Genetic algorithm-based linear blockade formation optimization method for multiple magnetic detection unmanned aerial vehicles

Abstract

The invention belongs to the technical field of ocean anti-submergence, and particularly relates to a multi-magnetic detection unmanned aerial vehicle linear blockade formation optimization method and device based on a genetic algorithm. The method comprises the following steps: S1, coding by taking the distance and the flight speed of the unmanned aerial vehicle as parameters to be optimized to form an initial population; s2, for each individual, calculating a fitness value of a fitness function constructed based on the coverage range, the detection probability, the anti-interference efficiency, the total energy consumption and the positioning error; s3, screening a plurality of individuals with relatively high fitness values, and performing crossover variation to form a new population; and S4, the step S2 and the step S3 are repeated until iteration reaches the maximum number of times or fitness value convergence is carried out, and the optimal combination of the distance and the flight speed of the unmanned aerial vehicle is determined. According to the invention, the detection capability, the positioning precision, the anti-interference capability and the energy utilization efficiency of the unmanned aerial vehicle cluster in the anti-submarine task are significantly improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of marine anti-submarine technology, and particularly relates to a multi-magnetic exploration unmanned aerial vehicle linear blockade array optimization method and device based on a genetic algorithm. BACKGROUND

[0002] Traditional anti-submarine means (such as sonar buoys and shipborne magnetic exploration instruments) have problems such as small coverage range, slow response speed, and susceptibility to marine environment interference. Although unmanned aerial vehicle clusters can improve detection flexibility, existing linear blockade arrays have the following defects:

[0003] Coverage gap: The spacing between unmanned aerial vehicles depends on experience and does not consider the magnetic anomaly signal attenuation characteristics, resulting in a detection blind area (such as missing targets when the spacing is too large).

[0004] Insufficient anti-interference capability: The influence of electromagnetic interference (EMI) on the linear array is not quantified, and there is a lack of dynamic adjustment strategy.

[0005] Therefore, there is an urgent need for a linear blockade array simulation method based on magnetic gradient tensor cooperation to dynamically optimize the spacing, speed, and detection efficiency of unmanned aerial vehicles and provide theoretical support for anti-submarine tasks. SUMMARY

[0006] To solve the above problems, the application provides a multi-magnetic exploration unmanned aerial vehicle linear blockade array optimization method and device based on a genetic algorithm.

[0007] The application provides a multi-magnetic exploration unmanned aerial vehicle linear blockade array optimization method based on a genetic algorithm, mainly including:

[0008] Step S1, encoding the initial population with the unmanned aerial vehicle spacing and flight speed as the parameters to be optimized;

[0009] Step S2, for each individual, calculating the fitness value of the fitness function constructed based on the coverage range, detection probability, anti-interference efficiency, total energy consumption, and positioning error;

[0010] Step S3, selecting a plurality of individuals with higher fitness values for crossover and mutation to form a new population;

[0011] Step S4, repeating step S2 and step S3 until the iteration reaches the maximum number of times or the fitness value converges, and determining the optimal combination of unmanned aerial vehicle spacing and flight speed.

[0012] Preferably, step S1 further includes:

[0013] Encoding the actual values of the unmanned aerial vehicle spacing and flight speed; or

[0014] The UAV spacing and flight speed are discretized into binary strings respectively to form UAV spacing encoding and flight speed encoding.

[0015] Preferably, in step S2, the fitness function is:

[0016]

[0017] wherein, α1, α2, α3, α4, α5 are weight coefficients, W cover is the coverage width, P detect is the detection probability, SNR total is the signal-to-noise ratio improvement factor, E total is the total energy consumption, σ pos is the lower bound of positioning error;

[0018] wherein, the coverage width W cover is calculated by the following formula:

[0019]

[0020] wherein, r is the detection radius of a single magnetometer, N is the number of UAVs, D is the UAV spacing, and θ is the azimuth angle of the magnetic anomaly signal decay to the detection threshold;

