A comprehensive aperture direction finding method, device, equipment and medium

Abstract

The application belongs to the technical field of microwave radiation measurement, and provides a synthetic aperture direction finding method, device, equipment and medium, which are used for improving the direction finding accuracy and real-time performance of the synthetic aperture array. Based on the multi-path synchronous complex baseband signals after data preprocessing, the initial population satisfying the preset constraint condition is constructed after the UV plane sampling set is obtained. Based on the initial population and the UV plane sampling set, the target individual indicating the direction of arrival of the radiation source is obtained through iterative calculation by the differential evolution algorithm. In this way, the optimal value of the space spectrum peak is quickly and globally searched by the differential evolution through the adaptive dynamic adjustment of the mutation and crossover operators, so that the direction finding accuracy and real-time performance are improved.

Description

Technical Field

[0001] This invention relates to the field of microwave radiation measurement technology, and more specifically, to a comprehensive aperture direction finding method, apparatus, equipment, and medium. Background Technology

[0002] Synthetic aperture array (SAP) direction finding technology possesses unique advantages of passive high precision and plays a crucial role in electromagnetic information acquisition. SAP obtains the visibility function through complex cross-correlation operations between array elements and estimates the signal arrival direction through spatial spectrum peak inversion. It boasts core advantages such as high direction finding accuracy, no need for large-size solid aperture arrays, and full-space two-dimensional direction finding without blind zones, making it widely applicable in numerous fields.

[0003] Currently, existing synthetic aperture array (SAP) direction finding technology achieves peak search of the spatial spectrum through digital beamforming. Its core idea is to generate a grid of discrete angles within a known field of view at a fixed step size, perform digital beamforming based on the visibility function for the incoming wave direction corresponding to each grid point, construct the spatial spectrum by calculating the output power value of each beam, and finally extract the angle corresponding to the maximum value of the spectral peak as the direction finding result.

[0004] However, the direction finding accuracy of existing processing methods is limited by the inherent accuracy bottleneck of discrete grid size and poor real-time performance of full-space direction finding, making it difficult to meet the practical application requirements of transient and short-time signal direction finding. Summary of the Invention

[0005] This application aims to provide a comprehensive aperture direction finding method, apparatus, equipment, medium, and product to solve the problems of poor real-time performance and accuracy of existing processing methods.

[0006] Firstly, this application provides a comprehensive aperture direction finding method, comprising: Acquire multiple synchronous complex baseband signals after data preprocessing; Based on multiple synchronous complex baseband signals, a UV plane sampling set is obtained; the UV plane sampling set includes the visibility function value of each sampling point and the corresponding UV coordinates, and the sampling points are independent array element pairs in a planar array. Construct an initial population that meets the preset constraints; each individual in the initial population is used to indicate the direction of incoming radiation from the corresponding candidate radiation source. Based on the initial population and the UV plane sampling set, the target individuals used to indicate the direction of the incoming radiation source are obtained through iterative calculation using the differential evolution algorithm.

[0007] The technical solution provided in this application offers at least the following advantages: Based on preprocessed multi-channel synchronous complex baseband signals, after obtaining a UV-plane sampling set, an initial population satisfying preset constraints is constructed. Based on the initial population and the UV-plane sampling set, iterative calculations are performed using a differential evolution algorithm to obtain target individuals used to indicate the direction of arrival of radiation sources. Thus, this application improves direction finding accuracy and real-time performance by using adaptively dynamically adjusted mutation and crossover operators to perform rapid global search for the optimal value of the spatial spectral peak through differential evolution.

[0008] One possible implementation involves acquiring multiple synchronous complex baseband signals after data preprocessing, including: Radio frequency signals from far-field radiation sources are received via a planar array; The radio frequency signal is down-converted, analog-to-digital converted, and synchronously calibrated to obtain multiple synchronous complex baseband signals.

[0009] One possible implementation involves obtaining a UV-plane sample set based on multiple synchronous complex baseband signals, including: Traverse all independent element pairs in the planar array to calculate the normalized cross-correlation of the multi-channel synchronous complex baseband signals for each independent element pair, and obtain the normalized visibility function value of the corresponding independent element pair. By utilizing the conjugate symmetry of the visibility function, the sampling points of the UV plane are completed to obtain a complete UV plane sampling set.

