Aircraft antenna positioning method, system and computer storage medium

Abstract

The present invention relates to the field of aircraft communications and discloses an aircraft antenna positioning method, system and computer storage medium. The aircraft antenna positioning method is applied to an aircraft antenna system, wherein the aircraft antenna system includes an antenna array and a beam control unit. The positioning method includes: obtaining motion information of an aircraft received by the antenna array, and determining a target search area corresponding to the motion information based on an unscented Kalman filter algorithm; determining prediction information of the target search area based on a preset optimization positioning algorithm; determining angle information based on the prediction information and collected position information, and determining an angle control instruction corresponding to the angle information; and driving the antenna array through the beam control unit based on the angle control information, thereby improving the tracking and positioning accuracy of the antenna system by improving the tracking and positioning algorithm of the aircraft antenna system.

Description

Technical Field

[0001] The present invention relates to the field of aircraft communications, and in particular to an aircraft antenna positioning method, system and computer storage medium. Background Art

[0002] In many engineering fields, it's often impossible to directly obtain the true values ​​of the required state variables. For example, in radar detection of aerial targets and drone target positioning and tracking, random interference often adds random noise to the observed signals. Separating the precise motion state variables of an aircraft or land target from these noise-laden observation signals is difficult, and the only way to determine these state variables is to estimate or predict them based on the observed signals. In linear systems, the Kalman filter algorithm can effectively reduce the impact of random noise and improve system tracking and positioning accuracy. Consequently, the Kalman filter algorithm has become increasingly popular. Its applications are widespread, encompassing areas such as navigation and guidance, target positioning and tracking, communications and signal processing, digital image processing, and speech signal processing. However, real systems often contain varying degrees of nonlinear factors, which can easily affect filtering effectiveness. Nonlinear factors are present in systems such as aircraft flight status and satellite navigation.

[0003] Traditional aircraft antenna systems use a single Kalman filter algorithm or a particle filter algorithm to achieve tracking and positioning of the aircraft by the antenna system. This type of aircraft antenna system has major defects. The nonlinearity of the Kalman filter algorithm causes poor normal filtering effects, and the particle filter algorithm causes particle degradation as sampling errors accumulate. That is, this type of aircraft antenna system will have poor normal filtering effects due to the nonlinearity of the single Kalman filter algorithm, and the particle filter algorithm causes particle degradation as sampling errors accumulate, resulting in low tracking and positioning accuracy of the aircraft antenna system. Summary of the Invention

[0004] The main purpose of the present invention is to propose an aircraft antenna positioning method, system and computer storage medium, aiming to improve the tracking and positioning algorithm of the aircraft antenna, thereby improving the tracking and positioning accuracy of the antenna.

[0005] To achieve the above-mentioned object, the present invention provides an aircraft antenna positioning method, which is applied to an aircraft antenna system, wherein the aircraft antenna system includes an antenna array and a beam control unit. The aircraft antenna positioning method includes:

[0006] Acquiring the motion information of the aircraft received by the antenna array, and determining a target search area corresponding to the motion information based on an unscented Kalman filter algorithm;

[0007] Determining prediction information of the target search area according to a preset optimized positioning algorithm;

[0008] Determine angle information based on the prediction information and the collected position information, and determine an angle control instruction corresponding to the angle information;

[0009] The antenna array is driven by the beam control unit based on the angle control information.

[0010] Optionally, after the step of determining the prediction information of the target search area according to a preset optimized positioning algorithm, the method further includes:

[0011] The motion information of the aircraft is updated based on the prediction information, and the step of determining the target search area corresponding to the motion information based on the unscented Kalman filter algorithm is performed.

[0012] Optionally, the step of determining the target search area corresponding to the motion information based on an unscented Kalman filter algorithm includes:

[0013] Constructing a state equation and an observation equation based on the motion information, wherein the state equation is the relationship information between the position of the aircraft and its corresponding acceleration and velocity, and the observation equation is the relationship information between the position of the aircraft, the motion information, and preset interference information;

[0014] Determine the predicted position corresponding to the aircraft based on the unscented Kalman filter algorithm, the state equation and the observation equation;

[0015] A preset disturbance range is determined, and a target search area corresponding to the predicted position is determined based on the disturbance range.

[0016] Optionally, the step of determining the prediction information of the target search area according to a preset optimized positioning algorithm includes:

[0017] Determine a multi-population fusion cultural genetic algorithm in the optimization positioning algorithm;

[0018] Determining all candidate populations corresponding to the target search area, and determining candidate individuals corresponding to each candidate population; the candidate individuals include positions within the target search area, and velocities and accelerations corresponding to the positions;

[0019] Determining the evaluation value of the candidate individual based on the fitness function in the multi-population fusion cultural genetic algorithm;

[0020] Sorting the evaluation values ​​of the candidate individuals in ascending order, and determining the first preset number of candidate individuals in each candidate population in the sorting as target individuals;

[0021] Generating offspring individuals of each target individual based on the influence function in the multi-population fusion cultural genetic algorithm;

[0022] Determining the evaluation value of the offspring individual according to the fitness function;

[0023] Performing secondary sorting on the evaluation values ​​of the offspring individuals based on the order from small to large, determining the first offspring individual in the secondary sorting as the first prediction information, and updating the receiver function of the multi-population fusion cultural genetic algorithm based on the first prediction information;

[0024] Prediction information is determined according to the evaluation value of the offspring individual and the updated receiving function.

