Electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process

Abstract

This invention relates to an electromagnetic signal target threat assessment method based on dynamic evidence theory and the analytic hierarchy process (AHP), comprising: acquiring an electromagnetic signal to be assessed in real time; using the AHP, determining multiple threat weights corresponding to each other based on multiple threat assessment indicators, wherein the values ​​of the multiple threat assessment indicators are obtained from the electromagnetic signal to be assessed; calculating the multiple threat weights using a dynamic BPA generation method to obtain a threat probability value corresponding to each threat assessment indicator; weighting and fusing the threat probability values ​​corresponding to the multiple threat assessment indicators to obtain a comprehensive threat probability value; and outputting the threat assessment result corresponding to the electromagnetic signal to be assessed based on the comprehensive threat probability value. This method can effectively identify and assess the threat level of electromagnetic signals, providing an efficient and accurate assessment means for electromagnetic security.

Description

Technical Field

[0001] This invention belongs to the field of radar communication technology, specifically relating to an electromagnetic signal target threat assessment method based on dynamic evidence theory and hierarchical analysis. Background Technology

[0002] With the rapid advancement of smart city construction, the electromagnetic signal environment in key urban areas, including but not limited to international airports, government office areas, large sports venues, and financial centers, is exhibiting a significant trend towards high dynamism and increasing complexity. In typical urban security scenarios, the target airspace may simultaneously contain three core civilian signals: walkie-talkie communication signals, Wi-Fi / Bluetooth hotspot signals, and cellular mobile communication signals, as well as potential threat sources such as illegal fake base station signals, unauthorized spectrum occupation signals, and covert information theft signals. All of these signals pose potential security threats to the electromagnetic airspace of the security area.

[0003] However, existing threat signal assessment systems suffer from superficial data analysis and struggle to simultaneously achieve real-time performance and accuracy, severely hindering emergency response effectiveness. Summary of the Invention

[0004] To address the aforementioned problems in existing technologies, this invention provides an electromagnetic signal target threat assessment method based on dynamic evidence theory and the analytic hierarchy process (AHP). The technical problem to be solved by this invention is achieved through the following technical solution:

[0005] This invention provides a method for assessing electromagnetic signal target threats based on dynamic evidence theory and the analytic hierarchy process, comprising:

[0006] An electromagnetic signal to be evaluated is acquired in real time; using the Analytic Hierarchy Process (AHP), multiple threat weights are determined based on multiple threat assessment indicators, the values ​​of which are obtained from the electromagnetic signal to be evaluated; using a dynamic BPA generation method, the multiple threat weights are calculated to obtain the threat probability value corresponding to each threat assessment indicator; the threat probability values ​​corresponding to the multiple threat assessment indicators are weighted and fused to obtain a comprehensive threat probability value; based on the comprehensive threat probability value, the threat assessment result corresponding to the electromagnetic signal to be evaluated is output.

[0007] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0008] To address the shortcomings of existing threat signal assessment systems, such as superficial data analysis and difficulty in simultaneously achieving real-time performance and accuracy, this invention provides an electromagnetic signal target threat assessment method based on dynamic evidence theory and the analytic hierarchy process (AHP), which offers significant technical advantages. First, the AHP determines the weights of multiple threat assessment indicators, comprehensively considering factors such as relative distance, signal identity, signal center frequency, signal duration, and signal modulation method to ensure the comprehensiveness and accuracy of the assessment. Second, a dynamic BPA generation method is used to calculate the threat probability value corresponding to each threat assessment indicator, adapting to complex and changing electromagnetic environments and improving the real-time performance and adaptability of the assessment. Finally, by weighted fusion of multiple threat probability values, a comprehensive threat probability value is obtained and the threat assessment result is output, providing scientific and reliable decision support. This method can effectively identify and assess the threat level of electromagnetic signals, providing an efficient and accurate assessment tool for electromagnetic security. Attached Figure Description

[0009] Figure 1 This is a flowchart of the electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process provided in this embodiment of the invention;

[0010] Figure 2 This is a schematic diagram of the structure of AHP provided in an embodiment of the present invention;

[0011] Figure 3 This is a schematic diagram of an AHP model based on multiple threat assessment indicators provided in an embodiment of the present invention;

[0012] Figure 4 This is a comparison chart showing the threat index assessment of walkie-talkie signals using existing threat signal assessment systems and the electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process provided by this invention. Detailed Implementation

[0013] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0014] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0015] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0016] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings, disclosure, and appended claims in carrying out the claimed invention. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0017] The electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process proposed in this invention will now be described in detail with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart of an electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process provided in an embodiment of the present invention. Figure 1 As shown, the method includes steps 110-150. Specifically:

[0019] Step 110: Acquire an electromagnetic signal to be evaluated in real time.