[0010] One possible implementation involves pre-setting constraints including an initial search range and three-dimensional spatial vector constraints; Specifically, the initial search range of the first and second included angles is determined based on the maximum unambiguous field of view of the planar array; the first included angle is used to indicate the angle between the incoming wave direction and the positive X-axis direction of the array, and the second included angle is used to indicate the angle between the incoming wave direction and the positive Y-axis direction of the array. Three-dimensional space vector constraints are expressed by the following formula: ; in, The first included angle, This is the second included angle.

[0011] One possible implementation involves iterative computation using a differential evolution algorithm, based on an initial population and a UV plane sampling set, including: Nonlinear mutation is performed on individuals in the initial population to obtain multiple mutated individuals; Multiple mutated individuals were cross-crossed with individuals from the initial population to obtain multiple experimental individuals; The fitness function value of the experimental individuals is calculated based on the UV plane sampling set. Between the fitness function values ​​of experimental individuals corresponding to the same array element and the fitness function values ​​of individuals in the initial population, individuals with higher fitness function values ​​are retained to enter the next generation of the population to achieve iterative updates of the population. When the preset convergence condition is met, the algorithm is considered to have converged, and the individual with the largest fitness function value in the population at this time is marked as the target individual.

[0012] One possible implementation involves calculating the fitness function value of the experimental individuals based on the UV plane sampling set, using the following formula: ; in, M This represents the total number of samples in the UV plane. This corresponds to the fitness function value of the experimental individual. It is the first Visibility function value of each sampling point It is the first UV coordinates of each sampling point This corresponds to the first included angle of the experimental individual. This corresponds to the second included angle of the experimental individual.

[0013] One possible implementation is to pre-determine the convergence conditions, including any one of the following: When the number of iterations of the differential evolution algorithm reaches the preset maximum number of iterations; When the maximum fitness change rate of a population over consecutive T generations is less than a preset threshold.

[0014] Secondly, this application provides a comprehensive aperture direction finding device, comprising: The acquisition module is used to acquire multiple synchronous complex baseband signals after data preprocessing; The processing module is used to obtain a UV plane sampling set based on multiple synchronous complex baseband signals. The UV plane sampling set includes the visibility function value of each sampling point and the corresponding UV coordinates. The sampling points are independent array element pairs in the planar array. The processing module is also used to construct an initial population that meets preset constraints; each individual in the initial population is used to indicate the direction of incoming radiation from the corresponding candidate radiation source. The processing module is also used to perform iterative calculations based on the initial population and the UV plane sampling set using a differential evolution algorithm to obtain target individuals that indicate the direction of the incoming radiation source.

[0015] Thirdly, this application provides an electronic device comprising: a processor and a memory; the memory storing processor-executable instructions; when the processor is configured to execute the instructions, causing the electronic device to implement the method of the first aspect described above.

[0016] Fourthly, this application provides a computer-readable storage medium comprising: computer software instructions; which, when executed in an electronic device, cause the electronic device to implement the method described in the first aspect.

[0017] The beneficial effects of the second to fourth aspects mentioned above are described in the corresponding description of the first aspect and will not be repeated here. Attached Figure Description

[0018] Figure 1 A schematic flowchart of a comprehensive aperture direction finding method provided in an embodiment of this application; Figure 2 This is a schematic diagram of a simulation scene provided in this embodiment; Figure 3 This is a schematic diagram of the direction finding error results provided in the embodiments of this application; Figure 4 Comparison chart of computation time provided for embodiments of this application; Figure 5 This is a schematic diagram illustrating the composition of an integrated aperture direction finding device provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] The terms “comprising” and “having”, and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0020] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0021] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0022] The following description, in conjunction with the accompanying drawings, details a comprehensive aperture direction finding method provided in the embodiments of this application.

