New method for suppressing sudden change of intermediate-frequency radar wind field based on antenna contribution value

Abstract

The invention discloses a novel intermediate-frequency radar wind field abrupt change suppression method based on antenna contribution values, and relates to the technical field of intermediate-frequency radar signal processing and atmospheric exploration, and the method comprises the steps: employing echo signals collected by at least three antenna SA systems or a correlation function obtained through calculation to obtain an autocorrelation function ACF and a cross-correlation function CCF; and calculating a calibrated cross-correlation function (CCF) 'for FCA inversion in cooperation with the antenna array configuration data and the empirical error threshold. According to the method, through an improved calibration mechanism, for data whose deviation does not exceed a threshold value, according to a theoretical value, a cross-correlation function zero lag point target value is reversely deduced and is matched with a current zero lag point, the amplitude / correlation of the cross-correlation function is calibrated, and the calibrated cross-correlation function is output to improve the accuracy and stability of FCA inversion.

Description

Technical Field

[0001] This invention relates to the fields of intermediate frequency radar signal processing and atmospheric detection technology, specifically to a novel method for suppressing sudden changes in wind field in intermediate frequency radar based on antenna contribution values. Background Technology

[0002] In mid-to-upper atmospheric wind field inversion using MF radar, the SA antenna system and FCA framework are commonly employed: cross-correlation and autocorrelation functions are constructed from the echo signals, and wind field parameters are solved accordingly. Existing MF radar engineering processes often use polynomial fitting to smooth and denoise the cross-correlation function. While this has the advantages of low complexity and suitability for online inversion, its robustness under strong noise conditions is limited, and the smoothing effect degrades significantly with increasing noise levels, making it difficult to fundamentally suppress wind field instability.

[0003] Noise can disturb the amplitude and phase of echo signals, altering the values ​​of cross-correlation / autocorrelation functions. This can lead to a decrease in the amplitude of the correlation function, degradation of its shape, or even distortion, thereby weakening the stability of correlation parameter solutions and introducing uncertainty into wind field inversion. Ultimately, this manifests as abnormal fluctuations, abrupt changes, or discontinuities in the wind field time series. Signal-level preprocessing and noise reduction schemes (such as MF-DCP) can improve wind field quality to some extent by preprocessing the received signal to suppress noise effects and improve the shape of the correlation function. However, abrupt changes and instability may still not be effectively suppressed during low SNR periods, indicating that relying solely on signal-level denoising or correlation function smoothing is insufficient to fundamentally solve the problem. Summary of the Invention

[0004] This invention provides a novel method for suppressing sudden changes in wind field in mid-frequency radar based on antenna contribution values, thus solving the problems of existing technologies.

[0005] In a first aspect, the present invention provides a novel method for suppressing abrupt changes in wind field in mid-frequency radar based on antenna contribution values, comprising:

[0006] The autocorrelation function (ACF) and cross-correlation function (CCF) are calculated using echo signals acquired by at least a three-antenna SA system. Combined with antenna array configuration data and empirical error thresholds, the calibrated cross-correlation function (CCF') used for FCA inversion is calculated.

[0007] Furthermore, including:

[0008] Step 1: Input noisy echo signal and Where k is the index of the time sampling point;

[0009] Step 2: According to calculate and The cross-correlation function is obtained. Lag time τ ⟨ represents taking the complex conjugate of the sampled value of the signal from antenna 2 at time k + τ, and ⟨·> represents taking the statistical average or time average of the time series.

[0010] Step 3: [Regarding...] Perform polynomial fitting to obtain the denoised result. ;

[0011] Step 4: Utilize the noise-reduced... Antenna parameters are based on the formula and Theoretical values ​​were derived and observed values Among them, the observed values The antenna contribution value is the observed value calculated based on the denoised data; CCF(0): the cross-correlation function after denoising. The value at zero hysteresis (τ=0), Δx: the physical distance between receiving antenna 1 and antenna 2, theoretical value. The theoretical value of the antenna contribution is determined by the antenna hardware configuration. This is the half-power beamwidth of the transmitting antenna. The half-power beamwidth weighting function for the receiving antenna. The wavelength weighting function for radar operation;

[0012] Step 5: Using antenna parameters and theoretical values According to the formula The conclusion is ;

[0013] Step 6: If the theoretical value and observed values Error greater than 0.01: Data is unreasonable, discard the data;

[0014] Step 7: If the theoretical value and observed values Error less than or equal to 0.01: Use After calibration and noise reduction After calibration ;

[0015] Step 8: Output the calibrated output .