[0021] The detection probability P detect is calculated by the following formula:

[0022] P detect = 1-e -λ·t·N ;

[0023] wherein, λ is the detection probability density, t is the task time, and v is the flight speed;

[0024] The signal-to-noise ratio improvement factor SNR total is calculated by the following formula:

[0025]

[0026] S i is the signal power, I is the intensity of the interference source, r i is the UAV position, and r 干扰 is the interference source position;

[0027] The total energy consumption E total is calculated by the following formula:

[0028]

[0029] wherein, P fly is the flight power, and P mag is the magnetometer power;

[0030] Lower bound of positioning error σ pos Calculated using the following formula:

[0031]

[0032] Where σ is the standard deviation of the magnetic detector noise. This represents the magnitude of the magnetic field gradient.

[0033] Preferably, step S3 further includes using roulette or tournament methods to screen multiple individuals with high fitness.

[0034] The second aspect of this application provides a device for optimizing the linear blockade formation of a multi-magnetic probe UAV based on a genetic algorithm, mainly comprising:

[0035] The initial population encoding module is used to encode the initial population using the drone spacing and flight speed as parameters to be optimized.

[0036] The fitness value calculation module is used to calculate the fitness value for each individual based on a fitness function constructed from coverage, detection probability, anti-interference performance, total energy consumption, and positioning error.

[0037] The crossover mutation module is used to select multiple individuals with high fitness values ​​for crossover mutation to form a new population.

[0038] The iterative control module is used to repeatedly call the fitness value calculation module and the crossover and mutation module until the maximum number of iterations is reached or the fitness value converges, thereby determining the optimal combination of UAV spacing and flight speed.

[0039] Preferably, the initial population coding module includes:

[0040] Direct encoding units are used to encode the actual values ​​of UAV spacing and flight speed; or

[0041] The mapping encoding unit is used to discretize the UAV spacing and flight speed into binary strings, respectively, to form UAV spacing encoding and flight speed encoding.

[0042] Preferably, in the fitness value calculation module, the fitness function is:

[0043]

[0044] Where α1, α2, α3, α4, and α5 are weighting coefficients, and W cover For the coverage width, P detect For detection probability, SNR total E is the signal-to-noise ratio enhancement factor. total For total energy consumption, σ pos This is the lower bound of the positioning error;

[0045] Among them, the coverage width W cover Calculated using the following formula:

[0046]

[0047] Where r is the detection radius of a single magnetic detector, N is the number of UAVs, D is the distance between UAVs, and θ is the azimuth angle at which the magnetic anomaly signal attenuates to the detection threshold;

[0048] Detection probability P detect Calculated using the following formula:

[0049] P detect =1-e -λ·t·N ;

[0050] Where λ is the detection probability density. t is the mission time, and v is the flight speed;

[0051] Signal-to-noise ratio (SNR) enhancement factor total Calculated using the following formula:

[0052]

[0053] S i Let I be the signal power, I be the interference source strength, and r be the signal power. i For the drone's location, r 干扰 Location of the interference source;

[0054] Total energy consumption E total Calculated using the following formula:

[0055]

[0056] Among them, P fly For flight power, P mag Power of the magnetic detector;

[0057] Lower bound of positioning error σ pos Calculated using the following formula:

[0058]

[0059] Where σ is the standard deviation of the magnetic detector noise. This represents the magnitude of the magnetic field gradient.

[0060] Preferably, a roulette wheel or tournament method is used to select multiple individuals with high fitness.