[0023] Figure 1 This is a schematic flowchart illustrating a comprehensive aperture direction finding method provided in an embodiment of this application. (Combined with...) Figure 1 The following describes a comprehensive aperture direction finding method provided in an embodiment of this application: S101. Acquire the multi-channel synchronous complex baseband signal after data preprocessing.

[0024] In one possible implementation, radio frequency signals from a far-field radiation source are received via a planar array; the radio frequency signals are then down-converted, analog-to-digital converted, and synchronously calibrated to obtain multiple synchronous complex baseband signals.

[0025] Specifically, a two-dimensional planar array is used to receive radio frequency signals emitted by a far-field radiation source, wherein the planar array contains... Each array element is an independent array element, arranged in a predetermined topology within a plane. Since the electromagnetic waves arriving at the array from the far-field radiation source can be approximated as plane waves, the signals received by each array element only exhibit phase differences due to path differences.

[0026] Among them, the down-conversion processing of data preprocessing refers to shifting the radio frequency signals of each channel to the baseband frequency band through a mixer and outputting an analog baseband signal to reduce the rate requirements of subsequent sampling and digital processing and adapt to the working bandwidth of conventional digital signal processing devices.

[0027] Analog-to-digital conversion in data preprocessing refers to the synchronous sampling of various analog baseband signals and the conversion of continuous analog signals into discrete digital signals through a high-speed analog-to-digital converter, resulting in multiple digital baseband signals.

[0028] Synchronous calibration of data preprocessing refers to calibrating the channel amplitude and phase consistency of multiple digital baseband signals to eliminate the inherent amplitude and phase errors and sampling clock deviations between each array element channel, ensuring the phase accuracy of subsequent cross-correlation calculations, and avoiding direction finding deviations introduced by channel mismatch.

[0029] After the above data preprocessing, multiple synchronous complex baseband signals are obtained. ,in , t represents the total number of array elements in the planar array, and t represents the sampling time.

[0030] S102. Obtain the UV plane sampling set based on multiple synchronous complex baseband signals.

[0031] The UV plane sampling set includes the visibility function value and corresponding UV coordinates of each sampling point, and the sampling points are independent array element pairs in the planar array.

[0032] In one possible implementation, all independent element pairs in the planar array are traversed to calculate the normalized cross-correlation of the multi-channel synchronous complex baseband signals for each independent element pair, thereby obtaining the normalized visibility function value for the corresponding independent element pair.

[0033] Specifically, it involves traversing all independent element pairs in a planar array. Furthermore, each pair of array elements is calculated only once to avoid duplicate sampling. For each independent array element pair, the normalized cross-correlation of the two complex baseband signals is calculated to obtain the normalized visibility function value corresponding to that array element pair. The calculation formula is shown in equation (1) below: (1); in, This indicates that time averaging and normalization can eliminate the influence of inconsistent channel gains among different array elements on the direction finding results. This is the normalized visibility function value.

[0034] In one possible implementation, the sampling points of the UV plane are completed by using the conjugate symmetry of the visibility function to obtain a complete UV plane sampling set.

[0035] Specifically, for each set of independent array elements, the UV coordinates of a sampling point on the corresponding UV plane are determined by the ratio of the spatial position difference between independent array element i and independent array element j to the signal wavelength.

[0036] Furthermore, based on the conjugate symmetry of the visibility function, the sampling points of the UV plane are completed, and the completion rule is as follows: .

[0037] By using conjugate symmetric completion, the number of effective sampling points in the UV plane can be doubled without increasing the number of hardware channels, thus enriching the constraint information of the spatial spectrum and improving the direction finding accuracy and spatial resolution.

[0038] After completing the completion, you will get the following: M A UV plane sampling set of sampling points, each sampling point containing the corresponding visibility function value and UV coordinates, provides an evaluation basis for subsequent differential evolution optimization.

[0039] S103. Construct an initial population that meets the preset constraints.

[0040] The preset constraints include the initial search range and three-dimensional spatial vector constraints. Each individual in the initial population is used to indicate the direction of incoming radiation from the corresponding candidate radiation source.

