T / R assembly calibration control method

Abstract

The invention relates to the technical field of radar systems, in particular to a T / R assembly calibration control method, which comprises the following steps of: sending a calibration signal to a T / R array to enable each T / R assembly in the T / R array to generate assembly state parameters based on the calibration signal; based on the physical layout relation of the T / R array, spatial correlation analysis is carried out on the component state parameters of all the T / R components, and effective state parameters of all the T / R components are determined; and according to the effective state parameter of each T / R assembly, updating the calibration parameter of each T / R assembly. According to the invention, the real change of the performance of the T / R assembly and the environmental interference can be effectively distinguished, and the stability of calibration parameters in a complex working environment is ensured.

Description

Technical Field

[0001] This application relates to the field of radar system technology, and in particular to a T / R component calibration control method. Background Technology

[0002] In phased array radar systems, T / R (Transmit / Receive) module calibration is a critical step in ensuring stable radar performance. Phased array radars rely on the precise amplitude and phase control of hundreds or thousands of T / R modules to achieve beamforming, scanning, and null generation. Any amplitude or phase error in any module will lead to a decrease in the main lobe gain, an increase in the sidelobe level, and beam pointing deviation in the antenna pattern, severely impacting the radar's detection range and anti-jamming capabilities.

[0003] Existing calibration control methods for T / R components all attempt to balance the core indicators of calibration accuracy, real-time performance, system complexity, and environmental adaptability. Offline calibration methods offer high accuracy but lack real-time performance; internal monitoring methods have online capabilities but are hardware-complex and may interrupt the task; OTA methods offer high system-level accuracy but have stringent implementation conditions; and blind calibration methods offer good real-time performance but lack reliability.

[0004] In traditional technologies, T / R module calibration methods typically employ closed-loop calibration systems based on automated testing equipment and digital signal processing to optimize key parameters such as amplitude, phase, and linearity of their transmit and receive channels. However, in complex real-world operating environments (such as temperature variations, component aging, and multipath interference), traditional static calibration parameters based on ideal laboratory conditions quickly become ineffective, leading to drift in T / R module performance (such as amplitude and phase consistency).

[0005] Especially when the radar is in a complex electromagnetic environment or an environment with severe multipath interference, environmental factors can cause drastic fluctuations in the measured state parameters. Traditional calibration algorithms usually employ a simple closed-loop feedback mechanism, which makes it difficult to distinguish whether such fluctuations are caused by performance drift of the T / R component hardware itself (which requires calibration) or by external environmental interference (such as multipath effects and clutter, which do not require calibration).

[0006] If the system incorrectly identifies environmental interference as component performance drift and forcibly updates parameters, it will not only fail to improve performance but will also introduce incorrect compensation amounts, leading to beam performance degradation and even system oscillation. Therefore, accurately identifying environmental interference and extracting the true and effective state parameters of the T / R components in complex dynamic environments is a pressing problem that current phased array radar calibration technology needs to solve. Summary of the Invention

[0007] Therefore, it is necessary to provide a T / R component calibration control method to ensure the stability of calibration parameters in complex working environments, addressing the aforementioned technical problems.

[0008] This invention provides a T / R component calibration control method, the method comprising: A calibration signal is sent to the T / R array, enabling each T / R component in the T / R array to generate component status parameters based on the calibration signal; Based on the physical layout relationship of the T / R array, spatial correlation analysis is performed on the component state parameters of each T / R component to determine the effective state parameters of each T / R component. Update the calibration parameters of each T / R component based on the valid status parameters of each T / R component.

[0009] In one embodiment, based on the physical layout relationship of the T / R array, spatial correlation analysis is performed on the component state parameters of each T / R component to determine the effective state parameters of each T / R component, specifically including: For each T / R component, calculate the correlation index between the component state parameters of the T / R component and the component state parameters of the corresponding neighboring T / R components in the T / R array; Based on the correlation index, the effective state parameters of the T / R component are determined.

[0010] In one embodiment, the correlation index between the component state parameters of the T / R component and the component state parameters of its neighboring T / R components in the T / R array is calculated, specifically including: Calculate the statistical dispersion between the component state parameters of the T / R component and the component state parameters of the corresponding neighboring T / R components in the T / R array; Based on statistical dispersion, the similarity between the component state parameters of a T / R component and the component state parameters of neighboring T / R components is determined, and the similarity is used as a correlation index.

[0011] In one embodiment, based on statistical dispersion, the similarity between the component state parameters of a T / R component and the component state parameters of neighboring T / R components is determined, specifically including: The component status parameters of this T / R component are compared with the statistical median of the component status parameters of neighboring T / R components to obtain the comparison results; Based on statistical dispersion, the range of the judgment threshold is determined; When the comparison result is within the judgment threshold range, the similarity between the component state parameters of the T / R component and the component state parameters of the neighboring T / R components is determined to be high similarity.

[0012] In one embodiment, before performing spatial correlation analysis on the component state parameters of each T / R component, the method further includes: The filtering contribution weight of each T / R component is determined based on the spatial distance between each T / R component and the preset reference point; wherein the filtering contribution weight decreases as the spatial distance increases. Based on the filtering contribution weight of each T / R component, the component state parameters of each T / R component are weighted and averaged to obtain the filtered state parameters of each T / R component. Specifically, the spatial correlation analysis of the component state parameters of each T / R component includes: performing spatial correlation analysis on the filtered state parameters of each T / R component.

[0013] In one embodiment, spatial correlation analysis is performed on the filtered state parameters of each T / R component, specifically including: Obtain the electromagnetic coupling coefficient matrix of the T / R array; where the electromagnetic coupling coefficient matrix characterizes the electromagnetic coupling strength between each T / R component in the T / R array; Based on the electromagnetic coupling coefficient matrix and the filtered state parameters of each T / R component, the parameter offset caused by the mutual coupling effect is calculated. The mutual coupling correction state parameters of each T / R component are obtained by subtracting the parameter offset from the filtered state parameters of each T / R component. Spatial correlation analysis was performed on the mutual coupling correction state parameters of each T / R component.

[0014] In one embodiment, after determining the effective state parameters of the T / R component based on the correlation index, the method further includes: When the component state parameter of the T / R component is identified as an outlier parameter, the time-domain fluctuation characteristics and frequency-domain distribution characteristics of the outlier parameter are analyzed. Based on the time-domain fluctuation characteristics and frequency-domain distribution features, the fault type corresponding to the outlier parameter is determined; When the fault type is a component hardware fault, the T / R component is marked as unavailable and its calibration parameters are not updated. When the fault type is environmental interference, the calibration parameters of the T / R component are updated based on the mutual coupling correction state parameters of the neighboring T / R components.

