A method for realizing electromagnetic spectrum map based on improved inverse distance interpolation method

Abstract

The invention discloses a method for realizing an electromagnetic spectrum map based on an improved inverse distance interpolation method, comprising the steps of Step 1. collecting and preprocessing spectrum data at different positions in space; Step 2. determining a variable search radius R; Step 3. sorting N known measurement points within the variable search radius R in Step 2 from small to large according to distance; Step 4. calculating a weight coefficient according to a weight formula; Step 5. calculating and storing a field intensity value E at a point to be interpolated (x, y); Step 6. repeating Step 2 to Step 5 until all points to be interpolated in a region are calculated. The method improves the inverse distance interpolation method, constructs a method with a variable search radius, eliminates data far from a point to be interpolated, and increases the weight of a point close to the point, thereby improving interpolation accuracy and calculation efficiency, and has the characteristics of simple algorithm structure, high interpolation accuracy and calculation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of spectrum map construction, and in particular to a method for realizing an electromagnetic spectrum map based on an improved inverse distance interpolation method. Background Art

[0002] With the development of cognitive radio technology, people's demand for spectrum perception is increasing. Spectrum maps are used to describe radio environments, provide network services and prior knowledge, and can visualize multi-dimensional information such as time, frequency, geographic location, and energy. They are dynamic and interactive, and have important practical significance for understanding the electromagnetic environment, improving network performance, resolving spectrum conflicts, managing spectrum resources, and maximizing the use of spectrum resources.

[0003] Due to factors such as terrain and cost, it is difficult to obtain regional electromagnetic spectrum data by measuring all nodes. Usually, incomplete regional spectrum measurement data can only be obtained by sampling. Therefore, how to use incomplete data to restore the overall spectrum map has become an important part of spectrum map construction.

[0004] The current methods used for spatial interpolation construction of radio spectrum maps mainly include inverse distance weighted method and Kriging interpolation method, among which:

[0005] (1) Kriging interpolation achieves the purpose of unbiased estimation by optimizing weights, but its algorithm complexity is greatly increased compared to the inverse distance weighted method, making it difficult to meet the needs of fast calculation and having poor practicality;

[0006] (2) The inverse distance weighted method is a representative of spatial interpolation methods. The main principle of this method is to use the characteristic that the similarity between two points in space decreases as the distance increases. The interpolation point is estimated by calculating the distance between the interpolation point and the known point. Since the inverse distance weighted method weights all known measurement points in space, its error is large and difficult to control.

[0007] Therefore, it is urgent to design a new electromagnetic spectrum map implementation method to solve the problems existing in the above-mentioned prior art. Summary of the invention

[0008] In view of the above-mentioned problems, the present invention aims to provide a method for realizing an electromagnetic spectrum map based on an improved inverse distance interpolation method. The method improves the inverse distance interpolation method, constructs a method of a variable search radius, eliminates data that are far away from the point to be interpolated, and increases the weights of points that are closer, thereby improving the interpolation accuracy and calculation efficiency. The method has the characteristics of simple algorithm structure, high interpolation accuracy and calculation efficiency.

[0009] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0010] A method for realizing an electromagnetic spectrum map based on an improved inverse distance interpolation method includes the steps of Step 1. collecting and preprocessing spectrum data at different positions in space;

[0011] Step 2. Determine the variable search radius R;

[0012] Step 3. Sort the N known measurement points within the variable search radius R in Step 2 in ascending order of distance, find the first M data points and store them;

[0013] Step 4. Calculate the weight coefficient according to the weight formula;

[0014] Step 5. Calculate the field strength value E at the interpolation point (x, y) and store it according to the weight coefficient determined in step 4 and the M known data points determined in step 3;

[0015] Step 6. Repeat Step 2 to Step 5 until all the interpolation points in the area are calculated.

[0016] Preferably, the spectrum data collection process described in step 1 includes:

[0017] Step 101. Deploy spectrum monitoring nodes and collect the longitude coordinates of data collection points in the deployment area [lon] i , data collection point latitude coordinate set [lat] i , field strength set of data collection points[e] i ;

[0018] Step 102. Then record the collected information as E = {[lon] i , [lat] i ,[e] i}.

