Anti-drone array direction finding calibration method and device

Abstract

The application discloses a kind of anti-UAV array direction finding calibration method and device, it is related to anti-UAV technical field, realize multi-dimensional error correction, realize the accurate calibration of all error sources, all directions.The first time that array element position error is included in anti-UAV array direction finding calibration system, through GPS / inertial navigation module real-time acquisition array element position, calculate the influence coefficient of position deviation to direction finding, and into calibration formula, solve the direction finding error problem caused by outdoor array element installation deviation;Meanwhile, discard single 0 degree auxiliary signal source, add 30°, 60°, 90° and so on 7 direction multi-angle calibration signal source, establish the angle-error mapping table of all directions, realize the accurate calibration of different angle incident UAV signal, eliminate direction dependent error, cover the full direction incident demand of UAV signal in anti-UAV scene.

Description

Technical Field

[0001] This invention relates to the field of anti-drone technology, and in particular to an anti-drone array direction finding calibration method and apparatus. Background Technology

[0002] With the rapid development of drone technology, the popularity of consumer and industrial drones has increased significantly. At the same time, the problem of unauthorized drone flights, leading to public safety, airspace management, and privacy breaches, has become increasingly prominent. Counter-drone technology has become a key research focus in national defense and civilian security. Radio direction finding technology is a core component of counter-drone systems. By determining the direction of arrival of radio waves emitted by a drone, it enables the location, tracking, and handling of drone targets. Super-resolution array direction finding technology, due to its high accuracy and high resolution, is widely used in various counter-drone direction finding systems.

[0003] Super-resolution array direction finding technology uses an antenna array formed by arranging multiple direction finding elements at different locations to collect and process the radio signals from UAVs, thereby achieving super-resolution estimation of the direction of arrival. However, in practical anti-UAV applications, array errors can severely affect direction finding accuracy and even lead to failure. The main sources of these errors include: 1. Element hardware errors: Inconsistencies in antenna manufacturing processes and RF channel device performance lead to deviations in amplitude and phase responses of each channel, i.e., amplitude and phase errors; 2. Element installation errors: During on-site installation, it is impossible to guarantee that each element is strictly in its theoretical design position, resulting in three-dimensional coordinate positional deviations, i.e., positional errors. These errors directly alter the propagation path difference of electromagnetic waves between elements, leading to deviations in direction finding calculations; 3. Direction-dependent errors: Traditional calibration... 4. Error drift: Anti-drone systems often operate continuously outdoors for extended periods. Changes in ambient temperature, humidity, and electromagnetic interference can cause slow drift in equipment performance, gradually invalidating the original fixed pre-calibration data and generating drift errors. 5. Single calibration benchmark: Traditional methods often use channel 1 as a single reference benchmark. If this channel experiences hardware failure or signal interference, it will directly lead to deviations in the entire calibration system, resulting in low system fault tolerance.

[0004] Existing array direction finding calibration methods mainly include adaptive calibration and auxiliary source calibration. Adaptive calibration does not require an external auxiliary signal source and estimates errors based on the signal's own characteristics. However, its global convergence is difficult to guarantee, and in complex electromagnetic environments like anti-drone scenarios, it is prone to getting trapped in local optima, resulting in low calibration accuracy. Auxiliary source calibration calibrates errors by adding an external auxiliary signal source. Its computational logic is relatively simple, but existing technologies have significant drawbacks: First, they only calibrate amplitude and phase errors, neglecting the impact of array element position errors on direction finding. Anti-drone arrays are often deployed outdoors, where element position deviations are a significant error source. Second, they often use a single 0-degree auxiliary signal source, failing to achieve omnidirectional error calibration and resulting in poor calibration effects for drone signals incident at different angles. Third, pre-calibration data is stored in a fixed location, failing to consider error drift during long-term equipment operation, leading to poor timeliness of the calibration data. Fourth, using a single channel as a reference standard results in low system fault tolerance; a single-channel failure directly leads to calibration failure.

[0005] Therefore, how to provide a multi-dimensional, dynamic, and highly fault-tolerant array direction finding and accurate calibration method suitable for anti-drone scenarios is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of the above problems, the present invention provides a method and apparatus for calibrating the direction finding of an anti-drone array to overcome or at least partially solve the above problems. Through three core improvements—multi-dimensional error correction, dynamic updating of pre-calibration data, and optimization of calibration reference redundancy—the invention addresses issues such as existing methods not considering element position errors, incomplete coverage of single-angle calibration, failure of fixed pre-calibration data, and low single-channel reference fault tolerance. This achieves accurate, comprehensive, and dynamic calibration of various errors in the anti-drone array direction finding system, improving direction finding accuracy and system stability, and meeting the engineering application requirements of anti-drone scenarios.

[0007] This invention provides the following solution: A method for calibrating the direction finding of an anti-UAV array, comprising: Step S1: Set the number of array element channels of the antenna array to M, and select K uniformly distributed reference channels, where K is a natural number greater than 1 and less than M; initialize the GPS / inertial navigation module, the multi-angle calibration signal source, and the error detection module. The positioning accuracy of the GPS / inertial navigation module is not less than 0.1 meters. The multi-angle calibration signal source covers 7 directions: 0°, 30°, 60°, 90°, 120°, 150°, and 180°, and the signal frequency covers the 2.4 GHz, 5.8 GHz, and 1.2 GHz frequency bands. The error detection module is set with a deviation threshold of 5%. Step S2: sequentially turn on the auxiliary signal sources in each direction, and obtain the amplitude-phase error calibration matrix at each angle; collect calibration signal data of each channel, take the average value of data collected by K reference channels as the unified reference benchmark, calculate the relative error between channels, and then calculate the amplitude-phase error before the channels; collect the actual three-dimensional coordinates of each array element through the GPS / inertial navigation module, compare with the designed theoretical coordinates to calculate the position deviation, and calculate the position error coefficient in combination with the signal incident angle and the position deviation of the array element; establish an angle-error mapping table containing the corresponding relationship among signal incident angle, array element channel number, amplitude-phase error and position error coefficient, and store it in a local database; Step S3: during the period when there is no unmanned aerial vehicle target signal, the error detection module collects environmental background data according to the detection period, continues to use the average value of the K reference channels as the reference benchmark, calculates the real-time relative error between channels, retrieves the historical data of relative error between channels under the last pre-calibrated reference angle from the local database, calculates the deviation rate between the real-time relative error between channels and the historical data, and when the deviation rate is greater than 5%, triggers repeated execution of step S2 to update the angle-error mapping table; Step S4: after the unmanned aerial vehicle target signal is detected, collect the data of the signal to be measured; collect the three-dimensional coordinates of each array element in real time through the GPS / inertial navigation module, update the real-time position deviation and update the position error coefficient in combination with the preliminarily estimated direction angle of the unmanned aerial vehicle signal; turn on the closest calibration signal source according to the preliminarily estimated direction angle, collect real-time calibration signal data and calculate the real-time relative error between channels; retrieve the amplitude-phase error corresponding to the angle from the angle-error mapping table, and based on the real-time relative error between channels, the amplitude-phase error and the updated position error coefficient, perform synchronous joint calibration of amplitude-phase error and position error on the data of the signal to be measured; continue to use the average value of the K reference channels as the reference benchmark again, normalize the data of each channel after joint calibration, complete direction consistency calibration to obtain calibrated signal data, and calculate the covariance matrix based on the calibrated signal data; Step S5: perform incoming wave direction estimation by using a super-resolution DOA estimation algorithm based on the covariance matrix.

[0008] Preferably: the selection rule of the K reference channels in step S1 is: when M≤10, K=2; when 10<M≤20, K=3; when M>20, K=5.

