. There are also many and mature processing methods for spectrum data collection, display, statistical information analysis and signal data analysis. However, there is relatively little work in this field on the basis of discrete acquisition sample points, the comprehensive use of multiple interpolation methods, the acquisition of the spectrum of the entire target area, and the analysis of the spectrum information of the entire area, the output of auxiliary decision information and the location of the transmitting signal source.

The spectrum data collected by spectrum sensing equipment is discrete point data. How to obtain the spectrum situation in the target area through inference, completion and other processing from the spectrum data of discrete location points, and to display it in a visual way and support subsequent applications is a key technical point in the comprehensive processing of spectrum situation.

Summary of the invention

The purpose of the present invention is to overcome the defects of the prior art and propose a situation awareness visualization system for complex electromagnetic spectrum.

In order to achieve the above-mentioned purpose, the present invention proposes a situation awareness visualization system for complex electromagnetic spectrum, characterized in that the system comprises: a perception layer, a data layer and a function layer; wherein,

The perception layer is used to receive spectrum perception data collected from discrete locations;

The data layer is used to store and manage the acquired spectrum sensing data, to store and manage map information data and information related to the acquisition equipment of the spectrum sensing data, and to store and manage the result data processed by the function layer;

The functional layer is used to fuse the spectrum sensing data of discrete location points to form a regional electromagnetic spectrum situation distribution, present it in combination with map information data, and locate the radiation source.

As an improvement of the above system, the functional layer includes a fusion processing module, and the processing process of the fusion processing module includes:

Get the spectrum sensing data of the specified segment;

Extract frequency point data according to frequency band and step size;

Filter invalid data and fix erroneous data;

Eliminate the influence of fast fading through cluster filtering on the repaired spectrum sensing data;

Select the corresponding interpolation algorithm according to the spectrum sensing data to generate a single frequency point situation map;

The situation maps of each frequency point are synthesized to obtain the regional electromagnetic spectrum situation distribution.

As an improvement of the above system, the corresponding interpolation algorithm includes natural neighbor interpolation processing, thin plate spline interpolation processing and Kriging interpolation processing.

As an improvement of the above system, the natural neighbor interpolation process specifically includes:

Find the N discrete locations closest to the query point and establish the corresponding N Voronoi diagrams;

For the interpolation point z, the polygon that changes due to the addition of z is defined as the neighborhood of z. The intersection of the new polygon and the original polygon determines the influence weight of the corresponding discrete position point on the interpolation point z.

The spectrum sensing data of the N discrete location points are interpolated by applying weights in proportion based on the size of the Voronoi diagram of the N discrete location points.

As an improvement of the above system, the thin plate spline interpolation processing process specifically includes:

The spectrum data collected at a certain area position is used as the spline interpolation data p _i , p _j at the corresponding area position, and the radial basis function U (p _i , p _j ) is calculated according to the following formula:

U(p _i ,p _j )=σ(p _i -p _j ) ² log(σ(p _i -p _j ))

Among them, σ(pi _{- pj} ) corresponds to the Euclidean distance between two points on the two-dimensional plane, _lngi , _lngj represent the longitude of the i-th and j-th collected data, _lati , _latj represent the latitude of the i-th and j-th collected data;

Based on the radial basis function U( _pi , _pj ), we get the weight w _i,x of the i-th sample point to the predicted point x.

The value V(x) of the point to be predicted is obtained by weighted calculation:

Where w _i,x is and V( _xi ) is the value of the i-th sample point.

As an improvement of the above system, the Kriging interpolation processing process specifically includes:

Based on the corresponding relationship between the path loss of electromagnetic radiation and the distance, an unbiased estimation matrix of the measurement difference and distance between data location points is established to achieve interpolation prediction.

As an improvement of the above system, the functional layer includes a situation positioning module, and the processing process of the situation positioning module specifically includes:

According to the spectrum situation map generated by the fusion processing module, combined with the pre-set signal strength threshold and geographic information data, the radiation source is located.