[0015] In one embodiment, the neighboring T / R components of the T / R component in the T / R array are determined in the following manner: Identify the location type of the T / R component in the T / R array; If the location type is an array edge location or an array corner location, then based on the principle of array symmetry, a virtual T / R component is created outside the boundary of the T / R array; Based on the component state parameters of the existing T / R components in the T / R array, determine the virtual state parameters of the virtual T / R component; Include the virtual T / R component in the selection range of the neighboring T / R components of this T / R component.

[0016] In one embodiment, the method further includes, before sending a calibration signal to the T / R array: Send a synchronization calibration signal to the T / R array to measure the clock path delay difference and local oscillator phase difference of each T / R component in the T / R array; Based on the clock path delay difference, the delay compensation parameters for each T / R component are determined; Based on the local oscillator phase difference, the phase compensation parameters of each T / R component are determined; When sending calibration signals to the T / R array, digital domain compensation processing is performed on the calibration signals of each T / R component based on the time delay compensation parameters and phase compensation parameters of each T / R component.

[0017] In one embodiment, the component state parameters include phase error values ​​and amplitude error values.

[0018] The beneficial effects of this invention are: (1) The T / R component calibration control method designed in this invention sends calibration signals to the T / R array to obtain the state parameters of each component. Instead of directly using these parameters for calibration, it performs spatial correlation analysis on the component state parameters based on the physical layout relationship of the T / R array. By examining the correlation characteristics of parameters between adjacent T / R components, it identifies abnormal parameters affected by environmental interference and determines the valid state parameters. Finally, it updates the calibration parameters only based on these valid state parameters. This approach breaks through the limitations of traditional static calibration, enabling the system to distinguish between true performance drift caused by temperature changes and component aging and temporary parameter fluctuations caused by environmental factors such as multipath interference. This avoids misjudging environmental interference as hardware performance changes, resulting in incorrect updates of calibration parameters. It ensures the continuous effectiveness of calibration parameters in complex actual working environments and fundamentally solves the technical problem of T / R component performance consistency drifting due to environmental factors.

[0019] (2) This invention employs statistical dispersion and adaptive thresholds to improve the accuracy of anomaly detection. Unlike traditional fixed threshold judgments, this application introduces statistical dispersion and median-based similarity judgment methods. This method can dynamically adjust the judgment threshold according to the overall fluctuation (dispersion) of the current array, achieving adaptive judgment criteria. In harsh environments (large dispersion), the criteria are automatically relaxed to reduce false alarms, and in stable environments, the criteria are tightened to reduce missed alarms, effectively solving the problem that a single fixed threshold is difficult to adapt to dynamic environments.

[0020] (3) This invention combines electromagnetic mutual coupling correction and spatial filtering to restore the true physical state of the components. Addressing the inherent mutual coupling effect and measurement noise of the array antenna, this invention effectively filters out high-frequency measurement random noise through spatial distance-weighted filtering. More importantly, it calculates and subtracts the mutual coupling offset using the electromagnetic coupling coefficient matrix. This step achieves physical-level "decoupling" of the signal, ensuring that subsequent correlation analysis is based on the true performance parameters of the T / R components under "vacuum" conditions, thereby significantly improving the physical authenticity and calibration accuracy of the calibration parameters.

[0021] (4) This invention realizes intelligent fault diagnosis and array "self-healing" function; this invention not only eliminates anomalies, but also further analyzes the time-domain fluctuation and frequency-domain distribution characteristics of outlier parameters, thereby intelligently distinguishing between "component hardware failure" and "temporary environmental interference". For hardware failure, the system can automatically isolate to prevent beam contamination; for environmental interference, it uses the mutual coupling correction value of neighborhood information to reconstruct parameters. This mechanism gives the array a certain "self-healing" capability, greatly reduces maintenance costs, and improves the survivability of the system.

[0022] (5) This invention solves the edge effect problem and ensures the consistency of the entire array calibration. Addressing the issue of decreased calibration accuracy due to a lack of neighborhood information in array edge or corner components, this invention proposes an innovative scheme to create virtual T / R components based on the principle of array symmetry. By completing the boundary conditions, edge components can apply spatial correlation analysis algorithms with the same accuracy as the central components, effectively ensuring the consistency of the amplitude and phase of the entire array. This is particularly crucial for reducing the sidelobe level of the phased array antenna. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the accompanying drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below.

[0024] Figure 1 This is a flowchart illustrating the T / R component calibration control method provided in an embodiment of this application; Figure 2 This is a block diagram of the T / R component calibration control system in one embodiment; Figure 3 This is a flowchart illustrating the steps for determining the valid state parameters of each T / R component provided in this embodiment; Figure 4 This is a flowchart illustrating the steps for performing spatial correlation analysis on the filtered state parameters of each T / R component provided in this embodiment. Figure 5 This is a flowchart illustrating the steps for determining the neighboring T / R components in the T / R array corresponding to the T / R component provided in this embodiment; Figure 6 This is a flowchart illustrating the steps for digital domain compensation processing of the calibration signals of each T / R component provided in this embodiment. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the application will be described in further detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0027] A phased array radar system consists of a large number of transceiver (T / R) modules forming an array antenna, with each T / R module responsible for signal transmission and reception. Due to differences in manufacturing processes, environmental temperature variations, and component aging, the phase and amplitude characteristics of each T / R module can vary, leading to a deterioration in radar system performance, such as beam pointing deviation, increased sidelobe levels, and reduced gain.

[0028] Related techniques typically employ an independent calibration method, which calibrates each T / R component separately, ignoring the spatial correlation between T / R components. This method is simple to implement, but when some T / R components malfunction or are subject to environmental interference, the calibration results are prone to significant errors.

[0029] This application provides a T / R component calibration control method. By utilizing the physical layout relationship of the T / R array, spatial correlation analysis is performed on the component state parameters of each T / R component to determine the effective state parameters of each T / R component, thereby improving calibration accuracy and system robustness. This T / R component calibration control method is applied to a T / R component calibration control system.

[0030] See Figure 1 The following will combine Figure 1 The steps shown illustrate the T / R component calibration control method provided in the embodiments of this application.

[0031] In step S101, a calibration signal is sent to the T / R array, so that each T / R component in the T / R array generates component state parameters based on the calibration signal.

[0032] In this embodiment, a stable single-frequency signal (e.g., a 9.5 GHz continuous wave signal) is generated by a calibration signal generator, and this signal is distributed to each T / R component of the T / R array via a calibration network. In some embodiments, the calibration network is constructed using Wilkinson power dividers to ensure the amplitude and phase consistency of the calibration signal.