[0009] Preferably: the specific method for calculating the relative error between channels in step S2 is: obtain the average value of data collected by the K reference channels at the angle , wherein represents the data of the th reference channel; process the data collected by each channel​ The relative error between channels is obtained by comparing the value with the average value. ; Set the initial values ​​of the amplitude and phase error calibration matrix for all reference channels. .

[0010] Preferably: the amplitude and phase error before the channel in step S2 The calculation formula is:

[0011] In the formula: This represents the amplitude and phase error calibration matrix. This indicates the relative error between channels.

[0012] Preferably: the deviation rate in step S3 The calculation formula is:

[0013] In the formula: This represents the relative error between real-time channels. Indicates reference angle Historical data on relative errors between channels.

[0014] Preferably, the synchronous joint calibration of amplitude and phase error and position error in step S4 is achieved by the following formula:

[0015] In the formula: This represents the signal data of each channel after multi-dimensional error joint calibration. This represents the signal data to be measured collected from each channel. This represents the relative error between real-time channels. Indicates the direction and angle relative to the preliminary estimate. The corresponding amplitude and phase errors, This represents the real-time position error coefficient.

[0016] Preferably, the orientation consistency calibration in step S4 is achieved in the following manner: Obtain the calibration data for K reference channels. average ; Data after joint calibration of each channel Normalization is performed to obtain the signal data after orientation consistency calibration. .

[0017] Preferably, in step S4, if the preliminary estimated direction angle θ′ of the UAV signal is between the directions of the two calibration signal sources, the amplitude and phase error and position error coefficients corresponding to the closest calibration signal source direction are retrieved from the angle-error mapping table for calibration.

[0018] Preferably, the super-resolution DOA estimation algorithm in step S5 includes any one of the MUSIC algorithm, ESPRIT algorithm, and weighted subspace fitting algorithm; the covariance matrix R is obtained by multiplying the calibrated signal data by its conjugate transpose matrix, i.e. .

[0019] An anti-drone array direction finding calibration device is provided for performing the above-described anti-drone array direction finding calibration method. The device includes: The system initialization and parameter setting unit is used to set the number of array element channels of the antenna array to M, and select K uniformly distributed reference channels, where K is a natural number greater than 1 and less than M; it initializes the GPS / inertial navigation module, the multi-angle calibration signal source, and the error detection module. The GPS / inertial navigation module has a positioning accuracy of not less than 0.1 meters. The multi-angle calibration signal source covers seven directions: 0°, 30°, 60°, 90°, 120°, 150°, and 180°, and the signal frequency covers the 2.4 GHz, 5.8 GHz, and 1.2 GHz frequency bands. The error detection module has a set deviation threshold of 5%. The multi-angle pre-calibration and angle-error mapping table establishment unit is used to sequentially activate auxiliary signal sources in each direction to obtain amplitude and phase error calibration matrices at each angle; collect calibration signal data from each channel, use the average value of the data collected from K reference channels as a unified reference benchmark, calculate the relative error between channels, and then calculate the amplitude and phase error before the channel; collect the actual three-dimensional coordinates of each array element through the GPS / inertial navigation module, compare them with the theoretical coordinates of the design to calculate the position deviation, and calculate the position error coefficient by combining the signal incident angle and the position deviation of the array element; establish an angle-error mapping table containing the correspondence between the signal incident angle, array element channel number, amplitude and phase error, and position error coefficient, and store it in a local database; The error drift detection and pre-calibration data dynamic update unit is used to collect environmental background data by the error detection module according to the detection cycle during the period when there is no UAV target signal. The average value of the K reference channels is used as the reference reference to calculate the real-time inter-channel relative error. The historical data of inter-channel relative error under the reference angle of the most recent pre-calibration is retrieved from the local database. The deviation rate between the real-time inter-channel relative error and the historical data is calculated. When the deviation rate is greater than 5%, step S2 is triggered to be repeated to update the angle-error mapping table. A multi-dimensional real-time joint calibration unit for UAV signals is used to collect the signal data to be tested after detecting the UAV target signal; it collects the three-dimensional coordinates of each array element in real time through the GPS / inertial navigation module, updates the real-time position deviation, and updates the position error coefficient by combining the preliminary estimated direction angle with the UAV signal; it activates the closest calibration signal source according to the preliminary estimated direction angle, collects real-time calibration signal data, and calculates the real-time inter-channel relative error; it retrieves the amplitude and phase error of the corresponding angle from the angle-error mapping table, and performs synchronous joint calibration of amplitude and phase error and position error on the signal data to be tested based on the real-time inter-channel relative error, amplitude and phase error, and the updated position error coefficient; it again uses the average value of the K reference channels as the reference benchmark to normalize the data after joint calibration of each channel, completes the direction consistency calibration, obtains the calibrated signal data, and calculates the covariance matrix based on the calibrated signal data; The direction of arrival estimation unit is used to estimate the direction of arrival based on the covariance matrix using a super-resolution DOA estimation algorithm.

[0020] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This application provides a method and apparatus for calibrating the direction finding of an anti-drone array, achieving multi-dimensional error correction and precise calibration across all error sources and directions. For the first time, array element position errors are incorporated into the anti-drone array direction finding calibration system. Array element positions are acquired in real-time via GPS / inertial navigation modules, the influence coefficient of position deviation on direction finding is calculated, and integrated into the calibration formula, solving the direction finding error problem caused by outdoor array element installation deviations. Simultaneously, the single 0-degree auxiliary signal source is abandoned, and multi-angle calibration signal sources in seven directions (30°, 60°, 90°, etc.) are added, establishing a full-directional angle-error mapping table. This enables precise calibration of drone signals incident at different angles, eliminating direction-dependent errors and covering the omnidirectional incident requirements of drone signals in anti-drone scenarios. Multi-dimensional error correction comprehensively suppresses amplitude and phase errors, position errors, direction consistency errors, and direction-dependent errors, significantly improving the completeness and accuracy of calibration.

[0021] The pre-calibration data is dynamically updated to ensure long-term calibration stability. An error detection module is added, which compares real-time channel errors with historical pre-calibration errors by periodically collecting background data during periods without a target, calculating the deviation rate. When the deviation rate exceeds a 5% threshold, the pre-calibration data is automatically updated. This mechanism automates and dynamically updates the pre-calibration data, solving the problem of error drift and failure caused by long-term equipment operation and environmental changes in traditional fixed pre-calibration data. It ensures that the calibration data always matches the actual operating state of the equipment, improving the long-term stability of calibration and making it suitable for long-term continuous outdoor operation scenarios of anti-drone systems.

[0022] Optimizing calibration reference redundancy significantly improves the system's fault tolerance and reliability. A multi-channel reference is used instead of the traditional single-channel reference. K uniformly distributed and stable reference channels are selected based on the number of array element channels, and normalized calibration is performed by calculating the average error of the multiple reference channels. This method effectively reduces calibration deviations caused by hardware failures, electromagnetic interference, and performance drift of a single channel. Even if individual reference channels fail, effective calibration can still be achieved through the remaining reference channels, greatly improving the system's fault tolerance and reliability. Simultaneously, the averaging process of the multi-channel reference effectively suppresses the impact of environmental noise on calibration, further improving calibration accuracy.

[0023] The computational logic is simple and highly applicable to engineering. While achieving multi-dimensional, dynamic, and highly fault-tolerant calibration, it maintains a simple computational logic. All calibration formulas are based on linear operations and normalization, without complex iteration and optimization processes, resulting in high computational efficiency. It can run in real time on the embedded hardware platform of anti-drone systems. At the same time, the hardware modification of this invention only requires the addition of a multi-angle calibration signal source, a GPS / inertial navigation module, and an error detection module. All modules are commercially mature products, easy to integrate and deploy, with low modification costs. It is suitable for upgrading and modifying various existing anti-drone array direction finding systems, and has extremely strong engineering applicability.