As an improvement of the above system, the functional layer further includes an available frequency recommendation module, and the processing process of the available frequency recommendation module specifically includes:

According to the spectrum sensing data, determine whether the field strength value of each frequency point in the service frequency band exceeds the set threshold value one by one. If it is judged to be yes, the frequency point is occupied. Otherwise, it is not occupied, and channel quality analysis is performed;

Statistics on available frequencies within the service frequency band are combined with channel quality analysis to output a recommended list of available frequencies.

Compared with the prior art, the advantages of the present invention are:

1. Compared with the existing similar technologies, the biggest feature of this system is that it collects spectrum data in the area. It not only displays the spectrum of the data sampling points, but also forms the spectrum situation data of the entire target area through interpolation, completion and extrapolation.

2. The present invention supports the comprehensive use of multiple interpolation methods based on the existing interpolation processing methods. Currently, it supports three methods: natural neighbor interpolation method, Kriging interpolation method and thin plate spline interpolation method. Users can choose the appropriate processing method according to different application scenarios;

3. The statistical analysis function based on regional spectrum is another feature of this system, including the spectrum occupancy of the target area, the recommended available frequency at any location in the area, and other results. These results can be displayed in a visual way in this system.

4. The present invention can provide positioning based on regional spectrum situation. Based on the results of regional spectrum situation, the area that meets the situation characteristics of the transmitting source signal can be found, so that the transmitting signal source can be located. By applying the present invention, after selecting a suitable situation interpolation processing method, when the position coverage of the collected data reaches a certain level, a relatively accurate regional spectrum situation can be generated, so that the candidate position of the transmitting signal source can be located.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 is a system composition diagram of the present invention;

Fig. 2 is a diagram of the software system composition;

Fig. 3 is a diagram showing the relationship between data and interface protocols;

FIG4 is a flowchart of available frequency recommendation;

FIG5 is an example of a recommended list of available frequencies;

Fig. 6 is a situation positioning flow chart;

Figure 7 is a fusion processing flow;

FIG8 is a schematic diagram of a natural neighbor interpolation algorithm;

FIG9 is a schematic diagram of a thin plate spline algorithm;

Figure 10 is a block diagram of a single-machine task deployment mode;

FIG11 is a block diagram of a local networking deployment model.

DETAILED DESCRIPTION

The present invention mainly applies the existing multiple interpolation methods to the processing of spectrum acquisition data from point to surface, and calculates and completes the spectrum sensing sample data of the mobile device to obtain the spectrum situation results above the target area. On this basis, the spectrum situation is analyzed to display the results of spectrum occupancy statistical analysis, available frequency, transmission signal positioning, etc. in a visual way.

The technical solution of the present invention is described in detail below with reference to the accompanying drawings and embodiments.

Example

As shown in FIG1 , Embodiment 1 of the present invention provides a situation awareness visualization system for complex electromagnetic spectrum, the system comprising: a perception layer, a data layer and a function layer; wherein:

The perception layer is used to receive spectrum perception data collected from discrete locations;

The data layer is used to store and manage the acquired spectrum sensing data and map information data and the device data and result data of the corresponding acquisition device;

The functional layer is used to fuse the spectrum sensing data of discrete locations to form a regional electromagnetic spectrum situation distribution, present it in combination with map information data, and locate the radiation source location

The perception layer is a digital signal comprehensive analysis unit that collects spectrum data; the data layer is the data used for analysis, result data, and map data for display; the service layer includes spectrum acquisition, background spectrum, statistical analysis, available frequency recommendation, and situation positioning.

1. Software composition

The specific software composition is shown in Figure 2. It mainly includes data management and business functions. Data management includes geographic information data management, spectrum data management, acquisition equipment management and result data management; business functions include spectrum acquisition, background spectrum learning, statistical analysis, available frequency recommendation and situation positioning to meet business needs.

2. Data and interface protocols

The spectrum data collected by the digital signal comprehensive analysis unit is analyzed by fusion algorithm and visualized, as shown in Figure 3.

3. Function and performance index design

1. Background spectrum learning function

Through in-depth analysis of the monitoring data collected from the spectrum, the background spectrum is learned, and the starting frequency, step, and threshold value are set according to different business frequency bands, and steady-state background spectra are generated in different time periods.

Table 1 List of background spectrum learning indicators

Set the starting frequency, learning step, and time period to support generating independent background spectrum every hour, day, and week.