[0025] Optionally, the fitness function is the sum of the root mean square errors of position, velocity and acceleration.

[0026] Optionally, after the step of secondary sorting the evaluation values ​​of the offspring individuals in ascending order, the method further comprises:

[0027] Counting the number of iterations of the multi-population fusion cultural genetic algorithm, and detecting whether the number of iterations is equal to a preset iteration threshold;

[0028] If they are equal, the first offspring individual in the secondary sorting is determined to be the second prediction information, and the particle filter algorithm in the optimization positioning algorithm is used to determine the prediction information of the second prediction information;

[0029] If not, the step of determining the first offspring individual in the secondary sorting as the first prediction information is executed.

[0030] Optionally, the step of determining prediction information based on the evaluation value of the offspring individual and the updated receiving function includes:

[0031] Determining a preset number of offspring individuals with evaluation values ​​of the offspring individuals in each candidate population as new target individuals;

[0032] The offspring individuals of the target individual are generated based on the influence function and the updated receiving function, and the step of determining the evaluation value of the offspring individuals according to the fitness function is performed to determine the prediction information.

[0033] Optionally, the aircraft antenna system further includes an omnidirectional antenna, and before the step of obtaining the motion information of the aircraft received by the antenna array, the following steps are included:

[0034] Acquire data information received by the omnidirectional antenna, and detect whether the signal strength of the data information is less than a preset threshold signal strength;

[0035] If the signal strength of the data information is less than the preset threshold signal strength, the antenna switching instruction is triggered, and based on the antenna switching instruction, the omnidirectional antenna is turned off and the antenna array is turned on. Based on the turned-on antenna array, the step of obtaining the motion information of the aircraft received by the antenna array is performed.

[0036] In addition, to achieve the above-mentioned objectives, the present invention also provides an aircraft antenna system, comprising: a memory, a processor, and an aircraft antenna program stored in the memory and executable on the processor, wherein the aircraft antenna program, when executed by the processor, implements the steps of the above-mentioned aircraft antenna positioning method.

[0037] In addition, to achieve the above-mentioned purpose, the present invention further provides a computer storage medium, on which an aircraft antenna program is stored. When the aircraft antenna program is executed by a processor, the steps of the above-mentioned aircraft antenna positioning method are implemented.

[0038] The present invention discloses a method for positioning an aircraft antenna. The method is applied to an aircraft antenna system, wherein the aircraft antenna system includes an antenna array and a beam control unit. The positioning method obtains motion information of an aircraft received by the antenna array, determines a target search area corresponding to the motion information based on an unscented Kalman filter algorithm, determines prediction information of the target search area based on a preset optimized positioning algorithm, determines angle information based on the prediction information and collected position information, and determines an angle control instruction corresponding to the angle information, and drives the antenna array through the beam control unit based on the angle control information. The positioning method combines the unscented Kalman filter algorithm with the optimized positioning algorithm to enable the antenna array to accurately position the aircraft, so that the embodiment of the present application has good real-time and robustness, and improves the accuracy of tracking and positioning of the antenna system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic diagram of the structure of an aircraft antenna system in the hardware operating environment involved in an embodiment of the present invention;

[0040] Figure 2 A schematic flow chart of an embodiment of a method for positioning an aircraft antenna according to the present invention;

[0041] Figure 3 A schematic diagram of the structural framework of an aircraft antenna system according to an embodiment of the present invention;

[0042] Figure 4 This is a flowchart of the cultural genetic algorithm;

[0043] Figure 5This is a schematic diagram of the process of the multi-population fusion culture genetic algorithm in the aircraft antenna positioning method of the present invention;

[0044] Figure 6 Schematic diagram of the flow of the improved unscented Kalman filter algorithm in the aircraft antenna positioning method of the present invention;

[0045] Figure 7 Schematic diagram of the improved particle filter algorithm flow in the aircraft antenna positioning method of the present invention;

[0046] Figure 8 This is a scene diagram of another embodiment of the aircraft antenna positioning method of the present invention;

[0047] Figure 9 The figure is a schematic diagram of the implementation flow of another embodiment of the aircraft antenna positioning method of the present invention.

[0048] Description of Figure Numbers:

[0049] Label name Label name 10 Power supply unit 20 Control Center 30 Communication transceiver 40 GPS 50 Omnidirectional antenna 60 Beam Steering System 70 Phase-shifted feed network 80 antenna array 0001 Communication bus 0002 Get interface 0003 processor 0004 Processing interface 0005 Memory

[0050] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0052] The term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0053] In the description and claims of the embodiments of this application, the terms "first" and "second" are used to distinguish different objects, rather than to describe a specific order of objects. For example, the terms "first target object" and "second target object" are used to distinguish different objects, rather than to describe a specific order of objects.

[0054] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0055] In the description of the embodiments of this application, unless otherwise specified, "multiple" means two or more. For example, "multiple processing units" means two or more processing units; "multiple systems" means two or more systems.