[0061] This application significantly improves the detection capability, positioning accuracy, anti-interference capability, and energy utilization efficiency of UAV swarms in anti-submarine missions. It can effectively address the shortcomings of traditional anti-submarine methods, greatly enhance the effectiveness of marine anti-submarine warfare, and provide important theoretical support and practical guidance for the future development of anti-submarine technology. Attached Figure Description

[0062] Figure 1 This is a flowchart of a preferred embodiment of the multi-magnetic probe UAV linear blockade formation optimization method based on genetic algorithm in this application. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are only some, not all, of the embodiments of this application. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0064] The first aspect of this application provides a method for optimizing the linear blockade formation of multi-magnetic probe UAVs based on genetic algorithms, such as... Figure 1 As shown, it mainly includes:

[0065] Step S1: Using the distance between UAVs and their flight speed as parameters to be optimized, encode them to form an initial population;

[0066] Step S2: For each individual, calculate the fitness value based on the fitness function constructed based on coverage, detection probability, anti-interference performance, total energy consumption, and positioning error;

[0067] Step S3: Select multiple individuals with high fitness values ​​and perform crossover mutation to form a new population;

[0068] Step S4: Repeat steps S2 and S3 until the maximum number of iterations is reached or the fitness value converges, and determine the optimal combination of UAV spacing and flight speed.

[0069] In step S1, an initial population (e.g., 50-100 individuals) is randomly generated, with each individual representing a possible combination of (UAV spacing D, flight speed v). For example:

[0070]

[0071] The initial range must satisfy the constraints (such as D). min ≤D≤2r,v min ≤v≤v max ), where the subscripts max and min represent the upper and lower limits respectively, and r is the detection radius of the single-unit magnetic detector.

[0072] In some alternative implementations, step S1 further includes:

[0073] Encode using the actual values ​​of drone spacing and flight speed; or

[0074] The distance between drones and their flight speed are discretized into binary strings to form drone distance encoding and flight speed encoding, respectively.

[0075] In this embodiment, encoding the actual values ​​means directly using the values ​​of the drone spacing D and flight speed v as gene segments (e.g., D = 1.5 km, v = 60 km / h), while binary encoding means discretizing the parameters into binary strings (e.g., 8 bits represent the speed range of 0-120 km / h). The fitness needs to be calculated after decoding into the actual values.

[0076] In step S2, this application constructs a fitness function to evaluate the merits of each group (D,v), which requires comprehensive consideration of indicators such as detection probability, positioning error, and anti-interference index to quantify array performance.

[0077] In some optional implementations, in step S2, the fitness function is:

[0078]

[0079] Where α1, α2, α3, α4, and α5 are weighting coefficients reflecting task priority, W cover For the coverage width, P detect For detection probability, SNR total E is the signal-to-noise ratio enhancement factor. total For total energy consumption, σ pos This represents the lower bound of the positioning error.

[0080] Among them, the coverage width W cover Calculated using the following formula:

[0081]

[0082] Where r is the detection radius of a single magnetic detector, N is the number of UAVs, D is the distance between UAVs, and θ is the azimuth angle at which the magnetic anomaly signal attenuates to the detection threshold; constraints need to be introduced here:

[0083] D≤2r sinθ, to ensure no blind zone.

[0084] Detection probability P detect It follows a Poisson distribution, specifically calculated using the following formula:

[0085] P detect =1-e -λ·t·N ;

[0086] Where λ is the detection probability density. t is the mission time, and v is the flight speed;

[0087] Signal-to-noise ratio (SNR) enhancement factor total The anti-interference performance of the system is reflected by the following formula:

[0088]

[0089] S i Let I be the signal power, I be the interference source strength, and r be the signal power. i For the drone's location, r 干扰 Location of the interference source;

[0090] Total energy consumption E total Calculated using the following formula:

[0091]

[0092] Among them, P fly For flight power, P mag Power of the magnetic detector;

[0093] Based on magnetic gradient tensor cooperative measurement, the lower bound of the positioning error σ is determined. pos Calculated using the following formula:

[0094]

[0095] Where σ is the standard deviation of the magnetic detector noise. This represents the magnitude of the magnetic field gradient.

[0096] In some alternative implementations, step S3 further includes using roulette or tournament methods to screen multiple individuals with high fitness.