[0020] For example, a search is conducted within a security area (radius of 5000) with a step size of 10 meters and a signal acquisition time of 1 second. Within the current period, one electromagnetic signal to be evaluated is acquired. After receiving the electromagnetic signal to be evaluated, steps 120-150 are used to analyze the evaluation signal.

[0021] Step 120: Using the Analytic Hierarchy Process (AHP), based on multiple threat assessment indicators, determine multiple threat weights that correspond one-to-one. The values ​​of the multiple threat assessment indicators are obtained from the electromagnetic signals to be assessed.

[0022] Here, we will first introduce the Analytic Hierarchy Process (AHP). AHP is an analytical method that integrates qualitative and quantitative approaches. It provides a more efficient estimation tool based on human thinking patterns. It performs well in scenarios involving complex problems with multiple objectives, influencing factors, and criteria, thereby improving the reliability, feasibility, and effectiveness of decision-making. AHP aims to divide complex objectives into several independent parts and, based on this, establish a hierarchical structure to better estimate the importance of each part, thus determining the final decision. Complex decision problems often involve multidimensional objectives and fuzzy nonlinear relationships. Directly comparing all factors on the same plane can easily lead to information clutter and confused weight allocation. The hierarchical structure generally includes three layers: the objective layer, the criteria layer, and the alternative layer. The objective layer defines the goals of the problem, anchoring the decision boundary and avoiding divergence in the analysis process. The criteria layer transforms abstract objectives into operational evaluation dimensions by building an evaluation index system. The alternative layer compares the advantages and disadvantages of specific strategies under given criteria, such as... Figure 2 As shown. After the hierarchical structure was constructed, a judgment matrix was built for each type of indicator. Expert experience was used to accurately measure the relative importance between different indicators, and quantitative analysis was performed using the TL Statyd 1-9 proportional scaling method. The analytic hierarchy process (AHP) basically divides the importance levels into five different levels: equally important, slightly important, significantly important, strongly important, and extremely important, and assigns quantitative values ​​of 1, 3, 5, 7, and 9 respectively; four other items fall between the five importance levels and are assigned quantitative values ​​of 2, 4, 6, and 8. For clarity, Table 1 illustrates the importance levels in the AHP.

[0023] Table 1

[0024]

[0025] Here, step 120 specifically includes: ranking multiple threat assessment indicators according to their importance to obtain ranked threat assessment indicators, wherein the multiple threat assessment indicators include: relative distance, signal identity, signal center frequency, signal duration, and signal modulation method; establishing an electromagnetic signal threat assessment template based on the ranked threat assessment indicators; constructing a judgment matrix based on the electromagnetic signal threat assessment template, wherein the number of rows and columns of the judgment matrix is ​​the total number of the multiple threat assessment indicators; calculating the judgment matrix to obtain the corresponding feature vector, and using the feature vector as multiple threat weights.

[0026] Here, obtaining the signal to be evaluated allows us to obtain the specific content of the aforementioned threat assessment indicators. The reason for choosing relative distance, signal identity, signal center frequency, signal duration, and signal modulation method as threat assessment indicators is that signals close to the protected area require focused screening. The signal center frequency directly relates to frequency band compliance and the legitimate use of public resources. For example, detecting a sudden high-power 5.8GHz signal in the 2.4GHz civilian WiFi band suggests unauthorized drone image transmission equipment illegally occupying the frequency band, significantly increasing the threat level. Signal duration can determine the presence of illegal eavesdropping or location tracking devices. Signal identity typically includes three categories: abnormal signals, unknown signals, and whitelisted signals; signal fingerprints can distinguish the legitimacy of devices. Signal modulation method can infer device functionality. For example, OFDM modulation is used for regular WiFi, while sudden frequency-hopping FSK modulation suggests malicious behavior. Furthermore, more complex modulation methods also represent a higher degree of signal processing complexity, requiring appropriate attention to their threat level.

[0027] It should be noted that threat assessment indicators can also include signal bandwidth and signal reception power. This is because broadband signals may occupy public communication resources, disrupting normal services within the area. For example, a malicious interference router's continuous frequency sweep signal across the WiFi band with a bandwidth of 40MHz may be detected. Furthermore, abnormally high-power signals may indicate the presence of high-power jamming devices, which could negatively impact the area's electromagnetic security.

