ADS-B data credibility evaluation method based on radar reference source

Abstract

The invention provides an ADS-B (Automatic Dependent Surveillance-Broadcast) data credibility evaluation method based on a radar reference source. The ADS-B data credibility evaluation method comprises the following steps: constructing an ADS-B data credibility evaluation index system of an ADS-B; calculating the comprehensive weight of the indexes, and sorting to obtain a weight vector; and constructing a membership function of each index element, obtaining a grade evaluation matrix of the fuzzification vector containing all the index elements, and calculating the dot product of the weight vector and the grade evaluation matrix to obtain the comprehensive credibility of the ADS-B data. According to the method disclosed by the invention, the problem of low credibility of the ADS-B data of the broadcast type automatic dependent surveillance system can be solved on the premise of not introducing other data by fully utilizing an operation mode that the existing broadcast type automatic dependent surveillance system ADS-B and the radar coexist in an airport in China.

Description

Technical Field

[0001] The invention belongs to the technical field of aviation monitoring, and in particular relates to an ADS-B data credibility evaluation method based on a radar reference source. Background Art

[0002] Surveillance technology is an important technical foundation for modern civil aviation operations and air traffic management. Existing technologies are currently mainly implemented through technical means such as the Aircraft Communication Addressing and Reporting System ACARS, the Automatic Dependent Surveillance-Broadcast System ADS-B and radar (including S-mode radar, A / C-mode radar, etc.). As an emerging radar-like surveillance means, the Automatic Dependent Surveillance-Broadcast System ADS-B has been identified by ICAO as the main surveillance technology in the future due to its advantages such as low construction cost, accurate target positioning and high data update rate.

[0003] However, since the relevant surveillance is completely dependent on the airborne navigation information source and the ADS-B system itself does not have the function of verifying the information source, if there are inaccurate navigation position information, active aircraft deception, malicious interference or interception of aircraft and similar situations, the ground station equipment cannot effectively identify them, which will greatly reduce the credibility of the ADS-B data and restrict its application. Many airports currently use the ADS-B and radar coexistence operation mode, and radar data is more reliable than ADS-B data, so it can be used as a basis for evaluating the credibility of ADS-B data. Summary of the invention

[0004] Purpose of the invention: The technical problem to be solved by the present invention is to provide a method for evaluating the credibility of ADS-B data based on a secondary radar reference source in view of the deficiencies of the prior art. The present invention uses three types of information sources, such as S-mode radar, A / C-mode radar, and flight plan, as reference sources for evaluating the credibility of ADS-B data, establishes a comprehensive evaluation index system for the credibility of ADS-B, and constructs a comprehensive evaluation model for the credibility of ADS-B data. The present invention makes full use of the current coexistence of ADS-B and radar in domestic airports, and can effectively solve the problem of low credibility of ADS-B data without introducing other data.

[0005] Technical solution: The present invention specifically provides a method for evaluating the credibility of ADS-B data based on a radar reference source, comprising the following steps:

[0006] Step 1: Define the consistency index of ADS-B data based on secondary radar reference source by "qualitative + quantitative" method, and build an ADS-B data credibility evaluation index system including 3 criterion layer indicators and 8 factor layer indicators;

[0007] Step 2: Calculate the comprehensive weights of the three criterion-level indicators and the eight factor-level indicators, and sort them to obtain a comprehensive weight vector;

[0008] Step 3: construct the membership function of each indicator element, obtain the grade evaluation matrix containing the fuzzy vectors of all indicator elements, and calculate the dot product of the comprehensive weight vector and the grade evaluation matrix to obtain the comprehensive credibility of the ADS-B data.