[0024] This invention adapts to the electromagnetic environment and signal characteristics of anti-drone scenarios. The multi-angle calibration signal source frequency covers the commonly used 2.4GHz, 5.8GHz, and 1.2GHz frequency bands for anti-drone operations, matching the radio signal frequencies of drones. The detection cycle of the error detection module can be flexibly adjusted according to the complex outdoor electromagnetic environment, enabling rapid error detection and updating. Super-resolution DOA estimation is adaptable to the characteristics of rapid drone target movement, weak signals, and complex electromagnetic interference in anti-drone scenarios, ensuring real-time performance and accuracy of direction finding. This invention is fully adapted to the application scenarios of anti-drone operations, and the calibration effect is significant. Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0026] Figure 1 This is a flowchart of an anti-UAV array direction finding calibration method provided in an embodiment of the present invention; Figure 2is a flow chart of multi-angle pre-calibration and angle-error mapping table establishment provided by an embodiment of the present invention; Figure 3 is a flow chart of error drift detection and pre-calibration data update provided by an embodiment of the present invention; Figure 4 is a flow chart of multi-dimensional real-time joint calibration for UAV signal provided by an embodiment of the present invention; Figure 5 is a schematic diagram of an anti-UAV array direction finding calibration apparatus provided by an embodiment of the present invention; Figure 6 is a schematic diagram of an anti-UAV array direction finding calibration device provided by an embodiment of the present invention. Detailed Description of the Embodiments

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by a person skilled in the art shall fall within the protection scope of the present invention.

[0028] See Figure 1 , which is an anti-UAV array direction finding calibration method provided by an embodiment of the present invention, as shown in Figure 1 , the method may include: Step S1: Set the number of array element channels of the antenna array as M, select K uniformly distributed reference channels, where K is a natural number greater than 1 and less than M; initialize the GPS / inertial navigation module, multi-angle calibration signal source and error detection module, wherein the positioning accuracy of the GPS / inertial navigation module is not lower than 0.1 meter, the multi-angle calibration signal source covers 7 directions of 0°, 30°, 60°, 90°, 120°, 150° and 180°, the signal frequency covers 2.4 GHz, 5.8 GHz and 1.2 GHz frequency bands, and the error detection module sets a deviation threshold of 5%; in specific implementation, the embodiment of the present application may provide the selection rule for the K reference channels as follows: when M≤10, K=2; when 10<M≤20, K=3; when M>20, K=5.

[0029] Step S2: Sequentially activate auxiliary signal sources in each direction to obtain amplitude and phase error calibration matrices at each angle; collect calibration signal data from each channel, using the average value of data collected from K reference channels as a unified reference benchmark, calculate the relative error between channels, and then calculate the amplitude and phase error before the channel; collect the actual three-dimensional coordinates of each array element through the GPS / inertial navigation module, compare them with the theoretical coordinates to calculate the position deviation, and calculate the position error coefficient by combining the signal incident angle and the position deviation of the array element; establish an angle-error mapping table containing the correspondence between the signal incident angle, array element channel number, amplitude and phase error, and position error coefficient, and store it in a local database; In specific implementation, the embodiment of this application can provide the following specific method for calculating the relative error between channels: Find the K reference channels at the angle Average value of collected data ,in Indicates the first Data from one reference channel; Collect data from each channel The relative error between channels is obtained by comparing the value with the average value. ; Set the initial values ​​of the amplitude and phase error calibration matrix for all reference channels. .

[0030] Furthermore, the amplitude and phase error before the channel The calculation formula is:

[0031] In the formula: This represents the amplitude and phase error calibration matrix. This indicates the relative error between channels.

[0032] Step S3: During periods without UAV target signals, the error detection module collects environmental background data according to the detection cycle. Using the average of the K reference channels as the reference benchmark, it calculates the real-time inter-channel relative error (retrieving historical data of inter-channel relative error at the most recent pre-calibrated reference angle from the local database) and calculates the deviation rate between the real-time inter-channel relative error and the historical data. When the deviation rate is greater than 5%, it triggers the re-execution of step S2 to update the angle-error mapping table. In specific implementations, the embodiments of this application can provide the deviation rate. The calculation formula is:

[0033] In the formula: This represents the relative error between real-time channels. Indicates reference angle Historical data on relative errors between channels.

[0034] Step S4: After detecting the UAV target signal, acquire the signal data to be tested; acquire the three-dimensional coordinates of each array element in real time through the GPS / inertial navigation module, update the real-time position deviation, and update the position error coefficient by combining the UAV signal with the preliminary estimated direction angle; activate the closest calibration signal source according to the preliminary estimated direction angle, acquire real-time calibration signal data, and calculate the real-time inter-channel relative error; retrieve the amplitude and phase error of the corresponding angle from the angle-error mapping table, and perform synchronous joint calibration of amplitude and phase error and position error on the signal data to be tested based on the real-time inter-channel relative error, amplitude and phase error, and the updated position error coefficient; again use the average value of the K reference channels as the reference, normalize the data after joint calibration of each channel, complete the direction consistency calibration, obtain the calibrated signal data, and calculate the covariance matrix based on the calibrated signal data; in specific implementation, the embodiment of this application can provide the synchronous joint calibration of amplitude and phase error and position error through the following formula:

[0035] In the formula: This represents the signal data of each channel after multi-dimensional error joint calibration. This represents the signal data to be measured collected from each channel. This represents the relative error between real-time channels. Indicates the direction and angle relative to the preliminary estimate. The corresponding amplitude and phase errors, This represents the real-time position error coefficient.

[0036] The orientation consistency calibration is achieved in the following way: Obtain the calibration data for K reference channels. average ; Data after joint calibration of each channel Normalization is performed to obtain the signal data after orientation consistency calibration. .

[0037] If the preliminary estimated direction angle θ′ of the UAV signal is between the directions of the two calibration signal sources, then the amplitude and phase error and position error coefficients corresponding to the closest calibration signal source direction are retrieved from the angle-error mapping table for calibration.

[0038] Step S5: Based on the covariance matrix, a super-resolution DOA estimation algorithm is used to estimate the direction of arrival. Specifically, in this application embodiment, the super-resolution DOA estimation algorithm may include any one of the MUSIC algorithm, ESPRIT algorithm, and weighted subspace fitting algorithm; the covariance matrix R is obtained by multiplying the calibrated signal data by its conjugate transpose matrix, i.e. .

[0039] The anti-UAV array direction finding calibration method provided by the embodiments of the present application adds a multi-angle calibration signal source, a GPS / inertial navigation module and an error detection module on the basis of the original array direction finding calibration system, wherein: the multi-angle calibration signal source is configured to provide omnidirectional auxiliary calibration signals, covering the common signal incident angles of UAVs; the GPS / inertial navigation module is configured to collect the three-dimensional coordinate position of each array element in real time, so as to realize accurate calculation of array element position errors; the error detection module is configured to periodically detect channel error drift and trigger automatic update of pre-calibration data. The method adopts the core idea of combining pre-calibration and real-time calibration, integrates the technical means of multi-dimensional error correction, dynamic update of pre-calibration data, and optimization of calibration benchmark redundancy, completes the joint calibration of amplitude-phase error, direction consistency error and array element position error of array signals, and then performs super-resolution DOA estimation.

[0040] It can be understood that the anti-UAV scenario requires "silent calibration without target signal" (it cannot rely on continuous pilot signals like communication base stations), so a special mechanism of multi-angle auxiliary signal source + target-free background detection must be introduced. This method is fundamentally different from general communication array calibration.

[0041] The following is a detailed introduction to the anti-UAV array direction finding calibration method provided by the embodiments of the present application.

[0042] As Figure 1 shown, the anti-UAV array direction finding calibration method provided by the present application specifically includes the following steps: Step 1: System initialization and parameter setting.