[0028] An AHP model is built using multiple threat assessment metrics, such as Figure 3 As shown in Table 1, multiple threat assessment indicators were ranked in descending order of threat level, in the following order: relative distance, signal identity, center frequency, duration, and modulation method. Subsequently, the importance of the above five indicators was compared pairwise according to the quantification scale in Table 1 to obtain the electromagnetic signal threat assessment template, and the specific results are shown in Table 2.

[0029] Table 2

[0030] relative distance Duration Center frequency Signal Identity Modulation method relative distance 1 5 4 2 6 Duration 1 / 5 1 4 / 5 2 / 5 6 / 5 Center frequency 1 / 4 5 / 4 1 1 / 2 6 / 4 Signal Identity 1 / 2 5 / 2 2 1 6 / 2 Modulation method 1 / 6 5 / 6 4 / 6 2 / 6 1

[0031] Based on Table 2, a judgment matrix is ​​established to determine the importance of each pair of adjacent elements. When one indicator is more important than another, its relative importance quantification value is greater than 1. When one indicator is less important than another, its relative importance quantification value is less than 1; furthermore, the values ​​in the diagonal squares should be reciprocals of each other.

[0032] In one possible implementation, the judgment matrix J1 is represented as:

[0033]

[0034] After calculation, the largest eigenvalue λ of the judgment matrix J1 was obtained. max =4.999, and then using the largest eigenvalue λ max After standardization, the standardized feature vector is obtained as v = [0.47, 0.09, 0.12, 0.23, 0.08]. T The standardized feature vector also represents the threat weight for each threat assessment metric. For example, the threat weight for relative distance is 0.47, and the threat weight for modulation method is 0.08.

[0035] After calculating the threat probability value corresponding to each threat assessment indicator, a hierarchical consistency check is required on the weight allocation results to ensure the logical consistency of expert judgments and the objectivity and rationality of model construction. This step aims to verify whether the pairwise comparison scales assigned by expert experience are mathematically consistent. If a logical loop of "A is better than B and B is better than C, but C is better than A" appears, it indicates that the expert's judgment may be due to cognitive bias. In this case, it is necessary to backtrack and adjust the assignment parameters of the judgment matrix until the test indicators meet the acceptable threshold before proceeding to the final global weight synthesis stage. The specific steps are as follows:

[0036] Step 1: Calculate the Consistency Index (CI): Where, λ max Let CI represent the largest eigenvalue of the judgment matrix C, and n represent the number of threat indicators. The closer CI is to zero, the more the judgment matrix C meets the test requirements. However, relying solely on the value CI as a standard for satisfactory consistency is insufficient. To estimate the magnitude of CI, this invention introduces the average random consistency index to measure the reliability of the results. The average random consistency index RI is shown in Table 3 for n = 1, 2, ..., 10.

[0037] Table 3

[0038]

[0039] Step 2: Calculate the consistency ratio (CR): Wherein, CR represents the random consistency ratio. If CR≤0.1, the consistency of the judgment matrix can be considered to have met the requirements, that is, it is considered to have satisfactory consistency, and the current result is accepted. If CR>0.1, the judgment matrix needs to be readjusted and a consistency check needs to be performed to ensure that it meets the consistency requirements.

[0040] Based on Step 1 and Step 2, the calculated value of CI is 0.0351 and the value of CR is 0.0313. Since CR < 0.10, the consistency of the judgment matrix A is acceptable. That is, the threat weights for relative distance, signal identity, center frequency, duration, and modulation method are 0.47, 0.09, 0.12, 0.23, and 0.08, respectively.

[0041] Step 130: Using the dynamic BPA generation method, calculate multiple threat weights to obtain the threat probability value corresponding to each threat assessment indicator.

[0042] Before introducing the specific steps for generating the BPA probabilistic assignment function, we first need to introduce the concept of a Frame of Discernment. The Frame of Discernment is defined as follows: Suppose there is a decision problem to be addressed. We can define the possible outcomes of this problem as a set space Θ. All subsets contained in this space represent any proposition of the problem we are interested in. Assume the set space Θ = {θ1, θ2, ..., θ...} n} is a non-empty finite set consisting of n elements. The power set of Θ is 2. Θ It can be defined as: By setting the recognition frame as Θ={θ1,θ2,…,θ n}, and based on cognitive experience, select evaluation schemes for each indicator element: D1, D2, ..., D N D i ∈2 Θ (i = 1, ..., N). The evaluation and identification framework Θ under different factors are compared, and the appropriateness of each evaluation is quantified. Assume that for evaluation D... i The quantization value is r i The results of the comparison are used to form a knowledge matrix.