[0016] Furthermore, the theoretical value of the antenna contribution is calculated using the antenna array configuration data, including the radar operating wavelength, the antenna half-beamwidth weighting function, and the antenna spacing.

[0017] Furthermore, in step S4, the antenna contribution value is calculated based on the value of the cross-correlation function after noise reduction at the zero hysteresis point, the value of the corresponding autocorrelation function at the zero hysteresis point, and the antenna spacing.

[0018] Furthermore, in step S7, the specific calibration process is as follows:

[0019] Based on the theoretical value of the antenna contribution and the antenna spacing, the target value of the zero hysteresis point of the cross-correlation function is derived.

[0020] The current value of the noise-reduced cross-correlation function at the zero hysteresis point is adjusted to the target value, and the amplitude of the entire cross-correlation function is scaled proportionally to complete the calibration.

[0021] The present invention provides a novel method for suppressing sudden changes in mid-frequency radar wind field based on antenna contribution values. Through an improved calibration mechanism, for data whose deviation does not exceed the threshold, the target value of the zero-hysteresis point of the cross-correlation function is inversely derived from the theoretical value and matched with the current zero-hysteresis point. The amplitude / correlation of the cross-correlation function is calibrated, and the calibrated cross-correlation function is output to improve the accuracy and stability of FCA inversion. Attached Figure Description

[0022] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and constitute a part of this invention, are not intended to limit the scope of the invention. In the drawings:

[0023] Figure 1 A flowchart of a novel method for suppressing sudden changes in wind field in mid-frequency radar based on antenna contribution values, provided as an exemplary embodiment of the present invention. Detailed Implementation

[0024] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention.

[0025] Terminology - Definitions

[0026] MF radar: Medium Frequency radar used to detect the average wind field in the middle and upper atmosphere (e.g., 50–100 km). Wind field inversion is often based on the spatially divided antenna (SA) system and the full correlation analysis (FCA) framework.

[0027] SA system: Spaced Antenna system, with at least three spatially distributed receiving antennas, which solve for wind field parameters by sampling through cross-correlation / autocorrelation functions.

[0028] FCA algorithm: Full Correlation Analysis wind measurement algorithm, the core of which is to use the parameters of the correlation function to solve the drift velocity of the diffraction pattern and then invert the true wind speed.

[0029] ACF: Autocorrelation Function.

[0030] CCF: Cross-Correlation Function.

[0031] SNR: Signal-to-Noise Ratio.

[0032] MF-DCP: A delay correlation parameter estimation algorithm based on median filtering, which can improve the shape of the correlation function, but abrupt changes in low SNR periods may still not be fundamentally suppressed.

[0033] MF-AH: It adopts a correlation function-based noise reduction and quality control algorithm based on antenna contribution value. It introduces antenna contribution value criteria on the basis of polynomial fitting to achieve elimination and calibration, and suppress abrupt changes in low SNR wind field.

[0034] Existing solutions (represented by polynomial fitting and smoothing) mainly suffer from the following technical drawbacks:

[0035] (1) Sensitive to noise and lack of robustness under low SNR: The distortion of the correlation function caused by noise is difficult to be stably corrected, resulting in abnormal fluctuations and frequent abrupt changes in the inverted wind field.

[0036] (2) Lack of objective quality evaluation criteria that can be decoupled from noise: In scenarios with low SNR or strong background disturbance, it is easy to be dominated by noise when determining whether the correlation function meets the FCA solution requirements, thus making it difficult to form an effective anomaly removal and calibration loop.

[0037] (3) Signal preprocessing or smoothing alone is not enough: Even if preprocessing such as MF-DCP is used to improve the shape of the correlation function, abrupt changes may still exist during low SNR periods.

[0038] This invention, within the framework of a space-division antenna (SA) system and full correlation analysis (FCA) wind measurement, focuses on noise reduction, quality discrimination, and calibration processing of cross-correlation / autocorrelation functions. It is used to suppress abnormal fluctuations, abrupt changes, and discontinuities in wind field inversion results under complex observation conditions such as low signal-to-noise ratio (SNR), thereby improving the stability and usability of mid-to-upper atmospheric wind field products.

[0039] Technical concept of the present invention:

[0040] This invention introduces an "antenna contribution value parameter" as a quality criterion for the correlation function. The theoretical value of this parameter is determined solely by the antenna array configuration and is independent of the noise level. It naturally possesses the advantage of weak correlation / decoupling with the noise term and can be used to determine whether the noise-reduced correlation function meets the requirements for FCA solution.