[0033] After receiving the calibration signal, each T / R component processes the signal and outputs a feedback signal. In some embodiments, the T / R component includes key components such as a low-noise amplifier, mixer, phase shifter, attenuator, and power amplifier, the characteristics of which affect the generation of component state parameters.

[0034] Component status parameters refer to a set of parameters reflecting the operating state of a T / R component. In some embodiments, component status parameters include phase error values ​​and amplitude error values. The phase error value represents the deviation between the actual phase and the ideal phase of the T / R component, and the amplitude error value represents the deviation between the actual gain and the ideal gain of the T / R component.

[0035] In this embodiment, the phase error and amplitude error values ​​of each T / R component can be obtained by measuring the phase difference and amplitude difference between the output signal and the reference signal of the T / R component. Specifically, the phase error value can be obtained through I / Q demodulation and digital signal processing, and the amplitude error value can be obtained through power detection and comparison.

[0036] In some embodiments, to improve measurement accuracy, an averaging method based on multiple measurements can be used. For example, five calibration signals are sent consecutively, the component status parameters are recorded after each measurement, and the average of the five measurement results is taken as the final component status parameters.

[0037] In step S102, based on the physical layout relationship of the T / R array, spatial correlation analysis is performed on the component state parameters of each T / R component to determine the effective state parameters of each T / R component.

[0038] The physical layout of a T / R array refers to the relative positions and topology of the T / R components in space. In some embodiments, the T / R array can be a rectangular array, a circular array, or an array of any shape. For a rectangular array, the T / R components are arranged in rows and columns; for a circular array, the T / R components are distributed at equal intervals around the circumference.

[0039] Spatial correlation analysis refers to assessing the reliability of component status parameters by utilizing the spatial correlation between T / R components. In phased array radar systems, adjacent T / R components typically exhibit high correlation in their component status parameters due to their close physical location and similar operating environments. When the component status parameters of a particular T / R component differ significantly from those of its neighboring T / R components, it may indicate an anomaly in that T / R component.

[0040] The implementation process is as follows: First, for each T / R component, its neighboring T / R components in the T / R array are determined. In some embodiments, the neighboring T / R components can be the four or eight T / R components closest to the current T / R component, and the specific number can be determined according to the array density and application scenario.

[0041] Then, the correlation index between the component state parameters of the T / R component and the component state parameters of its neighboring T / R components is calculated. In some embodiments, the correlation index can be represented by statistical dispersion or similarity. The smaller the statistical dispersion, the smaller the difference between the component state parameters, and the higher the correlation.

[0042] Finally, based on the correlation index, the valid state parameters of the T / R component are determined. In some embodiments, when the correlation index is higher than a preset threshold, the component state parameters of the T / R component are considered reliable, and the original measured values ​​are directly used as valid state parameters; when the correlation index is lower than the preset threshold, the component state parameters of the T / R component may be abnormal, and the weighted average of the component state parameters of neighboring T / R components is used as valid state parameters.

[0043] In this embodiment, spatial correlation analysis can effectively identify and handle abnormal T / R components, improving the reliability of calibration results. For example, when a T / R component is subjected to external interference, its component state parameters will differ significantly from those of neighboring T / R components. Spatial correlation analysis can identify this anomaly, and the parameters of neighboring T / R components can be used for compensation, avoiding the impact of abnormal T / R components on the overall calibration results.

[0044] In step S103, the calibration parameters of each T / R component are updated according to the effective status parameters of each T / R component.

[0045] Calibration parameters are parameters used to compensate for phase and amplitude errors in the T / R module. In some embodiments, calibration parameters include phase calibration values ​​and amplitude calibration values. The phase calibration value is used to compensate for phase errors, and the amplitude calibration value is used to compensate for amplitude errors.

[0046] The specific implementation process is as follows: First, the calibration parameters are calculated based on the valid state parameters. In some embodiments, the phase calibration value is equal to the negative of the phase error value, and the amplitude calibration value is equal to the reciprocal of the amplitude error value. For example, if the phase error of a certain T / R component is +5°, then its phase calibration value is -5°; if the amplitude error is 0.9 (indicating that the gain is 10% lower than the ideal value), then its amplitude calibration value is 1 / 0.9≈1.111.

[0047] The calibration parameters are then written to the control register of the T / R component. In some embodiments, the T / R component contains a digital control unit that can receive and store the calibration parameters. The phase calibration value is used to control the phase shifter, and the amplitude calibration value is used to control the attenuator or gain control unit.

[0048] Finally, verify the calibration effect. In some embodiments, the calibration signal can be sent again to measure the state parameters of the calibrated T / R component and evaluate the calibration effect. If the calibrated state parameters meet the preset accuracy requirements, the calibration is complete; otherwise, the calibration process can be repeated until satisfactory results are achieved.

[0049] In this embodiment of the invention, updating calibration parameters by using effective state parameters determined based on spatial correlation analysis can significantly improve calibration accuracy and system robustness. Test results show that, compared with the traditional independent calibration method, the method of this application embodiment reduces phase calibration error from ±3° to within ±1° and amplitude calibration error from ±0.5dB to within ±0.2dB, significantly improving the performance of the phased array radar system.

[0050] See Figure 2 In some embodiments, the T / R component calibration control system includes: Calibration signal generator 10: Used to generate a stable calibration signal; T / R Array 20: An array system consisting of M×N T / R components; Signal acquisition unit 30: used to acquire the output signals of the T / R components, i.e. the component status parameters of each T / R component; Calibration control sheet 40: used to receive the component status parameters of each T / R component, execute the T / R component calibration control method of this application embodiment, and output the valid status parameters of each T / R component; Control bus 50: Connects the calibration control unit to the T / R array and is used to transmit the valid status parameters of each T / R component.

[0051] In some embodiments, the T / R array 20 may be a planar array, a conformal array, or other types of array. The calibration signal generator 10 may be a vector signal generator capable of generating a highly stable single-frequency signal. The signal acquisition unit 30 may be a high-speed ADC and a digital signal processing unit for accurately measuring the output signal characteristics of the T / R component. The calibration control unit 40 may be an FPGA, a DSP, or a microprocessor for executing the calibration algorithm.

[0052] In this embodiment, the calibration control unit 40 performs the above steps S101 to S103 to achieve automatic calibration of the T / R array.

[0053] In one exemplary embodiment, see Figure 3 This embodiment provides a specific implementation method for determining the effective state parameters of each T / R component, including the following main steps: In step S201, for each T / R component, the neighboring T / R components in the T / R array corresponding to that T / R component are determined.

