Radar sector arrangement optimization method and device based on firefly optimization algorithm

Abstract

The invention provides a radar sector arrangement optimization method and device based on a firefly optimization algorithm. The method comprises the following steps: sequentially substituting solutions in an initial solution set into a cost function to obtain a plurality of solution brightness, and sorting the solution brightness to obtain a solution brightness sequence and a current optimal solution; selecting a mobile solution according to the brightness degree of each solution in the solution brightness sequence and a roulette model, and performing attraction calculation on the mobile solution according to a preset attraction calculation rule to obtain an attraction result; obtaining the moving direction and the moving distance of the moving solution according to the attraction degree result; according to the method, the to-be-optimized sector arrangement parameters are flexibly set by adopting the firefly algorithm, so that sector beam arrangement of different radars is met, and coupling among the parameters is avoided while the universality of the method is improved; and secondly, iterative optimization is carried out on the solution brightness according to the adaptation degree, so that the problem of falling into a local optimal solution is avoided on the premise of ensuring the calculation precision, and the radar resource utilization rate is improved.

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and in particular to a radar sector arrangement optimization method and device based on a firefly optimization algorithm. Background Art

[0002] Sector arrangement or sector beam arrangement is the process of dividing the coverage space into multiple sectors to improve the accuracy of target perception when the radar indicator antenna arranges the transmission and reception of electromagnetic waves in space. Reasonable planning of sector beams can improve the utilization rate of radar resources and is of great significance in the fields of signal processing, data processing and resource scheduling.

[0003] In the prior art, a globally optimized genetic algorithm is generally used to obtain radar sector arrangement parameters. For example, "Phase-controlled array radar pulse interleaved beam dwell scheduling algorithm based on genetic algorithm" (patent application number CN202110029477.2, publication number CN112749804A) discloses an adaptive beam dwell scheduling algorithm involving phased array radar and applying pulse interleaving technology. The algorithm pre-screens the radar task requests in each scheduling interval, obtains the final scheduling sequence through genetic algorithm crossover, and uses adaptive processing to effectively reduce the task loss rate and improve the realization value rate and correction value rate. However, the key factors (crossover rate and mutation rate) of the implementation carrier of the algorithm-genetic algorithm require a lot of experiments to obtain, and the fixed probability used by the algorithm may not always provide the best response to the optimal solution. When dealing with the optimal solution of more information dimensions and more coupling relationship problems, it is easy to fall into the local optimal solution and produce the problem of slow iteration speed and long time consumption.

[0004] Therefore, the existing radar sector arrangement method has the problems of slow iteration speed and easy to fall into the local optimal solution, which leads to low radar resource utilization. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a radar sector arrangement optimization method and device based on the firefly optimization algorithm.

[0006] The technical problem to be solved by the present invention is achieved through the following technical solutions: In a first aspect, the present invention provides a radar sector arrangement optimization method based on a firefly optimization algorithm, comprising: S101, obtaining the radar's sector arrangement parameters to be optimized and the radar's sector initial parameters, and generating a cost function according to the sector arrangement parameters to be optimized; S102, using the initial sector parameters to calculate the initial solution position of the sector arrangement parameters to be optimized, to obtain an initial solution set; S103, substituting the solutions in the initial solution set into the cost function in turn to obtain multiple solution brightnesses, and sorting the multiple solution brightnesses to obtain a solution brightness sequence and a current optimal solution; S104, selecting a moving solution according to the brightness of each solution in the solution brightness sequence and the roulette model, and calculating the attractiveness of the moving solution according to a preset attractiveness calculation rule to obtain an attractiveness result; S105, obtaining the moving direction and moving distance of the moving solution according to the attraction result; S106, updating the solution position of the initial solution set in S103 according to the result of S105, obtaining a sector update parameter, and using the sector update parameter as the initial solution set in S103; S107, repeatedly executing S103-S106 until the distance between the sector update parameter in S106 and the current optimal solution in S103 is less than a preset threshold; S108. When the condition of S107 is met, output the current optimal solution obtained when S103 was most recently executed, and use the current optimal solution as the optimized sector arrangement parameter of the radar.