2. Statistical analysis function

The system provides a variety of data analysis functions, including frequency scanning data (maximum, minimum, average), single frequency stability statistics, frequency scanning waterfall chart analysis, and frequency occupancy statistics in different time periods.

The functions are as follows:

Frequency scan data (maximum, minimum, average)

Analyze the maximum and minimum values that appear in the scan data over a period of time, and calculate the average value of the scan data over the cumulative time.

Single frequency stability statistics

Continuously monitor a single frequency point and record the changes in the signal strength of the frequency over a period of time.

Waterfall chart analysis

The scan data is accumulated over time and the signal strength of the scan data is represented by different colors.

·Occupancy statistics of different time and frequency bands

Use manually or automatically calculated signal thresholds to analyze whether the signal strength over a period of time reaches or exceeds the threshold of a certain frequency. Count the number of times the threshold is reached or exceeded, and the percentage of this number to the total number of measurements is the time occupancy.

Frequency occupancy refers to the percentage of occupied frequencies to total frequencies.

3. Available frequency recommendation

Based on the frequency sweep test data, the occupied and unoccupied frequency points on different channels are obtained. When the frequency point field strength value exceeds the set threshold value, the system feedbacks that the frequency point is occupied. When the frequency point field strength does not exceed the set threshold value, the system feedbacks that the frequency point is unoccupied. Statistics are collected on the available frequencies in the service frequency band. As shown in Figure 4, the available frequency recommendation flow chart is shown. Figure 5 is an example of the available frequency recommendation list.

Analyze frequency usage at a certain location based on existing monitoring data and its frequency allocation.

4. Situation Positioning

The system generates a spectrum situation diagram by fusion analysis of spectrum acquisition and monitoring data combined with an interpolation algorithm, analyzes changes in field strength values within the area, and locates the radiation source.

Function input:

矢量：待分析的区域，用户可使用矢量工具绘制一个临时矢量参与计算，也可选择系统已配置的矢量区域进行计算分析。

Vector: The area to be analyzed. Users can use the vector tool to draw a temporary vector to participate in the calculation, or select the vector area configured by the system for calculation and analysis.

分析类型：系统可按频点和频段两种模式进行分析。频点分析用于分析单个频点的态势分布情况，频段分析用于分析频段内的所有频点的综合态势分布情况。

Analysis type: The system can perform analysis in two modes: frequency point and frequency band. Frequency point analysis is used to analyze the situation distribution of a single frequency point, and frequency band analysis is used to analyze the comprehensive situation distribution of all frequency points in the frequency band.

无线电业务：根据所选无线电业务分析某个频段范围内的综合结果。

Radio Service: Analyzes the overall results within a frequency band according to the selected radio service.

分析频率：在频点分析模式中，需设置分析的频道。

Analysis frequency: In frequency analysis mode, you need to set the channel to be analyzed.

门限：设置信号强度门限。

Threshold: Set the signal strength threshold.

Function Output

Radiation source location information. Figure 6 shows the situation positioning flow chart.

For scenes with high requirements on processing speed and large sample size, natural neighbor interpolation is used. The natural neighbor method is an enhanced form of the Thiessen polygon method. It has average performance in terms of approximation and good performance in inference ability. The method is relatively simple and more in line with people's inherent thinking. However, it is relatively limited in scope of use and is mainly suitable for scenes with small areas and low spatial variability. Because the processing method is relatively simple, this method has good performance in terms of processing speed and can use a larger sample size of the scene;

For situations with a large number of data sample points, thin plate spline interpolation is used. This method can complete the calculation within an acceptable time range. The thin plate spline interpolation method simulates physical shape deformations. Almost all biological deformations can be approximated by this method. Using thin plate spline interpolation can achieve relatively good results in the calculation of electromagnetic spectrum data. In terms of theoretical approximation, this method has certain disadvantages, but it has great advantages in terms of calculation speed and calculation ability.