[0043] (1) Set the number of array element channels of the antenna array as M. According to the value of the number of array element channels M, select K reference channels following the principles of uniform distribution, consistent performance and high stability, wherein K is a natural number greater than 1 and less than M. The specific selection rule is: when M≦10, K=2; when 10<M≦20, K=3; when M>20, K=5. After the reference channels are selected, they remain fixed and serve as the reference benchmark for subsequent calibration.

[0044] (2) Initialize the GPS / inertial navigation module, complete the positioning calibration of the module, ensure that its positioning accuracy is not lower than 0.1m, and can collect the three-dimensional coordinate position of each array element in real time Initialize the multi-angle calibration signal source, which includes auxiliary signal transmission units in seven directions: 0°, 30°, 60°, 90°, 120°, 150°, and 180°. The signal frequencies cover the commonly used anti-drone frequency bands of 2.4GHz, 5.8GHz, and 1.2GHz. The auxiliary signals in each direction can be turned on individually or simultaneously according to actual needs. Initialize the error detection module, set the deviation threshold to 5%, and the detection cycle to 1h~24h. The detection cycle can be adjusted according to the environmental complexity of the anti-drone application scenario. It can be set to 1h~6h in complex outdoor electromagnetic environments and 12h~24h in stable indoor environments.

[0045] Step 2: Multi-angle pre-calibration and establishment of angle-error mapping table, such as... Figure 2 As shown.

[0046] Pre-calibration is performed before the system's first operation or after equipment maintenance. Its core function is to complete the initial calculation of amplitude and phase errors and array element position errors at various angles, and to establish an omnidirectional angle-error mapping table to provide fundamental data for subsequent real-time calibration. Specifically, this includes: (1) Obtaining the amplitude and phase error calibration matrix at multiple angles: The auxiliary signal sources in the directions of 0°, 30°, 60°, 90°, 120°, 150° and 180° are turned on in sequence. For each angle θ, the amplitude and phase error calibration matrix at that angle is obtained by using the auxiliary signal source method for that angle. ,in This refers to the array element channel number. Set the initial values ​​of the amplitude and phase error calibration matrices for all reference channels. (k is the reference channel number, This ensures the consistency of the calibration reference for the reference channel.

[0047] (2) Multi-angle calibration signal data acquisition: For each directional angle θ, control each channel of the antenna array to synchronously acquire calibration signal source data at that angle. Set the number of snapshots to M, and record the data acquired by each channel as follows: .

[0048] (3) Calculation of relative error between channels in multi-channel reference: Abandoning the traditional single-channel reference method, K reference channels are used as multiple reference references. First, the average value of the data collected by the K reference channels at angle θ is calculated. Then, the data collected from each channel is normalized with this average value to obtain the relative error between channels at each angle. And the relative error between the reference channels .

[0049] (4) Calculation of amplitude and phase error in multi-angle channels: Based on the amplitude and phase error calibration matrix at each angle and relative error between channels Calculate the amplitude and phase errors in front of the channel at various angles. This parameter reflects the inherent amplitude and phase deviation of each channel at different angles.

[0050] (5) Calculation of array element position error coefficient: The actual three-dimensional coordinates of each array element are collected by the GPS / inertial navigation module. Compare the design theoretical coordinates of each array element Calculate the positional deviation of each array element. Combining the electromagnetic wave propagation model, based on the signal incident angle θ and the array element position deviation... Calculate the influence coefficient of position deviation on direction finding. , The position error coefficient is related to the signal direction angle. Its physical meaning is: the degree of influence of the electromagnetic wave propagation path difference caused by the array element position deviation on the signal phase. The calculation formula is derived based on the electromagnetic wave path difference-phase difference conversion relationship and combined with the geometric model of array direction finding.

[0051] (6) Establishment of angle-error mapping table: integrate amplitude and phase errors at various angles θ and position error coefficient Establish an omnidirectional angle-error mapping table, which includes angle θ, array element channel number i, and amplitude and phase error. Position error coefficient The table shows the one-to-one correspondence of the four core parameters and covers the entire directional angle range from 0° to 180°, providing a basis for retrieving errors in UAV signals at different angles during subsequent real-time calibration.

[0052] (7) Pre-calibration data storage: The established angle-error mapping table and the initial calibration data (amplitude and phase error calibration matrix, relative error between channels) of K reference channels are stored in the local database of the system as the initial reference data for subsequent real-time calibration.

[0053] Step 3: Error drift detection and dynamic updating of pre-calibration data, such as... Figure 3 As shown.

[0054] To address the error drift problem caused by long-term equipment operation, this invention utilizes an error detection module to perform periodic error detection during periods without a target, and automatically triggers pre-calibration data updates based on deviations, ensuring the timeliness of calibration data. Specifically, this includes: (1) Background Data Acquisition Without a Target: The error detection module monitors the signal reception status of the system in real time. During periods without UAV target signals, it automatically controls each channel to acquire environmental background data according to a preset detection cycle. The background data acquired by each channel is recorded as follows: The number of snapshots collected was consistent with the pre-calibration, both being M.

[0055] (2) Real-time inter-channel relative error calculation: Based on multi-channel reference benchmarks, the average value of background data from K reference channels is calculated. Then, the background data of each channel is normalized with this average value to obtain the real-time relative error between channels. .

[0056] (3) Deviation rate calculation and judgment: Retrieve the reference angle of the most recent pre-calibration from the system's local database. Historical data of relative error between channels Calculate the deviation rate between real-time inter-channel relative error and historical data. The deviation rate η is compared with a preset 5% threshold to determine whether a pre-calibration data update should be triggered.

[0057] (4) Automatic update of pre-calibration data: If the deviation rate η exceeds the 5% threshold, it means that the error drift generated by the operation of the equipment has affected the calibration accuracy. The system automatically triggers the pre-calibration data update process, repeats all the operations in step 2, re-collects calibration signal data at each angle, calculates the amplitude and phase error and position error coefficients, updates the angle-error mapping table, and overwrites the original data stored in the local database of the system with the new calibration data; if the deviation rate η does not exceed the 5% threshold, it means that the equipment error drift is within the allowable range. The original pre-calibration data remains unchanged, and the subsequent real-time direction finding calibration continues.

[0058] The core advantage of this step is that it solves the problem of traditional fixed pre-calibration data becoming invalid during long-term equipment operation by automatically detecting errors and updating data without human intervention. This ensures that the calibration data always matches the actual operating state of the equipment, thus improving the long-term stability of the calibration.

[0059] Step 4: Multi-dimensional real-time joint calibration of UAV signals, such as... Figure 4 As shown.

[0060] Real-time calibration is performed synchronously each time the UAV target is headed. Its core is based on an angle-error mapping table established through pre-calibration, combined with the actual incident angle of the UAV signal, to complete multi-dimensional joint calibration of amplitude and phase errors, position errors, and orientation consistency errors. Multi-channel references are used to ensure the calibration's fault tolerance. Specifically, this includes: (1) Acquisition of signal data from the UAV under test: When the anti-UAV system detects the radio signal of the UAV target, it controls each channel of the antenna array to synchronously acquire the radio signal data of the UAV. The number of snapshots is set to M, and the signal data acquired by each channel are recorded as follows: .

[0061] (2) Real-time position error update of array elements: The three-dimensional coordinate position of each array element is collected in real time through GPS / inertial navigation module. Considering that outdoor array elements may have slight position changes due to environmental factors (such as wind and ground subsidence), the real-time position deviation of each array element is recalculated. And combined with the preliminary estimated direction angle of the UAV signal Update position error coefficient The initial estimated direction angle θ' of the UAV signal can be obtained through traditional direction finding methods, which have low accuracy requirements and are only used to retrieve calibration data for the corresponding angle from the angle-error mapping table.