[0054] In this embodiment, the T / R array is a 32×32 rectangular array, and the T / R component spacing is 15mm. For any T / R component (i, j) in the array, its neighboring T / R components are determined as follows: In some embodiments, a 4-neighborhood approach is used to determine neighboring T / R components, namely T / R components in the four directions of up, down, left, and right. For example, for T / R component (15, 15), its neighboring T / R components are (14, 15), (16, 15), (15, 14), and (15, 16).

[0055] In some embodiments, neighboring T / R components are determined using an 8-neighborhood approach, which includes T / R components along the diagonal direction. For example, for T / R component (15, 15), its neighboring T / R components include (14, 15), (16, 15), (15, 14), (15, 16), (14, 14), (14, 16), (16, 14), and (16, 16).

[0056] In some embodiments, the neighborhood size can be dynamically adjusted based on the size of the T / R array and the operating frequency. For arrays operating below 10 GHz, a 4-neighborhood is used; for arrays operating above 10 GHz, an 8-neighborhood is used.

[0057] In this embodiment, considering that the T / R array operates at a frequency of 9.5 GHz, an 8-neighborhood approach is used to determine neighboring T / R components. This approach can more comprehensively capture the spatial correlation between T / R components and improve the accuracy of the analysis.

[0058] In particular, for T / R components located at the edge or corner of the array, the number of their neighboring T / R components is reduced. For example, for T / R component (1, 15) located at the top edge of the array but not at the corner, there are only 5 neighboring T / R components: (1, 14), (1, 16), (2, 14), (2, 15), and (2, 16).

[0059] This step identifies each T / R component's neighboring T / R components in the T / R array, providing a basis for subsequent calculations of statistical dispersion and similarity.

[0060] In step S202, the statistical dispersion between the component state parameters of the T / R component and the component state parameters of neighboring T / R components is calculated.

[0061] In this embodiment, the component state parameters include phase error values ​​and amplitude error values. Taking the phase error value as an example, the calculation process of statistical dispersion is as follows: First, the phase error values ​​of the T / R module and its neighboring T / R modules are collected. Assuming an 8-neighborhood approach is used, there are 8 neighboring T / R modules, resulting in 9 phase error values ​​(including the central T / R module itself).

[0062] Then, the statistical median of these phase error values ​​is calculated. The statistical median is the value in the middle when the data are arranged in ascending order; it is more resistant to the influence of outliers than the mean.

[0063] Next, the deviation of the phase error value of each neighboring T / R component from the statistical median is calculated. Specifically, the average of the absolute deviations of the phase error values ​​of all neighboring T / R components from the statistical median is calculated; this average is the statistical dispersion.

[0064] In some embodiments, statistical dispersion can also be represented by calculating the standard deviation of the phase error values ​​of neighboring T / R components. However, this embodiment uses the mean absolute deviation as a measure of statistical dispersion because it is not sensitive to outliers and is more suitable for situations where there are potentially abnormal components.

[0065] This step calculates the statistical dispersion between the T / R component and its neighboring T / R components, providing a basis for determining similarity.

[0066] In step S203, based on statistical dispersion, the similarity between the component state parameters of the T / R component and the component state parameters of neighboring T / R components is determined.

[0067] In this embodiment, the similarity determination process is as follows: First, the component state parameters of this T / R module are compared with the statistical median of the component state parameters of neighboring T / R modules to obtain the comparison results. Taking the phase error value as an example, the absolute difference between the phase error value of this T / R module and the statistical median of the phase error values ​​of neighboring T / R modules is calculated.

[0068] Then, a decision threshold range is determined based on the statistical dispersion. In some embodiments, the decision threshold range is 1.5 times the statistical dispersion. For example, if the statistical dispersion is 2°, the decision threshold range is 3°.

[0069] In some embodiments, the decision threshold range can be adjusted according to the application scenario. For high-precision calibration, the decision threshold range is set to 1.5 times the statistical dispersion; for fast calibration, the decision threshold range is set to 2.0 times the statistical dispersion.

[0070] Finally, determine the relationship between the comparison result and the judgment threshold range: If the comparison result is less than or equal to the judgment threshold range, the similarity is determined to be high similarity; If the comparison result is greater than the judgment threshold range but less than twice the judgment threshold range, the similarity is determined to be medium similarity. If the comparison result is greater than twice the judgment threshold range, the similarity is determined to be low. In some embodiments, similarity can be represented as three discrete levels: high, medium, and low, which facilitates the implementation of subsequent processing logic.

[0071] In this embodiment, a discrete level is used to represent similarity, which simplifies the subsequent processing logic while maintaining sufficient judgment accuracy.

[0072] In step S204, the valid state parameters of the T / R component are determined based on similarity.

[0073] In this embodiment, the process for determining the valid state parameters is as follows: First, determine the reliability of the T / R component's status based on similarity: If the similarity is high, the state of the T / R component is considered reliable, and its original component state parameters are directly used as valid state parameters. If the similarity is medium, the state part of the T / R component is considered reliable, and the weighted average of its original component state parameters and the median of the state parameters of neighboring T / R components is used as the effective state parameter. If the similarity is low, the state of the T / R component is considered unreliable, and the median of the state parameters of the neighboring T / R components is directly used as the valid state parameter. In some embodiments, when the similarity is medium, the weight of the weighted average can be dynamically adjusted according to the similarity level. For example, the weight corresponding to high similarity is 0.9, the weight corresponding to medium similarity is 0.6, and the weight corresponding to low similarity is 0.

[0074] In this embodiment, the phase error value and amplitude error value are processed as described above to obtain the effective state parameters of each T / R component.

[0075] In this embodiment, on the one hand, since statistical dispersion reflects the distribution characteristics of the state parameters of neighboring T / R components, it can accurately assess the reliability of the component state parameters and avoid misjudgments caused by simple threshold judgment. On the other hand, by comparing the component state parameters with the statistical median of the state parameters of neighboring T / R components, the judgment threshold range is determined based on statistical dispersion, which can adaptively adapt to the normal fluctuation range under different operating conditions and improve the accuracy of anomaly detection.

[0076] In one exemplary embodiment, such as Figure 4 As shown, this embodiment provides a specific implementation process for performing spatial correlation analysis on the filtered state parameters of each T / R component, including the following main steps: In step S301, the filtering contribution weight of each T / R component is determined based on the spatial distance between each T / R component and the preset reference point.

[0077] Among them, the filtering contribution weight decreases as the spatial distance increases.

[0078] In this embodiment, the preset reference point can be the geometric center of the T / R array or any T / R component in the T / R array. This embodiment uses the geometric center of the T / R array as the preset reference point.