For spectrum sensing data of discrete sampling points, Kriging interpolation is used. Ordinary Kriging interpolation has the characteristics of good approximation, strong inference ability and wide application range. However, in terms of processing speed, the interpolation algorithm of ordinary Kriging requires a lot of calculation, especially when there are a large number of sample points, the calculation of this method takes a long time. It is also for this reason that in the application of actual electromagnetic wave spectrum situation generation, when the number of sample points exceeds 100, it is no longer suitable to use this method for processing.

5. Situation process design

In order to realize the fusion of spectrum sensing data collected by sensing nodes at discrete locations into regional electromagnetic spectrum situation distribution and then present it in combination with geographic information system, this scheme intends to adopt three interpolation algorithms for data fusion processing: natural neighbor interpolation, thin plate spline interpolation, and Kriging interpolation.

The data fusion processing flow is shown in Figure 7.

The following is a detailed introduction to the three interpolation algorithms.

1) Natural Neighbor Interpolation

The natural neighbor algorithm finds the subset of input samples closest to the query point and interpolates by applying weights to these samples in proportion to the area size. This interpolation is also called Sibson or "area-stealing" interpolation. This interpolation algorithm is also divided into two steps. First, the Voronoi diagram is established with the monitoring points, and then each grid point is interpolated. The establishment and interpolation of the Voronoi diagram are explained below.

Voronoi diagram, also called Thiessen polygon or Dirichlet diagram, is a continuous polygon composed of a set of perpendicular bisectors connecting two adjacent points. N distinct points on a plane divide the plane according to the nearest neighbor principle; each point is associated with its nearest neighbor region. Delaunay triangle is a triangle formed by connecting related points that share an edge with adjacent Voronoi polygons. The center of the circumscribed circle of a Delaunay triangle is a vertex of the Voronoi polygon associated with the triangle. Voronoi triangle is the dual graph of Delaunay diagram.

If the point set consists of N points, the region of points closer to Pi than to other points is the intersection of the N-1 half-planes containing Pi. These N-1 half-planes are determined by the perpendicular bisectors of point Pi and other points. V(i) is a polygon composed of some perpendicular bisector segments. The region of each point is made using the above method to form a point Voronoi diagram. It divides the entire plane into N regions, each of which contains a point. This region is the region of this point. The line segment or ray in it is called a Voronoi edge, which must be a segment of the perpendicular bisector of the line connecting the two points. The two points are called the related points of the Voronoi edge, and the intersection between the Voronoi edges is called the Voronoi vertex. The related points of the Voronoi edge are also the related points of the Voronoi vertex. In addition, if point (x, y)∈V(i), then Pi is the closest point to point (x, y). As shown in Figure 8.

About interpolation: If x is the point to be interpolated in the figure, the original Voronoi will change due to the addition of point x. As shown in the figure above, the polygon that changes due to the addition of x is the neighboring area of x. The intersection of the new polygon and the original polygon determines the influence weight of the sample point on the interpolation point x, so the value of x can be calculated using the following formula:

Where ai is the value of the sample point in the i-th neighboring area, wi is the weight of the i-th neighboring area, and the weight wi is calculated as follows:

2) Thin plate spline method

Thin Plate Spline (TPS) is an interpolation method constructed by constructing a smooth function expression. When this interpolation is applied to radio wave propagation, it comprehensively considers the radial variation law of the measurement point and its surrounding points. Based on the comprehensive consideration of the distance and loss of the propagation model, the gradient variation matrix of the electromagnetic prediction point and the measurement point is constructed. This interpolation has a certain outward extension.

Thin plate spline interpolation is to imagine an interpolation function as bending a thin steel plate so that it passes through a given point, which requires a bending energy. This interpolation method ensures that all control points can match as much as possible, and how to minimize the bending of the steel plate. Figure 9 shows a schematic diagram of the control points and interpolation surface.

The principle of the thin plate spline method is based on the fact that the surface represented by it can minimize a functional consisting of the norm of the second-order derivatives of the elements in the Hilbert space, that is,

The functional is rotationally invariant and can approximately represent the bending energy of the thin plate determined by the binary function Φ. The surface obtained by the thin plate spline method is equivalent to the surface obtained by deforming a thin plate under a certain pressure. This pressure acts at each observation point (known scattered points), and its magnitude is proportional to the attribute value of the observation data point. The thin plate spline has the property of minimum bending, and Φ becomes a spline with minimum bending.