[0009] Step 1 includes the following steps:

[0010] Step 1-1, using the three types of information sources, namely, S-mode radar, A / C-mode radar, and flight plan, as reference sources for credibility evaluation of ADS-B data of the automatic dependent surveillance-broadcast system, selecting common data in the ADS-B data of the automatic dependent surveillance-broadcast system and the reference source data for comparison and consistency analysis, and constructing a comprehensive evaluation index system for credibility of ADS-B data of the automatic dependent surveillance-broadcast system, which specifically includes 3 criterion-level indicators and 8 factor-level indicators, wherein the 3 criterion-level indicators are parameter consistency B1 with the S-mode secondary radar, parameter consistency B2 with the A / C-mode secondary radar, and parameter consistency B3 with the flight plan, and the 8 factor-level indicators are S-mode 24-bit address code consistency C1, S-mode flight number consistency C2, S-mode secondary code consistency C3, S-mode position information consistency C4; A / C mode secondary code consistency C5, A / C mode position information consistency C6; flight plan flight number consistency C7, and planned route and target position consistency C8; the indicators are divided into two categories: quantitative indicators and qualitative indicators;

[0011] Step 1-2: The position information is a quantitative indicator. For the quantitative indicator, the relative error of the comparison data is used to represent the consistency of the two sets of data. The position information of the reference source and the ADS-B data are matched in pairs, and the relative error E of the longitude of the comparison data group is calculated respectively. lat , relative error of latitude E lon , relative error of height E h :

[0012]

[0013]

[0014]

[0015] Where n represents the dimension of the data set, lat ref ,lon ref 、h ref are the longitude, latitude and altitude values ​​of the reference source data, lat ADS-B ,lon ADS-B 、h ADS-B They are the longitude, latitude and altitude values ​​of the Automatic Dependent Surveillance-Broadcast system ADS-B data respectively;

[0016] Then calculate the mean value C i As an indicator of consistency:

[0017] C i =(E lat +E lon +E h ) / 3 (4)

[0018] Applicable factor-level indicators include C4, C6, and C8;

[0019] Steps 1-3, for qualitative indicators, use a scoring method to match the reference source with the ADS-B data in pairs. In a pair of compared data, the code value that is consistent is scored as 1 point, the highest score is equal to the number of code bits, and the lowest score is 0. If N pairs of data are compared, the average score Qci of the N pairs of data is calculated as the consistency index Ci. Applicable factor layer indicators include C1, C2, C3, C5, and C7.

[0020] Step 2 includes the following steps:

[0021] Step 2-1, according to the 1-9 scaling method, the relative importance of the criterion layer indicators B1, B2, B3, and the factor layers C1, C2, C3, C4, C5, C6, C7, and C8 are judged, and the judgment matrix P1 of the target layer A (i.e., the credibility of the broadcast automatic dependent surveillance system ADS-B data) relative to the criterion layers B1, B2, and B3, the judgment matrix P2 of the criterion layer B1 relative to the factor layers C1, C2, C3, and C4, the judgment matrix P3 of the criterion layer B2 relative to the factor layers C5 and C6, and the judgment matrix P4 of the criterion layer B3 relative to the factor layers C7 and C8 are constructed, and the corresponding weight vectors (i.e., eigenvectors) and the maximum eigenroot are calculated;

[0022] For P1:

[0023] The target layer A is relative to the criterion layers B1, B2, and B3. B1 is more important than B2 and more important than B3, and the scale values ​​are set to 3 and 7; while B2 is more important than B3, and the scale value is 5, so the judgment matrix P1 is obtained:

[0024]

[0025] The sum method is used to calculate the approximate eigenvector and the approximate maximum eigenroot, that is, first normalize each column to obtain the column normalized matrix of P1

[0026]

[0027] Then sum each row to get The row sum matrix

[0028]

[0029] Continue normalizing to get the weight vector w:

[0030]

[0031] Continue to calculate P1w:

[0032]

[0033] Finally, the maximum characteristic root λ of P1 is obtained max :

[0034]

[0035] where w i is the i-th value of w;

[0036] Step 2-2, calculate the consistency ratio CR of the matrix. When CR<0.1, it is determined that the judgment matrix passes the consistency test, and the obtained weight coefficient is logically acceptable. The specific calculation formula is:

[0037]

[0038] Among them, CI is the consistency index of the judgment matrix; λ max is the maximum eigenvalue of the matrix; n is the dimension of the judgment matrix; RI is the average random consistency index;