[0007] Optionally, the sector arrangement parameters to be optimized include: the center beam width in the antenna normal direction, the antenna scanning speed, the antenna servo scanning speed, the beam dwell time, the beam pulse repetition frequency and the target detection probability.

[0008] Optionally, the cost function includes: a first cost function and a second cost function; The first cost function is used to calculate the total accumulation time of target detection ; The second cost function is used to calculate the cumulative detection probability of target detection ; ; ; in, Indicates The effective integration time of a beam irradiating the target is Indicates Scan the airspace, Indicates the cumulative number of beams emitted by the antenna in the target direction. Indicates the preset number of scans of the airspace. Indicates the sector coverage rate, represents the target detection probability, represents the cumulative multiplication process, Indicates sum processing; ; ; ; in, represents the beam pulse repetition frequency, represents the target data rate, represents the area swept by the antenna, represents the total area of ​​the beam arrangement, Indicates the antenna scanning speed, represents the beam dwell time, Indicates the deviation from the antenna normal The width of the beam, represents the target signal-to-noise ratio, represents the target false alarm probability; ; represents the central beam width in the normal direction of the antenna, Indicates the deviation from the antenna normal The angular difference of the beams; ; ; Indicates the antenna servo scanning speed.

[0009] Optionally, the sector initial parameters include: iterative fixed parameters, cost function parameter range, target solution set number And the problem dimension ; Iterative fixed parameters include: target data rate, target signal-to-noise ratio, light intensity attraction coefficient, maximum attraction factor, moving step factor, maximum number of iterations, minimum accuracy, and minimum accuracy iteration number; The cost function parameter range is the parameter range of the sector arrangement parameters to be optimized.

[0010] Optionally, the initial solution position of the sector arrangement parameters to be optimized is calculated using the initial sector parameters to obtain an initial solution set, including: Use Latin hypercube sampling method to generate a preset number of random numbers; the preset number is the number of target solution sets And the problem dimension The product of Substitute each random number into the mapping function in turn to calculate the corresponding solution and obtain an initial solution set; The mapping processing function is expressed as: ; in, represents the lower boundary of the cost function parameter range, Represents the upper boundary of the cost function parameter range, Represents a random number, represents a solution in the initial solution set.

[0011] Optionally, the solutions in the initial solution set are sequentially substituted into the cost function to obtain multiple solution brightnesses, and the multiple solution brightnesses are sorted to obtain a solution brightness sequence and a current optimal solution, including: Substituting the solutions in the initial solution set into the first cost function in turn to obtain a plurality of first solution brightnesses; Arrange the plurality of first solution brightnesses in descending order to obtain a first solution brightness sequence, and use the first solution brightness sequence as a solution brightness sequence; The minimum value in the brightness sequence of the first solution is taken as the current optimal solution. Optionally, the solutions in the initial solution set are sequentially substituted into the cost function to obtain multiple solution brightnesses, and the multiple solution brightnesses are sorted to obtain a solution brightness sequence and a current optimal solution, including: Substituting the solutions in the initial solution set into the second cost function in turn to obtain multiple second solution brightnesses; Arrange the plurality of second solution brightnesses in descending order to obtain a second solution brightness sequence, and use the second solution brightness sequence as a solution brightness sequence; The maximum value in the second solution brightness sequence is taken as the current optimal solution.

[0012] Optionally, a moving solution is selected according to the brightness of each solution in the solution brightness sequence and the roulette model, and the attractiveness of the moving solution is calculated according to a preset attractiveness calculation rule to obtain an attractiveness result, including: The solutions in the solution brightness sequence are input into the roulette model according to the corresponding brightness level to obtain the moving solution; The Euclidean distance calculation method is used to calculate the mutual attraction between the moving solutions and obtain the attraction result.

[0013] Optionally, the moving direction and moving distance of the moving solution are obtained according to the attraction result, including: The maximum value among the attraction results is used as the moving direction of the moving solution; The maximum value in the attraction result and the preset step size factor are used to update the position of the moving solution to obtain the moving distance of the moving solution.

