A multi-domain situation information fusion method based on geographic and electromagnetic data

Abstract

The application discloses a kind of multi-domain situation information fusion methods based on geography and electromagnetic data, belong to multi-domain data fusion technical field.It includes the following processes: obtaining DEM data and electromagnetic data, the DEM data and electromagnetic data obtained are saved as json file respectively, stored to local to facilitate subsequent call;Draw heat map, code reads the json file saved to local, draw DEM data and electromagnetic data heat map;Calculate correlation matrix, correlation matrix can quantitatively describe the similarity between data, according to the correlation between DEM data and electromagnetic data Corresponding correlation matrix is established;Data registration, data registration is to find the corresponding relationship between different data, so that data changes to the same coordinate system, obtains the correlation fusion result of data.The application effectively solves the reliable fusion problem of multi-domain situation information in unknown environment, achieves the expected effect.

Description

Technical Field

[0001] This invention belongs to the field of multi-domain data fusion technology, and specifically refers to a method for fusion of multi-domain situational information based on geographic and electromagnetic data. Background Technology

[0002] In recent years, the rapid development of information technology has brought new challenges to situational awareness technology. Field-map-based situational awareness requires a multi-domain approach. The adaptability of a single sensing field to the sensor environment, detection performance, and differences in field spatial distribution are highly correlated. Situational awareness based on a single field suffers from insufficient reliability. Multi-domain field interaction can fully utilize the spatial distribution characteristics of different fields and sensor properties, resulting in stronger adaptability. Since multiple sensors are affected by various factors during data sensing, the data involved in fusion is uncertain. Therefore, how to use multi-domain data registration and cross-domain data fusion methods to register and fuse data from different sensors on distributed nodes, thereby improving the reliability of the fusion results, has become a research hotspot. Summary of the Invention

[0003] This invention proposes a multi-domain situational information fusion method based on geographic and electromagnetic data, aiming to conduct research on data fusion technology based on multi-domain situational information and solve the problem of multi-domain data fusion in indoor and outdoor environments. It studies multi-domain differentiated data association and data fusion techniques to address the reliable fusion of multi-domain situational information in unknown environments.

[0004] This invention is achieved through the following technical solution:

[0005] A multi-domain situational information fusion method based on geographic and electromagnetic data includes the following steps:

[0006] Step 1: Acquire DEM data and electromagnetic data, and save the acquired DEM data and electromagnetic data separately;

[0007] Step 2: Draw heatmaps of DEM and electromagnetic data;

[0008] Step 3: Based on the similarity measurement method, calculate the correlation degree for each DEM data and electromagnetic data that falls within the correlation threshold, and establish the corresponding correlation matrix based on the calculated correlation degree;

[0009] Step 4: Calculate the association success rate based on the association matrix.

[0010] Furthermore, the specific method for step 3 is as follows:

[0011] 1) Extract cognitive information from the DEM heatmap and describe it in matrix form:

[0012]

[0013] Where m and n are the width and length of the heatmap, and d ij The value is 1 when it is greater than the association threshold, and 0 when it is less than the association threshold, i = 1, 2, ..., m, j = 1, 2, ..., n;

[0014] Extract cognitive information from electromagnetic thermograms and describe it in matrix form:

[0015]

[0016] Where m and n are the width and length of the heatmap, e ij The value is 1 when it is greater than the association threshold, and 0 when it is less than the association threshold, i = 1, 2, ..., m, j = 1, 2, ..., n;

[0017] 2) Calculate the association matrix R using the extracted cognitive information:

[0018]

[0019]

[0020] Where m and n are the width and length of the heatmap, i = 1, 2, ..., m, j = 1, 2, ..., n.

[0021] Furthermore, the specific calculation method for the association success rate S in step 4 is as follows:

[0022]

[0023] The beneficial effects of this invention are as follows:

[0024] This invention proposes a multi-domain situational information fusion method based on geographic and electromagnetic data. By acquiring DEM and electromagnetic data, drawing distribution heat maps, calculating correlation matrices, and data registration, it solves the problem of reliable fusion of multi-domain situational information in unknown environments and has good application prospects. Attached Figure Description

[0025] Figure 1 This is a heatmap of DEM data from the present invention.