[0039] Step 2-3, multiply the weight vector of the judgment matrix P1 by the weight vectors of P2, P3, and P4 respectively, and calculate the comprehensive weights of the factor layer indicators C1, C2, C3, C4, C5, C6, C7, and C8, and then obtain the comprehensive weight ranking of all indicator elements, and obtain the comprehensive weight vector W of each factor layer indicator in the comprehensive evaluation of the credibility of the broadcast automatic dependent surveillance system ADS-B data:

[0040] W=(w1,w2,w3,w4,w5,w6,w7,w8)=(0.1932,0.0370,0.0793,0.3396,0.0930,0.1860,0.0240,0.0479).

[0041] Among them, w1, w2, w3, w4, w5, w6, w7, and w8 are the comprehensive weight values ​​of factor layer indicators C1, C2, C3, C4, C5, C6, C7, and C8 respectively.

[0042] In step 2, the weight values ​​of the three criterion layer indicators (i.e., the values ​​of the weight vector of the judgment matrix P1) are from high to low: parameter consistency of the S-mode secondary radar, parameter consistency with the A / C-mode secondary radar, and parameter consistency with the flight plan.

[0043] In step 2, the comprehensive weight values ​​of the eight criterion layer indicators (i.e., the value of the product of the weight vector of the judgment matrix P1 and the weight vectors of P2, P3, and P4 respectively) are from high to low: S mode position information, S mode 24-bit address code, A / C mode position information, A / C mode secondary code, S mode secondary code, planned route and target position, S mode flight number, and flight plan flight number.

[0044] Step 3 includes the following steps:

[0045] Step 3-1, perform fuzzy processing on each indicator element and construct the membership function:

[0046]

[0047]

[0048]

[0049]

[0050] Where x is the measured value of the indicator element; the meanings of x1, x2, x3, and x4 are: given a reasonable range of values ​​for the measured value x, it is equally divided into five value ranges: […, x1], (x1, x2), (x2, x3), (x3, x4), and [x4,…]. The measured value x is only distributed in the five value ranges. At this time, f(x) I Characterizes the membership of the measured value x in the range […,x2); f(x) II Characterizes the membership of the measured value x in the value range (x1, x3); f(x) III Characterizes the membership of the measured value x in the value range (x2, x4); f(x) IV It represents the membership of the measured value x in the range (x3,…];

[0051] Step 3-2, substitute the measured values ​​of factor layer indicators C1, C2, C3, C4, C5, C6, C7, and C8 into equations (6) to (9), and calculate their membership within the reasonable value range respectively, and then calculate the fuzzy vector R of each factor layer indicator score i for:

[0052] R i =(f(C i ) I ,f(C i ) II ,f(C i ) III ,f(c i ) IV ),i=1,2,3,4,5,6,7,8 (10)

[0053] And construct the rank evaluation matrix R containing the fuzzy vectors of all indicator elements;

[0054] Step 3-3, calculate the comprehensive credibility C of the automatic dependent surveillance-broadcast system ADS-B data after comprehensively considering various factors.

[0055] In step 3-2, the rating matrix R is:

[0056] R=(R1,R2,R3,R4,R5,R6,R7,R8) T (11)

[0057] Where T represents the matrix transpose.

[0058] In step 3-3, the comprehensive credibility C of the ADS-B data is calculated by the following formula:

[0059] C=W·R=(w1,w2,w3,w4,w5,w6,w7,w8)·(R1,R2,R3,R4,R5,R6,R7,R8) T (12).

[0060] The present invention also provides an electronic device, comprising a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the steps of the described method.

[0061] The present invention also provides a storage medium storing a computer program or instruction. When the computer program or instruction is run on a computer, the steps of the method described are executed.

[0062] Compared with the prior art, the present invention has the following significant advantages: (1) The reference sources for comprehensive credibility evaluation of ADS-B data come from S-mode radar, A / C-mode radar, and flight plan, all of which have high credibility and can be used to evaluate the credibility of ADS-B data. (2) The index elements for comprehensive credibility evaluation of ADS-B data are relatively easy to obtain and relatively easy to match in pairs. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more clear.