[0014] In a second aspect, the present invention provides a radar sector arrangement optimization device based on a firefly optimization algorithm, comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the radar sector arrangement optimization device based on the firefly optimization algorithm is running, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to execute the steps of the radar sector arrangement optimization method based on the firefly optimization algorithm in the first aspect mentioned above.

[0015] The present invention provides a radar sector arrangement optimization method and device based on the firefly optimization algorithm. Among them, a radar sector arrangement optimization method based on the firefly optimization algorithm includes: S101, obtaining the radar sector arrangement parameters to be optimized and the radar sector initial parameters, and generating a cost function according to the sector arrangement parameters to be optimized; S102, using the sector initial parameters to calculate the initial solution position of the sector arrangement parameters to be optimized, and obtaining an initial solution set; S103, substituting the solutions in the initial solution set into the cost function in turn to obtain multiple solution brightnesses, and sorting the multiple solution brightnesses to obtain a solution brightness sequence and the current optimal solution; S104, selecting a moving solution according to the brightness of each solution in the solution brightness sequence and the roulette model, and sorting the moving solution according to the preset attraction calculation rules. Perform attraction calculation to obtain attraction result; S105, obtain the moving direction and moving distance of the moving solution according to the attraction result; S106, update the solution position of the initial solution set in S103 according to the result of S105, obtain the sector update parameter, and use the sector update parameter as the initial solution set in S103; S107, repeat S103-S106 until the distance between the sector update parameter in S106 and the current optimal solution in S103 is less than a preset threshold; S108, when the condition of S107 is met, output the current optimal solution obtained when S103 was most recently executed, and use the current optimal solution as the optimized sector arrangement parameter of the radar. In the present invention, by adopting the firefly algorithm, the parameters of the optimized sector arrangement can be flexibly set to meet the sector beam arrangement of different radars, thereby improving the versatility of the method and avoiding coupling between parameters; secondly, by iteratively optimizing the solution brightness according to the degree of adaptation, while ensuring the calculation accuracy, the problem of falling into the local optimal solution is avoided, the iteration speed is improved, and the resource utilization of the radar is effectively improved.

[0016] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a flow chart of a radar sector arrangement optimization method based on a firefly optimization algorithm provided by an embodiment of the present invention; Figure 2 A schematic diagram of a radar sector beam arrangement obtained after optimization using a method according to an embodiment of the present invention provided in an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a radar sector arrangement optimization device based on a firefly optimization algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0019] In order to improve the resource utilization of radar, an embodiment of the present invention provides a radar sector arrangement optimization method based on a firefly optimization algorithm. Figure 1 The present invention provides a flowchart of a radar sector arrangement optimization method based on a firefly optimization algorithm. Figure 1 As shown, the method includes: S101, obtaining the radar's sector arrangement parameters to be optimized and the radar's sector initial parameters, and generating a cost function according to the sector arrangement parameters to be optimized.

[0020] Optionally, the sector arrangement parameters to be optimized include: the center beam width in the antenna normal direction, the antenna scanning speed, the antenna servo scanning speed, the beam dwell time, the beam pulse repetition frequency and the target detection probability.

[0021] It should be noted that the parameters of the optimized sector arrangement can also be flexibly adjusted according to the different radar types and application scenarios. In addition, since the firefly algorithm used has no coupling relationship between its various parameters, it avoids the problem of slow iteration speed and long time caused by falling into the local optimal solution when dealing with the optimal solution of problems with more information dimensions and more coupling relationships.

[0022] In addition, the number of sector arrangement parameters to be optimized can be based on the problem dimension to set it up.

