Unmanned aerial vehicle battlefield environment complexity assessment method based on bat algorithm optimization consistency check

Abstract

The invention relates to the technical field of unmanned aerial vehicle systems, in particular to an unmanned aerial vehicle battlefield environment complexity assessment method based on bat algorithm optimization consistency check. The method comprises the following steps: further dividing the environmental complexity of the unmanned aerial vehicle into three factors of weather, electromagnetism and geography, and establishing an index system based on an analytic hierarchy process; on the basis of weather, electromagnetism and geography, representative indexes are selected and quantified, and an index system is perfected; in the analytic hierarchy process, the consistency check part of the matrix has certain subjectivity, and the bat algorithm is asked to optimize the matrix. By simulating a behavior of searching a target in a space by a bat, updating of a consistency check fitness function is analogous to iteration of a bat position vector, so that subjectivity of the part can be reduced, solving of an optimization problem is realized, a more accurate result is obtained, and the method is suitable for large-scale popularization and application. Therefore, an operator can quickly and accurately know the change and the danger degree of the battlefield environment and make a relatively correct judgment according to the change and the danger degree.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle systems, and in particular to a method for evaluating the complexity of an unmanned aerial vehicle battlefield environment based on a bat algorithm optimized consistency check. Background Art

[0002] UAVs are often affected by the environment during use, from meteorological environment to electromagnetic environment to terrain constraints, all of which put higher demands on the performance of UAVs. In the face of severe weather conditions, the trajectory of the UAV has to be replanned when conditions are suitable. In relatively complex natural weather conditions, the complex weather conditions must also be evaluated to determine whether the UAV can complete the mission safely.

[0003] At present, there are few studies on the evaluation of the complexity of the battlefield environment of unmanned aerial vehicles in China, and there are also few systematic modeling for the evaluation of the complexity of the battlefield environment of unmanned aerial vehicles, and the methods used have high requirements for subjective experience. Luo Yuzhi et al. proposed to use Bayesian networks to model and evaluate the meteorological threat level. Wang Jun et al. used Bayesian networks to conduct in-depth research on the meteorological threat level of unmanned aerial vehicles under mountainous conditions. Wang Maosheng et al. used Bayesian networks to study the meteorological threat level of unmanned aerial vehicles under plateau conditions. Chang Sha et al. established a marine meteorological simulation environment evaluation model based on matrix gray correlation theory and gray clustering method.

[0004] In reality, since the Bayesian algorithm is overly dependent on expert knowledge and is highly subjective, it is not suitable for evaluating the overall environmental complexity. In addition to natural meteorological conditions, the electromagnetic environment is complex and changeable, there is no detailed data on the natural magnetic field, there are many frequency-using devices, and signal overlap will have a great impact on functions such as communication between drones, thereby affecting the efficiency of mission completion. When performing some mountain, jungle or urban combat missions, drones also face terrain challenges. It is necessary to extract terrain indicators for quantification and use this to judge the probability of the drone completing the mission successfully, so as to facilitate on-the-spot judgment by combat commanders.

[0005] Therefore, it is necessary to develop a new method for evaluating the complexity of UAV battlefield environment to overcome the above problems. Summary of the invention

[0006] The purpose of the present invention is: to address the shortcomings of the prior art, the present invention proposes a method for evaluating the complexity of the UAV battlefield environment based on a bat algorithm optimized consistency test, which can properly handle multiple indicators and a comprehensive evaluation model that requires a combination of quantitative analysis and qualitative analysis, and reduce the subjectivity and uncertainty of the model by optimizing the consistency test with a bat algorithm.

[0007] The technical solution adopted by the present invention is as follows:

[0008] The complexity evaluation method of the UAV battlefield environment based on the bat algorithm optimization consistency test includes the following steps:

[0009] Step 1: According to the complexity and diversity of the UAV combat environment, three factors, namely meteorological complexity, electromagnetic complexity and geographical complexity, are selected as the next level of environmental complexity factors. Representative indicators are selected from each factor to construct an indicator system for environmental complexity assessment.