[0043] In one possible implementation, the knowledge matrix can be represented as:

[0044]

[0045] Among them, R k This represents the knowledge matrix constructed for the k-th evaluation element. Each row and column of the matrix represents the evaluation scheme formed for the k-th evaluation element, where "1" represents a comparison between the scheme and itself, and "0" represents no comparison. k r represents the weight value of the i-th evaluation element after AHP calculation. nk The suitability is taken from Table 4.

[0046] Table 4

[0047]

[0048] By calculating the knowledge matrix R k The largest eigenvalue and its corresponding eigenvector, and the normalized eigenvector I k This represents the basic probability assignment m(X) for each set of evaluation schemes. For the determined (N+1)×(N+1) knowledge matrix, let λ be the largest eigenvalue. N+1(max) The corresponding normalized feature vector (fundamental probability assignment) is (m1, m2, ..., m N ,m N+1 If ), then its calculation formula can be expressed as:

[0049]

[0050] Here, based on the above description, the identification framework is first set as Θ={H,M,L}, where H, M, and L represent the high, medium, and low threats that the threat signal may pose to us. The evaluation schemes selected in this invention example are: {H}, {M}, {L}, {H,M}, and {M,L}.

[0051] It should be noted that the calculation methods for threat probability values ​​differ depending on the threat assessment indicator. Specifically, the calculation method for threat probability values ​​corresponding to relative distance for threat assessment indicators differs from the calculation methods for other threat assessment indicators. This is because, assuming a security area is defined as having a core importance zone within a 500-meter radius of the center point, when constructing the BPA probability assignment for signal distance, we define the threat probability value for distances less than or equal to 500 meters as: m(H) = 6 × v1 / (6 × v1 + 1). If the signal appears 390 meters from the center point after 5 seconds and then 180 meters after another 5 seconds, using the traditional DS / AHP method would result in the threat value and threat level of these three locations remaining unchanged, potentially interfering with command personnel's decision-making. Therefore, a detailed explanation of the situation when the threat assessment indicator is based on relative distance is necessary.

[0052] When the threat assessment indicator is relative distance, step 130 includes: determining the area where the electromagnetic signal to be assessed is located based on the relative distance between the electromagnetic signal to be assessed and the area to be protected. The area where the electromagnetic signal to be assessed is located includes: high-risk area, medium-high risk area, medium-risk area, medium-low risk area and low-risk area; and calculating multiple threat weights based on the area where the electromagnetic signal to be assessed is located using the dynamic BPA generation method to obtain the threat probability value corresponding to the relative distance.

[0053] Furthermore, when the area where the electromagnetic signal to be evaluated is located is a high-risk area, a medium-risk area, or a low-risk area, a first high-risk knowledge matrix, a first medium-risk knowledge matrix, or a first low-risk knowledge matrix is ​​constructed using multiple threat weights and the relative distance between the electromagnetic signal to be evaluated and the area to be protected. The maximum eigenvector of the first high-risk knowledge matrix, the first medium-risk matrix, or the first low-risk knowledge matrix is ​​obtained to obtain the first high-risk eigenvector, the first medium-risk eigenvector, or the first low-risk eigenvector. The first high-risk eigenvector, the first medium-risk eigenvector, or the first low-risk eigenvector is normalized to obtain the first threat probability value corresponding to the relative distance. The first threat probability value includes the first high-threat probability value, the first medium-threat probability value, and the first low-threat probability value.

[0054] For example, a high-risk area is defined as an area whose relative distance from the protected area is less than or equal to 300; a medium-risk area is defined as an area whose relative distance from the protected area is less than 1500 and greater than or equal to 800; and a low-risk area is defined as an area whose relative distance from the protected area is greater than or equal to 3000.

[0055] Therefore, the first low-risk knowledge matrix can be represented as: Correspondingly, the highest threat probability value can be expressed as: Here, v1 refers to the threat weight corresponding to the relative distance, with a value of 0.47.

[0056] The first type of risk knowledge matrix can be represented as: Correspondingly, the probability value of the first type of threat can be expressed as:

[0057] Furthermore, the first high-risk knowledge matrix can be represented as: Correspondingly, the highest threat probability value can be expressed as:

[0058] Here, the method further includes: when the area where the electromagnetic signal to be evaluated is located is a medium-to-high-risk area, constructing a medium-to-high-risk knowledge matrix using multiple threat weights and the relative distance between the electromagnetic signal to be evaluated and the area to be protected; obtaining the maximum eigenvector of the medium-to-high-risk knowledge matrix to obtain the corresponding second high-risk eigenvector and second medium-risk eigenvector; and performing normalization processing on the second high-risk eigenvector and the second medium-risk eigenvector respectively to obtain the second high-threat probability value and the second medium-threat probability value corresponding to the relative distance.

