Firefly optimization algorithm-based radar sector arrangement optimization method and device

Abstract

The application provides a firefly optimization algorithm-based radar sector arrangement optimization method and device. The method comprises the following steps: sequentially substituting solutions in an initial solution set into a cost function to obtain multiple solution brightnesses, and sorting the solution brightnesses to obtain a solution brightness sequence and a current optimal solution; selecting a moving solution according to the brightness of each solution in the solution brightness sequence and a roulette bet model, calculating the attraction degree of the moving solution according to a preset attraction degree calculation rule to obtain an attraction degree result; and obtaining the moving direction and moving distance of the moving solution according to the attraction degree result. In the application, the firefly algorithm is used to flexibly set the sector arrangement parameters to be optimized, so that the sector beam arrangement of different radars is met, the universality of the method is improved, and the coupling between parameters is avoided. Furthermore, the solution brightness is iteratively optimized according to the adaptability, the problem of falling into a local optimal solution is avoided under the premise of ensuring the calculation accuracy, and the radar resource utilization rate is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of radar technology, in particular to a radar sector arrangement optimization method and device based on a glowworm optimization algorithm. BACKGROUND

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

[0003] In the prior art, a global optimization genetic algorithm is generally used to obtain radar sector arrangement parameters. For example, an adaptive beam dwell scheduling algorithm for phased array radar using pulse interleaving technology is disclosed in "Pulse interleaved beam dwell scheduling algorithm for phased array radar based on genetic algorithm" (Patent Application No. CN202110029477.2, Publication No. CN112749804A). 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 of the implementation carrier of the algorithm-genetic algorithm (crossover rate and mutation rate) need to be obtained through a large number of experiments, and the fixed probability used in the algorithm may not always provide the best response to the optimal solution. When dealing with optimal solutions with more information dimensions and more coupling relationships, it is easy to fall into local optimal solutions, resulting in 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 local optimal solution, which further leads to low utilization rate of radar resources. SUMMARY

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

[0006] The technical problem to be solved by the present application is solved by the following technical scheme:

[0007] In a first aspect, the present application provides a radar sector arrangement optimization method based on a glowworm optimization algorithm, comprising:

[0008] S101, obtaining the to-be-optimized sector arrangement parameters of the radar and the initial parameters of the sectors of the radar, and generating a cost function according to the to-be-optimized sector arrangement parameters;

[0009] S102, calculating the initial solution position of the to-be-optimized sector arrangement parameters using the initial parameters of the sectors to obtain an initial solution set;

[0010] S103, substituting the solutions in the initial solution set into the cost function in sequence to obtain multiple solution brightnesses, and sorting the multiple solution brightnesses to obtain a solution brightness sequence and a current optimal solution;

[0011] 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;

[0012] S105, obtaining the moving direction and moving distance of the moving solution according to the attraction result;

[0013] 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;

[0014] S107, repeating 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;

[0015] 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.

[0016] Optionally, the sector arrangement parameters to be optimized include: antenna normal center beam width, antenna scanning speed, antenna servo scanning speed, beam dwell time, beam pulse repetition frequency, and target detection probability.

[0017] Optionally, the cost function includes: a first cost function and a second cost function;

[0018] 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 ;

[0019] ;

[0020] ;

[0021] in, Indicates the The effective integration time of a beam irradiating the target is Indicates the 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 sector coverage, represents the target detection probability, Indicates cumulative multiplication processing, Indicates sum processing;

[0022] ;

[0023] ;

[0024] ;

[0025] 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;

[0026] ;

[0027] 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;

[0028] ;

[0029] ;

[0030] Indicates the antenna servo scanning speed.

[0031] Optionally, the sector initial parameters include: iterative fixed parameters, cost function parameter range, target solution set number and problem dimensions ;

[0032] 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;

[0033] The cost function parameter range is the parameter range of the sector arrangement parameters to be optimized.

[0034] 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:

[0035] generating a preset number of random numbers by using a Latin hypercube sampling method; the preset number is a number of target solution sets and a product of a problem dimension .