[0097] In this embodiment, roulette wheel selection refers to allocating selection probabilities based on fitness values, with individuals possessing high fitness being more likely to be retained. For example, the top 10% of individuals by fitness directly enter the next generation. Tournament selection involves randomly selecting several individuals to compete, with the best one winning, thus avoiding premature convergence.

[0098] Step S3 can use single-point crossover or arithmetic crossover, and mutation can use Gaussian mutation, boundary mutation, or elite preservation. Single-point crossover involves randomly selecting gene locations and exchanging some genes from the parent individuals. For example, exchanging D and v from two parents generates offspring. Arithmetic crossover involves linearly combining genes encoded by real numbers (e.g., offspring D = 0.6D). 父1 +0.4D 父2 Gaussian mutation refers to applying random perturbations to parameters to enhance local search capabilities. Boundary mutation means that if the parameters exceed the bounds after mutation, they are reset to random values ​​within the constraints. Elite preservation means retaining a number of optimal individuals in each generation to ensure that superior genes are not destroyed.

[0099] This application proposes a method for optimizing the linear blockade formation of multi-magnetic probe UAVs based on genetic algorithms, and its beneficial effects are reflected in the following aspects:

[0100] First, it offers wider coverage. Traditional anti-submarine warfare (ASW) methods are limited by the coverage area and operational flexibility of the equipment, often resulting in blind spots and missed target detection. This application, however, optimizes the spacing parameters of the UAVs using a genetic algorithm, ensuring effective detection of magnetic anomaly signals and avoiding detection blind spots. The optimized array increases the coverage width, exposing a larger sea area and significantly improving detection efficiency and coverage.

[0101] Secondly, the detection probability is significantly improved. By optimizing the UAV speed and mission time parameters, and combining them with a Poisson distribution model, the optimized formation can more effectively detect randomly distributed underwater targets. The total detection probability is maximized, which means that in anti-submarine missions, the likelihood of successful target detection is greatly increased, thereby improving mission reliability.

[0102] Third, positioning accuracy is improved. The use of magnetic gradient tensor co-measurement technology means that the positioning accuracy of targets is significantly improved in complex and ever-changing marine environments, providing a reliable prerequisite for subsequent precision strikes.

[0103] Fourth, enhanced anti-interference capability. Traditional anti-submarine equipment suffers severe performance degradation in electromagnetic interference environments. However, this application optimizes parameter combinations to maximize the signal-to-noise ratio enhancement factor, effectively improving the array's anti-interference capability in complex electromagnetic interference environments and ensuring the stability and reliability of detection.

[0104] Fifth, energy consumption optimization. By optimizing the UAV's flight speed and formation spacing, the total energy consumption model is minimized. This not only extends the UAV's endurance but also improves mission execution efficiency, enabling anti-submarine missions to continue for longer periods, which is particularly significant in resource-constrained environments.

[0105] Sixth, dynamic adjustment capability. The introduction of genetic algorithms allows the UAV formation to be adjusted in real time according to actual mission requirements and environmental changes. This dynamic optimization capability enhances the system's adaptability, enabling it to maintain high anti-submarine performance under different conditions.

[0106] Seventh, reduce the false alarm rate. The optimized array and detection algorithm reduce the possibility of false alarms and improve the reliability of the system. This is especially important in actual anti-submarine missions, avoiding the consumption of unnecessary resources and time due to false alarms.

[0107] Eighth, improve overall mission efficiency. Through a multi-objective weighted fitness function design, several key performance indicators, including coverage width, detection probability, anti-interference capability, energy consumption, and positioning error, are comprehensively optimized. This not only improves the overall performance of the system but also maximizes the overall efficiency of anti-submarine missions, demonstrating significant application value.

[0108] In summary, the proposed method for optimizing the linear blockade formation of multi-magnetic probe UAVs based on genetic algorithms significantly improves the detection capability, positioning accuracy, anti-interference capability, and energy efficiency of UAV swarms in anti-submarine warfare through intelligent optimization. The implementation of this method effectively addresses the shortcomings of traditional anti-submarine methods, greatly enhances the effectiveness of marine anti-submarine warfare, and provides important theoretical support and practical guidance for the future development of anti-submarine technology.