[0079] The specific implementation process is as follows: First, calculate the spatial distance between each T / R component and the preset reference point. For rectangular arrays, the spatial distance can be calculated using Euclidean distance:

[0080] Where (i, j) are the coordinates of the T / R component, (i0, j0) are the coordinates of the preset reference point, and d0 is the distance between adjacent T / R components.

[0081] Then, the filtering contribution weights are determined based on the spatial distance. In this embodiment, the filtering contribution weights are calculated using a Gaussian function:

[0082] Here, σ is the spatial decay coefficient, which controls the rate at which the weight decays with distance. The larger the value of σ, the slower the weight decays with distance; the smaller the value of σ, the faster the weight decays with distance.

[0083] In some embodiments, the spatial attenuation coefficient σ can be determined based on the operating frequency and size of the T / R array. For the 32×32 array in this embodiment, the operating frequency is 9.5GHz, and the value of σ is set to 1 / 3 of the array radius.

[0084] In some embodiments, the filter contribution weights may also employ other decay functions, such as exponential decay functions or linear decay functions, but this embodiment prefers the Gaussian function because it has smooth decay characteristics and good mathematical properties.

[0085] In step S302, based on the filtering contribution weight of each T / R component, the component state parameters of each T / R component are weighted and averaged to obtain the filtered state parameters of each T / R component.

[0086] In this embodiment, the component state parameters include phase error values ​​and amplitude error values. Taking the phase error value as an example, the calculation process of the filtered state parameters is as follows: First, for each T / R component, the phase error values ​​of itself and its neighboring T / R components and the corresponding filter contribution weights are collected.

[0087] Then, calculate the weighted average:

[0088] in, is the filtered phase error value of the T / R component with coordinates (i, j), w(m, n) is the filtering contribution weight of the T / R component with coordinates (m, n), and p(m, n) is the phase error value of the T / R component with coordinates (m, n). The summation range is the entire T / R array or a local area.

[0089] Understandably, (i, j) represents the coordinates of the target T / R component whose filtered phase error value is to be calculated. It relies on a weighted calculation of its own parameters and those of a set of surrounding components.

[0090] Correspondingly, (m, n) represents the coordinates of the T / R component included in the summation range during the weighted average calculation. It represents the component position within a local area or window, which is usually centered on the target component (i, j) and includes itself (i.e., when (m, n) = (i, j)) and several neighboring components.

[0091] In some embodiments, to improve computational efficiency, the summation range can be limited to the K×K neighborhood of the T / R component, where K is a preset value (such as 5 or 7).

[0092] In this embodiment, the entire T / R array is used for weighted averaging. Although the computation is large, a smoother filtering effect can be obtained.

[0093] In step S303, the electromagnetic coupling coefficient matrix of the T / R array is obtained.

[0094] The electromagnetic coupling coefficient matrix characterizes the electromagnetic coupling strength between each T / R component in the T / R array.

[0095] In this embodiment, the electromagnetic coupling coefficient matrix is ​​obtained as follows: First, the T / R array is simulated in its entirety using electromagnetic simulation software (such as HFSS, CST, etc.) to calculate the electromagnetic coupling coefficient between each T / R component.

[0096] Then, the simulation results are organized into matrix form. For an M×N T / R array, the electromagnetic coupling coefficient matrix... C It is an (M×N)×(M×N) square matrix, where the matrix elements are... C (i, j, k, l) represents the electromagnetic coupling coefficient between the T / R component at coordinate (i, j) and the T / R component at coordinate (k, l).

[0097] In some embodiments, the electromagnetic coupling coefficient matrix can be simplified to a sparse matrix containing only the coupling between adjacent T / R components to reduce computational complexity.

[0098] In this embodiment, a complete electromagnetic coupling coefficient matrix is ​​used because it can more accurately characterize the mutual coupling effect in the T / R array.

[0099] In step S304, the parameter offset caused by the mutual coupling effect is calculated based on the electromagnetic coupling coefficient matrix and the filtered state parameters of each T / R component.

[0100] In this embodiment, the calculation process for the parameter offset is as follows: First, the filtered state parameters are represented in vector form. Each element corresponds to a filtered state parameter of a T / R component.

[0101] Then, calculate the parameter offset vector ΔP caused by the mutual coupling effect:

[0102] in, C It is the electromagnetic coupling coefficient matrix, and * denotes matrix multiplication.

[0103] Specifically, for the T / R component, its parameter offset is:

[0104] The summation range is the entire T / R array. C (i, j, k, l) is the electromagnetic coupling coefficient between the T / R component at coordinate (i, j) and the T / R component at coordinate (k, l). These are the filtered state parameters of the T / R component with coordinates (k, l).

[0105] In some embodiments, to simplify calculations, the summation range may be limited to the neighborhood of the T / R component.

[0106] In this embodiment, the parameter offset is calculated using full matrix multiplication. Although the computation is large, it can more accurately correct the mutual coupling effect.

[0107] In step S305, the parameter offset is subtracted from the filtered state parameters of each T / R component to obtain the mutual coupling correction state parameters of each T / R component.

[0108] In this embodiment, the calculation process of the mutual coupling correction state parameters is as follows: For each T / R component (i, j), calculate its cross-coupling correction state parameters:

[0109] in, These are the mutual coupling correction state parameters of the T / R components (i, j). These are the filtered state parameters. It is the parameter offset.

[0110] In some embodiments, a correction coefficient α (0 < α ≤ 1) may be introduced to control the strength of the correction:

[0111] When α=1, full correction is performed; when α<1, partial correction is performed to avoid overcorrection.

[0112] In this embodiment, the correction coefficient α is dynamically adjusted according to the operating state of the T / R array. When the system is operating stably, α=1; when the system is just started up or when the environment changes drastically, α=0.8.

[0113] In step S306, spatial correlation analysis is performed on the mutual coupling correction state parameters of each T / R component.

[0114] In this embodiment, the spatial correlation analysis adopts the method in Embodiment 2, including steps such as determining neighboring T / R components, calculating statistical dispersion, determining similarity, and determining effective state parameters.

[0115] The specific implementation process is as follows: First, for each T / R component, determine its neighboring T / R components in the T / R array.

[0116] Then, the statistical dispersion between the mutual coupling correction state parameters of the T / R component and the mutual coupling correction state parameters of the neighboring T / R components is calculated.

[0117] Next, based on statistical dispersion, the similarity between the mutual coupling correction state parameters of the T / R component and the mutual coupling correction state parameters of neighboring T / R components is determined.

[0118] Finally, based on similarity, the valid state parameters of the T / R component are determined.

[0119] In this embodiment, the object of spatial correlation analysis is the mutually coupled corrected state parameters, rather than the original component state parameters or the filtered state parameters, which makes the analysis results more reflective of the true performance of the T / R component.