[0059] Specifically, medium- and high-risk areas are those located at a relative distance of less than 800 units and greater than or equal to 300 units from the area requiring protection. The medium- and high-risk knowledge matrix can be represented as follows:

[0060]

[0061] The corresponding second highest threat probability value is expressed as: And, the corresponding second threat probability value is expressed as:

[0062] Here, the method further includes: when the area where the electromagnetic signal to be evaluated is located is a medium-low risk area, constructing a medium-low risk knowledge matrix using multiple threat weights and the relative distance between the electromagnetic signal to be evaluated and the area to be protected; obtaining the maximum eigenvector of the medium-low risk knowledge matrix to obtain the corresponding third medium-risk eigenvector and second low-risk eigenvector; and performing normalization processing on the third medium-risk eigenvector and the second low-risk eigenvector respectively to obtain the third medium-threat probability value and the second low-threat probability value corresponding to the relative distance.

[0063] Specifically, low-to-medium risk areas are those located at a relative distance of less than 3000 units and greater than or equal to 1500 units from the area requiring protection. The low-to-medium risk knowledge matrix can be represented as follows:

[0064]

[0065] Here, assuming the area where the electromagnetic signal to be evaluated is located is a low-to-medium risk area, after calculating the low-to-medium risk knowledge matrix, the probability value of the third threat corresponding to the relative distance can be expressed as: The second lowest threat probability value can be expressed as:

[0066]

[0067] Here, when the threat assessment indicators are signal identity, signal center frequency, signal duration, and signal modulation method, the traditional DS / AHP method is used for calculation. The calculation formula for the threat probability value (BPA value) corresponding to each threat assessment indicator is shown in Table 5.

[0068] Table 5

[0069]

[0070] For example, when calculating the threat probability value corresponding to each threat assessment indicator, the specific values ​​are categorized according to the corresponding indicator and then substituted into the corresponding formula for calculation. Furthermore, different threat assessment indicators may correspond to different threat probability values. For example, the threat probability value corresponding to relative distance is a high threat probability value, and the threat probability value corresponding to signal modulation method is a low threat probability value.

[0071] Step 140: Weight and fuse the threat probability values ​​corresponding to multiple threat assessment indicators to obtain a comprehensive threat probability value.

[0072] Here, step 140 specifically includes: obtaining the probability value of the same type of threat, which includes high threat probability value, medium threat probability value or low threat probability value corresponding to multiple threat assessment indicators; using multiple threat weights, weighted summing with the probability value of the same type of threat to obtain a comprehensive threat probability value, which includes: high comprehensive threat probability value, medium comprehensive threat probability value or low comprehensive threat probability value.

[0073] For example, the high threat probability value corresponding to each threat assessment indicator is obtained. If a threat assessment indicator has no high threat probability value, then the high threat probability value of that indicator is set to 0. Then, the threat weight corresponding to each threat assessment indicator is used to perform a weighted summation with the threat probability value corresponding to that indicator to summarize multiple high threat probability values ​​and obtain a high comprehensive threat probability value.

[0074] Specifically, the high integrated threat probability value can be expressed as: m ave (H)=v1*m(H)+v2*s(H)+v3*p(H)+v4*q(H)+v5*r(H), where v2 is the threat weight corresponding to the signal identity, v3 is the threat weight corresponding to the signal center frequency, v4 is the threat weight corresponding to the signal duration, and v5 is the threat weight corresponding to the signal modulation method.

[0075] Furthermore, if a threat assessment indicator has no medium threat probability value, then the medium threat probability value for that indicator is set to 0. Specifically, the medium overall threat probability value can be expressed as: m ave (M)=v1*m(M)+v2*s(M)+v3*p(M)+v4*q(M)+v5*r(M); If a certain threat assessment indicator has no low threat probability value, then the corresponding low threat probability value is 0. The low comprehensive threat probability value is expressed as: m ave (L)=v1*m(L)+v2*s(L)+v3*p(L)+v4*q(L)+v5*r(L).

[0076] Step 150: Based on the comprehensive threat probability value, output the threat assessment result corresponding to the electromagnetic signal to be assessed.

[0077] Specifically, step 150 includes: obtaining the first preset weight, the second preset weight, and the third preset weight; multiplying the first preset weight by the high comprehensive threat probability value to obtain the first product, multiplying the second preset weight by the medium comprehensive threat probability value to obtain the second product, and multiplying the third preset weight by the low comprehensive threat probability value to obtain the third product; adding the first product, the second product, and the third product to obtain the comprehensive threat value corresponding to the electromagnetic signal to be evaluated; determining the corresponding threat level according to the comprehensive threat value corresponding to the electromagnetic signal to be evaluated, and outputting the comprehensive threat value and the threat level corresponding to the electromagnetic signal to be evaluated as the threat assessment result.