[0019] Preferably, the process of determining the variable search radius R in step 2 includes:

[0020] Step 201. For a point to be interpolated with coordinates (x, y), set the interpolation search radius R with the point as the center;

[0021] Step 202. Determine whether the known data point is within the search radius, and calculate the distance d between the known data point within the search radius and the point to be interpolated i And store, and at the same time store the number N of known data points within the search radius;

[0022] Step 203. Compare the size of N and M

[0023] If N ≥ M, store the values ​​of R and N;

[0024] If N<M, the search radius R is enlarged and the process returns to Step 202 to recalculate and judge until N≥M;

[0025] Where M is the number of points closest to the point to be interpolated, which is given in a pre-set manner.

[0026] Preferably, the calculation process of the weight coefficient described in step 4 includes:

[0027] Step 401. Calculate the interpolation point (x, y) and the known measurement point (x i ,y i ) i

[0028]

[0029] Where 1≤i≤M;

[0030] Step 402. Calculate weight coefficients

[0031]

[0032] Where: p is any positive real number, generally p=2; λ i To update the weights.

[0033] Preferably, the function of the field strength value E in step 5 is:

[0034]

[0035] Preferably, the electromagnetic spectrum map realization method based on the improved inverse distance interpolation method further comprises the steps of:

[0036] Step 7. Root mean square error adjustment process

[0037] The root mean square error is the square root of the ratio of the square of the deviation between the estimated value and the true value and the number of observations n, which is calculated as follows:

[0038]

[0039] Where E represents the radiation source matrix, represents the interpolated radiation source matrix;

[0040] The RMSE is processed logarithmically, that is

[0041] RMSE = 10log 10 (RMSE).

[0042] The beneficial effects of the present invention are as follows: the present invention discloses a method for realizing an electromagnetic spectrum map based on an improved inverse distance interpolation method. Compared with the prior art, the improvements of the present invention are as follows:

[0043] (1) The present invention proposes a method for realizing an electromagnetic spectrum map based on an improved inverse distance interpolation method. The method realizes the construction of a spectrum map under incomplete conditions. By measuring the electromagnetic spectrum data of some points, the inverse distance interpolation algorithm is improved to obtain a regional electromagnetic spectrum map; the workload and difficulty of spectrum measurement are reduced;

[0044] (2) This method has a small amount of calculation and a simple principle. By introducing a variable search radius R, the problem of excessive calculation caused by the weight of distant data points being too small and not participating in the actual calculation in the inverse distance interpolation algorithm is improved. It also avoids the data deviation problem caused by using all measurement points for calculation. At the same time, compared with the Kriging algorithm, the amount of calculation is significantly reduced.

[0045] (3) This method does not need to consider the influence of radio wave propagation model and terrain, and makes full use of spatial correlation for calculation, which improves the accuracy of spectrum map construction and effectively reduces the number of measurement points required in the process of geographic spectrum map construction. It is more economical and convenient, and has the advantages of simple algorithm structure, high interpolation accuracy and calculation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The present invention is an algorithm flow chart of the electromagnetic spectrum map implementation method based on the improved inverse distance interpolation method.

[0047] Figure 2 Flow chart of the algorithm for determining the variable search radius R of the present invention.

[0048] Figure 3 It is the original spectrum map under the simulation scenario of 5 radiation sources of the present invention.

[0049] Figure 4 It is the original spectrum map under the simulation scenario of three radiation sources of the present invention.

[0050] Figure 5 This is the reconstructed spectrum map for five radiation source scenarios according to the electromagnetic spectrum map implementation method based on the improved inverse distance interpolation method according to Example 2 of the present invention.

[0051] Figure 6 This is the reconstructed spectrum map for three radiation source scenarios according to the electromagnetic spectrum map implementation method based on the improved inverse distance interpolation method according to Example 2 of the present invention.