[0056] To make the description of the following embodiments clear and concise, a brief introduction to an implementation of an aircraft positioning method is first given:

[0057] Among aircraft positioning algorithms, the Kalman filter improves system tracking accuracy only in linear systems because it can effectively reduce the effects of random noise. However, in real-world applications, actual systems often exhibit varying degrees of nonlinearity, which can easily affect filtering effectiveness. For example, nonlinearity exists in many systems, such as flying cars and satellite navigation, and Kalman filtering struggles to address this nonlinearity. Existing particle filter algorithms used in nonlinear systems can accumulate errors due to random sampling, even leading to filter divergence and slowing particle degradation. To address the shortcomings of these algorithms, the present embodiment proposes an aircraft antenna positioning method.

[0058] The antenna array receives the motion information of the aircraft, and determines the target search area corresponding to the motion information based on the unscented Kalman filter algorithm; determines the prediction information of the target search area based on a preset optimized positioning algorithm; determines the angle information based on the predicted information and the collected position information, and determines the angle control instruction corresponding to the angle information; and drives the antenna array through the beam control unit based on the angle control information. By combining the unscented Kalman filter algorithm and the optimized positioning algorithm, the positioning method can achieve accurate positioning of the aircraft by the antenna array, making the embodiment of the present application have better real-time and robustness, and improving the tracking and positioning accuracy of the antenna system.

[0059] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of an aircraft antenna system in the hardware operating environment involved in an embodiment of the present invention.

[0060] like Figure 1As shown, the aircraft antenna system may include: a processor 0003, such as a central processing unit (CPU), a communication bus 0001, an acquisition interface 0002, a processing interface 0004, and a memory 0005. Among them, the communication bus 0001 is used to realize the connection and communication between these components. The acquisition interface 0002 may include an information acquisition device, an acquisition unit such as a computer, and the acquisition interface 0002 may optionally include a standard wired interface or a wireless interface. The processing interface 0004 may optionally include a standard wired interface or a wireless interface. The memory 0005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk storage. The memory 0005 may optionally be a storage device independent of the aforementioned processor 0003.

[0061] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the aircraft antenna system, and may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0062] like Figure 1 As shown, the memory 0005 as a computer storage medium may include an operating system, an acquisition interface module, a processing interface module, and an aircraft antenna program.

[0063] exist Figure 1 In the aircraft antenna system shown, the communication bus 0001 is mainly used to realize the connection and communication between components; the acquisition interface 0002 is mainly used to connect to the background server and communicate data with the background server; the processing interface 0004 is mainly used to connect to the deployment end (user end) and communicate data with the deployment end; the processor 0003 and the memory 0005 in the aircraft antenna system of the present invention can be set in the aircraft antenna system, and the aircraft antenna system calls the aircraft antenna program stored in the memory 0005 through the processor 0003, and executes the control steps of the aircraft antenna system provided by the embodiment of the present invention.

[0064] Based on the above hardware structure, an embodiment of the aircraft antenna positioning method of the present invention is proposed.

[0065] The embodiment of the present invention provides an aircraft antenna positioning method, referring to Figure 2 , Figure 2 This is a flow chart of an embodiment of a method for positioning an aircraft antenna according to the present invention. The method is applied to an aircraft antenna system, which includes an antenna array and a beam control unit. The method includes:

[0066] Step S10, obtaining the motion information of the aircraft received by the antenna array, and determining the target search area corresponding to the motion information based on the unscented Kalman filter algorithm;

[0067] In this embodiment, the controller receives motion information from an aircraft via an antenna array. The aircraft is a flying mobile object, which may include but is not limited to flying cars and drones. This motion information may include the object's motion mode and its currently detected position. Motion modes may include uniformly accelerated linear motion, uniformly velocity linear motion, and projectile motion. The motion information is processed using an unscented Kalman filter algorithm to predict the target search area corresponding to the aircraft's motion information. The target search area includes the variation of the three variables, position, velocity, and acceleration, within a disturbance range.

[0068] Step S20, determining prediction information of the target search area according to a preset optimized positioning algorithm;

[0069] In this embodiment, a preset optimized positioning algorithm is used to process the target search area obtained above, thereby determining prediction information within the target search area. The prediction information includes a predicted position, a predicted velocity, and a predicted acceleration, where the predicted position is the center position of the optimal target search area.

[0070] Since a preset optimized positioning algorithm is used to process the target search area obtained by the unscented Kalman filter algorithm, more accurate prediction information is obtained, thereby achieving more accurate positioning of the aircraft.

[0071] Step S30, determining angle information based on the prediction information and the collected position information, and determining an angle control instruction corresponding to the angle information;

[0072] In this embodiment, the controller can determine the angle information based on the location of the predicted information and the location information collected by itself, and then generate corresponding angle control instructions based on the angle information, wherein the location information refers to the longitude, latitude, altitude and other information of the base station or instrument controlled by the controller, and the angle information refers to the angle required by the predicted information that the antenna array in the base station or instrument needs to point to the aircraft.

[0073] Step S40: driving the antenna array through the beam control unit based on the angle control information.

[0074] In this embodiment, after the controller generates angle control information, it will drive the antenna array based on the angle control information through the base station or the internal beam control unit of the instrument, so that each radiating unit in the antenna array is pointed toward the aircraft to ensure accurate communication between the aircraft and the antenna array.