[0078] Exemplarily, if the first preset weight is 0.7, the second preset weight is 0.2, and the third preset weight is 0.1, the calculation formula for the comprehensive threat value corresponding to the electromagnetic signal to be evaluated can be expressed as: Threat = 0.7 × m ave (H) + 0.2 × m ave (M) + 0.1 × m ave (L).

[0079] Here, determining the corresponding threat level according to the comprehensive threat value corresponding to the electromagnetic signal to be evaluated includes: if the comprehensive threat value corresponding to the electromagnetic signal to be evaluated is less than the first threshold and greater than or equal to the second threshold, determining the threat level of the electromagnetic signal to be evaluated as a high-level threat; if the comprehensive threat value corresponding to the electromagnetic signal to be evaluated is greater than or equal to the third threshold and less than the second threshold, determining the threat level of the electromagnetic signal to be evaluated as a medium-level threat; if the comprehensive threat value corresponding to the electromagnetic signal to be evaluated is greater than the fourth threshold and less than the third threshold, determining the threat level of the electromagnetic signal to be evaluated as a low-level threat. Moreover, the method further includes: generating an alarm message when it is determined that the threat level of the electromagnetic signal to be evaluated is a medium-level threat or a high-level threat.

[0080] Exemplarily, 0.8 ≤ Threat < 1, the threat level is a high-level threat. In this case, there is an urgent threat, and immediate action should be taken to avoid or counter it. 0.4 ≤ Threat < 0.8, the threat level is a medium-level threat. This abnormal signal has an interference or attack intention, and decisions can be made according to the actual situation to respond. 0 < Threat < 0.4, the threat level is a low-level threat. This abnormal signal poses a threat but the threat is relatively small, and its threat can be appropriately monitored.

[0081] To address the shortcomings of existing threat signal assessment systems, such as superficial data analysis and difficulty in simultaneously achieving real-time performance and accuracy, this invention provides an electromagnetic signal target threat assessment method based on dynamic evidence theory and the analytic hierarchy process (AHP). The AHP method determines the weights of multiple threat assessment indicators, comprehensively considering factors such as relative distance, signal identity, signal center frequency, signal duration, and signal modulation method, ensuring the comprehensiveness and accuracy of the assessment. Simultaneously, a dynamic probability probability (BPA) generation method is used to dynamically generate threat probability values ​​based on real-time acquired electromagnetic signals, adapting to complex and changing electromagnetic environments and significantly improving the real-time performance and adaptability of the assessment. Furthermore, consistency checks ensure the logical consistency of expert judgments and the objectivity and rationality of model construction, avoiding weight allocation confusion caused by cognitive biases and further enhancing the reliability of the assessment results. In the comprehensive assessment stage, the threat probability values ​​corresponding to multiple threat assessment indicators are weighted and fused to obtain a comprehensive threat probability value. This comprehensive threat value is then calculated using preset weights to ultimately determine the threat level, providing clear and scientific threat assessment results. The method also includes an alarm mechanism that generates alarm information based on the threat level, promptly alerting relevant personnel to take countermeasures, enhancing the system's practicality and security. In summary, this method, through multi-level and multi-dimensional assessment and dynamic adjustment, can effectively identify and evaluate the threat level of electromagnetic signals, providing scientific and reliable decision support for electromagnetic security.

[0082] To verify the superior characteristics of this invention compared to existing threat signal assessment systems, Figure 4 This is a comparison chart showing the threat index assessment of walkie-talkie signals using a system that utilizes an existing threat signal assessment system and a system that uses the electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process provided by this invention. (See attached image.) Figure 4 As shown, the trajectory of a walkie-talkie signal is simulated to generate input parameters, causing the walkie-talkie signal to continuously move closer to the center area, and its threat index is sampled in steps of 10 meters.