[0041] Specifically, based on the maximum unambiguous field of view of the planar array Determine the initial search range for the first and second included angles. The first included angle is used to indicate the angle between the incoming wave direction and the positive X-axis direction of the array, and the second included angle is used to indicate the angle between the incoming wave direction and the positive Y-axis direction of the array.

[0042] The maximum unambiguous field of view is determined by the array element spacing and the signal wavelength, and is the maximum angular range in which the array can achieve grating lobe-free and unambiguous direction finding.

[0043] The three-dimensional spatial vector constraint is shown by the following formula (2): (2); in, The first included angle, This is the second included angle.

[0044] Angles that do not satisfy the above three-dimensional spatial vector constraints do not exist in the physical space and belong to the invalid search space, and need to be directly removed during the population initialization stage.

[0045] Based on the above constraints, the initial population size NP is set. The population size is negatively correlated with the number of array elements and the signal-to-noise ratio: the more array elements there are, the sharper the peak of the spatial spectrum, the lower the difficulty of optimization, and the smaller the required population size; the higher the signal-to-noise ratio, the more obvious the peak characteristics of the spatial spectrum, the less local extremum interference, and the smaller the required population size.

[0046] In practical applications, upper and lower limits can be set for the population size, such as setting the lower limit to 20 and the upper limit to 100, to avoid premature convergence and getting stuck in local optima due to an excessively small population, and to avoid redundant computation and reduced real-time performance due to an excessively large population.

[0047] After determining the population size, initial individuals are generated within the effective search space using uniform sampling to ensure a uniform distribution of the population within the search space. Efficient sampling methods include Latin hypercube sampling, stratified sampling, and quasi-Monte Carlo sampling. Among these, Latin hypercube sampling can achieve uniform coverage of the parameter space with a smaller sample size, making it suitable for initial population generation in this scenario.

[0048] Each individual in the initial population =[ , ], corresponding to the direction of incoming radiation from a candidate source, where The first included angle of this individual. This is the second included angle of the individual.

[0049] This step addresses the shortcomings of the general differential evolution algorithm, such as the initial population containing a large number of illegal angles and a high proportion of invalid searches. By using dual constraints to filter the effective search space, a high-quality initial population is generated, thereby improving the algorithm's convergence speed and search efficiency from the starting point of the iteration.

[0050] S104. Based on the initial population and the UV plane sampling set, the target individuals used to indicate the direction of the incoming radiation source are obtained through iterative calculation using the differential evolution algorithm.

[0051] In one possible implementation, individuals in the initial population are subjected to nonlinear mutation to obtain multiple mutated individuals. These multiple mutated individuals are then cross-crossed with individuals in the initial population to obtain multiple experimental individuals. The fitness function value of the experimental individuals is calculated based on the UV plane sampling set.

[0052] Specifically, for each target individual in the current population Randomly select 3 distinct indices that are related to the target individual. i Different individuals , , Generate mutant individuals based on differential mutation strategy The calculation formula (3) is shown below: (3); in, A mutated individual, F The scaling factor is calculated as shown in equation (4); (4); in, , , This represents the current iteration number. This represents the maximum number of iterations.

[0053] In the early stages of iteration, the scaling factor is larger, which can expand the search range of individuals, maintain population diversity, and prevent the algorithm from getting trapped in local optima. As the number of iterations increases, the scaling factor decreases non-linearly and becomes smaller in the later stages of iteration, which can shrink the search step size, improve local search accuracy, and accelerate the convergence speed of the algorithm.

[0054] The above method of crossing multiple mutant individuals with individuals from the initial population to obtain multiple experimental individuals is explained below: For the target individuals in the initial population With variant individuals Crossover is performed to generate experimental individuals. ,in Each dimension of the variable is calculated using the following formula (5): (5); in, As a dimensional index for individual variables, in the embodiments of this application The values ​​are 1 and 2, respectively corresponding to ; The dimension index is randomly selected so that at least one dimension in the experimental individuals comes from the variant individuals, thus avoiding crossover failure; It is a random number drawn evenly from between 0 and 1; The preset crossover probability is calculated using the following formula (6): (6); in, , In the early stages of iteration, the crossover probability... A higher crossover probability can ensure population diversity and improve global search capabilities; in the later stages of iteration, the crossover probability... A smaller value ensures the stability of the algorithm's convergence.