In the above formula, Φ is defined as follows:

Where m ₀ , m ₁ and m ₂ are real numbers, and ω _i is the weight of the observation sample point. is the radial basis function. This function is a combination of the interpolation method and the method of this patent, and is mainly introduced in the following paragraphs.

According to the requirement of minimum bending energy, ω _i , m ₀ , m ₁ and m ₂ can be solved, so that the value of any point can be predicted. The solution formula is as follows:

(ω ₁ ,ω ₂ ,...,ω _n ,m ₀ ,m ₁ ,m ₂ ) ^T ＝L ^-1 *Y

Y＝(v ₁ ,v ₂ ,...,v _n ,0,0,0) ^T

The estimated value Z(p _x,y ) at any point can be calculated using the following formula

The thin plate spline interpolation method is applied in this system, and the spectrum data collected at a certain regional position is used as the input data of the interpolation method. The distribution of the collected data itself at the regional position corresponds to the p _i of the thin plate spline interpolation data. The processing process uses the radial basis function to calculate the weight. The expression formula of the radial basis function U(p _i ,p _j ) is as follows:

U(p _i ,p _j )=σ(p _i ,p _j ) ² log(σ(p _i ,p _j ))

where σ(pi _{- pj} ) corresponds to the Euclidean distance between two points on the two-dimensional plane

Wherein, lng _i and lng _j represent the longitude of the i-th and j-th collected data, and lat _i and lat _j represent the latitude of the i-th and j-th collected data.

3) Kriging interpolation

The Kriging method generates continuous surface data from discrete reference point data according to relevant attribute values. Its regionalized variables have two characteristics: 1) Randomness: It is characterized by local, irregular, random and unpredictable characteristics; 2) Structurality: The random variables Z(x) and Z(x+h) of two points x and x+h in space have autocorrelation characteristics. This autocorrelation feature is related to the distance h between the two points in space and the variable characteristics.

The perception data Z(x,y) can be regarded as a regionalized research variable, which is a two-dimensional spatial distribution data. Assuming that Z(m) represents the variable of the perception data measurement value, on the one hand, when a point m in space is fixed, the perception data measurement value Z(m) is uncertain and can be regarded as a random variable, which reflects its randomness; on the other hand, the perception data measurement values Z(m) and Z(m+h) at two different points m and m+h in space (here h represents the distance vector in the two-dimensional space, and its modulus |h| represents the distance between point m and m+h) have a certain degree of autocorrelation. Generally speaking, the smaller h is, the better the correlation is. This autocorrelation reflects a certain continuity and correlation of the perception data measurement value variable, and reflects its structural aspect.

There is a research area D. For the regionalized research variable Z(x)∈D, x ₁ ,x ₂ ,…,x _n are n observation points in the region, and Z(x ₁ ),Z(x ₂ ),…,Z(x _n ) are the corresponding observation values. There is an unsampled point x ₀ in the region, and its estimated value is z ^* (x ₀ ). z ^* (x ₀ ) can be estimated by a linear relationship:

In the above formula, λ _i is the unknown weight of the measured value at the ith position. Therefore, the purpose of Kriging interpolation is to obtain the weight. Since Kriging interpolation is an unbiased optimization interpolation, unbiasedness and minimum estimated variance become the selection criteria for weight λ, and the equation group of Kriging interpolation can be obtained.

Write the above equations in the form of a matrix

[K']·[λ']＝[M']

To obtain the attribute values of unsampled points through Kriging interpolation, it is necessary to analyze the data of known sampling points, obtain the experimental variogram values, fit the discrete experimental variogram values through the theoretical variogram model, and obtain the variogram model of the sampling point data.

Solve the system of equations to obtain the weight values of Kriging interpolation.

λ'＝K' ^-1 *M'

是半方差函数，由于空间上的相似性属性，认为

半方差函数和空间距离d(x_i，x_j)之间关系可以用线性、指数或对数的关系进行拟合。在本方法中，使用克里金指数模型函数进行拟合。Substitute the solved weights into the formula to obtain the attribute values of the unsampled points.

is the semivariogram function. Due to the spatial similarity property, it is considered that

The relationship between the semivariance function and the spatial distance d( _xi , _xj ) can be fitted using a linear, exponential or logarithmic relationship. In this method, the Kriging exponential model function is used for fitting.