[0083] When using the electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process (AHP) provided in this invention to assess the threat index of a walkie-talkie signal, the signal first appeared at a distance of 900 meters from the center area, its identity was determined to be unknown, its center frequency was 470MHz, it used conventional 4FSK modulation, and its appearance time was less than 5 seconds, resulting in a threat index of 0.2292 (low threat). When the signal moved to 800 meters, its identity was updated to abnormal, the frequency band was still occupied at 470MHz, the modulation switched to an unauthorized DMR encryption mode, and its appearance time exceeded 30 seconds, indicating increased threat intent, and the threat index jumped to 0.5839 (medium threat). Subsequently, the signal intruded into the 500-meter range of the center area, its identity was confirmed as an abnormal signal, the center frequency briefly jumped to the 148MHz aviation band, it used FHSS frequency hopping, and the signal appearance time exceeded 2 minutes, causing the threat index to surge to 0.8131 (high threat). We found that the threat index increases exponentially as the distance approaches the center area, indicating that our dynamic BPA generation mechanism is effective. The entire process relies on the real-time fusion of five elements—distance, identity, frequency band, duration, and modulation—to determine the threat index of the signal.

[0084] When existing threat signal assessment systems evaluate the threat index of walkie-talkie signals, although the threat index jumps when parameters such as area, center frequency, and adjustment method change, the threat index remains unchanged when the signal is in a certain distance range because the system uses a fixed suitability to assign BPA to the "relative distance" parameter. This makes it impossible to reasonably explain the dynamic changes in the signal threat index as the signal distance approaches.

[0085] based on Figure 4 It can be seen that the electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process provided by this invention can improve the shortcomings of traditional evidence fusion rule processing methods for continuous parameters, and finally output the threat index and level through probability transformation, so as to achieve accurate assessment of multi-source signals in complex electromagnetic environments.

[0086] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. An electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process, characterized in that, The method comprises the following steps: real-time acquisition of an electromagnetic signal to be evaluated; determining a one-to-one correspondence between a plurality of threat weights based on a plurality of threat evaluation indexes using an analytic hierarchy process, wherein the values of the plurality of threat evaluation indexes are obtained from the electromagnetic signal to be evaluated; calculating the plurality of threat weights using a dynamic BPA generation method to obtain a threat probability value corresponding to each threat evaluation index; weighting and fusing the threat probability values corresponding to the plurality of threat evaluation indexes to obtain a comprehensive threat probability value; outputting a threat evaluation result corresponding to the electromagnetic signal to be evaluated based on the comprehensive threat probability value; wherein, when the threat evaluation index is a relative distance, the calculation of the plurality of threat weights using the dynamic BPA generation method to obtain a threat probability value corresponding to each threat evaluation index comprises: determining the region where the electromagnetic signal to be evaluated is located according to the relative distance between the electromagnetic signal to be evaluated and the protected region, wherein the region where the electromagnetic signal to be evaluated is located includes a high-risk region, a medium-high-risk region, a medium-risk region, a medium-low-risk region, and a low-risk region; wherein the high-risk region is defined as a region with a relative distance from the protected region less than or equal to 300, the medium-risk region is defined as a region with a relative distance from the protected region less than 1500 and greater than or equal to 800, and the low-risk region is defined as a region with a relative distance from the protected region greater than or equal to 3000; calculating the plurality of threat weights using a dynamic BPA generation method according to the region where the electromagnetic signal to be evaluated is located to obtain a threat probability value corresponding to the relative distance; the calculation of the plurality of threat weights using a dynamic BPA generation method according to the region where the electromagnetic signal to be evaluated is located to obtain a threat probability value corresponding to the relative distance comprises: in the case where the region where the electromagnetic signal to be evaluated is located is the high-risk region, the medium-risk region, or the low-risk region, constructing a first high-risk knowledge matrix, a first medium-risk knowledge matrix, or a first low-risk knowledge matrix using the plurality of threat weights and the relative distance between the electromagnetic signal to be evaluated and the protected region; The first low-risk knowledge matrix is represented as: ; The first medium-risk knowledge matrix is represented as: ; The first high-risk knowledge matrix is represented as: ; obtaining a first high-risk feature vector, a first medium-risk feature vector, or a first low-risk feature vector by solving the maximum characteristic vector of the first high-risk knowledge matrix, the first medium-risk knowledge matrix, or the first low-risk knowledge matrix; normalizing the first high-risk feature vector, the first medium-risk feature vector, or the first low-risk feature vector to obtain a first threat probability value corresponding to the relative distance, wherein the first threat probability value includes a first high-threat probability value, a first medium-threat probability value, and a first low-threat probability value.