[0062] (3) Real-time calibration signal data acquisition: Based on the preliminary estimated direction angle of the UAV signal. Find the calibration signal source direction angle closest to it from the angle-error mapping table, the system automatically turns on the auxiliary signal source in that direction, repeats the operation in step 2 (2)-(3), collects the calibration signal data at that angle, calculates and imports the real-time inter-channel relative error. .

[0063] (4) Multi-dimensional error joint calibration: retrieve the angles from the angle-error mapping table in the system's local database, which are consistent with the preliminary estimated directions. Corresponding amplitude and phase error Combined with the updated real-time position error coefficient in step 4(2) The relative error between real-time channels imported in step 4(3) Multi-dimensional joint calibration of amplitude and phase error plus position error is performed. The calibration calculation formula is as follows:

[0064] in, For the signal data of each channel after multi-dimensional error joint calibration, this formula incorporates the array element position error into the calibration system, realizing the synchronous suppression of amplitude and phase errors and position errors, and solving the defect of traditional methods that only calibrate amplitude and phase errors.

[0065] Multi-channel reference directional consistency calibration: The purpose of directional consistency calibration is to eliminate signal amplitude and phase inconsistencies caused by pattern distortion in each channel. This invention abandons the traditional single-channel reference and uses K reference channels as multiple references. First, the average value of the data from the K reference channels after multi-dimensional error joint calibration is calculated. Then convert each channel Normalizing the signal with this average value yields the signal data after orientation consistency calibration. This method reduces calibration deviations caused by single-channel failures and signal interference through multi-channel averaging, significantly improving the system's fault tolerance.

[0066] (6) Covariance matrix calculation: based on signal data after orientation consistency calibration Calculate the covariance matrix of the array signal. This matrix provides an accurate signal data basis for subsequent super-resolution DOA estimation.

[0067] Step 5: Super-resolution DOA estimation.

[0068] Based on the calibrated covariance matrix R from step 4, a super-resolution DOA estimation algorithm suitable for anti-drone scenarios is used to accurately estimate the incoming direction of UAV radio waves. Commonly used algorithms include the MUSIC algorithm, the ESPRIT algorithm, and the weighted subspace fitting algorithm, etc. The specific implementation process is as follows: (1) Perform eigenvalue decomposition on the covariance matrix R to obtain eigenvalues ​​and corresponding eigenvectors; (2) Based on the magnitude of the eigenvalues, the eigenvectors are divided into a signal subspace and a noise subspace, where the signal subspace corresponds to the eigenvectors of the UAV target signal and the noise subspace corresponds to the eigenvectors of the environmental noise. (3) Using the orthogonality of the signal subspace and the noise subspace, spatial spectral peak search is performed, and the angle corresponding to the spectral peak is the precise direction of the incoming UAV radio wave.

[0069] The calibrated covariance matrix R eliminates the effects of amplitude and phase errors, position errors, and orientation consistency errors, ensuring the orthogonality of the signal subspace and noise subspace, thereby significantly improving the accuracy of super-resolution DOA estimation and enabling precise orientation finding of UAV targets.

[0070] To make the objectives, technical solutions, and advantages of this invention clearer, this invention will be described in detail through specific implementation methods in conjunction with the actual application scenario of the anti-drone array direction finding system. This embodiment takes an anti-drone direction finding system with a 16-channel antenna array as an example. The number of array element channels M=16, and K=3 reference channels are selected according to the selection principle, namely channel 4, channel 8, and channel 12. These three channels are evenly distributed in the physical position of the array, have consistent hardware performance, and have high operational stability.

[0071] Example system configuration: Multi-angle calibration signal source: Includes auxiliary signal transmission units in 7 directions: 0°, 30°, 60°, 90°, 120°, 150° and 180°. The signal frequency covers 2.4GHz, 5.8GHz and 1.2GHz. The transmission power is adjustable and the signal-to-noise ratio is ≥30dB.

[0072] GPS / Inertial Navigation Module: Adopts a high-precision dual-frequency GPS module with a positioning accuracy of ±0.05m. It can collect the three-dimensional coordinate positions of 16 array elements in real time with a data update frequency of 10Hz.

[0073] Error detection module: Integrated into the main control unit of the system, implemented in software, with a preset deviation threshold of 5% and a detection cycle of 2 hours (outdoor complex electromagnetic environment).

[0074] Main control unit: adopts an embedded ARM+FPGA architecture. The ARM is responsible for the operation of the calibration algorithm and data storage, while the FPGA is responsible for the synchronous acquisition and preprocessing of array signals. The acquisition rate is 100MSps, and the number of snapshots is set to 16 (consistent with the number of array element channels M).

[0075] Super-resolution DOA estimation algorithm: The MUSIC algorithm is adopted, with a spectral peak search step size of 0.1° and a direction finding accuracy of ≤0.5°.

[0076] Specific implementation steps: Step 1: System Initialization and Parameter Setting (1) Set the number of array element channels M=16, select channels 4, 8 and 12 as reference channels, K=3, and store the reference channel numbers in the main control unit.

[0077] (2) Initialize the GPS / inertial navigation module, complete the positioning calibration, and ensure the positioning accuracy is ±0.05m; initialize the multi-angle calibration signal source, test the working status of the signal transmission unit in each direction, and ensure that the signal is transmitted normally; initialize the error detection module, set the deviation threshold to 5%, the detection cycle to 2h, and enable the targetless signal monitoring function.

[0078] Step 2: Multi-angle pre-calibration and establishment of angle-error mapping table (1) Sequentially turn on the auxiliary signal sources in the directions of 0°, 30°, 60°, 90°, 120°, 150°, and 180°, and perform a test for each angle. The amplitude and phase error calibration matrix is ​​obtained by using an auxiliary signal source method with corresponding angles. (i=1~16), set the reference channels 4, 8, and 12. .

[0079] (2) For each angle θ, calibration signal data is synchronously acquired through 16 channels controlled by FPGA, with 16 snapshots. The data from each channel is recorded as follows: .

[0080] (3) Calculate each angle The average value of the data collected from the next three reference channels Normalization yields the relative error between channels. The reference channel .

[0081] (4) Calculate the amplitude and phase error in front of the channel at each angle. .

[0082] (5) The actual three-dimensional coordinates of the 16 array elements were collected by the GPS / inertial navigation module. Comparison of design theory coordinates Calculate positional deviation Based on the electromagnetic wave propagation model, the position error coefficient at each angle is calculated. .

[0083] (6) Fusion and Establish an angle-error mapping table from 0° to 180°, containing the angle θ, channel number i, and so on. , Four parameters.

[0084] (7) Store the angle-error mapping table and the initial calibration data of the reference channel in the local database of the main control unit.

[0085] Step 3: Error drift detection and dynamic updating of pre-calibration data.

[0086] (1) The error detection module monitors the system signal in real time. When there is no UAV target signal, it controls each channel to collect environmental background data once every 2 hours, which is recorded as follows: (i=1~16), 16 snaps.

[0087] (2) Calculate the average value of the background data from the three reference channels. Normalization yields the real-time relative error between channels. .

[0088] (3) Retrieve the most recent pre-calibrated 0° reference angle Calculate the deviation rate .

[0089] (4) If η>5%, the main control unit automatically triggers the pre-calibration data update, repeats all operations in step 2, updates the angle-error mapping table and overwrites the original data; if η≦5%, the original data remains unchanged.

[0090] Step 4: Multi-dimensional real-time joint calibration of UAV signals.

[0091] (1) When the system detects a drone signal in the 2.4GHz band, it controls 16 channels via FPGA to synchronously acquire drone signal data. The number of snapshots is 16, denoted as... (i=1~16).