When considering electromagnetic spectrum interpolation, based on the correspondence between the path loss of electromagnetic radiation and the distance (based on the electromagnetic wave propagation model), an unbiased estimation matrix of the measurement difference and distance between monitoring points is established to realize Kriging interpolation prediction.

The application of the Kriging interpolation method in combination with the present invention is to convert the distance d( _xi , _xj ) between data points into a formula for calculating the covariance. This conversion uses a Kriging exponential function model, as shown in the above formula. In the formula, _c0 is the nugget constant of the Kriging exponential model, which is generally taken as 0. c is the arch height in the model, and _d0 is the range in the model. c and _d0 can take better empirical values according to the results of actual sample data processing.

4. Software Deployment

The system supports two working deployment modes: stand-alone deployment and networking.

The stand-alone task is to connect the receiver to analyze the spectrum information through a handheld device or laptop, as shown in Figure 10.

The local networking deployment mode consists of multiple units and a control terminal, and the devices achieve networking and collaborative communication through a local area network, as shown in Figure 11.

5. Environmental and Adaptability Design

The system is compatible with Windows and Android systems and can be used on both computers and mobile phones, making it convenient to use in different scenarios.

6. Reliability Design

The system can work continuously for 24 hours a day, 7 days a week. To ensure the reliability of the system, the following strategies are adopted:

(1) Data segmentation strategy

After the system software starts collecting spectrum data, the spectrum data is automatically saved in the specified appendix and saved as a file with a size of 200M to avoid the problem of slow loading due to large files.

(2) Software Layered Structure

Software structure can be roughly divided into four levels: program, subprogram, module and program unit. Program refers to a set of instructions in the software that can run independently and perform complete functions; subprogram refers to a major functional subset in the program; module refers to a logically separate part of the program; program unit refers to a process or routine. A good software hierarchical structure should be a tree structure with a clear trunk and context and distinct levels.

(3) Avoid design complexity

Software design should implement the principle of simplicity and reliability, and the logical structure and algorithm in the program should be clear and easy to read. The number of parallel modules, the number of multiple loops, and the depth of nesting layers should be controlled within 7; the number of executable source code statements for each program unit should not exceed 200 at most, and not exceed 60 on average; each program should begin with a brief description of the function, input and output, local variables, etc., and the program should be supplemented with an appropriate amount of comments (generally not less than 20% of the source code).

(4) Fault-tolerant design

Software fault tolerance refers to the use of fault-tolerant software structure design. When a failure occurs, the software automatically restores its functions or controls the impact caused by the failure without human intervention.

7. Maintainability Design

Take the following measures to ensure system maintainability:

(1) The modular design of the system facilitates maintenance and troubleshooting. The system software has an operation log file that records the software's operation log, making it easy to trace problems.

(2) System design and coding specifications. System software implementation is carried out according to the specifications to facilitate software understanding and modification.

(3) The system has a complete and simple system installation tool that can support local or full system automatic installation and deployment, quick repair, version upgrade and other functions.

(4) Logs of key operations and abnormal situations during system operation help software operation and maintenance personnel to quickly locate, reproduce, and solve problems when troubleshooting.

Innovation

Spectrum situation synthesis and presentation technology based on geographic information

In order to solve the bottleneck problems such as the three-dimensionalization of electromagnetic space and spectrum fragmentation, which make traditional spectrum sensing methods unable to obtain complete and wide-area spectrum situation information, this paper takes GIS information as the data basis and uses adaptive interpolation algorithm to fuse spectrum data from the whole domain of time, space and frequency to present multi-dimensional spectrum situation.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention is described in detail with reference to the embodiments, it should be understood by those skilled in the art that any modification or equivalent replacement of the technical solutions of the present invention does not depart from the spirit and scope of the technical solutions of the present invention and should be included in the scope of the claims of the present invention.