[0064] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0065] like Figure 1 As shown, an embodiment of the present invention provides an ADS-B data credibility evaluation method based on a radar reference source, comprising the following steps:

[0066] Step 1: Define the consistency index of ADS-B data based on secondary radar reference source by "qualitative + quantitative" method, and build an ADS-B data credibility evaluation index system including 3 criterion layer indicators and 8 factor layer indicators; specifically include:

[0067] Step 1-1, using three types of information sources such as S-mode radar, A / C-mode radar, and flight plan as reference sources for the credibility evaluation of ADS-B data, selecting common data between ADS-B data and reference source data for comparison and consistency analysis, and constructing a comprehensive evaluation index system for the credibility of ADS-B data as shown in Table 1. There are two types of indicators, quantitative and qualitative, so different calculation methods are required.

[0068] Table 1

[0069]

[0070]

[0071] Step 1-2, for quantitative indicators, the relative error of the control data is used to represent the consistency of the two sets of data. In this study, the main information is location information, so the location information of the reference source and the broadcast automatic dependent surveillance system ADS-B data are matched in pairs, and the relative errors of longitude, latitude, and altitude of the comparison data group are calculated respectively: Elat, Elon, and Eh:

[0072]

[0073]

[0074]

[0075] Where n represents the dimension of the data set, lat ref ,lon ref 、h ref are the longitude, latitude and altitude values ​​of the reference source data, lat ADS-B ,lon ADS-B 、h ADS-B They are the longitude, latitude and altitude values ​​of the Automatic Dependent Surveillance-Broadcast system ADS-B data respectively;

[0076] Then calculate the average of the three as the consistency indicator:

[0077] C i =(E lat +E lon +E h ) / 3 (4)

[0078] Applicable factor-level indicators include consistency of position information (S mode) C4, consistency of position information (A / C mode) C6, and consistency of planned route and target position C8.

[0079] Step 1-3, for qualitative indicators, use the scoring method to match the reference source with the ADS-B data in pairs. In a pair of comparison data, the code value is consistent with 1 point (the highest score is equal to the number of code bits, the lowest is 0 points); if N pairs of data are compared, the average score Qci of the N pairs of data is calculated as the consistency index Ci. Applicable factor layer indicators include the consistency C1 of the 24-bit address code (S mode), the consistency C2 of the flight number (S mode), the consistency C3 of the secondary code (S mode), the consistency C5 of the secondary code (A / C mode), and the consistency C7 of the flight number (flight plan).

[0080] Step 2: Calculate the comprehensive weights of the three criterion-level indicators and the eight factor-level indicators, and sort them to obtain a comprehensive weight vector;

[0081] According to the 1-9 scaling method (see Table 2), the judgment matrix of the target layer A and the criterion layer B1 / B2 / B3 is constructed respectively, and the corresponding weights are calculated by the maximum eigenvalue method. In order to ensure the logical correctness of the judgment matrix when comparing each factor pairwise, the judgment matrix needs to be checked for consistency.

[0082] Table 2

[0083] Scale value meaning 1 The two factors are equally important. 3 Compared with the two factors, the former is slightly more important than the latter. 5 Compared with the two factors, the former is obviously more important than the latter. 7 Compared with the two factors, the former is more important than the latter. 9 Compared with the two factors, the former is extremely more important than the latter. 2,4,6,8 The importance is between the two adjacent evaluation scales mentioned above

[0084] Step 2-1, by judging the relative importance of the criterion layers B1, B2, B3 and the factor layers C1, C2, C3, C4, C5, C6, C7, C8, a total of four judgment matrices are constructed, namely, the judgment matrix P1 of the target layer A relative to the criterion layers B1, B2, B3, the judgment matrix P2 of the criterion layer B1 relative to the factor layers C1, C2, C3, C4, the judgment matrix P3 of the criterion layer B2 relative to the factor layers C5 and C6, and the judgment matrix P4 of the criterion layer B3 relative to the factor layers C7 and C8, as shown in Tables 3 to 6.