[0092] (2) The GPS / inertial navigation module collects the three-dimensional coordinates of 16 array elements in real time and updates the position deviation. The initial estimated direction angle θ' = 45° of the UAV signal is obtained through traditional direction finding methods, and the position error coefficient is updated. .

[0093] (3) The system automatically turns on the 30° calibration signal source that is closest to 45°, collects calibration signal data at 30°, and calculates the real-time inter-channel relative error. And import.

[0094] (4) Retrieve the amplitude and phase error at 30° from the angle-error mapping table. , combined and Perform multi-dimensional joint error calibration: This system was designed to address the specific technical problem of "the failure of traditional fixed calibration due to slight changes in the position of outdoor array elements over time (wind / settlement)". The division structure in the formula is not arbitrarily chosen, but rather to eliminate the linear superposition effect of different error sources on the signal phase.

[0095] (5) Calculate the average value of the data after calibration of the three reference channels. Normalization completes the directional consistency calibration: .

[0096] (6) Calculate the covariance matrix The matrix is ​​then transmitted to the ARM for super-resolution DOA estimation.

[0097] Step 5: Super-resolution DOA estimation.

[0098] The ARM performs eigenvalue decomposition on the covariance matrix R to obtain 16 eigenvalues ​​and corresponding eigenvectors. Based on the magnitude of the eigenvalues, the signal subspace and noise subspace are divided. The MUSIC algorithm is used to search for spectral peaks with a step size of 0.1°. The angle corresponding to the spectral peak is 45.2°, which is the precise direction of the incoming radio wave of the UAV. The direction finding accuracy reaches 0.2°, which is much higher than the direction finding accuracy of traditional calibration methods.

[0099] Implementation effect verification: This embodiment applies the calibration method to a 16-channel anti-UAV array direction finding system and compares it with a traditional single-angle, single-channel reference, fixed pre-calibration method. The test scenario is a complex outdoor electromagnetic environment, with the UAV target flying within the range of 0° to 180°. The test results are as follows: Direction finding accuracy: The average direction finding accuracy of this invention is ≤0.5°, while the average direction finding accuracy of traditional methods is ≥2°, resulting in an improvement of more than 75% in direction finding accuracy; Long-term stability: After 72 hours of continuous operation, the direction finding accuracy of the present invention did not decrease significantly, while the direction finding accuracy of the traditional method decreased to ≥3°, and calibration failed due to error drift; Fault tolerance: By artificially simulating a hardware failure in reference channel 8, the direction finding accuracy of this invention only drops to 0.8°, which still meets the direction finding requirements for anti-UAVs. Traditional methods, on the other hand, suffer from a drop in direction finding accuracy to ≥5° due to a single channel failure, resulting in direction finding failure. Omnidirectional calibration effect: At all angles from 0° to 180°, the direction finding accuracy of the present invention is ≤0.5°, while the direction finding accuracy of the traditional method at non-0° angles is ≥3°, demonstrating a significant omnidirectional calibration effect.

[0100] Test results show that the anti-drone array direction finding precision calibration method of the present invention can effectively improve direction finding accuracy, long-term stability and system fault tolerance, and fully meet the engineering application requirements of anti-drone scenarios.

[0101] Understandably, in practical applications, machine learning-based adaptive calibration schemes can also be used. These schemes primarily utilize algorithms such as neural networks or support vector machines to establish error prediction models based on historical calibration data and environmental parameters, replacing some hardware calibration modules. Machine learning can automatically extract the nonlinear relationship between array element errors and environmental factors, reducing reliance on multi-angle auxiliary signal sources. However, GPS / inertial navigation modules are still required to provide position data as input features.

[0102] A distributed collaborative calibration scheme can also be adopted, mainly using multi-base station collaborative direction finding technology. Error compensation is achieved through signal cross-verification between distributed antenna arrays, replacing the multi-angle calibration signals of a single system. The distributed system can eliminate local errors through the spatial baseline differences of multiple base stations, but the synchronization and data fusion issues of multiple base stations need to be addressed.

[0103] In summary, the anti-drone array direction finding calibration method provided in this application achieves multi-dimensional error correction, enabling accurate calibration across all error sources and directions. For the first time, array element position errors are incorporated into the anti-drone array direction finding calibration system. Array element positions are acquired in real-time via GPS / inertial navigation modules, the influence coefficient of position deviation on direction finding is calculated, and this is integrated into the calibration formula, solving the direction finding error problem caused by outdoor array element installation deviations. Simultaneously, the method abandons the single 0-degree auxiliary signal source and adds multi-angle calibration signal sources in seven directions (30°, 60°, 90°, etc.), establishing a full-directional angle-error mapping table. This enables accurate calibration of drone signals incident at different angles, eliminating direction-dependent errors and covering the omnidirectional incident requirements of drone signals in anti-drone scenarios. Multi-dimensional error correction comprehensively suppresses amplitude and phase errors, position errors, direction consistency errors, and direction-dependent errors, significantly improving the completeness and accuracy of calibration.

[0104] The pre-calibration data is dynamically updated to ensure long-term calibration stability. An error detection module is added, which compares real-time channel errors with historical pre-calibration errors by periodically collecting background data during periods without a target, calculating the deviation rate. When the deviation rate exceeds a 5% threshold, the pre-calibration data is automatically updated. This mechanism automates and dynamically updates the pre-calibration data, solving the problem of error drift and failure caused by long-term equipment operation and environmental changes in traditional fixed pre-calibration data. It ensures that the calibration data always matches the actual operating state of the equipment, improving the long-term stability of calibration and making it suitable for long-term continuous outdoor operation scenarios of anti-drone systems.

[0105] Optimizing calibration reference redundancy significantly improves the system's fault tolerance and reliability. A multi-channel reference is used instead of the traditional single-channel reference. K uniformly distributed and stable reference channels are selected based on the number of array element channels, and normalized calibration is performed by calculating the average error of the multiple reference channels. This method effectively reduces calibration deviations caused by hardware failures, electromagnetic interference, and performance drift of a single channel. Even if individual reference channels fail, effective calibration can still be achieved through the remaining reference channels, greatly improving the system's fault tolerance and reliability. Simultaneously, the averaging process of the multi-channel reference effectively suppresses the impact of environmental noise on calibration, further improving calibration accuracy.

[0106] The computational logic is simple and highly applicable to engineering. While achieving multi-dimensional, dynamic, and highly fault-tolerant calibration, it maintains a simple computational logic. All calibration formulas are based on linear operations and normalization, without complex iteration and optimization processes, resulting in high computational efficiency. It can run in real time on the embedded hardware platform of anti-drone systems. At the same time, the hardware modification of this invention only requires the addition of a multi-angle calibration signal source, a GPS / inertial navigation module, and an error detection module. All modules are commercially mature products, easy to integrate and deploy, with low modification costs. It is suitable for upgrading and modifying various existing anti-drone array direction finding systems, and has extremely strong engineering applicability.

[0107] This invention adapts to the electromagnetic environment and signal characteristics of anti-drone scenarios. The multi-angle calibration signal source frequency covers the commonly used 2.4GHz, 5.8GHz, and 1.2GHz frequency bands for anti-drone operations, matching the radio signal frequencies of drones. The detection cycle of the error detection module can be flexibly adjusted according to the complex outdoor electromagnetic environment, enabling rapid error detection and updating. Super-resolution DOA estimation is adaptable to the characteristics of rapid drone target movement, weak signals, and complex electromagnetic interference in anti-drone scenarios, ensuring real-time performance and accuracy of direction finding. This invention is fully adapted to the application scenarios of anti-drone operations, and the calibration effect is significant.