[0023] Optionally, the cost function includes: a first cost function and a second cost function; The first cost function is used to calculate the total accumulation time of target detection ; The second cost function is used to calculate the cumulative detection probability of target detection ; ; ; in, Indicates The effective integration time of a beam irradiating the target is Indicates Scan the airspace, Indicates the cumulative number of beams emitted by the antenna in the target direction. Indicates the preset number of scans of the airspace. Indicates the sector coverage rate, represents the target detection probability, represents the cumulative multiplication process, Indicates sum processing; ; ; ; in, represents the beam pulse repetition frequency, represents the target data rate, represents the area swept by the antenna, represents the total area of ​​the beam arrangement, Indicates the antenna scanning speed, represents the beam dwell time, Indicates the deviation from the antenna normal The width of the beam, represents the target signal-to-noise ratio, represents the target false alarm probability; ; represents the central beam width in the normal direction of the antenna, Indicates the deviation from the antenna normal The angular difference of the beams; ; ; Indicates the antenna servo scanning speed.

[0024] Optionally, the sector initial parameters include: iterative fixed parameters, cost function parameter range, target solution set number And the problem dimension ; Iterative fixed parameters include: target data rate, target signal-to-noise ratio, light intensity attraction coefficient, maximum attraction factor, moving step factor, maximum number of iterations, minimum accuracy, and minimum accuracy iteration number; The cost function parameter range is the parameter range of the sector arrangement parameters to be optimized.

[0025] S102: Calculate the initial solution position of the sector arrangement parameters to be optimized using the sector initial parameters to obtain an initial solution set.

[0026] Optionally, S102 may specifically include: Use Latin hypercube sampling method to generate a preset number of random numbers; the preset number is the number of target solution sets And the problem dimension The product of Substitute each random number into the mapping function in turn to calculate the corresponding solution and obtain an initial solution set; The mapping processing function is expressed as: ; in, represents the lower boundary of the cost function parameter range, Represents the upper boundary of the cost function parameter range, Represents a random number, represents a solution in the initial solution set.

[0027] It should be noted that, in the embodiment of the present invention, by performing mapping processing on the random number, it is possible to map the random number into the range of the cost function parameter.

[0028] S103, substituting the solutions in the initial solution set into the cost function in turn to obtain multiple solution brightnesses, and sorting the multiple solution brightnesses to obtain a solution brightness sequence and a current optimal solution.

[0029] Optionally, in an implementation manner, S103 may specifically include: Substituting the solutions in the initial solution set into the first cost function in turn to obtain a plurality of first solution brightnesses; Arrange the plurality of first solution brightnesses in descending order to obtain a first solution brightness sequence, and use the first solution brightness sequence as a solution brightness sequence; The minimum value in the brightness sequence of the first solution is taken as the current optimal solution.

[0030] Optionally, in another implementation, S103 may specifically include: Substituting the solutions in the initial solution set into the second cost function in turn to obtain multiple second solution brightnesses; Arrange the plurality of second solution brightnesses in descending order to obtain a second solution brightness sequence, and use the second solution brightness sequence as a solution brightness sequence; The maximum value in the second solution brightness sequence is taken as the current optimal solution.

[0031] S104, selecting a moving solution according to the brightness of each solution in the solution brightness sequence and the roulette model, and calculating the attractiveness of the moving solution according to a preset attractiveness calculation rule to obtain an attractiveness result.

[0032] Optionally, S104 may specifically include: The solutions in the solution brightness sequence are input into the roulette model according to the corresponding brightness level to obtain the moving solution; The Euclidean distance calculation method is used to calculate the mutual attraction between the moving solutions and obtain the attraction result.

[0033] Specifically, the calculation formula for the attraction result is as follows: ; ; ; Represents the mobile solution The brightness of its own Passing distance After the decay of The brightness of light produced when represents the light intensity attraction coefficient, represents the base of natural logarithms, Represents the mobile solution To mobile solution The distance Represents the mobile solution No. Quantity, Represents the mobile solution No. Quantity, Represents the problem dimension, Represents the mobile solution With mobile solution The attraction result between Represents the maximum attractiveness factor.

[0034] S105. Obtain the moving direction and moving distance of the moving solution according to the attraction result.

[0035] Optionally, S105 may specifically include: The maximum value among the attraction results is used as the moving direction of the moving solution; The maximum value in the attraction result and the preset step size factor are used to update the position of the moving solution to obtain the moving distance of the moving solution.