[0010] The meteorological complexity index at least includes wind shear, thunderstorm and turbulence;

[0011] The electromagnetic complexity index at least includes background noise intensity coefficient, signal power intensity coefficient, frequency occupancy coefficient, and frequency coincidence coefficient;

[0012] The geographic complexity index includes at least the altitude change rate, the maximum slope coefficient, the slope change rate and the obstruction density coefficient;

[0013] Step 2: Quantify the indicators of each selected factor;

[0014] Step 3: Compare the importance of indicators at the same level and indicators belonging to the same factor, and establish judgment matrices respectively;

[0015] Step 4: Optimize the consistency test part of the established judgment matrix based on the bat algorithm, and normalize the judgment matrix after the consistency test based on the square root method to obtain the weight of each indicator; specifically, the following steps are included:

[0016] The consistency test of the established judgment matrix is ​​carried out, and the consistency index of the judgment matrix is When CI is 0, the judgment matrix has complete consistency. If not, the judgment matrix is ​​readjusted;

[0017] Taking CI = 0 as the objective function, the consistency check is simplified to the following optimization problem:

[0018]

[0019] The bat algorithm is used to solve the above optimization problem by simulating the behavior of bats searching for targets in space. The state update formula used by bats in the search is:

[0020]

[0021] In the formula, f i 、v i t+1 、x i t+1They represent the frequency, speed and position of bat i at time t+1 respectively, β is a uniformly distributed random number between [0,1], x* represents the current optimal individual bat position; f max and f min Indicates the maximum and minimum values ​​of bat frequency;

[0022] In the search phase, a uniformly distributed random number rand1 is generated, and ri is the pulse emission frequency of bat i. If rand1>r i , then update a local new solution near the optimal solution:

[0023] X new =X old +εA t

[0024] In the formula, A t is the average pulse loudness of all bats in this generation, and ε is a random value in [-1,1];

[0025] Generate a second uniformly distributed random number rand2. If rand2 t , and f(x i )<f(x * ), then according to

[0026]

[0027] Update impulse loudness A i t and the pulse emission frequency r i ;

[0028] In the formula, r i 0 is the initial pulse emission frequency of bat i; α∈(0,1) is the pulse loudness attenuation coefficient; γ>0, is the pulse emission rate increase coefficient;

[0029] Sort the fitness function values ​​of each layer and select the optimal value x*; repeat the iteration, when t tends to infinity, A t , r both tend to 0, indicating that the bat has found the optimal solution and no longer sends pulses;

[0030] Use the square root method to normalize the judgment matrix and solve the indicator weights:

[0031]

[0032] Step 5: After calculating the weight of each indicator, convert the quantified indicator into a coefficient of [0,1] according to each quantitative indicator in step 2, multiply it with the corresponding weight and sum it up to calculate the meteorological complexity, electromagnetic complexity, geographical complexity and the final environmental complexity to be solved. ​

[0033] Preferably, in step three, the importance of indicators and factors at the same level are compared based on Santy's 1-9 scaling method.

[0034] Preferably, the wind shear refers to a sudden change in wind direction and wind speed in the horizontal or vertical direction, and its danger level is quantified by the change in wind speed of horizontal wind in the vertical direction within a unit height;

[0035] The threat level of the thunderstorm is inversely proportional to the distance between the UAV and the center of the thunderstorm;

[0036] The turbulence threat level is measured by its turbulence intensity σ.

[0037] Preferably, the background noise intensity coefficient refers to the ratio of the sum of the background electromagnetic noise intensities within the range to the maximum noise intensity that the device can withstand for normal operation;

[0038] The signal power intensity coefficient refers to the ratio of the electromagnetic signal field strength exceeding the anti-interference threshold level to the electromagnetic signal field strength at the location where the frequency-using device receives the signal;

[0039] The frequency occupancy factor refers to the ratio of the sum of the frequency ranges used by all frequency-using devices within the range to the range of all available frequency bands;

[0040] The frequency overlap coefficient refers to the ratio of the bandwidth of the overlapping working frequencies of the frequency-using equipment to the overall occupied frequency bandwidth.

[0041] Preferably, the altitude change rate refers to the ratio of the altitude difference between the altitude of the place to be crossed by the drone and the current altitude to the highest altitude of the place to be crossed;

[0042] The maximum slope coefficient refers to the normalized value of the maximum value of the angle between the position with the highest slope and the horizontal plane;

[0043] The slope change rate coefficient refers to the ratio of the maximum value of the terrain slope change to the distance between two adjacent points;

[0044] The obstruction density coefficient refers to the percentage of ground area occupied by obstructions.

[0045] Preferably, the environmental complexity is the target layer, the meteorological complexity, the electromagnetic complexity and the geographical complexity are the criterion layer, and the next level indicators of the criterion layer factors are used as the indicator layer.

[0046] Furthermore, each indicator under the indicator layer is further decomposed into a lower level, and is also analyzed using steps 2 to 5.

[0047] Furthermore, the criterion layer expands a number of complexity criteria, and decomposes the expanded complexity criteria into a next layer of indicators, and also adopts steps 2 to 5 for analysis.

[0048] Preferably, the complexity level of the drone battlefield environment is divided into several threat levels according to the calculated data results between 0 and 1.