[0036] calculating corresponding solutions by substituting each random number into the mapping processing function in sequence to obtain an initial solution set;

[0037] The mapping processing function is represented as:

[0038] .

[0039] wherein, represents a lower boundary of a parameter range of the cost function, represents an upper boundary of the parameter range of the cost function, represents a random number, represents a solution in the initial solution set.

[0040] Optionally, substituting the solutions in the initial solution set into the cost function in sequence to obtain a plurality of solution luminances, and sorting the plurality of solution luminances to obtain a solution luminance sequence and a current optimal solution, comprising:

[0041] substituting the solutions in the initial solution set into the first cost function in sequence to obtain a plurality of first solution luminances;

[0042] arranging the plurality of first solution luminances in descending order to obtain a first solution luminance sequence, and taking the first solution luminance sequence as the solution luminance sequence;

[0043] taking a minimum value in the first solution luminance sequence as the current optimal solution.

[0044] Optionally, substituting the solutions in the initial solution set into the cost function in sequence to obtain a plurality of solution luminances, and sorting the plurality of solution luminances to obtain a solution luminance sequence and a current optimal solution, comprising:

[0045] substituting the solutions in the initial solution set into the second cost function in sequence to obtain a plurality of second solution luminances;

[0046] arranging the plurality of second solution luminances in descending order to obtain a second solution luminance sequence, and taking the second solution luminance sequence as the solution luminance sequence;

[0047] taking a maximum value in the second solution luminance sequence as the current optimal solution.

[0048] Optionally, selecting a moving solution according to a light degree of each solution in the solution luminance sequence and a roulette model, and calculating an attraction degree of the moving solution according to a preset attraction degree calculation rule to obtain an attraction degree result, comprising:

[0049] inputting the solutions in the solution luminance sequence into the roulette model according to corresponding light degrees to obtain the moving solution;

[0050] The attraction degree result is obtained by calculating the mutual attraction degree between the mobile solutions by using the Euclidean distance calculation method.

[0051] Optionally, the moving direction and the moving distance of the mobile solution are obtained according to the attraction degree result, comprising:

[0052] The maximum value in the attraction degree result is taken as the moving direction of the mobile solution.

[0053] The moving distance of the mobile solution is obtained by updating the position of the mobile solution by using the maximum value in the attraction degree result and a preset step factor.

[0054] In a second aspect, the present application provides a radar sector arrangement optimization device based on a glowworm optimization algorithm, comprising a processor, a storage medium and a bus, the storage medium storing machine readable instructions executable by the processor, when the radar sector arrangement optimization device based on the glowworm optimization algorithm is running, the processor and the storage medium communicate 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 glowworm optimization algorithm in the first aspect.

[0055] 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 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 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 the 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 sector arrangement to be optimized 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.

[0056] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic diagram of a flow chart of a radar sector arrangement optimization method based on a firefly optimization algorithm provided in an embodiment of the present invention;

[0058] Figure 2 A schematic diagram of a radar sector beam arrangement obtained by optimizing the radar sector beam arrangement using the method of the embodiment of the present invention;

[0059] Figure 3 A schematic structural diagram of a radar sector arrangement optimization device based on a firefly optimization algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0061] In order to improve the resource utilization of radar, an embodiment of the present invention provides a radar sector arrangement optimization method based on the firefly optimization algorithm. Figure 1 The present invention provides a flow chart of a radar sector arrangement optimization method based on the firefly optimization algorithm. Figure 1 As shown, the method includes:

[0062] S101: Obtain the radar's sector arrangement parameters to be optimized and the radar's sector initial parameters, and generate a cost function according to the sector arrangement parameters to be optimized.

[0063] Optionally, the sector arrangement parameters to be optimized include: antenna normal center beam width, antenna scanning speed, antenna servo scanning speed, beam dwell time, beam pulse repetition frequency, and target detection probability.