[0036] In the embodiment of the present invention, select Maximum attraction value As The moving direction of The optimal position is guided to make the final iterative result more accurate.

[0037] S106. Update the solution position of the initial solution set in S103 according to the result of S105 to obtain sector update parameters, and use the sector update parameters as the initial solution set in S103.

[0038] S107. Repeat S103-S106 until the distance between the sector update parameter in S106 and the current optimal solution in S103 is smaller than a preset threshold.

[0039] S108. When the condition of S107 is met, output the current optimal solution obtained when S103 was most recently executed, and use the current optimal solution as the optimized sector arrangement parameter of the radar.

[0040] It should be noted that the value of the preset threshold can be flexibly adjusted according to the application scenario and accuracy requirements, and this value is not specifically limited in the embodiments of the present invention.

[0041] In addition, the iterative process can also be expressed by the formula as follows: ; in, represent exist The updated position after iterations, is the number of iterations, express In the number of iterations The position below express In the number of iterations The position below is the step size factor, It is generated randomly with equal probability A random number between .

[0042] The mobile calculation formula corresponding to the solution position update is as follows: ; In addition, when the number of iterations is close to the minimum precision iteration number, the step size factor can be reduced to achieve a more precise adjustment of the optimized sector arrangement parameters. Specifically, the step size factor The adjustment can be found in the following formula: ; Represents the adjusted number of iterations The step size factor is is the step size dynamic attenuation coefficient, which can be determined by exponential decay, for example , , Represents the number of iterations before adjustment The step size factor.

[0043] In addition, it should be noted that in the field of radar technology, radar sector arrangement specifically refers to the arrangement of radar sector beams. In order to illustrate the implementation effect of the embodiment of the present invention, Figure 2 A schematic diagram of a radar sector beam arrangement obtained after optimization using the method of an embodiment of the present invention is provided in an embodiment of the present invention. Figure 2 The horizontal coordinate of corresponds to the azimuth angle of the array surface in the spherical coordinate system, in degrees, and the vertical coordinate corresponds to the elevation angle of the array surface in the spherical coordinate system, in degrees. Figure 2 As shown, the middle area of ​​the azimuth of the array spherical coordinate system is the key area, which is manifested as a denser arrangement of the sector beams than the two sides, and the two sides of the azimuth of the array spherical coordinate system are non-key areas, which are manifested as a sparser arrangement of the sector beams. Therefore, the method provided by the embodiment of the present invention realizes the rational setting of the sector arrangement or sector beam arrangement.

[0044] Through the execution of the above method and the display of results, the embodiment of the present invention has the following advantages: 1. Adjustability of target dimension information: In the firefly algorithm optimization framework, the problem dimension and cost function can be flexibly defined to adapt to the sector beam arrangement of different radars. Both the problem dimension and the cost function can be adjusted by configuring the arrangement parameters, so it can adapt to a variety of target scenarios.

[0045] 2. Dynamic matching of sector beam arrangement: The Firefly optimization algorithm can accommodate various types of observation data, including antenna scanning speed, beam pulse repetition frequency, and target detection probability. Through the optimization search process, the best sector beam parameters are arranged without the need to recalculate the dwell time and beam search method for a specific target.

[0046] 3. Parallel processing and calculation optimization: In matrix operations when solving multiple solutions, parallel processing technology can be used to use multiple computing units to calculate and solve at the same time, thereby accelerating the problem-solving process.

[0047] 4. Global search for the optimal solution.

[0048] 5. Interface consistency: A unified interface can be designed, and users only need to input their configuration parameters through the interface. The interface is responsible for mapping the configuration parameters to the corresponding part of the solution space of the Firefly Optimization Algorithm. Users do not need to understand the underlying algorithm details, which reduces the threshold for use.

[0049] The embodiment of the present invention provides a radar sector arrangement optimization method based on the firefly optimization algorithm. By adopting the firefly algorithm, the parameters of the optimized sector arrangement can be flexibly set to meet the sector beam arrangement of different radars, thereby improving the versatility of the method and avoiding coupling between parameters. Secondly, by iteratively optimizing the solution brightness according to the degree of adaptation, while ensuring the calculation accuracy, the problem of falling into the local optimal solution is avoided, the iteration speed is improved, and the resource utilization of the radar is effectively improved.