[0109] The second aspect of this application provides a multi-magnetic probe UAV linear blockade array optimization device based on a genetic algorithm, corresponding to the above-described method, characterized in that it includes:

[0110] The initial population encoding module is used to encode the initial population using the drone spacing and flight speed as parameters to be optimized.

[0111] The fitness value calculation module is used to calculate the fitness value for each individual based on a fitness function constructed from coverage, detection probability, anti-interference performance, total energy consumption, and positioning error.

[0112] The crossover mutation module is used to select multiple individuals with high fitness values ​​for crossover mutation to form a new population.

[0113] The iterative control module is used to repeatedly call the fitness value calculation module and the crossover and mutation module until the maximum number of iterations is reached or the fitness value converges, thereby determining the optimal combination of UAV spacing and flight speed.

[0114] In some alternative implementations, the initial population coding module includes:

[0115] Direct encoding units are used to encode the actual values ​​of UAV spacing and flight speed; or

[0116] The mapping encoding unit is used to discretize the UAV spacing and flight speed into binary strings, respectively, to form UAV spacing encoding and flight speed encoding.

[0117] In some optional implementations, in the fitness value calculation module, the fitness function is:

[0118]

[0119] Where α1, α2, α3, α4, and α5 are weighting coefficients, and W cover For the coverage width, P detect For detection probability, SNR total E is the signal-to-noise ratio enhancement factor. total For total energy consumption, σ pos This is the lower bound of the positioning error;

[0120] Among them, the coverage width W cover Calculated using the following formula:

[0121]

[0122] Where r is the detection radius of a single magnetic detector, N is the number of UAVs, D is the distance between UAVs, and θ is the azimuth angle at which the magnetic anomaly signal attenuates to the detection threshold;

[0123] Detection probability P detect Calculated using the following formula:

[0124] P detect =1-e -λ·t·N ;

[0125] Where λ is the detection probability density. t is the mission time, and v is the flight speed;

[0126] Signal-to-noise ratio (SNR) enhancement factor total Calculated using the following formula:

[0127]

[0128] S i Let I be the signal power, I be the interference source strength, and r be the signal power. i For the drone's location, r 干扰 Location of the interference source;

[0129] Total energy consumption E total Calculated using the following formula:

[0130]

[0131] Among them, P fly For flight power, Pmag Power of the magnetic detector;

[0132] Lower bound of positioning error σ pos Calculated using the following formula:

[0133]

[0134] Where σ is the standard deviation of the magnetic detector noise. This represents the magnitude of the magnetic field gradient.

[0135] In some alternative implementations, roulette or tournament methods are used to select multiple individuals with high fitness.

[0136] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for optimizing the linear blockade formation of a multi-magnetic probe UAV based on a genetic algorithm, characterized in that, include: Step S1: Using the distance between UAVs and their flight speed as parameters to be optimized, encode them to form an initial population; Step S2: For each individual, calculate the fitness value based on the fitness function constructed based on coverage, detection probability, anti-interference performance, total energy consumption, and positioning error; Step S3: Select multiple individuals with high fitness values ​​and perform crossover mutation to form a new population; Step S4: Repeat steps S2 and S3 until the maximum number of iterations is reached or the fitness value converges, and determine the optimal combination of UAV spacing and flight speed.

2. The method for optimizing the linear blockade formation of multi-magnetic probe UAVs based on genetic algorithms as described in claim 1, characterized in that, Step S1 further includes: Encode using the actual values ​​of drone spacing and flight speed; or The distance between drones and their flight speed are discretized into binary strings to form drone distance encoding and flight speed encoding, respectively.