[0026] Figure 2 This is a thermal diagram of electromagnetic data from the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] A multi-domain situational information fusion method based on geographic and electromagnetic data includes the following steps:

[0029] Step 1: Obtain DEM data and electromagnetic data, and save the obtained DEM data and electromagnetic data as JSON files respectively, and store them locally for later use; among them, Digital Elevation Model (DEM) is a discrete mathematical representation of the topography of the Earth's surface.

[0030] 1) A DEM represents a finite sequence of three-dimensional vectors over a certain region, and can be described in functional form as follows:

[0031] V i =(x i ,y i ,z i i = 1, 2, ..., n

[0032] Among them, (x i ,y i ) is a planar coordinate, z i It is (x) i ,y i The corresponding elevation data.

[0033] 2) Place an electromagnetic source in the environment and use an electromagnetic sensor to detect electromagnetic data in the environment. The electromagnetic data is described in the form of a function:

[0034] E i =(x i ,y i ,s i i = 1, 2, 3, ..., n

[0035] Among them, (x i ,y i ) is a planar coordinate, s i It is (x) i ,y i The corresponding electromagnetic intensity.

[0036] Step 2: Draw heatmaps of DEM and electromagnetic data. The code reads the JSON file saved locally, and the drawing results are as follows. Figure 1 and Figure 2 As shown.

[0037] Step 3: Based on the similarity measurement method, calculate the correlation degree for each DEM and electromagnetic data falling within the correlation threshold, and establish a corresponding correlation matrix based on the calculated correlation degree. The correlation matrix quantitatively describes the degree of similarity between data, including data correlation measurement standards, logical principles, and similarity calculations, and is a key step in data correlation. The specific method is as follows:

[0038] 1) Extract cognitive information from the DEM heatmap and describe it in matrix form:

[0039]

[0040] Where m and n are the width and length of the heatmap, and d ij The value is 1 when it is greater than the association threshold, and 0 when it is less than the association threshold, i = 1, 2, ..., m, j = 1, 2, ..., n;

[0041] Extract cognitive information from electromagnetic thermograms and describe it in matrix form:

[0042]

[0043] Where m and n are the width and length of the heatmap, e ij The value is 1 when it is greater than the association threshold, and 0 when it is less than the association threshold, i = 1, 2, ..., m, j = 1, 2, ..., n;

[0044] 2) Calculate the association matrix R using the extracted cognitive information:

[0045]

[0046]

[0047] Where m and n are the width and length of the heatmap, i = 1, 2, ..., m, j = 1, 2, ..., n.

[0048] Step 4: Calculate the association success rate based on the association matrix.

[0049] The purpose of data registration is to find correspondences between different data sets, thereby transforming the data to the same coordinate system. Data registration is essentially the process of solving for transformation parameters. Data obtained under different conditions will have differences, and data registration can reduce these differences. Therefore, the success rate of data registration indirectly determines the quality of data fusion.

[0050] The specific calculation method for the success rate S of association is as follows:

[0051]

[0052] Table 1 shows the data association success rate in the embodiments of the present invention.

[0053] Table 1

[0054]

[0055] The foregoing has provided a detailed description of the multi-domain situational information fusion method based on geographic and electromagnetic data proposed in this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for fusing multi-domain situational information based on geographic and electromagnetic data, characterized in that, The method comprises the following steps: Step 1: obtaining DEM data and electromagnetic data, and saving the obtained DEM data and electromagnetic data respectively; Step 2: drawing a heat map of the DEM data and the electromagnetic data; Step 3: calculating the correlation degree of each DEM data and electromagnetic data falling within the correlation threshold according to a similarity measurement method, and establishing a corresponding correlation matrix according to the calculated correlation degree; Step 4: calculating a correlation success rate according to the correlation matrix; The specific mode of step 3 is as follows: 1) extracting cognitive information of the DEM heat map, which is described in the form of a matrix as follows: in, , The width and length of the heatmap. The value is 1 when it is greater than the correlation threshold, and 0 when it is less than the correlation threshold. , ; Extracting cognitive information in the electromagnetic heat map, which is described in the form of a matrix as follows: in, , The width and length of the heatmap. The value is 1 when it is greater than the correlation threshold, and 0 when it is less than the correlation threshold. , ; 2) Calculate the association matrix using the extracted cognitive information. : in, , The width and length of the heatmap. , .

2. The method for fusing multi-domain situational information based on geographic and electromagnetic data according to claim 1, characterized in that, Success rate of association in step 4 The specific calculation method is as follows: 。

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