[0050] The method provided in the embodiment of the present invention can be applied to an electronic device. Specifically, the electronic device can be: a desktop computer, a portable computer, an intelligent mobile terminal, a server, etc., which is not limited in the embodiment of the present invention.

[0051] Based on the same inventive concept, an embodiment of the present invention also provides a radar sector arrangement optimization device based on a firefly optimization algorithm. Figure 3A schematic diagram of the structure of a radar sector arrangement optimization device based on a firefly optimization algorithm provided by an embodiment of the present invention includes: a processor 310, a storage medium 320 and a bus 330, wherein the storage medium 320 stores machine-readable instructions executable by the processor 310, and when the radar sector arrangement optimization device based on the firefly optimization algorithm is running, the processor 310 communicates with the storage medium 320 via the bus 330, and the processor 310 executes the machine-readable instructions to execute the steps of the radar sector arrangement optimization method based on the firefly optimization algorithm. The specific implementation method and technical effect are similar and will not be repeated here.

[0052] The storage medium 320 may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk storage. Optionally, the storage medium 320 may also be at least one storage device located away from the processor 310.

[0053] The above-mentioned processor 310 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0054] It should be noted that the terms "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention.

[0055] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0056] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art can understand and implement other changes to the disclosed embodiments by viewing the drawings and the disclosed content. In the description of the present invention, the term "comprising" does not exclude other components or steps, "one" or "an" does not exclude multiple situations, and the meaning of "multiple" is two or more, unless otherwise clearly and specifically limited. In addition, certain measures are recorded in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0057] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A radar sector arrangement optimization method based on firefly optimization algorithm, characterized in that: include: S101, obtaining the radar's sector arrangement parameters to be optimized and the radar's sector initial parameters, and generating a cost function according to the sector arrangement parameters to be optimized; S102, using the initial sector parameters to calculate the initial solution position of the sector arrangement parameters to be optimized, to obtain an initial solution set; S103, substituting the solutions in the initial solution set into the cost function in sequence to obtain a plurality of solution brightnesses, and sorting the plurality of solution brightnesses to obtain a solution brightness sequence and a current optimal solution; S104, selecting a moving solution according to the brightness of each solution in the solution brightness sequence and the roulette model, and calculating the attractiveness of the moving solution according to a preset attractiveness calculation rule to obtain an attractiveness result; S105, obtaining a moving direction and a moving distance of the moving solution according to the attraction result; S106, updating the solution position of the initial solution set in S103 according to the result of S105, obtaining a sector update parameter, and using the sector update parameter as the initial solution set in S103; S107, repeatedly executing S103-S106 until the distance between the sector update parameter in S106 and the current optimal solution in S103 is less than a preset threshold; S108. When the condition of S107 is met, output the current optimal solution obtained when S103 was most recently executed, and use the current optimal solution as the optimized sector arrangement parameter of the radar.

2. The radar sector arrangement optimization method based on the firefly optimization algorithm according to claim 1 is characterized in that: The sector arrangement parameters to be optimized include: the center beam width in the antenna normal direction, the antenna scanning speed, the antenna servo scanning speed, the beam dwell time, the beam pulse repetition frequency and the target detection probability.

3. The radar sector arrangement optimization method based on the firefly optimization algorithm according to claim 1 is characterized in that: The cost function includes: a first cost function and a second cost function; The first cost function is used to calculate the total accumulation time of target detection ; The second cost function is used to calculate the cumulative detection probability of target detection ; ； ； in, Indicates The effective integration time of a beam irradiating the target is Indicates Scan the airspace, Indicates the cumulative number of beams emitted by the antenna in the target direction. Indicates the preset number of scans of the airspace. Indicates the sector coverage rate, represents the target detection probability, represents the cumulative multiplication process, Indicates sum processing; ； ； ； in, represents the beam pulse repetition frequency, represents the target data rate, represents the area swept by the antenna, represents the total area of ​​the beam arrangement, Indicates the antenna scanning speed, represents the beam dwell time, Indicates the deviation from the antenna normal The width of the beam, represents the target signal-to-noise ratio, represents the target false alarm probability; ； represents the central beam width in the normal direction of the antenna, Indicates the deviation from the antenna normal The angular difference of the beams; ； ； Indicates the antenna servo scanning speed.