[0108] See Figure 5 The present invention can also provide an anti-UAV array direction finding calibration device, such as... Figure 5 As shown, the device may include: The system initialization and parameter setting unit 501 is used to set the number of array element channels of the antenna array to M, and select K uniformly distributed reference channels, where K is a natural number greater than 1 and less than M; it initializes the GPS / inertial navigation module, the multi-angle calibration signal source, and the error detection module. The GPS / inertial navigation module has a positioning accuracy of not less than 0.1 meters. The multi-angle calibration signal source covers seven directions: 0°, 30°, 60°, 90°, 120°, 150°, and 180°, and the signal frequency covers the 2.4 GHz, 5.8 GHz, and 1.2 GHz frequency bands. The error detection module has a set deviation threshold of 5%. The multi-angle pre-calibration and angle-error mapping table establishment unit 502 is used to sequentially turn on the auxiliary signal sources in each direction to obtain the amplitude and phase error calibration matrix at each angle; collect calibration signal data of each channel, use the average value of the collected data of K reference channels as a unified reference benchmark, calculate the relative error between channels, and then calculate the amplitude and phase error before the channel; collect the actual three-dimensional coordinates of each array element through the GPS / inertial navigation module, compare them with the design theoretical coordinates to calculate the position deviation, and calculate the position error coefficient by combining the signal incident angle and the position deviation of the array element; establish an angle-error mapping table containing the correspondence between the signal incident angle, array element channel number, amplitude and phase error and position error coefficient, and store it in the local database; The error drift detection and pre-calibration data dynamic update unit 503 is used to collect environmental background data by the error detection module according to the detection cycle during the period when there is no UAV target signal. The average value of the K reference channels is used as the reference reference to calculate the real-time inter-channel relative error. The historical data of inter-channel relative error under the reference angle of the most recent pre-calibration is retrieved from the local database. The deviation rate between the real-time inter-channel relative error and the historical data is calculated. When the deviation rate is greater than 5%, step S2 is triggered to be repeated to update the angle-error mapping table. The multi-dimensional real-time joint calibration unit 504 for UAV signals is used to collect the signal data to be tested after detecting the UAV target signal; to collect the three-dimensional coordinates of each array element in real time through the GPS / inertial navigation module, update the real-time position deviation, and update the position error coefficient by combining the preliminary estimated direction angle with the UAV signal; to activate the closest calibration signal source according to the preliminary estimated direction angle, collect real-time calibration signal data, and calculate the real-time inter-channel relative error; to retrieve the amplitude and phase error of the corresponding angle from the angle-error mapping table, and to perform synchronous joint calibration of amplitude and phase error and position error on the signal data to be tested based on the real-time inter-channel relative error, amplitude and phase error, and the updated position error coefficient; to normalize the data after joint calibration of each channel again using the average value of the K reference channels as the reference, complete the direction consistency calibration, obtain the calibrated signal data, and calculate the covariance matrix based on the calibrated signal data; The incoming wave direction estimation unit 505 is used to estimate the incoming wave direction based on the covariance matrix using a super-resolution DOA estimation algorithm.

[0109] This invention can also provide an anti-UAV array direction finding calibration device, the device including a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the steps of the above-described anti-drone array direction finding calibration method according to the instructions in the program code.

[0110] like Figure 6 As shown in the figure, an anti-drone array direction finding calibration device provided in this embodiment of the invention may include: a processor 10, a memory 11, a communication interface 12, and a communication bus 13. The processor 10, memory 11, and communication interface 12 all communicate with each other through the communication bus 13.

[0111] In this embodiment of the invention, the processor 10 may be a central processing unit (CPU), an application-specific integrated circuit, a digital signal processor, a field-programmable gate array, or other programmable logic devices.

[0112] The processor 10 can call the program stored in the memory 11. Specifically, the processor 10 can execute the operations in the embodiment of the anti-UAV array direction finding calibration method.

[0113] The memory 11 is used to store one or more programs. The programs may include program code, which includes computer operation instructions. In this embodiment of the invention, the memory 11 stores at least a program for implementing the following functions: Step S1: Set the number of array element channels of the antenna array to M, and select K uniformly distributed reference channels, where K is a natural number greater than 1 and less than M; initialize the GPS / inertial navigation module, the multi-angle calibration signal source, and the error detection module. The positioning accuracy of the GPS / inertial navigation module is not less than 0.1 meters. The multi-angle calibration signal source covers 7 directions: 0°, 30°, 60°, 90°, 120°, 150°, and 180°, and the signal frequency covers the 2.4 GHz, 5.8 GHz, and 1.2 GHz frequency bands. The error detection module is set with a deviation threshold of 5%. Step S2: Sequentially activate the auxiliary signal sources in each direction to obtain the amplitude and phase error calibration matrix at each angle; collect calibration signal data for each channel, using the average value of the data collected from K reference channels as a unified reference benchmark, calculate the relative error between channels, and then calculate the amplitude and phase error before the channel; collect the actual three-dimensional coordinates of each array element through the GPS / inertial navigation module, compare them with the theoretical coordinates to calculate the position deviation, and calculate the position error coefficient by combining the signal incident angle and the position deviation of the array element; establish an angle-error mapping table containing the correspondence between the signal incident angle, array element channel number, amplitude and phase error, and position error coefficient, and store it in the local database; Step S3: During periods when there is no UAV target signal, the error detection module collects environmental background data according to the detection cycle, uses the average value of the K reference channels as the reference benchmark, calculates the real-time inter-channel relative error, retrieves the historical data of the inter-channel relative error under the most recent pre-calibrated reference angle from the local database, calculates the deviation rate between the real-time inter-channel relative error and the historical data, and when the deviation rate is greater than 5%, triggers the re-execution of step S2 to update the angle-error mapping table; Step S4: After detecting the UAV target signal, acquire the signal data to be tested; acquire the three-dimensional coordinates of each array element in real time through the GPS / inertial navigation module, update the real-time position deviation, and update the position error coefficient by combining the UAV signal with the preliminary estimated direction angle; activate the closest calibration signal source according to the preliminary estimated direction angle, acquire real-time calibration signal data, and calculate the real-time inter-channel relative error; retrieve the amplitude and phase error of the corresponding angle from the angle-error mapping table, and perform synchronous joint calibration of amplitude and phase error and position error on the signal data to be tested based on the real-time inter-channel relative error, amplitude and phase error, and the updated position error coefficient; again use the average value of the K reference channels as the reference, normalize the data after joint calibration of each channel, complete the direction consistency calibration, obtain the calibrated signal data, and calculate the covariance matrix based on the calibrated signal data; Step S5: Based on the covariance matrix, use the super-resolution DOA estimation algorithm to estimate the direction of arrival.

[0114] In one possible implementation, the memory 11 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function (such as file creation or data read / write). The data storage area may store data created during use, such as initialization data.

[0115] In addition, memory 11 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0116] Communication interface 12 can be an interface for the communication module, used to connect with other devices or systems.

[0117] Of course, it should be noted that, Figure 6 The structure shown does not constitute a limitation on the anti-UAV array direction finding calibration device in the embodiments of the present invention. In practical applications, the anti-UAV array direction finding calibration device may include more than Figure 6 More or fewer components as shown, or combinations of certain components.

[0118] This invention can also provide a computer-readable storage medium for storing program code for executing the steps of the above-described anti-UAV array direction finding calibration method.