[0064] It should be noted that the optimized sector arrangement parameters can be flexibly adjusted based on the radar type and application scenario. Furthermore, since the firefly algorithm employed has no coupling relationships between its parameters, it avoids the problem of slow and time-consuming iterations that can occur when trying to find the optimal solution for problems with multiple information dimensions and multiple coupling relationships, often resulting from falling into local optimal solutions.

[0065] In addition, the number of sector arrangement parameters to be optimized can be based on the problem dimension to set it up.

[0066] Optionally, the cost function includes: a first cost function and a second cost function;

[0067] 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 ;

[0068] ;

[0069] ;

[0070] in, Indicates the The effective integration time of a beam irradiating the target is Indicates the 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 sector coverage, represents a target detection probability, represents a multiplication processing, represents a summation processing;

[0071] ;

[0072] ;

[0073] ;

[0074] wherein, represents a beam pulse repetition frequency, represents a target data rate, represents an antenna scanning area, represents a total area of beam arrangement, represents an antenna scanning speed, represents a beam dwell time, represents a width of a beam deviated from an antenna normal direction, represents a target signal-to-noise ratio, represents a target false alarm probability;

[0075] ;

[0076] represents a central beam width of an antenna normal direction, represents an angle difference of a beam deviated from an antenna normal direction;

[0077] ;

[0078] ;

[0079] represents an antenna servo scanning speed.

[0080] Optionally, the sector initial parameters comprise: iteration fixed parameters, cost function parameter range, target solution set number and problem dimension ;

[0081] The iteration fixed parameters comprise: target data rate, target signal-to-noise ratio, light intensity attraction coefficient, maximum attraction degree factor, moving step factor, maximum iteration number, minimum precision and minimum precision iteration number.

[0082] The cost function parameter range is a parameter range of the sector arrangement parameters to be optimized.

[0083] S102, initial solution position calculation of the sector arrangement parameters to be optimized is performed by using the sector initial parameters, and an initial solution set is obtained. ​​

[0084] Optionally, S102 can specifically include:

[0085] generating a preset number of random numbers by using a Latin hypercube sampling method; the preset number is a number of target solution sets and a product of a problem dimension ;

[0086] calculating corresponding solutions by substituting each random number into a mapping processing function in sequence, to obtain an initial solution set;

[0087] The mapping processing function is represented as:

[0088] ;

[0089] wherein, a lower boundary of a parameter range of the cost function, an upper boundary of the parameter range of the cost function, a random number, a solution in the initial solution set.

[0090] It should be noted that, in the embodiment of the application, the random number can be mapped into the parameter range of the cost function by mapping processing on the random number.

[0091] 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.

[0092] Optionally, in one implementation, S103 can specifically include:

[0093] substituting the solutions in the initial solution set into a first cost function in sequence to obtain a plurality of first solution brightnesses;

[0094] arranging the plurality of first solution brightnesses in descending order to obtain a first solution brightness sequence, and taking the first solution brightness sequence as the solution brightness sequence;

[0095] taking a minimum value in the first solution brightness sequence as the current optimal solution.

[0096] Optionally, in another implementation, S103 can specifically include:

[0097] substituting the solutions in the initial solution set into a second cost function in sequence to obtain a plurality of second solution brightnesses;

[0098] arranging the plurality of second solution brightnesses in descending order to obtain a second solution brightness sequence, and taking the second solution brightness sequence as the solution brightness sequence;

[0099] taking a maximum value in the second solution brightness sequence as the current optimal solution.

[0100] S104, selecting a moving solution according to the light intensity of each solution in the solution light intensity sequence and the roulette model, calculating the attraction degree of the moving solution according to the preset attraction degree calculation rule to obtain an attraction degree result.

[0101] Optionally, S104 can specifically include:

[0102] inputting the solutions in the solution light intensity sequence into the roulette model according to the corresponding light intensity to obtain the moving solution;

[0103] calculating the mutual attraction degree between the moving solutions by using the Euclidean distance calculation method to obtain the attraction degree result.