2. The electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process according to claim 1, characterized in that, the determination of a one-to-one correspondence between a plurality of threat weights based on a plurality of threat evaluation indexes using an analytic hierarchy process comprises: sorting the plurality of threat evaluation indexes according to their importance to obtain a plurality of sorted threat evaluation indexes, wherein the plurality of threat evaluation indexes include a relative distance, a signal identity, a signal center frequency, a signal duration, and a signal modulation mode. According to the sorted threat evaluation indexes of the plurality of threat evaluation indexes, an electromagnetic signal threat evaluation template is established; Based on the electromagnetic signal threat evaluation template, a judgment matrix is constructed, and the number of rows and the number of columns of the judgment matrix are both the total number of the plurality of threat evaluation indexes; The judgment matrix is solved to obtain a corresponding eigenvector, and the eigenvector is taken as the plurality of threat weights.

3. The electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process according to claim 1, characterized in that, The plurality of threat weights are calculated by using a dynamic BPA generation method according to the region where the electromagnetic signal to be evaluated is located, to obtain a threat probability value corresponding to the relative distance, including: In the case that the region where the electromagnetic signal to be evaluated is located is the medium-high risk region, a medium-high risk knowledge matrix is constructed by using the plurality of threat weights and the relative distance between the electromagnetic signal to be evaluated and the region to be protected; A maximum eigenvector of the medium-high risk knowledge matrix is solved to obtain a second high-risk eigenvector and a second medium-risk eigenvector; The second high-risk eigenvector and the second medium-risk eigenvector are normalized respectively to obtain a second high-threat probability value and a second medium-threat probability value corresponding to the relative distance.

4. The electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process according to claim 1, characterized in that, The plurality of threat weights are calculated by using a dynamic BPA generation method according to the region where the electromagnetic signal to be evaluated is located, to obtain a threat probability value corresponding to the relative distance, including: In the case that the region where the electromagnetic signal to be evaluated is located is the medium-low risk region, a medium-low risk knowledge matrix is constructed by using the plurality of threat weights and the relative distance between the electromagnetic signal to be evaluated and the region to be protected; A maximum eigenvector of the medium-low risk knowledge matrix is solved to obtain a third medium-risk eigenvector and a second low-risk eigenvector; The third medium-risk eigenvector and the second low-risk eigenvector are normalized respectively to obtain a third medium-threat probability value and a second low-threat probability value corresponding to the relative distance.

5. The electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process according to any one of claims 1 to 4, characterized in that, The plurality of threat evaluation indexes are weighted and fused to obtain a comprehensive threat probability value, including: Same type threat probability values are obtained, and the same type threat probability values include high threat probability values, medium threat probability values or low threat probability values corresponding to the plurality of threat evaluation indexes; The same type threat probability values are weighted and summed by using the plurality of threat weights to obtain a comprehensive threat probability value, and the comprehensive threat probability value includes a high comprehensive threat probability value, a medium comprehensive threat probability value or a low comprehensive threat probability value.

6. The electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process according to claim 5, characterized in that, Based on the comprehensive threat probability value, a threat evaluation result corresponding to the electromagnetic signal to be evaluated is output, including: First, second and third preset weights are obtained; The first preset weight is multiplied by the high comprehensive threat probability value to obtain a first product, the second preset weight is multiplied by the medium comprehensive threat probability value to obtain a second product, and the third preset weight is multiplied by the low comprehensive threat probability value to obtain a third product; The first, second and third products are added to obtain the threat evaluation result. adding the first product, the second product and the third product to obtain a comprehensive threat value corresponding to the to-be-evaluated electromagnetic signal; determining a corresponding threat level according to the comprehensive threat value corresponding to the to-be-evaluated electromagnetic signal, and outputting the comprehensive threat value and the threat level corresponding to the to-be-evaluated electromagnetic signal as the threat evaluation result.

7. The electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process according to claim 6, characterized in that, The determining of the corresponding threat level according to the comprehensive threat value corresponding to the to-be-evaluated electromagnetic signal comprises: if the comprehensive threat value corresponding to the to-be-evaluated electromagnetic signal is less than a first threshold value and greater than or equal to a second threshold value, determining that the threat level of the to-be-evaluated electromagnetic signal is a high-level threat; if the comprehensive threat value corresponding to the to-be-evaluated electromagnetic signal is greater than or equal to a third threshold value and less than the second threshold value, determining that the threat level of the to-be-evaluated electromagnetic signal is a middle-level threat; if the comprehensive threat value corresponding to the to-be-evaluated electromagnetic signal is greater than a fourth threshold value and less than the third threshold value, determining that the threat level of the to-be-evaluated electromagnetic signal is a low-level threat.

8. The electromagnetic signal target threat assessment method based on dynamic evidence theory and analytic hierarchy process according to claim 7, characterized in that, The method further comprises: in a case where the threat level of the to-be-evaluated electromagnetic signal is determined to be the middle-level threat or the high-level threat, generating alarm information.

Images