[0052] Figure 7 It is a line graph of the root mean square error of the electromagnetic spectrum map implementation method based on the improved inverse distance interpolation method of the present invention. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0054] Embodiment 1: refer to the attached Figure 1-7 The method for realizing electromagnetic spectrum map based on improved inverse distance interpolation method includes the steps of

[0055] Step 1. Collect and preprocess spectrum data at different locations in space

[0056] Step 101. Deploy spectrum monitoring nodes to collect regional electromagnetic spectrum data, including the following information: longitude coordinate set of data collection points [lon] i , data collection point latitude coordinate set [lat] i , field strength set of data collection points[e] i ;

[0057] Step 102. Then record the collected information as E = {[lon] i , [lat] i ,[e] i};

[0058] Step 2. Determine the variable search radius R

[0059] Step 201. For a point to be interpolated with coordinates (x, y), set the interpolation search radius R with the point as the center;

[0060] Step 202. Determine whether the known data point is within the search radius, and calculate the distance d between the known data point within the search radius and the point to be interpolated i And store, and at the same time store the number N of known data points within the search radius;

[0061] Step 203. Compare the size of N and M

[0062] If N ≥ M, store the values ​​of R and N;

[0063] If N<M, the search radius R is enlarged and the process returns to Step 202 to recalculate and judge until N≥M;

[0064] Where M is the number of points closest to the point to be interpolated, which is given by a pre-set method;

[0065] Step 3. Sort the N known measurement points within the variable search radius R in Step 2 in ascending order of distance, find the first M data points and store them;

[0066] Step 4. Calculate the weight coefficient according to the weight formula, and the specific implementation is as follows:

[0067] Step 401. Calculate the interpolation point (x, y) and the known measurement point (x i ,y i ) i

[0068]

[0069] Where 1≤i≤M;

[0070] Step 402. Calculate weight coefficients

[0071]

[0072] Where: p is any positive real number, generally p=2; λ i To update the weights;

[0073] Step 5. Based on the weight coefficient determined in Step 4 and the M data points determined in Step 3, calculate the field strength value E at the interpolation point (x, y) according to the following interpolation formula and store it:

[0074]

[0075] Step 6. Repeat Step 2 to Step 5 until all the interpolation points in the area are calculated.

[0076] Preferably, in order to quantitatively estimate the error value of the interpolation algorithm, the electromagnetic spectrum map realization method based on the improved inverse distance interpolation method further includes the steps of:

[0077] Step 7. Root mean square error adjustment process

[0078] The root mean square error refers to the square root of the ratio of the square of the deviation between the estimated value and the true value and the number of observations n. The specific calculation is as follows:

[0079]

[0080] Where E represents the radiation source matrix, represents the interpolated radiation source matrix,

[0081] Since the RMSE value is small, it is processed logarithmically, that is,

[0082] RMSE = 10log 10 (RMSE).

[0083] Example 2: In order to verify the feasibility and superiority of the electromagnetic spectrum map implementation method based on the improved inverse distance interpolation method as described in Example 1 above, this example simulates a 100*100 grid area, in which three radiation sources are set, the simulated electromagnetic scene frequency is 1000MHz, the radiation source signal strength range is -50dBm to 50dBm, and the free space propagation model is used for calculation. The electromagnetic spectrum data measurement process is simulated through data missing, and the algorithm described in Example 1 is used to calculate the field strength value of the missing point to reconstruct the spectrum map;

[0084] The specific simulation implementation process is as follows:

[0085] 1. Region initialization

[0086] Divide the area into 100*100 grids, and use the grid vertex coordinates as the measurement point coordinates;

[0087] 2. Sampling and processing

[0088] The generated spectrum map is sampled at 50% sampling rate;

[0089] 3. Determine the variable search radius R,

[0090] 1) For the interpolation point with coordinates (x, y), set the interpolation search radius R with the point as the center;

[0091] 2) Find the sampling points within the range R and count their number N,

[0092] 3) Compare the sizes of N and M,

[0093] If N ≥ M, store the values ​​of R and N;

[0094] If N<M, expand the search radius R and return to step 2), recalculate and judge until N≥M;

[0095] Calculate the distance di from each of the N known measurement points to the point to be interpolated and store it;

[0096] 4. Determine the weight coefficient

[0097] The weight coefficient is calculated according to the weight formula, and the specific implementation is as follows;