[0104] Specifically, the attraction degree result calculation formula is as follows:

[0105]

[0106]

[0107]

[0108] represents the light intensity of the moving solution after the attenuation of the distance to the moving solution , represents the light intensity attraction coefficient, represents the base of the natural logarithm, represents the distance from the moving solution to the moving solution , represents the first component of the moving solution , represents the first component of the moving solution , represents the problem dimension, represents the attraction degree result between the moving solution and the moving solution , represents the maximum attraction degree factor. S105, obtaining the moving direction and the moving distance of the moving solution according to the attraction degree result.

[0109] Optionally, S105 can specifically include:

[0110] taking the maximum value in the attraction degree result as the moving direction of the moving solution;

[0111]

[0112] ​​​​​​The maximum value of 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.

[0113] In the embodiment of the present invention, select Maximum attraction value As The direction of movement to solve the movement The optimal guidance is provided for the position of the iteration, making the final iteration result more accurate.

[0114] S106 , updating the solution positions of the initial solution set in S103 according to the result of S105 , obtaining sector update parameters, and using the sector update parameters as the initial solution set in S103 .

[0115] S107 , repeatedly executing 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.

[0116] 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.

[0117] 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 embodiment of the present invention.

[0118] In addition, the iterative process can also be expressed as follows:

[0119] ;

[0120] 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 .

[0121] The movement calculation formula corresponding to the position update is as follows:

[0122] ;

[0123] Further, when the iteration number is close to the minimum precision iteration number, the step length factor can also be reduced to achieve more refined optimization of the sector arrangement parameter adjustment. Specifically, the adjustment of the step length factor can refer to the following formula:

[0124]

[0125] represents the step length factor after adjustment of the iteration number is a step length dynamic attenuation coefficient, which can be determined by exponential attenuation, for example represents the step length factor before adjustment of the iteration number

[0126] In addition, it should be noted that in the field of radar technology, the radar sector arrangement specifically refers to the arrangement of radar sector beams. In order to illustrate the implementation effect of the embodiments of the present application, Figure 2 is a radar sector beam arrangement diagram obtained after optimization by the method provided by the embodiments of the present application. Figure 2 The abscissa of the radar sector beam arrangement diagram corresponds to the azimuth angle of the array spherical coordinate system, and the ordinate corresponds to the elevation angle of the array spherical coordinate system. As shown in Figure 2 , the middle region of the azimuth angle of the array spherical coordinate system is the key region, which is manifested by the denser arrangement of sector beams compared to the two sides, and the two side regions of the azimuth angle of the array spherical coordinate system are non-key regions, which are manifested by the sparser arrangement of sector beams. Therefore, the method provided by the embodiments of the present application realizes the rationalization of sector arrangement or sector beam arrangement.

[0127] Through the execution and result display of the above method, the embodiments of the present application have the following advantages:

[0128] 1. Target dimension information adjustability: In the glowworm optimization framework, the problem dimension and cost function can be flexibly defined to adapt to different radar sector beam arrangements. The problem dimension and cost function can be adjusted by configuring the arrangement parameters, so it can adapt to multiple target scenarios.

[0129] 2. Dynamic matching of sector beam arrangement: The glowworm optimization algorithm can accommodate various types of observation data, including antenna scanning speed, beam pulse repetition frequency, and target detection probability. Through optimization search processing, the best sector beam parameters are arranged, and there is no need to recalculate the residence time and beam search mode for specific targets.

[0130] ​​​​​​3. Parallel processing and computing optimization: in the matrix operation when solving multiple solution luminance, parallel processing technology can be used to simultaneously calculate and solve by using multiple computing units to speed up the problem solving process.

[0131] 4. Global search for optimal solution.

[0132] 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 firefly optimization algorithm solution space, and users do not need to understand the details of the underlying algorithm, thereby reducing the use threshold.

[0133] The firefly optimization algorithm-based radar sector arrangement optimization method provided by the embodiment can flexibly set the sector arrangement parameters to be optimized by using the firefly algorithm, so that the sector beam arrangement of different radars is met, the universality of the method is improved, and the coupling between parameters is avoided. Secondly, by iteratively optimizing the solution luminance according to the adaptability, the problem of falling into a local optimal solution is avoided under the premise of ensuring the calculation accuracy, the iteration speed is improved, and the resource utilization rate of the radar is effectively improved.