[0085] Table 3

[0086]

[0087] Table 4

[0088]

[0089]

[0090] Table 5

[0091]

[0092] Table 6

[0093]

[0094] Step 2-2, using the commonly used judgment matrix consistency test method, that is, calculating the consistency ratio CR of the matrix. When CR<0.1, it is considered that the judgment matrix passes the consistency test and the obtained weight coefficient is logically acceptable. The specific calculation formula is as follows:

[0095]

[0096] Among them, CI is the consistency index of the judgment matrix; λ max is the maximum eigenvalue of the matrix; n is the dimension of the judgment matrix; RI is the average random consistency index, and its value is shown in Table 7.

[0097] Table 7

[0098] n 1 2 3 4 5 6 7 8 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41

[0099] Step 2-3, sort according to the calculated weights, and obtain the weight ranking of all indicator elements as shown in Table 8.

[0100] Table 8

[0101]

[0102] Finally, the weight vector W of each factor in the comprehensive evaluation of the credibility of ADS-B data is obtained:

[0103] W=(w1,w2,w3,w4,w5,w6,w7,w8)=(0.1932,0.0370,0.0793,0.3396,

[0104] 0.0930,0.1860,0.0240,0.0479)

[0105] Step 3: construct the membership function of each indicator element, obtain the grade evaluation matrix containing the fuzzy vectors of all indicator elements, and calculate the dot product of the comprehensive weight vector and the grade evaluation matrix to obtain the comprehensive credibility of the ADS-B data. Specifically, it includes the following steps:

[0106] Step 3-1, fuzzy processing is performed on each index element to construct a membership function. In the present invention, a trapezoidal distribution membership function is selected for construction, which is defined as:

[0107]

[0108]

[0109]

[0110]

[0111] Among them, x is the measured value of the indicator element; x1, x2, x3, and x4 are the cutoff values ​​of each indicator element, see Table 9. The higher the membership level, the better the data consistency.

[0112] Table 9

[0113]

[0114] Step 3-2: Calculate the fuzzy vector R of each evaluation index score using the above formula i for:

[0115] R i =(f(C i ) I ,f(C i ) II ,f(C i ) III ,f(C i ) IV ) (10)

[0116] And construct the grading evaluation matrix R containing the fuzzy vectors of all indicator elements:

[0117] R=(R1,R2,R3,R4,R5,R6,R7,R8) T (11)

[0118] Step 3-3, calculate the comprehensive credibility C of the ADS-B data after comprehensive consideration of various factors:

[0119] C=W·R=(w1,w2,w3,w4,w5,w6,w7,w8)·(R1,R2,R3,R4,R5,R6,R7,R8) T (12)

[0120] In the embodiment of the present invention, a set of ADS-B data is given. After consistency analysis, the consistency index values ​​of the data with the 24-bit address code, flight number, secondary code, and location information of the S-mode radar are 20, 5.2, 3.5, and 0.80, respectively; the consistency index values ​​of the data with the secondary code and location information of the A / C-mode radar are 3.2 and 0.90, respectively; the consistency index values ​​of the data with the flight number, planned route, and target location of the flight plan are 5.1 and 0.70, respectively. Substituting them into equations (6) to (8), the grade evaluation matrix of the data is calculated as follows:

[0121]

[0122] Then we get C=W·R=(0 0 0.0383 0.3113). At this time, according to the maximum membership principle, the consistency index of this group of data belongs to level IV and the comprehensive evaluation is excellent, that is, it is considered that the credibility of this group of automatic dependent surveillance-broadcast system ADS-B data is high. In fact, the consistency of this group of data with various indicators is indeed good, and the reliability of the evaluation method has been verified.