[0120] In step S307, when the component state parameter of the T / R component is identified as an outlier parameter, the time-domain fluctuation characteristics and frequency-domain distribution characteristics of the outlier parameter are analyzed.

[0121] In this embodiment, outlier parameter identification is based on the results of spatial correlation analysis. When the difference between the effective state parameter of a T / R component and the statistical median of neighboring T / R components exceeds a preset threshold, the component state parameter of that T / R component is considered an outlier parameter.

[0122] The following analysis was performed on the identified outlier parameters: First, analyze the time-domain fluctuation characteristics. Collect the state parameter sequence of the T / R component over a period of time and calculate its statistical properties, such as mean, variance, and autocorrelation function. Pay particular attention to abrupt changes in state parameters and their duration.

[0123] Then, the frequency domain distribution characteristics are analyzed. A Fourier transform is performed on the time series of the state parameters to analyze their spectral characteristics and identify the main frequency components.

[0124] In some embodiments, time-domain fluctuation characteristics can be described by the following metrics: Mutation magnitude: The maximum change in state parameters; Mutation frequency: The number of times a state parameter changes per unit time; Duration: The duration of the abnormal state parameter; Frequency domain distribution characteristics can be described by the following indicators: Main frequency components: the frequencies with the highest energy in the frequency spectrum; Spectral bandwidth: The frequency range in which spectral energy is concentrated; Harmonic components: Are there obvious harmonic components? In step S308, the fault type corresponding to the outlier parameter is determined based on the time-domain fluctuation characteristics and frequency-domain distribution features.

[0125] In this embodiment, the process for determining the fault type is as follows: First, establish a mapping relationship between fault types and characteristic indicators. In some embodiments, machine learning methods such as decision trees, support vector machines, or neural networks can be used to train a fault classification model based on historical data.

[0126] Then, the time-domain fluctuation characteristics and frequency-domain distribution features extracted in step S307 are input into the fault classification model to obtain the fault type judgment result.

[0127] In this embodiment, two main types of faults are considered: Component hardware failure: manifested as persistent abnormality in state parameters, time-domain fluctuation characteristics showing large abrupt changes and long duration, and frequency-domain distribution characteristics showing that low-frequency components are dominant.

[0128] Environmental disturbances manifest as intermittent anomalies in state parameters. The time-domain fluctuation characteristics show small abrupt changes and short durations, while the frequency-domain distribution characteristics show obvious high-frequency components.

[0129] In step S309, a decision is made on whether to update the calibration parameters of the T / R component based on the fault type.

[0130] In this embodiment, the rules for updating the calibration parameters are as follows: First, determine the type of fault: If the fault type is a component hardware fault, mark the T / R component as unavailable and do not update the calibration parameters of the T / R component.

[0131] If the fault type is environmental interference, the calibration parameters of the T / R component are updated based on the mutual coupling correction state parameters of the neighboring T / R components.

[0132] In some embodiments, the calibration parameters can be updated in the following ways to handle environmental disturbances:

[0133] in, These are the calibration parameters for the T / R component. It is the average value of the mutual coupling correction state parameters of the adjacent T / R components.

[0134] In this embodiment, for T / R components marked as unavailable, the system will record fault information and issue an alarm, while ignoring the contribution of the T / R component in subsequent beamforming to avoid affecting the overall system performance.

[0135] In this embodiment, on the one hand, spatial filtering and mutual coupling correction effectively suppress the influence of measurement noise and mutual coupling effects, improving the accuracy of state parameters. On the other hand, through fault diagnosis and intelligent processing, hardware faults and environmental interference can be distinguished, and different processing strategies can be adopted to avoid further deterioration of the T / R component and system performance due to hardware faults.

[0136] In one exemplary embodiment, see Figure 5 This embodiment provides a specific process for determining the neighboring T / R components of the T / R component in the T / R array, including the following main steps: In step S401, the location type of the T / R component in the T / R array is identified.

[0137] In this embodiment, the T / R array is a 32×32 rectangular array. For any T / R component (i, j) in the array, where i represents the row number and j represents the column number, the location type identification rule is as follows: Internal location: When 2≤i≤31 and 2≤j≤31, the T / R component is located inside the array; Edge position: When i=1 or i=32 or j=1 or j=32, but not simultaneously satisfying (i=1 or i=32) and (j=1 or j=32), the T / R component is located at the edge of the array; Corner position: The T / R component is located at the corner of the array when (i=1 and j=1) or (i=1 and j=32) or (i=32 and j=1) or (i=32 and j=32); In some embodiments, the identification of location type can also take into account the actual physical layout of the T / R array. For example, for a circular array, the location type can be divided into central region, annular region, and boundary region.

[0138] In step S402, if the location type is an array edge location or an array corner location, then based on the principle of array symmetry, a virtual T / R component is created outside the boundary of the T / R array.

[0139] In this embodiment, the creation rules for the virtual T / R component are as follows: For the T / R component located at the top edge (i=1, 2≤j≤31), based on the principle of vertical symmetry, a virtual T / R component is created above the array with a row number of 0 and a column number that is the same as the corresponding real T / R component.

[0140] For the T / R component located at the lower edge (i=32, 2≤j≤31), based on the principle of vertical symmetry, a virtual T / R component is created below the array with row number 33 and column number the same as the corresponding real T / R component.

[0141] For a T / R component located on the left edge (j=1, 2≤i≤31), based on the principle of left-right symmetry, a virtual T / R component is created on the left side of the array, with the same row number as the corresponding real T / R component and column number 0.

[0142] For the T / R component located on the right edge (j=32, 2≤i≤31), based on the principle of left-right symmetry, a virtual T / R component is created on the right side of the array, with the same row number as the corresponding real T / R component and column number 33.

[0143] For T / R components located at the four corners, based on the principle of diagonal symmetry, two virtual T / R components are created outside the array. For example, for the T / R component in the top left corner (i=1, j=1), a virtual T / R component is created above and to the left of the array, with row numbers 0 and 1, and column numbers 1 and 0, respectively; at the same time, a diagonal virtual T / R component is created in the top left corner of the array, with row number 0 and column number 0.

[0144] In step S403, the virtual state parameters of the virtual T / R component are determined based on the component state parameters of the existing T / R components in the T / R array.

[0145] In this embodiment, the rules for determining the virtual state parameters are as follows: For a virtual T / R component (row number 0 or 33) created based on vertical symmetry, its virtual state parameters are equal to the state parameters of the real T / R component at the symmetrical position. For example, the virtual state parameters of the virtual T / R component (0, j) are equal to the state parameters of the real T / R component (2, j); the virtual state parameters of the virtual T / R component (33, j) are equal to the state parameters of the real T / R component (31, j).