Claims (8)

1. A situation awareness visualization system oriented to complex electromagnetic spectrum, the system comprising: a sensing layer, a data layer and a functional layer; wherein,

the sensing layer is used for receiving spectrum sensing data collected by discrete position points;

the data layer is used for storing and managing the acquired spectrum sensing data, storing and managing map information data and acquisition equipment related information of the spectrum sensing data, and storing and managing result data processed by the functional layer;

the functional layer is used for carrying out fusion processing on the frequency spectrum sensing data of the discrete position points to form regional electromagnetic spectrum situation distribution, presenting the regional electromagnetic spectrum situation distribution by combining with map information data, and positioning the position of the radiation source.

2. The complex electromagnetic spectrum-oriented situation awareness visualization system according to claim 1, wherein the functional layer comprises a fusion processing module, and a processing procedure of the fusion processing module comprises:

acquiring spectrum sensing data of a designated section;

extracting frequency point data according to the frequency band and the step length;

filtering invalid data and repairing data with errors;

eliminating the influence of fast fading on the repaired spectrum sensing data through clustering filtering;

selecting a corresponding interpolation algorithm according to the frequency spectrum sensing data to generate a single-frequency point situation map;

and synthesizing the situation maps of the frequency points to obtain regional electromagnetic spectrum situation distribution.

3. The complex electromagnetic spectrum-oriented situation awareness visualization system according to claim 2, wherein the corresponding interpolation algorithm comprises natural neighborhood interpolation, thin-plate spline interpolation, and kriging interpolation.

4. The complex electromagnetic spectrum-oriented situation awareness visualization system according to claim 3, wherein the natural neighborhood interpolation processing specifically includes:

finding N discrete position points with the nearest distance to the point to be inquired, and establishing N corresponding Voronoi diagrams;

for a point z to be interpolated, defining a polygon which is changed due to the addition of z as a neighboring area of z, wherein the intersection of a new polygon and an original polygon determines the influence weight of a corresponding discrete position point on the interpolated point z;

and proportionally applying weights to the spectrum sensing data of the N discrete position points for interpolation based on the Voronoi diagram sizes of the N discrete position points.

5. The complex electromagnetic spectrum-oriented situational awareness visualization system according to claim 3, wherein the thin-plate spline interpolation processing specifically includes:

collecting frequency spectrum data at a certain region position as spline interpolation data p at a corresponding region position _i ，p _j The radial basis function U (p) is calculated according to the following formula _i ,p _j )：

U(p _i ,p _j )＝σ(p _i -p _j ) ² log(σ(p _i -p _j ))

Wherein, σ (p) _i -p _j ) Corresponding to the Euclidean distance, lng, between two points on a two-dimensional plane _i 、lng _j Longitude, lat, representing the ith, j data collected _i 、lat _j Representing the latitude of the ith and the jth acquired data;

based on radial basis function U (p) _i ,p _j ) Obtaining the weight w of the ith sample point to the predicted point x _i,x ，

Obtaining a numerical value V (x) of a point to be predicted by adopting weighting calculation:

wherein, V (x) _i ) Is the value of the ith sample point.

6. The complex electromagnetic spectrum-oriented situation awareness visualization system according to claim 3, wherein the kriging interpolation processing procedure specifically comprises:

and establishing an unbiased estimation matrix of the measurement difference and the distance between the data position points based on the corresponding relation between the path loss and the distance of the electromagnetic radiation, and realizing interpolation prediction.

7. The complex electromagnetic spectrum-oriented situation awareness visualization system according to claim 2, wherein the functional layer comprises a situation localization module, and a processing procedure of the situation localization module specifically comprises:

and positioning the radiation source by combining the preset signal intensity threshold and the geographic information data according to the frequency spectrum situation map generated by the fusion processing module.

8. The complex electromagnetic spectrum-oriented situation awareness visualization system according to claim 1, wherein the functional layer further comprises an available frequency recommendation module, and the processing procedure of the available frequency recommendation module specifically comprises:

judging whether the field intensity value of each frequency point in the service frequency band exceeds a set threshold value one by one according to the frequency spectrum sensing data, if so, judging that the frequency point is occupied, otherwise, judging that the frequency point is not occupied, and analyzing the channel quality;

and counting the available frequency conditions in the service frequency band and outputting an available frequency recommendation list by combining with channel quality analysis.

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