[0075] Based on the above embodiments of the aircraft antenna positioning method, a first embodiment of the aircraft antenna positioning system of the present application is proposed, referring to Figure 3 , Figure 3 The following is a schematic diagram of the structural framework of an embodiment of an aircraft antenna system. This aircraft antenna system does not limit its actual composition; modules can be added or removed based on actual needs. The aircraft antenna system includes an omnidirectional antenna 50, an antenna array 80, a phase-shifted feed network 70, a beam control unit 60, a power supply unit 10, a communication transceiver 30, a GPS 40, and a control center 20. The aircraft antenna system receives data transmitted from an aircraft via the omnidirectional antenna 50 or antenna array 80, and transmits it to the control center 20 for processing via the communication transceiver 30. The control center 20 extracts information such as longitude, latitude, and altitude from the data transmitted by the current aircraft, and simultaneously collects information such as the longitude, longitude, and altitude of the local system location via the GPS 40. Based on the algorithm in the aircraft positioning method, the pitch angle and azimuth angle of the antenna array 80 are calculated using two sets of data information from the aircraft and the local system. The control center 20 sends the pitch angle and azimuth angle as angle control instructions to the beam control unit 60. Finally, the beam control unit 60 converts the angle information into phase information for each antenna unit and drives the phase-shifted feed network 70 to adjust the angle of the antenna array 80. In this way, the beam scanning signal emitted by each radiating unit in the antenna array 80 is directed towards the aircraft, achieving accurate directional communication with the aircraft.

[0076] Furthermore, based on the above embodiments of the aircraft antenna positioning method and the embodiment of the aircraft antenna positioning system, another embodiment of the aircraft antenna positioning method of this embodiment is proposed, referring to Figure 8 , Figure 8 This is another scenario diagram of an embodiment of the aircraft antenna positioning method. The left figure is a scenario diagram of the aircraft antenna system in the communication base station of this embodiment communicating with a single aircraft. The right figure is a scenario diagram based on the expansion of one-to-one communication to achieve the simultaneous communication of aircraft antenna systems in multiple communication base stations with one aircraft, thereby achieving the communication of the same aircraft through the collaborative work of multiple base stations, thereby ensuring the communication accuracy. Figure 9 , Figure 9This figure illustrates the implementation flow of another embodiment of an aircraft antenna positioning method. The data fusion method for multiple communication base stations collects the state of the same aircraft at each base station and estimates the state using an optimized positioning algorithm to obtain the target's position information and covariance, ultimately achieving a local optimal estimate. Before the iteration ends, weights are updated using the data fusion algorithm, and the optimized positioning algorithm for each base station is executed. After the iteration ends, the estimated information from each base station is fused using the data fusion algorithm, outputting the globally optimal fused state. The simplest fusion method involves averaging the data, then calculating the deviation between each base station's local estimate and the mean of the N base stations. Weights are assigned based on the proportion of the deviation: larger deviations result in smaller weights, while smaller deviations result in larger weights. Ultimately, positioning the aircraft using multiple communication base stations ensures accurate positioning.

[0077] This embodiment discloses a method for positioning an aircraft antenna. The method is applied to an aircraft antenna system, which includes an antenna array and a beam control unit. The positioning method obtains the motion information of the aircraft received by the antenna array, determines the target search area corresponding to the motion information based on the unscented Kalman filter algorithm; determines the prediction information of the target search area based on a preset optimized positioning algorithm; determines the angle information based on the predicted information and the collected position information, and determines the angle control instruction corresponding to the angle information; and drives the antenna array through the beam control unit based on the angle control information. The positioning algorithm combines the unscented Kalman filter algorithm and the optimized positioning algorithm to enable the antenna array to accurately position the aircraft, so that the embodiment of the present application has good real-time and robustness, and improves the accuracy of the tracking and positioning of the antenna system.

[0078] In some embodiments, after the step S20 of determining the prediction information of the target search area according to a preset optimized positioning algorithm, the following steps are included:

[0079] Step A10: updating the motion information of the aircraft based on the prediction information, and executing the step of determining the target search area corresponding to the motion information based on the unscented Kalman filter algorithm.

[0080] In this embodiment, after obtaining the prediction information, the prediction information is used to update the motion information of the aircraft, and the step of determining the target search area corresponding to the motion information based on the unscented Kalman filter algorithm is performed according to the updated motion information, thereby obtaining new prediction information to iteratively update the prediction information and continuously locate the aircraft.

[0081] In some embodiments, the step S10 of determining the target search area corresponding to the motion information based on the unscented Kalman filter algorithm includes:

[0082] Step B10: constructing a state equation and an observation equation based on the motion information, wherein the state equation is the relationship between the position of the aircraft and its corresponding acceleration and velocity, and the observation equation is the relationship between the position of the aircraft, the motion information, and preset interference information;

[0083] Step B20, determining a predicted position corresponding to the aircraft based on the unscented Kalman filter algorithm, the state equation, and the observation equation;

[0084] Specifically, the unscented Kalman filter algorithm can effectively reduce the impact of random noise in nonlinear systems and improve system tracking and positioning accuracy. It is often used to predict the motion state of a tracked aircraft, reducing the size of the search area and improving the real-time and stability of tracking. Its applications are extensive, including in navigation and guidance, target positioning and tracking, communications and signal processing, digital image processing, speech signal processing, and many other fields.