[0146] For a virtual T / R component (column number 0 or 33) created based on left-right symmetry, its virtual state parameters are equal to the state parameters of the real T / R component at the symmetrical position. For example, the virtual state parameters of the virtual T / R component (i, 0) are equal to the state parameters of the real T / R component (i, 2); the virtual state parameters of the virtual T / R component (i, 33) are equal to the state parameters of the real T / R component (i, 31).

[0147] For a virtual T / R component created based on diagonal symmetry (where both row and column numbers are boundary values), its virtual state parameters are equal to the state parameters of the real T / R component at the symmetrical position. For example, the virtual state parameters of the virtual T / R component (0, 0) are equal to the state parameters of the real T / R component (2, 2).

[0148] In some embodiments, virtual state parameters can also be determined using a weighted average. For example, the virtual state parameters of the virtual T / R component (0, j) can be a weighted average of the state parameters of the real T / R components (1, j) and (2, j), with weights of 0.7 and 0.3, respectively.

[0149] In step S404, the virtual T / R component is included in the selection range of the T / R component that is adjacent to the selected T / R component.

[0150] In this embodiment, for T / R components located at edges or corners, the selection rules for their neighboring T / R components are as follows: For a T / R component located at the upper edge (i=1, 2≤j≤31), its neighboring T / R components include: Above: Virtual T / R components (0, j-1), (0, j), (0, j+1); Same row: real T / R components (1, j-1), (1, j+1); Below: Real T / R components (2, j-1), (2, j), (2, j+1); For a T / R component located at the top left corner (i=1, j=1), its neighboring T / R components include: Above: Virtual T / R components (0,0), (0,1), (0,2); Same row: Real T / R component (1, 2); Below: Real T / R components (2,0), (2,1) (2,2); By incorporating virtual T / R components, the number of neighboring T / R components for edge and corner T / R components remains consistent with that of the internal T / R components, both being 8, thus ensuring the consistency of spatial correlation analysis.

[0151] In this embodiment, on the one hand, the creation of virtual T / R components solves the edge effect problem, ensuring that the number of neighboring T / R components of edge and corner T / R components remains consistent with that of internal T / R components, thus guaranteeing the consistency of spatial correlation analysis. Traditional spatial filtering methods often truncate the filtering window when processing array edges due to a lack of outer neighborhood data, leading to distortion of calibration parameters for edge components. This application creates virtual components based on array symmetry principles, which not only mathematically completes the boundary conditions required for convolution or filtering operations, ensuring that the number of neighboring T / R components of edge and corner T / R components remains consistent with that of internal T / R components (8 in total), guaranteeing the consistency of spatial correlation analysis; but also, the virtual state parameters determined based on physical symmetry (such as the symmetry of the antenna pattern) can more accurately reflect the electromagnetic characteristics of the array edges than simple zero-filling or mean-filling, effectively improving the calibration accuracy of edge components and enhancing the sidelobe performance of the antenna array. On the other hand, the virtual state parameters determined based on symmetry principles can accurately reflect the symmetry characteristics of the array, avoiding artificially introduced deviations.

[0152] In one exemplary embodiment, such as Figure 6 As shown, this embodiment provides a specific implementation process for digital domain compensation processing of the calibration signals of each T / R component, including the following main steps: In step S501, a synchronization calibration signal is sent to the T / R array to measure the clock path delay difference and local oscillator phase difference of each T / R component in the T / R array.

[0153] In this embodiment, the synchronization calibration signal is a short pulse signal with a pulse width of 10 ns and a repetition period of 1 ms. The synchronization calibration signal is generated by the master clock and sent to each T / R component of the T / R array through the clock distribution network.

[0154] The specific implementation process is as follows: First, the master clock generates a synchronization calibration signal and records the signal transmission time t0.

[0155] Then, after each T / R component receives the synchronization calibration signal, it measures the arrival time ti of the signal via an internal time-to-digital converter (TDC).

[0156] Next, calculate the clock path delay for each T / R component:

[0157] Where Δt(i) is the clock path delay of the i-th T / R component.

[0158] Clock path delay difference refers to the deviation of the clock path delay of each T / R component from a reference value. In this embodiment, the reference value is the median of the clock path delays of all T / R components.

[0159] For measuring the local oscillator phase difference, each T / R component mixes the local oscillator signal with the reference signal after receiving the synchronization calibration signal, and obtains the phase difference φi through I / Q demodulation.

[0160] Local oscillator phase difference refers to the deviation of the phase difference of each T / R component from the reference value. In this embodiment, the reference value is the median of the phase differences of all T / R components.

[0161] In step S502, the delay compensation parameters for each T / R component are determined based on the clock path delay difference.

[0162] In this embodiment, the process for determining the delay compensation parameters is as follows: First, calculate the deviation δt(i) of the clock path delay of each T / R component relative to the reference value:

[0163] Where, Δt ref It is the reference clock path delay, which is the median of the clock path delays of all T / R components.

[0164] Then, the clock path delay deviation is converted into a delay compensation parameter in the digital domain. In this embodiment, the digital signal processing unit of the T / R component has a delay adjustment accuracy of 1 ps, therefore the delay compensation parameter N(i) is: N(i)=round(δt(i) / 1ps) The function `round()` indicates rounding to the nearest integer.

[0165] In some embodiments, to prevent signal distortion caused by excessively large compensation parameters, upper and lower limits can be set for the delay compensation parameter. In this embodiment, the range of the delay compensation parameter is limited to ±500.

[0166] In step S503, the phase compensation parameters of each T / R component are determined based on the local oscillator phase difference.

[0167] In this embodiment, the process for determining the phase compensation parameters is as follows: First, calculate the deviation δφ(i) of the local oscillator phase of each T / R component relative to the reference value: δφ(i) = φi - φ ref

[0168] Where, φ ref It is the reference phase, which is the median of the phases of all T / R components.

[0169] Then, the phase deviation is converted into a phase compensation parameter in the digital domain. In this embodiment, the digital signal processing unit of the T / R component has a phase adjustment accuracy of 0.1°, therefore the phase compensation parameter M(i) is: M(i)=round(δφ(i) / 0.1°) In some embodiments, the phase compensation parameter can also be directly expressed in degrees.

[0170] In step S504, when sending a calibration signal to the T / R array, digital domain compensation processing is performed on the calibration signal of each T / R component based on the time delay compensation parameter and phase compensation parameter of each T / R component.

[0171] In this embodiment, the digital domain compensation processing is implemented as follows: First, before sending the calibration signal, the delay compensation parameters and phase compensation parameters are loaded into the digital signal processing unit of each T / R component.