[0055] In the early stages of iteration, the crossover probability is relatively high, and more genes from mutated individuals enter the experimental individuals, which can ensure the diversity of the population and improve the global search capability. In the later stages of iteration, the crossover probability is relatively low, and more genes from the target individuals are preserved, which can ensure the stability of the algorithm's convergence and improve the final optimization accuracy.

[0056] For the above-mentioned UV plane sampling set, the fitness function value of the experimental individual is calculated. The fitness function value corresponds to the spatial spectrum amplitude under the candidate direction. The larger the value, the closer the direction is to the true incoming wave direction. The fitness function value is shown by the following formula (7): (7); in, M This represents the total number of samples in the UV plane. This corresponds to the fitness function value of the experimental individual. It is the first Visibility function value of each sampling point It is the first UV coordinates of each sampling point This corresponds to the first included angle of the experimental individual. This corresponds to the second included angle of the experimental individual.

[0057] Between the fitness function values ​​of experimental individuals corresponding to the same array element and the fitness function values ​​of individuals in the initial population, individuals with higher fitness function values ​​are retained to enter the next generation of the population, thereby achieving iterative updates of the population.

[0058] A greedy selection strategy was adopted to compare the experimental individuals. With target individuals The fitness value is used to retain individuals with higher fitness into the next generation of the population, as shown in equation (8): (8); in, The fitness function value of the experimental individual. The fitness function value of the target individual. For individuals with higher fitness.

[0059] This greedy selection strategy ensures that the fitness of the population increases monotonically, and that individuals in the population always iterate towards the optimal solution.

[0060] When the preset convergence condition is met, the algorithm is considered to have converged, and the individual with the largest fitness function value in the population at this time is marked as the target individual.

[0061] The preset convergence conditions include any one of the following: When the number of iterations of the differential evolution algorithm reaches the preset maximum number of iterations; When the maximum fitness change rate of a population over consecutive T generations is less than a preset threshold.

[0062] Specifically, the iteration count is updated to... Calculate the differences between individuals in the new population and individuals in the old population. If both differences are less than a given nonnegative constant... (For example Then the number of similarity iterations Otherwise, set it to 0. The iteration terminates when any of the following conditions are met; otherwise, nonlinear mutation is performed on individuals in the new population: Condition 1: When the number of iterations of the algorithm reaches the preset maximum number of iterations. ; Condition 2: Continuous The maximum fitness change rate of the population is less than a given threshold. ,Right now If the algorithm has converged, the iteration is terminated early.

[0063] After the algorithm iterates and terminates, a final population containing NP individuals is obtained. Calculate the fitness function value of each individual in the population, select the individual with the largest fitness function value as the target individual, and determine its corresponding angle. This is the final optimal direction finding angle, which is also the estimated result of the incoming wave direction of the radiation source.

[0064] In this embodiment, based on preprocessed multi-channel synchronous complex baseband signals, a UV-plane sampling set is obtained, and an initial population satisfying preset constraints is constructed. Based on the initial population and the UV-plane sampling set, iterative calculations are performed using a differential evolution algorithm to obtain target individuals used to indicate the direction of arrival of radiation sources. Thus, this application improves direction finding accuracy and real-time performance by using adaptively dynamically adjusted mutation and crossover operators to perform rapid global search for the optimal spatial spectral peak value through differential evolution, thereby enhancing the practicality and adaptability of integrated aperture direction finding technology.

[0065] To verify the effectiveness of the method proposed in this embodiment, this application also provides a simulation application embodiment to build a simulation test scenario for comparative experiments. Figure 2 This is a schematic diagram of a simulation scenario provided in this embodiment. Figure 3 This is a schematic diagram of the direction finding error results provided in an embodiment of this application. Figure 4 A comparison chart of computation time provided for embodiments of this application. Combined with... Figures 2 to 4 The following describes an example of a simulation application: Scenario Description: Under the condition that all array parameters, signal parameters, and observation platform are the same, the performance of different direction finding methods is compared to verify the effectiveness of the algorithm. The comparison indicators include the root mean square error of direction finding and the average time of a single direction finding.