[0085] The motion information may include the displacement equation s of the uniformly accelerated linear motion of the aircraft t =s0+vt+1 / 2at 2 and the velocity equation v t =v0+at and the observed position (i.e. the position detected for the aircraft).

[0086] State equations and observation equations can be constructed based on motion information.

[0087] The state equation is: X(k+1)=ΦX(k)+W(k),

[0088] in, W(k) is the system noise observation equation:

[0089] Among them, h is the observation matrix, V(k) is the observation noise, corresponding to the displacement observation noise Vr(k), the azimuth observation noise Pitch angle observation noise Vθ(k),

[0090]

[0091] Among them, x, y, and z are three coordinate axes, and the coordinate axes of x, y, and z can be used to identify the position of the tracking target. According to the observation position (x0, y0, z0), as well as the state equation and observation equation, the position information (x k ,y k , z k )’s predicted location.

[0092] Step B30: determining a preset disturbance range, and determining a target search area corresponding to the predicted position based on the disturbance range.

[0093] In this embodiment, the predicted position includes the first position (X k 、Y k , Z k ), speed (V xk 、V yk 、V zk ), acceleration (A xk 、A yk 、A zk ), and the preset disturbance range is α for the position, β for the speed, and γ for the acceleration. Then, combined with the first position (Xk, Yk, Zk), speed (V xk 、V yk 、V zk ), acceleration (A xk 、A yk 、A zk ) and the disturbance ranges α, β, and γ are used to construct the search area as the target search area.

[0094] Furthermore, based on the above unscented Kalman filter algorithm, a flow chart of an unscented Kalman filter algorithm of this embodiment is proposed, referring to Figure 6 , Figure 6 This is a flow chart of the improved unscented Kalman filter algorithm used in the aircraft antenna positioning method. The system determines the aircraft as the tracking target, establishes the system state equation (constant velocity or uniform acceleration) and observation equation (range, azimuth, pitch angle, etc.) for the tracking target, and initializes the state parameters and the multi-population fusion genetic algorithm (population size, crossover probability, number of iterations, and perturbation ranges for position, velocity, and acceleration) using the unscented Kalman filter algorithm. The unscented Kalman filter is then used to determine the next position of the predicted target as the observed position, and a search space is defined centered around the observed position. The algorithm then determines whether the aircraft has disappeared. If so, the search ends. Otherwise, the multi-population fusion genetic algorithm in the optimization positioning algorithm is used to search for the optimal predicted position. If the iterations are not complete, the unscented Kalman filter is used to determine the next position of the predicted target as the observed position. Otherwise, the loop ends, and the optimal position is output. This optimal position includes velocity, acceleration, and covariance information.

[0095] In some embodiments, step S20 of determining the prediction information of the target search area according to a preset optimized positioning algorithm includes:

[0096] Step C10, determining a multi-population fusion cultural genetic algorithm in the optimization positioning algorithm;

[0097] The cultural genetic algorithm (CGA) is a hybrid algorithm that combines a cultural algorithm with a genetic algorithm. It is a widely used optimization method with strong robustness and global optimization properties. It has been successfully applied in areas such as drone path planning, image processing, intelligent control, navigation and guidance, and missile target assignment. The CGA can be constructed by first initializing a population space, which can contain several populations, each of which can contain several individuals. Each individual is a variable, satisfying a predefined variable range. A fitness function is then calculated for each individual in the population space, generating a corresponding evaluation value to determine the relative performance of each individual. The multi-population CGA utilizes multiple populations for parallel evolutionary optimization, significantly overcoming the limitations of the CGA's single-population genetic evolution approach. Different populations are assigned different evolutionary parameters, enabling independent search to find their respective optimal states.