[0041] In engineering implementation, this invention adds an "antenna contribution value verification and calibration / removal" step to the traditional polynomial fitting smoothing: first, the theoretical value and the measured value are calculated and compared, and samples that exceed the threshold are judged as invalid and removed; samples that do not exceed the threshold are further used to back-calculate the target value of the zero hysteresis point of the cross-correlation function using the theoretical value, and the amplitude and correlation of the cross-correlation function are calibrated, thereby suppressing the wind speed error and wind field abrupt change introduced by amplitude attenuation / distortion.

[0042] The present invention provides a novel method for suppressing sudden changes in wind field in mid-frequency radar based on antenna contribution values, which aims to solve the above-mentioned technical problems in the prior art.

[0043] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0044] Example 1:

[0045] The steps are as follows Figure 1 As shown,

[0046] Step A1: Acquire echo signals using a three-antenna SA system;

[0047] Step A2: Calculate the autocorrelation function ACF and the cross-correlation function CCF.

[0048] Step A3: Using the antenna array configuration data and empirical error threshold, calculate the calibrated cross-correlation function CCF' for FCA inversion.

[0049] (1) Antenna contribution value as a quality criterion for weak correlation / decoupling with noise: Calculate the theoretical value of antenna contribution value determined only by antenna array configuration and use it as a reference benchmark for the effectiveness of correlation function / noise reduction standard.

[0050] (2) Measurement value construction method: The antenna contribution value AC_meas is directly calculated using the zero hysteresis point of the measured ACF / CCF, and is used to compare with the theoretical value to judge the quality of the correlation function.

[0051] (3) Threshold judgment + elimination mechanism: Based on the deviation index Δ and the empirical threshold ε, data with deviation exceeding the threshold are directly judged as invalid and eliminated to avoid the distortion correlation function entering FCA and causing sudden changes in the wind field.

[0052] (4) Calibration mechanism (core improvement step): For data whose deviation does not exceed the threshold, the target value of the zero lag point of the cross-correlation function is back-calculated based on the theoretical value and matched with the current zero lag point. The amplitude / correlation of the cross-correlation function is calibrated, and the calibrated cross-correlation function is output to improve the accuracy and stability of FCA inversion.

[0053] (5) Integrated protection of method and system: The steps in the instruction manual are modularized into a system structure (theoretical value calculation module, measured value calculation module, discrimination module, calibration module, FCA module) to achieve stable output of wind field online or offline.

[0054] A mid-frequency radar wind field abrupt change suppression system based on antenna contribution value is provided, comprising:

[0055] The signal acquisition and processing module is used to acquire echo signals and calculate cross-correlation functions.

[0056] A noise reduction module is used to perform noise reduction processing on the cross-correlation function;

[0057] The theoretical value calculation module is used to calculate the theoretical value of the antenna contribution based on the antenna array configuration parameters.

[0058] The measurement calculation module is used to calculate the antenna contribution measurement value based on the noise-reduced correlation function.

[0059] The discrimination module is used to compare the deviation between the theoretical value and the measured value, and to compare it with a preset threshold.

[0060] The calibration module is used to calibrate the cross-correlation function after noise reduction based on the theoretical value when the deviation does not exceed the preset threshold.

[0061] The data removal module is used to remove the current data when the deviation exceeds a preset threshold.

[0062] The system is integrated into the online wind field inversion link of the medium-frequency radar or the offline data processing system.

[0063] The core algorithm of this invention, the MF-AH algorithm, is as follows:

[0064] The cross-correlation function obtained after polynomial fitting and various antenna parameters For example.

[0065] Algorithm MF-AH

[0066] enter: , ;

[0067] Output: Calibrated ;

[0068] 1. According to the formula right Theoretical values ​​were obtained through calculation. ;

[0069] 2. According to the formula right Calculate to obtain the true value ;

[0070] 3. According to the formula right and Calculations were performed to obtain ;

[0071] 4.if ;

[0072] 5. Use calibration The range;

[0073] 6. Return the calibrated value. ;

[0074] 7.else

[0075] 8. return -1

[0076] 9.end if

[0077] The advantages of introducing the MF-AH algorithm are:

[0078] (1) Significantly suppresses abnormal fluctuations and abrupt changes under low SNR: In the low SNR region, the polynomial fitting results are prone to more frequent abnormal fluctuations; after introducing MF-AH, the low SNR region can still maintain relatively stable temporal changes, and the wind field evolution characteristics are more consistent with those of the high SNR region, thereby improving the stability and availability of the wind field.