[0066] like Figure 2 As shown, this embodiment employs a common synthetic aperture array (SAP) layout with a total of 16 array elements. One element is positioned at the origin, and the remaining elements are arranged in three arms with the center as the ray, each pair having an angle of 120°. Each arm contains six elements (including the central common element), and the spacing between adjacent elements on the same arm is 0.01m. The target signal wavelength is 0.1m, corresponding to a signal frequency of 3000MHz, and the signal-to-noise ratio is 20dB. 150 Monte Carlo simulation experiments were conducted, with the direction of arrival randomly selected within the maximum non-aliasing field of view for each experiment.

[0067] The traditional synthetic aperture direction finding algorithm uses a search step size of 0.2°. The population size of the method proposed in this invention is... Maximum number of iterations Similarity Iteration Difference Threshold and the upper limit of similar iterations .

[0068] Since the direction finding accuracy of traditional synthetic aperture direction finding algorithms depends on the size of the grid setting, combined with Figure 3 As shown, when the grid is set to 0.2°, the comparison of the direction finding error results of the traditional synthetic aperture direction finding algorithm and the method of the present invention shows that the method of the present invention is significantly better than the traditional synthetic aperture direction finding algorithm.

[0069] Meanwhile, traditional synthetic aperture direction finding algorithms require 400,000 spatial spectrum calculations when the grid step size is set to 0.2°. In contrast, the direction finding method based on differential evolutionary search algorithm provided in this application calculates at most 10,000 spatial spectra with a maximum of 200 iterations and a population size of 50. Figure 4 As shown in the comparison results of the computation time of different methods in multiple experiments, it can be seen that the computational efficiency of the method of the present invention is significantly better than that of the traditional grid search method.

[0070] In summary, the simulation results demonstrate that the proposed high-precision orientation finding method based on differential evolution and rapid global optimization significantly outperforms traditional synthetic aperture orientation finding algorithms in terms of orientation finding accuracy and real-time performance. It effectively resolves the contradiction between orientation finding accuracy and algorithm efficiency in existing synthetic aperture orientation finding technologies, meeting the application requirements of high-precision orientation finding, rapid and efficient solution, and full-scene adaptability. This method is of great significance for improving the performance and applicability of synthetic aperture orientation finding methods.

[0071] In some embodiments, this application also provides an integrated aperture direction finding device. This integrated aperture direction finding device may include one or more functional modules for implementing an integrated aperture direction finding method according to the above method embodiments.

[0072] For example, Figure 5 This is a schematic diagram illustrating the composition of an integrated aperture direction finding device provided in an embodiment of this application. Figure 5 As shown, the integrated aperture direction finding device includes: an acquisition module 201 and a processing module 202.

[0073] The acquisition module 201 is used to acquire multiple synchronous complex baseband signals after data preprocessing; Processing module 202 is used to obtain a UV plane sampling set based on multiple synchronous complex baseband signals; the UV plane sampling set includes the visibility function value of each sampling point and the corresponding UV coordinates, and the sampling points are independent array element pairs in the planar array; The processing module 202 is also used to construct an initial population that meets preset constraints; each individual in the initial population is used to indicate the direction of incoming radiation from the corresponding candidate radiation source. The processing module 202 is also used to perform iterative calculations based on the initial population and the UV plane sampling set using a differential evolution algorithm to obtain target individuals that indicate the direction of the incoming radiation source.

[0074] In some embodiments, the acquisition module 201 is specifically used to receive radio frequency signals from a far-field radiation source through a planar array; and to obtain multiple synchronous complex baseband signals by down-converting, analog-to-digital conversion and synchronous calibration of the radio frequency signals.

[0075] In some embodiments, the processing module 202 is specifically used to traverse all independent array element pairs in the planar array to calculate the normalized cross-correlation of the multi-channel synchronous complex baseband signals of each independent array element pair, and obtain the normalized visibility function value of the corresponding independent array element pair. By utilizing the conjugate symmetry of the visibility function, the sampling points of the UV plane are completed to obtain a complete UV plane sampling set.