[0098] 1) Calculate the interpolation point (x, y) and the known measurement point (x i ,y i ) i

[0099]

[0100] Where 1≤i≤M,

[0101] 2) Calculate the weight coefficient

[0102]

[0103] Where: p is any positive real number, generally p=2; λ i To update the weights;

[0104] 5. Based on the weight coefficient determined in step 4 and the M data points determined in step 3, the field strength value E at the interpolation point (x, y) is calculated and stored according to the following interpolation formula:

[0105]

[0106] 6. Repeat steps 2 to 5 until all the interpolation points in the area are calculated;

[0107] 7. In order to quantitatively estimate the error value of the interpolation algorithm, the root mean square error is introduced to evaluate the accuracy of the interpolation algorithm; the root mean square error refers to the square root of the ratio of the square of the deviation between the estimated value and the true value and the number of observations n, and the specific calculation is as follows:

[0108]

[0109] Where E represents the radiation source matrix, represents the interpolated radiation source matrix,

[0110] Since the RMSE value is small, it is processed logarithmically, that is,

[0111] RMSE = 10log 10 (RMSE).

[0112] Get as Figure 5 The usage method shown is based on the electromagnetic spectrum map implementation method of the improved inverse distance interpolation method to reconstruct the spectrum map under five radiation source scenes, such as Figure 6 The usage method shown is based on the electromagnetic spectrum map implementation method of the improved inverse distance interpolation method to reconstruct the spectrum map under three radiation source scenes, such as Figure 7 The method shown is based on a line graph of the root mean square error of the improved ID interpolation method.

[0113] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for realizing electromagnetic spectrum map based on improved inverse distance interpolation method, Features: Included steps Step 1. Collect and preprocess spectrum data at different locations in space; Step 2. Determine the variable search radius R; Step 201. For a point to be interpolated with coordinates (x, y), set the interpolation search radius R with the point as the center; Step 202. Determine whether the known data point is within the search radius, and calculate the distance d between the known data point within the search radius and the point to be interpolated i And store, and at the same time store the number N of known data points within the search radius; Step 203. Compare the size of N and M If N ≥ M, store the values ​​of R and N; If N<M, the search radius R is enlarged and the process returns to Step 202 to recalculate and judge until N≥M; Where: M is the number of points closest to the point to be interpolated, which is given by a pre-set method; Step 3. Sort the N known measurement points within the variable search radius R in Step 2 in ascending order of distance, find the first M data points and store them; Step 4. Calculate the weight coefficient according to the weight formula; Step 401. Calculate the interpolation point (x, y) and the known measurement point (x i ,y i ) i Where: 1≤i≤M; Step 402. Calculate weight coefficients Where: p is any positive real number; λ i To update the weights; Step 5. Calculate the field strength value E at the interpolation point (x, y) and store it according to the weight coefficient determined in step 4 and the M known data points determined in step 3; Step 6. Repeat Step 2 to Step 5 until all the interpolation points in the area are calculated.

2. According to claim 1, a method for realizing electromagnetic spectrum map based on improved inverse distance interpolation method, Features: The spectrum data acquisition process described in step 1 includes: Step 101. Deploy spectrum monitoring nodes, collect data collection point longitude coordinate set [lon]i, data collection point latitude coordinate set [lat]i, data collection point field strength set [e]i in the deployment area; Step 102. Then record the collected information as E = {[lon]i, [lat]i, [e]i}.

3. According to claim 1, a method for realizing electromagnetic spectrum map based on improved inverse distance interpolation method, Features: The function of the field strength value E described in step 5 is 4. According to claim 1, a method for realizing electromagnetic spectrum map based on improved inverse distance interpolation method, Features: The electromagnetic spectrum map realization method based on the improved inverse distance interpolation method also includes the steps of: Step 7. Root mean square error adjustment The root mean square error is the square root of the ratio of the square of the deviation between the estimated value and the true value and the number of observations n, which is calculated as follows: Where E represents the radiation source matrix, represents the interpolated radiation source matrix; The RMSE is processed logarithmically, that is RMSE=10log 10 (RMSE)。

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