[0134] The method provided by the embodiment can be applied to an electronic device. Specifically, the electronic device can be a desktop computer, a portable computer, a smart mobile terminal, a server, and the like, and the embodiment is not limited thereto.

[0135] Based on the same inventive concept, the embodiment of the present application also provides a firefly optimization algorithm-based radar sector arrangement optimization device. Figure 3 The structure diagram of the firefly optimization algorithm-based radar sector arrangement optimization device provided by the embodiment of the present application includes a processor 310, a storage medium 320, and a bus 330. The storage medium 320 stores machine-readable instructions executable by the processor 310. When the firefly optimization algorithm-based radar sector arrangement optimization device is running, the processor 310 and the storage medium 320 communicate through the bus 330. The processor 310 executes the machine-readable instructions to perform the steps of the above-mentioned firefly optimization algorithm-based radar sector arrangement optimization method. The specific implementation manner and technical effects are similar, and will not be repeated here.

[0136] The storage medium 320 can include a random access memory (RAM) and a non-volatile memory (NVM), such as at least one disk memory. Optionally, the storage medium 320 can also be at least one storage device located away from the aforementioned processor 310.

[0137] The processor 310 described above can be a general processor, including a central processing unit (CPU), a network processor (NP), etc.; can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0138] It should be noted that the terms "first", "second", and so on are used to distinguish similar objects, and do not necessarily have to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the application 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 application. Rather, they are merely examples of devices and methods consistent with some aspects of the present application.

[0139] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like 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 application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described can be combined in any appropriate manner in one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in the present specification.

[0140] Although the present application is described herein in conjunction with various embodiments, other variations of the disclosed embodiments can be understood and implemented by those skilled in the art with reference to the drawings and the disclosure. In the description of the present application, the word "comprising" does not exclude other components or steps, "one" or "an" does not exclude a plurality, and "plurality" means two or more, unless otherwise explicitly specified. In addition, some measures are described in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0141] The above is further detailed description of the present application in combination with specific preferred embodiments, and cannot be deemed as limitation of the specific implementation of the present application to these descriptions. For those skilled in the art to which the present application belongs, without departing from the concept of the present application, a number of simple deductions or substitutions can be made, and all of them shall be deemed as falling within the protection scope of the present application.

Claims

1. A radar sector arrangement optimization method based on the 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; wherein, The sector arrangement parameters to be optimized include: antenna normal center beam width, antenna scanning speed, antenna servo scanning speed, beam dwell time, beam pulse repetition frequency and target detection probability; 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 The effective integration time of a beam irradiating the target is Indicates the 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 sector coverage, represents the target detection probability, Indicates cumulative multiplication processing, 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; The sector initial parameters include: iterative fixed parameters, cost function parameter range, target solution set number and problem dimensions 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; 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 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 performing an attraction calculation on the moving solution according to a preset attraction calculation rule to obtain an attraction result; S105, obtaining 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 to obtain sector update parameters, and use the sector update parameters 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 calculating the initial solution position of the to-be-optimized sector arrangement parameters by using the initial sector 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 problem dimensions The product of Substituting each of the random numbers into the mapping processing 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 bound of the cost function parameter range, Represents the upper bound of the cost function parameter range, represents a random number, represents the solution in the initial solution set.

3. The radar sector arrangement optimization method based on the firefly optimization algorithm according to claim 1, 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, including: 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.

4. The radar sector arrangement optimization method based on the firefly optimization algorithm according to claim 1, 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, including: 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.

5. The radar sector arrangement optimization method based on the firefly optimization algorithm according to claim 1, 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 performing an attraction calculation on the moving solution according to a preset attraction calculation rule to obtain an attraction 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 mutual attraction degree between the moving solutions is calculated by using the Euclidean distance calculation method to obtain the attraction degree result.

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

7. A radar sector arrangement optimization device based on the 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 and the storage medium communicate via 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 6.

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