[0076] In some embodiments, the preset constraints in the processing module 202 include an initial search range and three-dimensional spatial vector constraints; Specifically, the initial search range of the first and second included angles is determined based on the maximum unambiguous field of view of the planar array; the first included angle is used to indicate the angle between the incoming wave direction and the positive X-axis direction of the array, and the second included angle is used to indicate the angle between the incoming wave direction and the positive Y-axis direction of the array. Three-dimensional space vector constraints are expressed by the following formula: ; in, The first included angle, This is the second included angle.

[0077] In some embodiments, the processing module 202 is specifically used to perform nonlinear mutation on individuals in the initial population to obtain multiple mutated individuals; Multiple mutated individuals were cross-crossed with individuals from the initial population to obtain multiple experimental individuals; The fitness function value of the experimental individuals is calculated based on the UV plane sampling set. Between the fitness function values ​​of experimental individuals corresponding to the same array element and the fitness function values ​​of individuals in the initial population, individuals with higher fitness function values ​​are retained to enter the next generation of the population to achieve iterative updates of the population. When the preset convergence condition is met, the algorithm is considered to have converged, and the individual with the largest fitness function value in the population at this time is marked as the target individual.

[0078] In some embodiments, the processing module 202 is specifically used to calculate the fitness function value of the experimental individuals based on the UV plane sampling set, as shown in the following formula: ; in, M This represents the total number of samples in the UV plane. This corresponds to the fitness function value of the experimental individual. It is the first Visibility function value of each sampling point It is the first UV coordinates of each sampling point This corresponds to the first included angle of the experimental individual. This corresponds to the second included angle of the experimental individual.

[0079] In some embodiments, the preset convergence condition in the processing module 202 includes any one of the following conditions: When the number of iterations of the differential evolution algorithm reaches the preset maximum number of iterations; When the maximum fitness change rate of a population over consecutive T generations is less than a preset threshold.

[0080] Figure 6This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device includes: a processor 602, a communication interface 603, and a bus 604. Optionally, the electronic device may also include a memory 601.

[0081] Processor 602 may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 602 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 602 may also be a combination of functions implementing computing capabilities, such as a combination of CPU0 and CPU1, a DSP, and a microprocessor.

[0082] The communication interface 603 includes a receiving unit and a transmitting unit, and is used to connect with other devices via a communication network. This communication network can be Ethernet, a wireless access network, a wireless local area network (WLAN), etc.

[0083] The memory 301 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0084] In one possible implementation, the memory 601 can exist independently of the processor 602. The memory 601 can be connected to the processor 602 via a bus 604 and is used to store instructions or program code. When the processor 602 calls the instructions or program code stored in the memory 601, it can implement the integrated aperture direction finding method provided in this embodiment of the invention.

[0085] In another possible implementation, the memory 601 can also be integrated with the processor 602.

[0086] Bus 604 can be an extended industry standard architecture (EISA) bus, etc. Bus 604 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0087] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by computer instructions instructing related hardware. The program can be stored in the aforementioned computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be any of the foregoing embodiments or memory. The aforementioned computer-readable storage medium can also be an external storage device of the aforementioned service invocation device, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the aforementioned service invocation device. Further, the aforementioned computer-readable storage medium can include both internal storage units of the aforementioned service invocation device and external storage devices. The aforementioned computer-readable storage medium is used to store the aforementioned computer program and other programs and data required by the aforementioned service invocation device. The aforementioned computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0088] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A comprehensive aperture direction finding method, characterized in that, include: Acquire multiple synchronous complex baseband signals after data preprocessing; Based on the aforementioned multi-channel synchronous complex baseband signals, a UV plane sampling set is obtained; The UV plane sampling set includes the visibility function value of each sampling point and the corresponding UV coordinates, and the sampling point is an independent array element pair in the plane array; An initial population that meets preset constraints is constructed; each individual in the initial population is used to indicate the direction of incoming radiation from the corresponding candidate radiation source. Based on the initial population and the UV plane sampling set, a target individual is obtained by iterative calculation using a differential evolution algorithm to indicate the direction of the incoming radiation source.