[0123] The present invention provides a method for evaluating the credibility of ADS-B data based on a radar reference source. There are many methods and approaches to implement the technical solution. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. A method for evaluating the credibility of ADS-B data based on a radar reference source, characterized in that: The following steps are involved: Step 1: define the consistency index of ADS-B data based on secondary radar reference source, and construct the ADS-B data credibility evaluation index system including criterion layer index and factor layer index; Step 2, calculate the comprehensive weights of the criterion layer indicators and the factor layer indicators, and sort them to obtain a comprehensive weight vector; Step 3: construct the membership function of each indicator element, obtain the grade evaluation matrix containing the fuzzy vectors of all indicator elements, and calculate the dot product of the comprehensive weight vector and the grade evaluation matrix to obtain the comprehensive credibility of the ADS-B data.

2. The method according to claim 1, characterized in that Step 1 includes the following steps: Step 1-1, using the three types of information sources, namely, S-mode radar, A / C-mode radar, and flight plan, as reference sources for credibility evaluation of ADS-B data of the automatic dependent surveillance-broadcast system, selecting common data in the ADS-B data of the automatic dependent surveillance-broadcast system and the reference source data for comparison and consistency analysis, and constructing a comprehensive evaluation index system for credibility of ADS-B data of the automatic dependent surveillance-broadcast system, which specifically includes 3 criterion-level indicators and 8 factor-level indicators, wherein the 3 criterion-level indicators are parameter consistency B1 with the S-mode secondary radar, parameter consistency B2 with the A / C-mode secondary radar, and parameter consistency B3 with the flight plan, and the 8 factor-level indicators are S-mode 24-bit address code consistency C1, S-mode flight number consistency C2, S-mode secondary code consistency C3, S-mode position information consistency C4; A / C mode secondary code consistency C5, A / C mode position information consistency C6; flight plan flight number consistency C7, and planned route and target position consistency C8; the indicators are divided into two categories: quantitative indicators and qualitative indicators; Step 1-2: The position information is a quantitative indicator. For the quantitative indicator, the relative error of the comparison data is used to represent the consistency of the two sets of data. The position information of the reference source and the ADS-B data are matched in pairs, and the relative error E of the longitude of the comparison data group is calculated respectively. lat , relative error of latitude E lon , relative error of height E h : Where n represents the dimension of the data set, lat ref ,lon ref 、h ref are the longitude, latitude and altitude values ​​of the reference source data, lat ADS-B ,lon ADS-B 、h ADS-B They are the longitude, latitude and altitude values ​​of the Automatic Dependent Surveillance-Broadcast system ADS-B data respectively; Then calculate the mean value C i As an indicator of consistency: C i =(And lat +E lon +E h ) / 3 (4) Applicable factor-level indicators include C4, C6, and C8; Steps 1-3, for qualitative indicators, use a scoring method to match the reference source with the ADS-B data in pairs. In a pair of compared data, the code value that is consistent is scored as 1 point, the highest score is equal to the number of code bits, and the lowest score is 0. If N pairs of data are compared, the average score Qci of the N pairs of data is calculated as the consistency index Ci. Applicable factor layer indicators include C1, C2, C3, C5, and C7.

3. The method according to claim 2, characterized in that Step 2 includes the following steps: Step 2-1, according to the 1-9 scaling method, the relative importance of the criterion layer indicators B1, B2, B3, and the factor layer C1, C2, C3, C4, C5, C6, C7, and C8 is judged, and the judgment matrix P1 of the target layer A relative to the criterion layer B1, B2, and B3, the judgment matrix P2 of the criterion layer B1 relative to the factor layer C1, C2, C3, and C4, the judgment matrix P3 of the criterion layer B2 relative to the factor layer C5 and C6, and the judgment matrix P4 of the criterion layer B3 relative to the factor layer C7 and C8 are constructed, and the corresponding weight vectors and the maximum characteristic root are calculated; For P1: The target layer A is relative to the criterion layers B1, B2, and B3. B1 is more important than B2 and more important than B3, and the scale values ​​are set to 3 and 7; while B2 is more important than B3, and the scale value is 5, so the judgment matrix P1 is obtained: The sum method is used to calculate the approximate eigenvector and the approximate maximum eigenroot, that is, first normalize each column to obtain the column normalized matrix of P1 Then sum each row to get The row sum matrix Continue normalizing to get the weight vector w: Continue to calculate P1w: Finally, the maximum characteristic root λ of P1 is obtained max : where w i is the i-th value of w; Step 2-2, calculate the consistency ratio CR of the matrix. When CR<0.1, it is determined that the judgment matrix passes the consistency test, and the obtained weight coefficient is logically acceptable. The specific calculation formula is: Among them, CI is the consistency index of the judgment matrix; λ max is the maximum eigenvalue of the matrix; n is the dimension of the judgment matrix; RI is the average random consistency index; Step 2-3, multiply the weight vector of the judgment matrix P1 by the weight vectors of P2, P3, and P4 respectively, and calculate the comprehensive weights of the factor layer indicators C1, C2, C3, C4, C5, C6, C7, and C8, and then obtain the comprehensive weight ranking of all indicator elements, and obtain the comprehensive weight vector W of each factor layer indicator in the comprehensive evaluation of the credibility of the broadcast automatic dependent surveillance system ADS-B data: W=(w1,w2,w3,w4,w5,w6,w7,w8)=(0.1932,0.0370,0.0793,0.3396,0.0930,0.1860,0.0240,0.0479) Among them, w1, w2, w3, w4, w5, w6, w7, and w8 are the comprehensive weight values ​​of factor layer indicators C1, C2, C3, C4, C5, C6, C7, and C8 respectively.