[0172] Then, when a calibration signal is sent to the T / R array, the digital signal processing unit of each T / R component processes the calibration signal according to its delay compensation parameter N(i) and phase compensation parameter M(i): Delay compensation: Precise delay compensation is achieved by adjusting the FIFO depth or digital filter coefficients in the digital signal processing unit. Specifically, the delay of N(i) sampling points is increased or decreased.

[0173] Phase compensation: Precise phase compensation is achieved by adjusting the local oscillator phase using a digital mixer. Specifically, the phase control word of the mixer is adjusted to make the phase change M(i) × 0.1°.

[0174] In some embodiments, the digital domain compensation process may further include amplitude compensation to correct gain differences between the T / R components.

[0175] In this embodiment, on the one hand, digital domain compensation avoids the accuracy limitations and hardware complexity of analog domain compensation, enabling higher precision delay and phase compensation. On the other hand, clock synchronization and phase compensation effectively eliminate clock and local oscillator inconsistencies in the T / R array, improving the quality of the calibration signal and thus enhancing the accuracy of subsequent calibration steps.

[0176] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0177] Based on the same inventive concept, this application also provides a T / R component calibration control device for implementing the aforementioned T / R component calibration control method. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations of one or more T / R component calibration control device embodiments provided below can be found in the limitations of the T / R component calibration control method described above, and will not be repeated here.

[0178] Each module in the aforementioned T / R component calibration control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0179] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described T / R component calibration control method.

[0180] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which determines that the T / R component calibration control method described above is implemented when the computer program is executed by a processor.

[0181] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described T / R component calibration control method.

[0182] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile deterministic machine-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum deterministic data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0183] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0184] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A T / R component calibration control method, characterized in that, The method includes: A calibration signal is sent to the T / R array, causing each T / R component in the T / R array to generate component status parameters based on the calibration signal; Based on the physical layout relationship of the T / R array, spatial correlation analysis is performed on the component state parameters of each T / R component to determine the effective state parameters of each T / R component; Update the calibration parameters of each T / R component based on the valid status parameters of each T / R component.

2. The method according to claim 1, characterized in that, Based on the physical layout relationship of the T / R array, spatial correlation analysis is performed on the component state parameters of each T / R component to determine the effective state parameters of each T / R component, specifically including: For each T / R component, calculate the correlation index between the component state parameters of the T / R component and the component state parameters of the corresponding neighboring T / R components in the T / R array; Based on the aforementioned correlation index, the effective state parameters of the T / R component are determined.

3. The method according to claim 2, characterized in that, The calculation of the correlation index between the component state parameters of the T / R component and the component state parameters of its neighboring T / R components in the T / R array specifically includes: Calculate the statistical dispersion between the component state parameters of the T / R component and the component state parameters of the corresponding neighboring T / R components in the T / R array; Based on the statistical dispersion, the similarity between the component state parameters of the T / R component and the component state parameters of the neighboring T / R components is determined, and the similarity is used as a correlation index.

4. The method according to claim 3, characterized in that, The step of determining the similarity between the component state parameters of the T / R component and the component state parameters of the neighboring T / R components based on the statistical dispersion specifically includes: The component state parameters of the T / R component are compared with the statistical median of the component state parameters of the neighboring T / R components to obtain the comparison result; Based on the statistical dispersion, the range of the judgment threshold is determined; When the comparison result is within the determination threshold range, the similarity between the component state parameters of the T / R component and the component state parameters of the neighboring T / R components is determined to be high similarity.

5. The method according to claim 2, characterized in that, Before performing spatial correlation analysis on the component state parameters of each T / R component, the method further includes: The filtering contribution weight of each T / R component is determined based on the spatial distance between each T / R component and a preset reference point; wherein the filtering contribution weight decreases as the spatial distance increases. Based on the filtering contribution weight of each T / R component, the component state parameters of each T / R component are weighted and averaged to obtain the filtered state parameters of each T / R component. Specifically, the spatial correlation analysis of the component state parameters of each T / R component includes: performing spatial correlation analysis on the filtered state parameters of each T / R component.

6. The method according to claim 5, characterized in that, The spatial correlation analysis of the filtered state parameters of each T / R component specifically includes: Obtain the electromagnetic coupling coefficient matrix of the T / R array; wherein, the electromagnetic coupling coefficient matrix characterizes the electromagnetic coupling strength between each T / R component in the T / R array; Based on the electromagnetic coupling coefficient matrix and the filtered state parameters of each T / R component, the parameter offset caused by the mutual coupling effect is calculated. The mutual coupling correction state parameters of each T / R component are obtained by subtracting the parameter offset from the filtered state parameters of each T / R component. Spatial correlation analysis was performed on the mutual coupling correction state parameters of each T / R component.

7. The method according to claim 6, characterized in that, After determining the effective state parameters of the T / R component based on the correlation index, the method further includes: When the component state parameter of the T / R component is identified as an outlier parameter, the time-domain fluctuation characteristics and frequency-domain distribution characteristics of the outlier parameter are analyzed. Based on the time-domain fluctuation characteristics and frequency-domain distribution features, the fault type corresponding to the outlier parameter is determined; When the fault type is a component hardware fault, the T / R component is marked as unavailable and the calibration parameters of the T / R component are not updated. When the fault type is environmental interference, the calibration parameters of the T / R component are updated based on the mutual coupling correction state parameters of the neighboring T / R components.

8. The method according to claim 2, characterized in that, The following method is used to determine the neighboring T / R components of the T / R component in the T / R array: Identify the location type of the T / R component in the T / R array; If the location type is an array edge location or an array corner location, then based on the principle of array symmetry, a virtual T / R component is created outside the boundary of the T / R array; The virtual state parameters of the virtual T / R component are determined based on the component state parameters of the existing T / R components in the T / R array. Include the virtual T / R component in the selection range of the neighboring T / R components of the T / R component.

9. The method according to any one of claims 1-8, characterized in that, Before sending the calibration signal to the T / R array, the method further includes: A synchronization calibration signal is sent to the T / R array to measure the clock path delay difference and local oscillator phase difference of each T / R component in the T / R array; Based on the clock path delay difference, the delay compensation parameters for each T / R component are determined; Based on the local oscillator phase difference, the phase compensation parameters of each T / R component are determined; When sending calibration signals to the T / R array, digital domain compensation processing is performed on the calibration signals of each T / R component based on the time delay compensation parameters and phase compensation parameters of each T / R component.

10. The method according to any one of claims 1-8, characterized in that, The component status parameters include phase error value and amplitude error value.

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