[0098] Further, refer to Figure 4 , Figure 4 The following is a schematic diagram of the cultural genetic algorithm process. By initializing the population space (i.e., information about position, velocity, acceleration, and their respective ranges of variation), an initial population of individuals, consisting of an NxM-dimensional real number vector, is randomly generated within a specified range. N refers to the number of individuals in the population, and M refers to the number of variables. Based on specific needs, an appropriate fitness function is set and calculated. Each individual in the population is evaluated, and qualified individuals are selected according to predefined rules. Based on the set variable ranges, the individuals in the initial population space, and their fitness values, an initial belief space is generated according to the structure of the cultural algorithm's belief space. Based on the new cultural algorithm's influence function, the optimal individuals generated in the previous genetic operation in the population space are mutated, generating N corresponding offspring individuals to participate in the next generation of genetic evolution. The basic genetic algorithm's selection operation and adaptive crossover and mutation operations produce correspondingly superior individuals. An acceptance function is set, and the knowledge in the belief space is refreshed according to a specific update rule. If the termination condition is not met, the algorithm returns to the fitness evaluation and continues. Otherwise, the algorithm terminates and outputs the most accurate position, velocity, acceleration, and other information. Based on the above cultural genetic algorithm, a multi-population fusion cultural genetic algorithm is proposed. Figure 5 , Figure 5This is a flowchart of a multi-population fusion cultural genetic algorithm for aircraft antenna positioning. Initialize the population (i.e., position, velocity, acceleration, and their respective ranges of variation). A fitness function (i.e., the sum of the root mean squared errors of each variable; smaller sums indicate better individuals, meaning more accurate positioning information) is used to calculate the corresponding values ​​for each individual in each population. The situational knowledge (optimal initial position information) and normative knowledge (position information (position, velocity, acceleration) range) are initialized for each population. Mutation is performed on each individual in the population space based on the influence function to generate the corresponding next generation of individuals. Genetic operations are then performed. A receiving function is used, and an update function updates the situational knowledge and normative knowledge, generating new knowledge to guide evolution. The cultural genetic algorithm is then applied to each of the M populations. An immigration operation is implemented, which periodically introduces the best individuals from each population to the other populations, enabling information exchange and updates between populations. An artificial selection operator is used to preserve the best individuals from each population during evolution to the elite population, preventing the destruction of dominant individuals. Determine whether the algorithm meets the convergence conditions. If so, terminate. Otherwise, continue to execute each single cultural genetic algorithm. After convergence, output the optimal result of the algorithm, that is, the optimal orientation information (i.e., position, speed, acceleration, etc.).

[0099] Step C20, determining all candidate populations corresponding to the target search area, and determining candidate individuals corresponding to each candidate population; the candidate individuals include positions within the target search area, and velocities and accelerations corresponding to the positions;

[0100] Step C30, determining the evaluation value of the candidate individual based on the fitness function in the multi-population fusion cultural genetic algorithm;

[0101] By determining all candidate populations corresponding to the target search area, the candidate individuals of each population are determined. The candidate individuals include the position in the target search area, as well as the speed and acceleration corresponding to the position. The candidate population refers to a population randomly composed of candidate individuals in the target search area. Each population simultaneously executes the population fusion cultural genetic algorithm. The evaluation value of the candidate individuals in each population is determined by determining the fitness function in the multi-population fusion cultural genetic algorithm.

[0102] Step C40, sorting the evaluation values ​​of the candidate individuals in ascending order, and determining the first preset number of candidate individuals in each candidate population in the sorting as target individuals;

[0103] The evaluation values ​​of the candidate individuals can be sorted in ascending order, and a preset number of candidate individuals (for example, the 9 with the smallest evaluation values ​​in the sorting) in the sorting are determined as target individuals. The embodiment of the present invention does not limit the number of target individuals.

[0104] Step C50, generating offspring individuals of each target individual based on the influence function in the multi-population fusion cultural genetic algorithm;

[0105] According to the influence function in the multi-population fusion cultural genetic algorithm, the target individuals in each population are mutated to obtain several offspring individuals of the optimal individuals;

[0106] The optimal individual determined by the fitness function in the population space can be mutated according to the influence function to obtain several offspring individuals of the optimal individual, wherein the offspring individual is the Nth generation individual obtained from the above optimal individual, where N≥1.

[0107] Step C60, determining the evaluation value of the offspring individual according to the fitness function;

[0108] Step C70, performing secondary sorting on the evaluation values ​​of the offspring individuals based on the order from smallest to largest, determining the first offspring individual in the secondary sorting as the first prediction information, and updating the receiver function of the multi-population fusion cultural genetic algorithm based on the first prediction information;

[0109] Step C80: Determine prediction information based on the evaluation value of the offspring individual and the updated receiving function.

[0110] Determine the evaluation values ​​of the individual offspring, perform a secondary sorting of the evaluation values ​​of the individual offspring in ascending order, determine all optimal offspring across all populations, and determine the first offspring individual among the optimal offspring as the first prediction information. That is, the optimal offspring of the population among all the optimal offspring is used as the first prediction information. The receiver function of the multi-population fusion cultural genetic algorithm is updated based on the optimal offspring of the population. The optimal offspring of the population refers to the optimal offspring among the multiple populations. Finally, the prediction information is determined based on the evaluation values ​​of the individual offspring and the updated receiver function.

[0111] Furthermore, the fitness function is the sum of the root mean square errors of position, velocity and acceleration.

[0112] The fitness function is determined by the root mean square error of position, velocity, and acceleration: Among them, △ represents position variable, velocity variable or acceleration variable, for example: △1 represents position variable, △2 represents velocity variable, △3 represents acceleration variable, and the fitness function is: ∑RESM.

[0113] In some other embodiments, after step C70 of secondary sorting the evaluation values ​​of the offspring individuals in ascending order, the following steps are included:

[0114] Step D10, counting the number of iterations of the multi-population fusion cultural genetic algorithm, and detecting whether the number of iterations is equal to a preset iteration threshold;

[0115] Step D20: If yes, then the first offspring individual in the secondary sorting is determined as the second prediction information, and the particle filter algorithm in the optimization positioning algorithm is used to determine the prediction information of the second prediction information;

[0116] Step D30: If not equal, then executing the step of determining the first offspring individual in the secondary sorting as the first prediction information.