[0049] Furthermore, 0-0.1 is risk-free, 0.11-0.25 is mild, 0.26-0.45 is relatively complicated, 0.46-0.65 is moderate, 0.66-0.8 is complicated, and 0.81-1 is severe.

[0050] Beneficial effects achieved by the present invention:

[0051] The evaluation method proposed in the present invention first analyzes the influence of complex electromagnetic, meteorological and geographical environments on the electronic information system of UAV, establishes an evaluation index system for the influence of complex electromagnetic spectrum, meteorological environment and geographical environment on UAV, and provides a solution method for each index of the index system. On this basis, a more objective and accurate evaluation method for the influence of complex electromagnetic, meteorological and geographical environments on UAV is provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of the method for evaluating the complexity of the UAV battlefield environment in Example 1 of the present invention.

[0053] Figure 2 This is a diagram of the system architecture of the environment complexity assessment index system in Example 1 of the present invention. DETAILED DESCRIPTION

[0054] The present invention will be described in detail below based on the accompanying drawings and preferred embodiments, and the purpose and effects of the present invention will become more clear. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] like Figure 1 As shown, a method for evaluating the complexity of a UAV battlefield environment based on bat algorithm optimization consistency check of the present invention specifically comprises the following steps:

[0056] Step 1: According to the complexity and diversity of the UAV combat environment, the environment is divided into three factors: meteorological, electromagnetic and geographical. Representative indicators are selected to construct an indicator system for environmental complexity assessment, such as Figure 2 As shown:

[0057] The index system for environmental complexity assessment includes a target layer, a criterion layer and an index layer, wherein the target layer is environmental complexity, and the criterion layer is divided into meteorological complexity, electromagnetic complexity and geographical complexity;

[0058] The meteorological complexity indicators include wind shear, thunderstorms and turbulence;

[0059] The electromagnetic complexity index includes background noise intensity coefficient, signal power intensity coefficient, frequency occupancy coefficient, and frequency coincidence coefficient.

[0060] The geographic complexity indicators include altitude change rate, maximum slope coefficient, slope change rate and obstruction density coefficient.

[0061] Step 2: Quantify the indicators.

[0062] Wind shear refers to the sudden change of wind direction and wind speed in the horizontal or vertical direction. The degree of danger is quantified by the change of horizontal wind speed in the vertical direction within a unit height.

[0063] The threat level of a thunderstorm is inversely proportional to the distance from the drone to the center of the thunderstorm;

[0064] The threat of turbulence is measured by its turbulence intensity σ;

[0065] The background noise intensity coefficient refers to the ratio of the sum of the background electromagnetic noise intensities within the range to the maximum noise intensity that the equipment can withstand for normal operation;

[0066] The signal power intensity coefficient refers to the ratio of the electromagnetic signal field strength exceeding the anti-interference threshold level to the electromagnetic signal field strength at the location where the frequency-using equipment receives the signal;

[0067] The frequency occupancy factor refers to the ratio of the sum of the frequency ranges used by all frequency-using devices within the range to the total usable frequency band range;

[0069] The frequency overlap coefficient refers to the ratio of the bandwidth of the overlapping working frequencies of the frequency-using equipment to the overall occupied frequency bandwidth;

[0070] The altitude change rate refers to the ratio of the altitude difference between the altitude of the place the drone is going to fly over and the current altitude to the highest altitude of the place to fly over;

[0071] The maximum slope coefficient refers to the normalized value of the maximum angle between the position with the highest slope and the horizontal plane;

[0072] The slope change rate coefficient refers to the ratio of the maximum value of terrain slope change to the distance between two adjacent points;

[0073] The occluder density factor is the percentage of ground area occupied by occluders.

[0074] Step 3: Compare the importance of indicators at the same level based on Santy's 1-9 scale method and establish a judgment matrix.

[0075] Set the importance of each indicator and establish a judgment matrix. If the indicator in the i-th row is significantly more important than the indicator in the j-th column, then the judgment matrix a ij The value of the position is 7, otherwise it is the reciprocal 1 / 7.