4. The radar sector arrangement optimization method based on the firefly optimization algorithm according to claim 1 is characterized in that: The sector initial parameters include: iterative fixed parameters, cost function parameter range, target solution set number And the problem dimension ; The iterative fixed parameters include: target data rate, target signal-to-noise ratio, light intensity attraction coefficient, maximum attraction factor, moving step factor, maximum number of iterations, minimum precision and minimum number of iterations; The cost function parameter range is the parameter range of the sector arrangement parameter to be optimized.

5. The radar sector arrangement optimization method based on the firefly optimization algorithm according to claim 4 is characterized in that: The using the initial sector parameters to calculate the initial solution position of the to-be-optimized sector arrangement parameters to obtain an initial solution set includes: Generate a preset number of random numbers using the Latin hypercube sampling method; the preset number is the number of target solution sets And the dimensions of the problem The product of Substituting each of the random numbers into the mapping function in turn to calculate the corresponding solution to obtain the initial solution set; The mapping processing function is expressed as: ； in, represents the lower boundary of the cost function parameter range, Represents the upper boundary of the cost function parameter range, Represents a random number, represents a solution in the initial solution set.

6. The radar sector arrangement optimization method based on the firefly optimization algorithm according to claim 3 is characterized in that: Substituting the solutions in the initial solution set into the cost function in sequence to obtain a plurality of solution brightnesses, and sorting the plurality of solution brightnesses to obtain a solution brightness sequence and a current optimal solution, comprises: Substituting the solutions in the initial solution set into the first cost function in sequence to obtain a plurality of first solution brightnesses; Arrange the plurality of first solution brightnesses in descending order to obtain a first solution brightness sequence, and use the first solution brightness sequence as the solution brightness sequence; The minimum value in the brightness sequence of the first solution is used as the current optimal solution.

7. The radar sector arrangement optimization method based on the firefly optimization algorithm according to claim 3 is characterized in that: Substituting the solutions in the initial solution set into the cost function in sequence to obtain a plurality of solution brightnesses, and sorting the plurality of solution brightnesses to obtain a solution brightness sequence and a current optimal solution, comprises: Substituting the solutions in the initial solution set into the second cost function in sequence to obtain a plurality of second solution brightnesses; Arrange the plurality of second solution brightnesses in descending order to obtain a second solution brightness sequence, and use the second solution brightness sequence as the solution brightness sequence; The maximum value in the second solution brightness sequence is used as the current optimal solution.

8. The radar sector arrangement optimization method based on the firefly optimization algorithm according to claim 1 is characterized in that: The step of selecting a moving solution according to the brightness of each solution in the solution brightness sequence and the roulette model, and calculating the attractiveness of the moving solution according to a preset attractiveness calculation rule to obtain an attractiveness result includes: Inputting the solutions in the solution brightness sequence into the roulette model according to the corresponding brightness levels to obtain a moving solution; The Euclidean distance calculation method is used to calculate the mutual attraction degree between the moving solutions to obtain the attraction result.

9. The radar sector arrangement optimization method based on the firefly optimization algorithm according to claim 1 is characterized in that: The step of obtaining the moving direction and moving distance of the moving solution according to the attraction result includes: The maximum value among the attraction results is used as the moving direction of the moving solution; The position of the mobile solution is updated using the maximum value in the attraction result and a preset step factor to obtain the moving distance of the mobile solution.

10. A radar sector arrangement optimization device based on firefly optimization algorithm, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the radar sector arrangement optimization device based on the firefly optimization algorithm is running, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to perform the steps of the radar sector arrangement optimization method based on the firefly optimization algorithm as described in any one of claims 1 to 9.

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