[0079] (2) Improve inversion accuracy and reduce wind speed error: In the range of SNR from -5 to 20 dB, MF-AH has more advantages in wind speed error control, especially under low SNR conditions, it can effectively reduce wind speed error.

[0080] (3) Available online: low computational cost: compared with traditional polynomial fitting, the average computation time of MF-AH only increases by about 0.001 seconds, but the improvement in error performance can cover the cost, making it suitable for online inversion in engineering.

[0081] (4) The criteria are more objective and decoupled from noise: The theoretical value of the antenna contribution is calculated only by the antenna configuration and is not related to noise. It can be used as a more objective and stable quality reference, avoiding the discrimination result being dominated by noise under low SNR.

[0082] (5) This invention can be directly applied to the online inversion link or offline reprocessing system of MF radar stations, outputting a more continuous and stable wind field sequence under complex observation conditions such as low SNR and strong background disturbance, improving the quality of middle and upper atmosphere sounding data, and providing more reliable input for middle and upper atmosphere dynamics research, wave / tidal analysis, long-term climate statistics, and data fusion complementary to meteor radar and other observations. Its online deployment feasibility is supported by the low additional overhead of "an increase in average computing time of about 0.001 seconds".

[0083] The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

[0084] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A novel method for suppressing sudden changes in wind field in mid-frequency radar based on antenna contribution values, characterized in that, include: The autocorrelation function (ACF) and cross-correlation function (CCF) are calculated using echo signals acquired by at least a three-antenna SA system. Combined with antenna array configuration data and empirical error thresholds, the calibrated cross-correlation function (CCF') used for FCA inversion is calculated.

2. The novel method for suppressing sudden changes in wind field in mid-frequency radar based on antenna contribution value according to claim 1, characterized in that, The specific steps include: Step 1: Input noisy echo signal and Where k is the index of the time sampling point; Step 2: According to calculate and The cross-correlation function is obtained. Lag time τ ⟨ represents taking the complex conjugate of the sampled value of the signal from antenna 2 at time k + τ, and ⟨·> represents taking the statistical average or time average of the time series. Step 3: [Regarding...] Perform polynomial fitting to obtain the denoised result. ; Step 4: Utilize the noise-reduced... Antenna parameters are based on the formula and Theoretical values ​​were derived and observed values Among them, the observed values The antenna contribution value is the observed value calculated based on the denoised data; CCF(0): the cross-correlation function after denoising. The value at zero hysteresis (τ=0), Δx: the physical distance between receiving antenna 1 and antenna 2, theoretical value. The theoretical value of the antenna contribution is determined by the antenna hardware configuration. The half-power beamwidth weighting function for the transmitting antenna. The half-power beamwidth weighting function for the receiving antenna. The wavelength for radar operation; Step 5: Using antenna parameters and theoretical values According to the formula The conclusion is ; Step 6: If the theoretical value and observed values Error greater than 0.01: Data is unreasonable, discard the data; Step 7: If the theoretical value and observed values Error less than or equal to 0.01: Use After calibration and noise reduction After calibration ; Step 8: Output the calibrated output .

3. The novel method for suppressing sudden changes in wind field in mid-frequency radar based on antenna contribution value according to claim 1, characterized in that, in, The theoretical value of the antenna contribution is calculated using the antenna array configuration data, including the radar operating wavelength, the antenna half-beamwidth weighting function, and the antenna spacing.

4. The novel method for suppressing sudden changes in wind field in mid-frequency radar based on antenna contribution value according to claim 3, characterized in that, include: In step S4, the measured antenna contribution value is calculated based on the value of the cross-correlation function after noise reduction at the zero hysteresis point, the value of the corresponding autocorrelation function at the zero hysteresis point, and the antenna spacing.

5. The novel method for suppressing sudden changes in wind field in mid-frequency radar based on antenna contribution value according to claim 4, characterized in that, include: In step S7, the specific calibration process is as follows: Based on the theoretical value of the antenna contribution and the antenna spacing, the target value of the zero hysteresis point of the cross-correlation function is derived. The current value of the noise-reduced cross-correlation function at the zero hysteresis point is adjusted to the target value, and the amplitude of the entire cross-correlation function is scaled proportionally to complete the calibration.

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