[0076] Thus, the meteorological complexity weight judgment matrix is ​​established:

[0077]

[0078] Electromagnetic complexity weight judgment matrix:

[0079]

[0080] Geographic complexity weight judgment matrix:

[0081]

[0082] Environmental complexity weight judgment matrix:

[0083]

[0084] Step 4: Optimize the consistency test part of the established judgment matrix based on the bat algorithm, and normalize the judgment matrix after the consistency test based on the square root method to obtain the weight of each indicator; specifically, the following steps are included:

[0085] The consistency test of the established judgment matrix is ​​carried out, and the consistency index of the judgment matrix is When CI is 0, the judgment matrix has complete consistency. If not, the judgment matrix is ​​readjusted;

[0086] Taking CI = 0 as the objective function, the consistency check is simplified to the following optimization problem:

[0087]

[0088] The bat algorithm is used to solve the above optimization problem by simulating the behavior of bats searching for targets in space. The state update formula used by bats in the search is:

[0089]

[0090] In the formula, f i 、v i t+1 、x i t+1 They represent the frequency, speed and position of bat i at time t+1 respectively, β is a uniformly distributed random number between [0,1], x* represents the current optimal individual bat position; f max and f min Indicates the maximum and minimum values ​​of bat frequency;

[0091] In the search phase, a uniformly distributed random number rand1 is generated, and ri is the pulse emission frequency of bat i. If rand1>ri , then update a local new solution near the optimal solution:

[0092] X new =X old +εA t

[0093] In the formula, A t is the average pulse loudness of all bats in this generation, and ε is a random value in [-1,1];

[0094] Generate a second uniformly distributed random number rand2. If rand2 t , and f(x i )<f(x * ), then according to

[0095]

[0096] Update impulse loudness A i t and the pulse emission frequency r i ;

[0097] In the formula, r i 0 is the initial pulse emission frequency of bat i; α∈(0,1) is the pulse loudness attenuation coefficient; γ>0, is the pulse emission rate increase coefficient;

[0098] Sort the fitness function values ​​of each layer and select the optimal value x*; repeat the iteration, when t tends to infinity, A t , r both tend to 0, indicating that the bat has found the optimal solution and no longer sends pulses;

[0099] Use the square root method to normalize the judgment matrix and solve the indicator weights:

[0100]

[0101] Step 5: After calculating the weights of each indicator, quantify the indicators calculated in step 2 into coefficients of [0,1], multiply them by the corresponding weights and sum them up to calculate the meteorological complexity, electromagnetic complexity, geographic complexity and the final environmental complexity to be solved.

[0102] Table 1 Classification of complexity levels of UAV battlefield environment

[0103] Threat Level No risk Mild More complex Moderate complex serious Threat Indicator T 0～0.1 0.11～0.25 0.26～0.45 0.46～0.65 0.66～0.8 0.81～1

[0104] ​The above includes a description of the preferred embodiments of the present invention, which is to illustrate the technical features of the present invention in detail rather than to limit the invention. A person skilled in the art may modify or make equivalent substitutions to the technical methods of the present invention. The subject matter of the present invention is defined by the claims, and other modifications and variations based on the subject matter of the present invention are also protected by this patent.

Claims

1. A method for evaluating the complexity of UAV battlefield environment based on bat algorithm optimization consistency test, It is characterized in that The steps include: Step 1: According to the complexity and diversity of the UAV combat environment, three factors, namely, meteorological complexity, electromagnetic complexity and geographical complexity, are selected as the next level of environmental complexity. Representative indicators are selected from each factor to construct an indicator system for evaluating the complexity of the battlefield environment. The meteorological complexity index at least includes wind shear, thunderstorm and turbulence; The electromagnetic complexity index at least includes background noise intensity coefficient, signal power intensity coefficient, frequency occupancy coefficient, and frequency coincidence coefficient; The geographic complexity index includes at least the altitude change rate, the maximum slope coefficient, the slope change rate and the obstruction density coefficient; Step 2: Quantify the indicators of each selected factor; Step 3: Compare the importance of indicators at the same level and indicators belonging to the same factor, and establish judgment matrices respectively; Step 4: Optimize the consistency test part of the established judgment matrix based on the bat algorithm, and normalize the judgment matrix after the consistency test based on the square root method to obtain the weight of each indicator; The specific steps include: The consistency test of the established judgment matrix is ​​carried out, and the consistency index of the judgment matrix is When CI is 0, the judgment matrix has complete consistency. If not, the judgment matrix is ​​readjusted; Taking CI = 0 as the objective function, the consistency check is simplified to the following optimization problem: The bat algorithm is used to solve the above optimization problem by simulating the behavior of bats searching for targets in space. The state update formula of the bats in the search is: In the formula, f i 、v i t+1 、x i t+1 They represent the frequency, speed and position of bat i at time t+1 respectively, β is a uniformly distributed random number between [0,1], x* represents the current optimal individual bat position; f max and f min Indicates the maximum and minimum values ​​of bat frequency; In the search phase, a uniformly distributed random number rand1 is generated, and ri is the pulse emission frequency of bat i. If rand1>r i , then update a local new solution near the optimal solution: X new =X old +εA t In the formula, A t is the average pulse loudness of all bats in this generation, and ε is a random value in [-1,1]; Generate a second uniformly distributed random number rand2. If rand2 t , and f(x i )<f(x * ), then according to​ Update impulse loudness A i t and the pulse transmission frequency r i ; In the formula, r i 0 is the initial pulse emission frequency of bat i; α∈(0,1) is the pulse loudness attenuation coefficient; γ>0, is the pulse emission rate increase coefficient; Sort the fitness function values ​​of each layer and select the optimal value x*; repeat the iteration, when t tends to infinity, A t , r both tend to 0, indicating that the bat has found the optimal solution and no longer sends pulses; Use the square root method to normalize the judgment matrix and solve the indicator weights: Step 5: After calculating the weight of each indicator, convert the quantified indicator into a coefficient according to each quantitative indicator in step 2, multiply it with the corresponding weight and sum it up to calculate the meteorological complexity, electromagnetic complexity, geographical complexity and the final environmental complexity to be solved.