2. The integrated aperture direction finding method according to claim 1, characterized in that, The acquisition of the preprocessed multi-channel synchronous complex baseband signals includes: The planar array receives radio frequency signals from far-field radiation sources. The radio frequency signal is down-converted, analog-to-digital converted, and synchronously calibrated to obtain the multi-channel synchronous complex baseband signal.

3. The integrated aperture direction finding method according to claim 1, characterized in that, The process of obtaining the UV plane sampling set based on the multi-channel synchronous complex baseband signal includes: Traverse all independent array element pairs in the planar array to calculate the normalized cross-correlation of each independent array element pair with the multi-channel synchronous complex baseband signal, and obtain the normalized visibility function value of the corresponding independent array element pair; By utilizing the conjugate symmetry of the visibility function, the sampling points of the UV plane are completed to obtain the complete sampling set of the UV plane.

4. The integrated aperture direction finding method according to claim 1, characterized in that, The preset constraints include the initial search range and three-dimensional spatial vector constraints; The initial search range of the first angle and the second angle is determined based on the maximum unambiguous field of view of the planar array; the first angle is used to indicate the angle between the incoming wave direction and the positive X-axis direction of the array, and the second angle is used to indicate the angle between the incoming wave direction and the positive Y-axis direction of the array. The three-dimensional space vector constraint is expressed by the following formula: ； in, The first included angle, This is the second included angle.

5. The integrated aperture direction finding method according to claim 1, characterized in that, The iterative calculation based on the initial population and the UV plane sampling set, using a differential evolution algorithm, includes: The individuals in the initial population are subjected to nonlinear mutation to obtain multiple mutated individuals; The multiple mutated individuals are cross-crossed with individuals in the initial population to obtain multiple experimental individuals; The fitness function value of the experimental individual is calculated based on the UV plane sampling set. Between the fitness function values ​​of the experimental individuals corresponding to the same array element and the fitness function values ​​of individuals in the initial population, individuals with higher fitness function values ​​are retained to enter the next generation of the population, so as to achieve iterative updates of the population. When the preset convergence condition is met, the algorithm is determined to have converged, and the individual with the largest fitness function value in the population at this time is marked as the target individual.

6. The integrated aperture direction finding method according to claim 5, characterized in that, The fitness function value of the experimental individual is calculated based on the UV plane sampling set, as shown in the following formula: ； in, M The total number of samples in the UV plane. This corresponds to the fitness function value of the experimental individual. It is the first Visibility function value of each sampling point It is the first UV coordinates of each sampling point This corresponds to the first included angle of the experimental individual. This corresponds to the second included angle of the experimental individual.

7. The integrated aperture direction finding method according to claim 5, characterized in that, The preset convergence condition includes any one of the following conditions: When the number of iterations of the differential evolution algorithm reaches the preset maximum number of iterations; When the maximum fitness change rate of a population over consecutive T generations is less than a preset threshold.

8. A comprehensive aperture direction finding device, characterized in that, include: The acquisition module is used to acquire multiple synchronous complex baseband signals after data preprocessing; The processing module is used to obtain a UV plane sampling set based on the multi-channel synchronous complex baseband signal; The UV plane sampling set includes the visibility function value of each sampling point and the corresponding UV coordinates, and the sampling point is an independent array element pair in the plane array; The processing module is also used to construct an initial population that meets preset constraints; each individual in the initial population is used to indicate the direction of incoming radiation from the corresponding candidate radiation source. The processing module is further configured to perform iterative calculations based on the initial population and the UV plane sampling set using a differential evolution algorithm to obtain target individuals used to indicate the direction of incoming radiation from the radiation source.

9. An electronic device, characterized in that, It includes a processor and a memory, the processor being coupled to the memory; the memory is used to store computer instructions, which are loaded and executed by the processor to enable the computer device to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes computer-executable instructions that, when executed on a computer, cause the computer to perform the method according to any one of claims 1 to 7.

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