4. The method according to claim 3, characterized in that: In step 2, the weight values ​​of the three criterion layer indicators are from high to low: parameter consistency of S-mode secondary radar, parameter consistency with A / C-mode secondary radar, and parameter consistency with the flight plan.

5. The method according to claim 4, characterized in that In step 2, the comprehensive weight values ​​of the eight criterion layer indicators are from high to low: S mode position information, S mode 24-bit address code, A / C mode position information, A / C mode secondary code, S mode secondary code, planned route and target position, S mode flight number, and flight plan flight number.

6. The method according to claim 5, characterized in that Step 3 includes the following steps: Step 3-1, perform fuzzy processing on each indicator element and construct a membership function: Where x is the measured value of the indicator element; the meanings of x1, x2, x3, and x4 are: given a reasonable range of values ​​for the measured value x, it is equally divided into five value ranges: […, x1], (x1, x2), (x2, x3), (x3, x4), and [x4,…]. The measured value x is only distributed in the five value ranges. At this time, f(x) I Characterizes the membership of the measured value x in the range […,x2); f(x) II Characterizes the membership of the measured value x in the value range (x1, x3); f(x) III Characterizes the membership of the measured value x in the value range (x2, x4); f(x) IV It represents the membership of the measured value x in the range of (x3,…]; Step 3-2, substitute the measured values ​​of factor layer indicators C1, C2, C3, C4, C5, C6, C7, and C8 into equations (6) to (9), and calculate their membership within the reasonable value range respectively, and then calculate the fuzzy vector R of each factor layer indicator score i for: R i =(f(C i ) I ,f(C i ) II ,f(C i ) III ,f(c i ) IV ),i=1,2,3,4,5,6,7,8 (10) And construct the rank evaluation matrix R containing the fuzzy vectors of all indicator elements; Step 3-3, calculate the comprehensive credibility C of the automatic dependent surveillance-broadcast system ADS-B data after comprehensively considering various factors.

7. The method according to claim 6, characterized in that In step 3-2, the rating matrix R is: R=(R1,R2,R3,R4,R5,R6,R7,R8) T (11) Where T represents the matrix transpose.

8. The method according to claim 7, characterized in that In step 3-3, the comprehensive credibility C of the ADS-B data is calculated by the following formula: <h2 style=";text-align:left;direction:ltr">C = W R = (w1, w2, w3, w4, w5, w6, w7, w8) (R1, R2, R3, R4, R5, R6, R7, R8)<h2 style=";text-align:left;direction:ltr"> T <h2 style=";text-align:left;direction:ltr"> (12) 9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores program codes, and when the program codes are executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 8.

10. A storage medium, characterized in that: A computer program or instruction is stored, and when the computer program or instruction is run on a computer, the steps of the method according to any one of claims 1 to 8 are executed.

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