[0117] In this embodiment, by counting the number of iterations of the multi-population fusion cultural genetic algorithm, the number of iterations refers to the number of times the loop is executed. If the number of iterations is equal to the preset iteration threshold, the iteration ends, and the first offspring individual in the secondary sorting is determined to be the second prediction information, that is, the optimal offspring of the secondary sorted population is used as the second prediction information, and the particle filter algorithm is executed through the second prediction information to obtain the prediction information of the second prediction information, and the prediction cycle ends. On the contrary, if the number of iterations is less than the preset iteration threshold, and the iteration is not ended, the offspring individual can be processed according to the preset operator, and then the step of determining that the first offspring individual in the secondary sorting is the first prediction information is executed; the preset operator includes at least one of the selection operator, crossover operator, reversal operator, and mutation operator. Further, referring to Figure 7 , Figure 7 This is a schematic diagram of the improved particle filter algorithm used in the aircraft antenna positioning method. By considering the sampling period, number of sampling points, process noise variance, observation noise variance, filtering results, and particle set initialization, an unscented Kalman filter is used to generate a particle set (better diversity, meaning greater selectivity and a higher probability of optimal results). State information (position, velocity, acceleration, and their variances) is obtained, and weights are calculated and normalized. Based on the preset particle diversity value, a decision is made to resample. If so, the data is resampled; otherwise, the data is directly updated. The loop is determined to be complete. If so, the final estimate (i.e., the position, velocity, acceleration, and other information closest to the true value) is output. Otherwise, the next loop is entered. It is worth noting that the particle filter algorithm can also be improved by increasing the number of particles and resampling techniques to alleviate particle degradation.

[0118] In some other embodiments, the step of determining prediction information according to the evaluation value of the offspring individual and the updated receiving function in step C80 includes:

[0119] Step E10, determining a preset number of offspring individuals with evaluation values ​​of the offspring individuals in each candidate population as new target individuals;

[0120] Step E20 , generating offspring individuals of the target individual based on the influence function and the updated receiving function, and executing the step of determining the evaluation value of the offspring individuals according to the fitness function to determine prediction information.

[0121] In this embodiment, the offspring individuals with the first preset number of evaluation values ​​of all offspring individuals in the candidate population are determined as new target individuals, and the first preset number of candidate individuals in the secondary sorting (for example, 15 with the smallest evaluation values ​​in the sorting) are determined as target individuals. The embodiment of the present invention does not limit the number of target individuals, and generates offspring individuals of the target individual based on the influence function and the updated receiving function. The prediction information is determined by executing the step of determining the evaluation value of the offspring individual according to the fitness function. On the one hand, the influence function can be set to different values ​​in different cultural genetic algorithms, and the crossover probability and mutation probability of each crossover mutation are also different. On the other hand, the range of random numbers in the receiving function after each crossover mutation will also converge with the optimal offspring of the population, thereby improving the efficiency of determining the optimal individual.

[0122] In some embodiments, the aircraft antenna system further includes an omnidirectional antenna, and before the step S10 of obtaining the motion information of the aircraft received by the antenna array, the following steps are included:

[0123] Step F10, obtaining data information received by the omnidirectional antenna, and detecting whether the signal strength of the data information is less than a preset threshold signal strength;

[0124] In step F20, if the signal strength of the data information is less than a preset threshold signal strength, the antenna switching instruction is triggered, and based on the antenna switching instruction, the omnidirectional antenna is turned off and the antenna array is turned on, and based on the turned-on antenna array, the step of obtaining the motion information of the aircraft received by the antenna array is performed.

[0125] In this embodiment, the omnidirectional antenna and antenna array in the aircraft antenna system can be switched based on the operating mode. That is, the aircraft antenna system selectively uses the omnidirectional antenna and antenna array to communicate with the aircraft based on the actual received signal state. When the antenna array is used for communication, a step is executed to obtain information about the aircraft's motion received by the antenna array. Conversely, when the omnidirectional antenna is used for communication, data information received by the omnidirectional antenna is obtained and the signal strength of the data information is detected to determine whether it is less than a preset threshold signal strength. Data information refers to data received from the aircraft, and only the signal strength at the time of the data information is considered. Signal strength refers to the relationship between signal strength and weakness. For example, a threshold signal strength of half the optimal communication signal strength can be defined. Below this threshold signal strength, the antenna array operates at high gain, while below this threshold, the omnidirectional antenna operates at low gain. When the data signal strength is less than the preset threshold signal strength, an antenna switching instruction is generated, which in turn turns off the omnidirectional antenna and turns on the antenna array. The antenna switching instruction refers to either turning on the omnidirectional antenna and turning off the antenna array, or turning off the omnidirectional antenna and turning on the antenna array. Finally, after the antenna array is turned on, the step of obtaining the movement information of the aircraft received by the antenna array is performed, so that the antenna can operate in different working modes according to the signal strength, which can ensure the accuracy of antenna communication and reduce the power consumption of the antenna operation.

[0126] The present invention also provides an aircraft antenna system.

[0127] The aircraft antenna system of the present invention includes: a memory, a processor, and an aircraft antenna program stored in the memory and executable on the processor. When the aircraft antenna program is executed by the processor, the steps of the aircraft antenna positioning method described above are implemented.

[0128] The present invention also provides a computer storage medium.