[0119] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0120] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0121] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0122] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for calibrating the direction finding of an anti-UAV array, characterized in that, Comprising the following steps: Step S1: setting the number of array element channels of an antenna array as M, and selecting K uniformly distributed reference channels, wherein K is a natural number greater than 1 and less than M; initializing a GPS / inertial navigation module, a multi-angle calibration signal source and an error detection module, wherein the positioning accuracy of the GPS / inertial navigation module is not lower than 0.1 meter, the multi-angle calibration signal source covers 7 directions of 0°, 30°, 60°, 90°, 120°, 150° and 180°, the signal frequency covers 2.4 gigahertz, 5.8 gigahertz and 1.2 gigahertz frequency bands, and the error detection module sets a deviation threshold as 5%; Step S2: sequentially turning on auxiliary signal sources in all directions, and acquiring an amplitude-phase error calibration matrix at each angle; acquiring calibration signal data of each channel, taking an average value of data acquired by the K reference channels as a unified reference, calculating a relative error between channels, and further calculating an amplitude-phase error before the channels; acquiring actual three-dimensional coordinates of each array element through the GPS / inertial navigation module, calculating a position deviation by comparing with designed theoretical coordinates, and calculating a position error coefficient by combining a signal incident angle and the position deviation of the array element; establishing an angle-error mapping table containing the corresponding relationship among signal incident angles, array element channel serial numbers, amplitude-phase errors and position error coefficients, and storing the angle-error mapping table in a local database; Step S3: in a time period when there is no unmanned aerial vehicle target signal, acquiring environmental background data by the error detection module according to a detection period, still taking the average value of the K reference channels as the reference, calculating a real-time relative error between channels, calling historical data of the relative error between channels under a reference angle of the most recent pre-calibration from the local database, calculating a deviation rate between the real-time relative error between channels and the historical data, and triggering repeated execution of step S2 to update the angle-error mapping table when the deviation rate is greater than 5%; Step S4: after detecting an unmanned aerial vehicle target signal, acquiring signal data to be measured; acquiring three-dimensional coordinates of each array element in real time through the GPS / inertial navigation module, updating a real-time position deviation and updating a position error coefficient by combining a preliminarily estimated direction angle of an unmanned aerial vehicle signal; turning on the closest calibration signal source according to the preliminarily estimated direction angle, acquiring real-time calibration signal data and calculating a real-time relative error between channels; calling an amplitude-phase error corresponding to an angle from the angle-error mapping table, performing synchronous joint calibration on the amplitude-phase error and the position error of the signal data to be measured based on the real-time relative error between channels, the amplitude-phase error and the updated position error coefficient; still taking the average value of the K reference channels as the reference again, normalizing data of all channels after joint calibration, completing direction consistency calibration to obtain calibrated signal data, and calculating a covariance matrix based on the calibrated signal data; Step S5: performing incoming wave direction estimation by using a super-resolution DOA estimation algorithm based on the covariance matrix.

2. The anti-UAV array direction finding calibration method according to claim 1, characterized in that, In step S1, the selection rule for the K reference channels is: when M≤10, K=2; when 10<M≤20, K=3; when M>20, K=5.

3. The anti-UAV array direction finding calibration method according to claim 1, characterized in that, In step S2, the specific method for calculating the relative error between channels is: Find the K reference channels at the angle Average value of collected data ,in Indicates the first Data from one reference channel; Collect data from each channel The relative error between channels is obtained by comparing the value with the average value. ; Set the initial values ​​of the amplitude and phase error calibration matrix for all reference channels. .

4. The anti-UAV array direction finding calibration method according to claim 1, characterized in that, The amplitude and phase error before the channel mentioned in step S2 The calculation formula is: In the formula: This represents the amplitude and phase error calibration matrix. This indicates the relative error between channels.

5. The anti-UAV array direction finding calibration method according to claim 1, characterized in that, The deviation rate mentioned in step S3 The calculation formula is: In the formula: This represents the relative error between real-time channels. Indicates reference angle Historical data on relative errors between channels.

6. The anti-UAV array direction finding calibration method according to claim 1, characterized in that, The synchronous joint calibration of amplitude and phase error and position error in step S4 is achieved by the following formula: In the formula: This represents the signal data of each channel after multi-dimensional error joint calibration. This represents the signal data to be measured collected from each channel. This represents the relative error between real-time channels. Indicates the direction and angle as initially estimated. The corresponding amplitude and phase errors, This represents the real-time position error coefficient.

7. The anti-UAV array direction finding calibration method according to claim 1, characterized in that, The orientation consistency calibration described in step S4 is achieved in the following way: Obtain the calibration data for K reference channels. average ; Data after joint calibration of each channel Normalization is performed to obtain the signal data after orientation consistency calibration. .

8. The anti-UAV array direction finding calibration method according to claim 1, characterized in that, In step S4, if the preliminary estimated direction angle θ′ of the UAV signal is between the directions of the two calibration signal sources, the amplitude and phase error and position error coefficients corresponding to the closest calibration signal source direction are retrieved from the angle-error mapping table for calibration.

9. The anti-UAV array direction finding calibration method according to claim 1, characterized in that, The super-resolution DOA estimation algorithm mentioned in step S5 includes any one of the MUSIC algorithm, ESPRIT algorithm, and weighted subspace fitting algorithm; the covariance matrix R is obtained by multiplying the calibrated signal data by its conjugate transpose matrix, i.e. .

10. A direction finding and calibration device for an anti-UAV array, characterized in that, The apparatus for performing the anti-UAV array direction finding calibration method according to any one of claims 1-7, the apparatus comprising: The system initialization and parameter setting unit is used to set the number of array element channels of the antenna array to M, and select K uniformly distributed reference channels, where K is a natural number greater than 1 and less than M; it initializes the GPS / inertial navigation module, the multi-angle calibration signal source, and the error detection module. The GPS / inertial navigation module has a positioning accuracy of not less than 0.1 meters. The multi-angle calibration signal source covers seven directions: 0°, 30°, 60°, 90°, 120°, 150°, and 180°, and the signal frequency covers the 2.4 GHz, 5.8 GHz, and 1.2 GHz frequency bands. The error detection module has a set deviation threshold of 5%. The multi-angle pre-calibration and angle-error mapping table establishment unit is used to sequentially activate auxiliary signal sources in each direction to obtain amplitude and phase error calibration matrices at each angle; collect calibration signal data from each channel, use the average value of the data collected from K reference channels as a unified reference benchmark, calculate the relative error between channels, and then calculate the amplitude and phase error before the channel; collect the actual three-dimensional coordinates of each array element through the GPS / inertial navigation module, compare them with the theoretical coordinates of the design to calculate the position deviation, and calculate the position error coefficient by combining the signal incident angle and the position deviation of the array element; establish an angle-error mapping table containing the correspondence between the signal incident angle, array element channel number, amplitude and phase error, and position error coefficient, and store it in a local database; The error drift detection and pre-calibration data dynamic update unit is used to collect environmental background data by the error detection module according to the detection cycle during the period when there is no UAV target signal. The average value of the K reference channels is used as the reference reference to calculate the real-time inter-channel relative error. The historical data of inter-channel relative error under the reference angle of the most recent pre-calibration is retrieved from the local database. The deviation rate between the real-time inter-channel relative error and the historical data is calculated. When the deviation rate is greater than 5%, step S2 is triggered to be repeated to update the angle-error mapping table. A multi-dimensional real-time joint calibration unit for UAV signals is used to collect the signal data to be tested after detecting the UAV target signal; it collects the three-dimensional coordinates of each array element in real time through the GPS / inertial navigation module, updates the real-time position deviation, and updates the position error coefficient by combining the preliminary estimated direction angle with the UAV signal; it activates the closest calibration signal source according to the preliminary estimated direction angle, collects real-time calibration signal data, and calculates the real-time inter-channel relative error; it retrieves the amplitude and phase error of the corresponding angle from the angle-error mapping table, and performs synchronous joint calibration of amplitude and phase error and position error on the signal data to be tested based on the real-time inter-channel relative error, amplitude and phase error, and the updated position error coefficient; it again uses the average value of the K reference channels as the reference benchmark to normalize the data after joint calibration of each channel, completes the direction consistency calibration, obtains the calibrated signal data, and calculates the covariance matrix based on the calibrated signal data; The direction of arrival estimation unit is used to estimate the direction of arrival based on the covariance matrix using a super-resolution DOA estimation algorithm.

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