2. According to claim 1, the method for evaluating the complexity of the UAV battlefield environment based on the bat algorithm optimization consistency check, It is characterized in that In the step three, the importance of indicators and factors at the same level is compared based on Santy's 1-9 scale method.

3. According to the method for evaluating the complexity of the UAV battlefield environment based on the bat algorithm optimization consistency check according to claim 1, It is characterized in that The wind shear refers to the sudden change of wind direction and wind speed in the horizontal or vertical direction, and its danger level is quantified by the change of wind speed in the vertical direction of horizontal wind within a unit height; The threat level of the thunderstorm is inversely proportional to the distance between the UAV and the center of the thunderstorm; The turbulence threat level is measured by its turbulence intensity σ.

4. According to claim 1, the method for evaluating the complexity of the UAV battlefield environment based on the bat algorithm optimization consistency check, It is characterized in that The background noise intensity coefficient refers to the ratio of the sum of the background electromagnetic noise intensities within the battlefield range to the maximum noise intensity that the equipment can withstand for normal operation; The signal power intensity coefficient refers to the ratio of the electromagnetic signal field strength exceeding the anti-interference threshold level to the electromagnetic signal field strength at the location where the frequency-using device receives the signal; The frequency occupancy coefficient refers to the ratio of the sum of the frequency ranges used by all frequency-using equipment within the battlefield to the range of all available frequency bands; The frequency overlap coefficient refers to the ratio of the bandwidth of the overlapping working frequencies of the frequency-using equipment to the overall occupied frequency bandwidth.

5. According to claim 1, the method for evaluating the complexity of the UAV battlefield environment based on the bat algorithm optimization consistency check, It is characterized in that The altitude change rate refers to the ratio of the altitude difference between the altitude of the place to be crossed by the drone and the current altitude to the highest altitude of the place to be crossed; The maximum slope coefficient refers to the normalized value of the maximum value of the angle between the position with the highest slope and the horizontal plane; The slope change rate coefficient refers to the ratio of the maximum value of the terrain slope change to the distance between two adjacent points; The obstruction density coefficient refers to the percentage of ground area occupied by obstructions.

6. According to claim 1, the method for evaluating the complexity of the UAV battlefield environment based on the bat algorithm optimization consistency check, It is characterized in that The environmental complexity is the target layer, the meteorological complexity, the electromagnetic complexity and the geographical complexity are the criterion layer, and the next level indicators of the criterion layer factors are used as the indicator layer.

7. The method for evaluating the complexity of the UAV battlefield environment based on the bat algorithm optimization consistency check according to claim 6, It is characterized in that Each indicator under the indicator layer is further decomposed into a lower level, and is also analyzed using steps 2 to 5.

8. According to claim 7, the method for evaluating the complexity of the UAV battlefield environment based on the bat algorithm optimization consistency check, It is characterized in that The criterion layer expands a number of complexity criteria, and decomposes the expanded complexity criteria into a next layer of indicators, and also adopts steps 2 to 5 for analysis.

9. The method for evaluating the complexity of the UAV battlefield environment based on the bat algorithm optimization consistency check according to claim 1, It is characterized in that Step 2: Each quantitative index is quantified into a coefficient of [0,1]. The complexity level of the UAV battlefield environment is divided into several threat levels according to the calculated data results between 0 and 1.

10. The method for evaluating the complexity of the UAV battlefield environment based on the bat algorithm optimization consistency check according to claim 9, Features 0-0.1 means no risk, 0.11-0.25 means mild, 0.26-0.45 means more complicated, 0.46-0.65 means moderate, 0.66-0.8 means complicated, and 0.81-1 means severe.

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