3. The method for optimizing the linear blockade formation of multi-magnetic probe UAVs based on genetic algorithms as described in claim 1, characterized in that, In step S2, the fitness function is: Where α1, α2, α3, α4, and α5 are weighting coefficients, and W cover For the coverage width, P detect For detection probability, SNR total E is the signal-to-noise ratio enhancement factor. total For total energy consumption, σ pos This is the lower bound of the positioning error; Among them, the coverage width W cover Calculated using the following formula: Where r is the detection radius of a single magnetic detector, N is the number of UAVs, D is the distance between UAVs, and θ is the azimuth angle at which the magnetic anomaly signal attenuates to the detection threshold; Detection probability P detect Calculated using the following formula: P detect =1-e -λ·t·N ; Where λ is the detection probability density. t is the mission time, and v is the flight speed; Signal-to-noise ratio (SNR) enhancement factor total Calculated using the following formula: S i Let I be the signal power, I be the interference source strength, and r be the signal power. i For the drone's location, r 干扰 Location of the interference source; Total energy consumption E total Calculated using the following formula: Among them, P fly For flight power, P mag Power of the magnetic detector; Lower bound of positioning error σ pos Calculated using the following formula: Where σ is the standard deviation of the magnetic detector noise. This represents the magnitude of the magnetic field gradient.

4. The method for optimizing the linear blockade formation of multi-magnetic probe UAVs based on genetic algorithms as described in claim 1, characterized in that, Step S3 further includes using roulette or tournament methods to select multiple individuals with high fitness.

5. A device for optimizing the linear blockade formation of a multi-magnetic probe UAV based on a genetic algorithm, characterized in that, include: The initial population encoding module is used to encode the initial population using the drone spacing and flight speed as parameters to be optimized. The fitness value calculation module is used to calculate the fitness value for each individual based on a fitness function constructed from coverage, detection probability, anti-interference performance, total energy consumption, and positioning error. The crossover mutation module is used to select multiple individuals with high fitness values ​​for crossover mutation to form a new population. The iterative control module is used to repeatedly call the fitness value calculation module and the crossover and mutation module until the maximum number of iterations is reached or the fitness value converges, thereby determining the optimal combination of UAV spacing and flight speed.

6. The multi-magnetic probe UAV linear blockade formation optimization device based on genetic algorithm as described in claim 5, characterized in that, The initial population coding module includes: Direct encoding units are used to encode the actual values ​​of UAV spacing and flight speed; or The mapping encoding unit is used to discretize the UAV spacing and flight speed into binary strings, respectively, to form UAV spacing encoding and flight speed encoding.

7. The multi-magnetic probe UAV linear blockade array optimization device based on genetic algorithm as described in claim 5, characterized in that, In the fitness value calculation module, the fitness function is: Where α1, α2, α3, α4, α5 are weighting coefficients, and W cover For the coverage width, P detect For detection probability, SNR total E is the signal-to-noise ratio enhancement factor. total For total energy consumption, σ pos This is the lower bound of the positioning error; Among them, the coverage width W cover Calculated using the following formula: Where r is the detection radius of a single magnetic detector, N is the number of UAVs, D is the distance between UAVs, and θ is the azimuth angle at which the magnetic anomaly signal attenuates to the detection threshold; Detection probability P detect Calculated using the following formula: P detect =1-e -λ·t·N ; Where λ is the detection probability density. t is the mission time, and v is the flight speed; Signal-to-noise ratio (SNR) enhancement factor total Calculated using the following formula: S i Let I be the signal power, I be the interference source strength, and r be the signal power. i For the drone's location, r 干扰 Location of the interference source; Total energy consumption E total Calculated using the following formula: Among them, P fly For flight power, P mag Power of the magnetic detector; Lower bound of positioning error σ pos Calculated using the following formula: Where σ is the standard deviation of the magnetic detector noise. This represents the magnitude of the magnetic field gradient.

8. The multi-magnetic probe UAV linear blockade formation optimization device based on genetic algorithm as described in claim 5, characterized in that, Roulette wheel or tournament methods are used to select multiple individuals with high fitness.

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