[0129] The computer storage medium of the present invention stores an aircraft antenna program, which implements the steps of the aircraft antenna positioning method described above when executed by a processor.

[0130] The method implemented when the aircraft antenna program running on the processor is executed can refer to the various embodiments of the aircraft antenna positioning method of the present invention, and will not be described in detail here.

[0131] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0132] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0133] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a computer storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0134] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for positioning an aircraft antenna, characterized in that: The aircraft antenna positioning method is applied to an aircraft antenna system, wherein the aircraft antenna system includes an antenna array and a beam control unit. The aircraft antenna positioning method includes: Acquiring the motion information of the aircraft received by the antenna array, and determining a target search area corresponding to the motion information based on an unscented Kalman filter algorithm; Determining prediction information of the target search area according to a preset optimized positioning algorithm, wherein the step of determining prediction information of the target search area according to the preset optimized positioning algorithm includes: Determine the multi-population fusion cultural genetic algorithm in the optimization positioning algorithm; determine all candidate populations corresponding to the target search area, and determine the candidate individuals corresponding to each candidate population; the candidate individuals include the position in the target search area, and the speed and acceleration corresponding to the position; determine the evaluation value of the candidate individuals based on the fitness function in the multi-population fusion cultural genetic algorithm; sort the evaluation values ​​of the candidate individuals in order from small to large, and determine the first preset number of candidate individuals in the first sorting in each candidate population as target individuals; generate offspring individuals of each target individual based on the influence function in the multi-population fusion cultural genetic algorithm; determine the evaluation value of the offspring individual according to the fitness function; sort the evaluation values ​​of the offspring individuals in order from small to large for the second time, determine the first offspring individual in the second sorting as the first prediction information, and update the receiving function of the multi-population fusion cultural genetic algorithm based on the first prediction information; determine the prediction information according to the evaluation value of the offspring individual and the updated receiving function; Determine angle information based on the prediction information and the collected position information, and determine an angle control instruction corresponding to the angle information; The antenna array is driven by the beam control unit based on the angle control instruction.

2. The aircraft antenna positioning method according to claim 1, wherein: After the step of determining the prediction information of the target search area according to the preset optimized positioning algorithm, the method further includes: The motion information of the aircraft is updated based on the prediction information, and the step of determining the target search area corresponding to the motion information based on the unscented Kalman filter algorithm is performed.

3. The aircraft antenna positioning method according to claim 1, wherein: The step of determining the target search area corresponding to the motion information based on the unscented Kalman filter algorithm includes: Constructing a state equation and an observation equation based on the motion information, wherein the state equation is the relationship information between the position of the aircraft and its corresponding acceleration and velocity, and the observation equation is the relationship information between the position of the aircraft, the motion information, and preset interference information; Determine the predicted position corresponding to the aircraft based on the unscented Kalman filter algorithm, the state equation and the observation equation; A preset disturbance range is determined, and a target search area corresponding to the predicted position is determined based on the disturbance range.

4. The aircraft antenna positioning method according to claim 1, wherein: The fitness function is the sum of the root mean square errors of position, velocity and acceleration.

5. The aircraft antenna positioning method according to claim 1, wherein: After the step of secondary sorting the evaluation values ​​of the offspring individuals in ascending order, the method includes: Counting the number of iterations of the multi-population fusion cultural genetic algorithm, and detecting whether the number of iterations is equal to a preset iteration threshold; If they are equal, the first offspring individual in the secondary sorting is determined to be the second prediction information, and the particle filter algorithm in the optimization positioning algorithm is used to determine the prediction information of the second prediction information; If not, the step of determining the first offspring individual in the secondary sorting as the first prediction information is executed.

6. The aircraft antenna positioning method according to claim 1, wherein: The step of determining prediction information based on the evaluation value of the offspring individual and the updated receiving function includes: Determining a preset number of offspring individuals with evaluation values ​​of the offspring individuals in each candidate population as new target individuals; The offspring individuals of the target individual are generated based on the influence function and the updated receiving function, and the step of determining the evaluation value of the offspring individuals according to the fitness function is performed to determine the prediction information.

7. The aircraft antenna positioning method according to any one of claims 1 to 6, characterized in that: The aircraft antenna system further includes an omnidirectional antenna. Before the step of obtaining the motion information of the aircraft received by the antenna array, the method includes: Acquire data information received by the omnidirectional antenna, and detect whether the signal strength of the data information is less than a preset threshold signal strength; If the signal strength of the data information is less than the preset threshold signal strength, the antenna switching instruction is triggered, and based on the antenna switching instruction, the omnidirectional antenna is turned off and the antenna array is turned on. Based on the turned-on antenna array, the step of obtaining the motion information of the aircraft received by the antenna array is performed.

8. An aircraft antenna system, characterized in that: The aircraft antenna system includes: a memory, a processor, and an aircraft antenna positioning program stored in the memory and executable on the processor. When the aircraft antenna positioning program is executed by the processor, the steps of the aircraft antenna positioning method according to any one of claims 1 to 7 are implemented.

9. A computer storage medium, characterized in that The computer storage medium stores an aircraft antenna positioning program, which, when executed by a processor, implements the steps of the aircraft antenna positioning method according to any one of claims 1 to 7.

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