Acousto-optic radar wind measurement data correction system based on adaptive noise suppression

Abstract

The invention discloses an acousto-optic radar wind measurement data correction system based on adaptive noise suppression, and relates to the technical field of data correction. Environmental parameters and acousto-optic radar system parameters are combined through a dynamic adjustment module to dynamically adjust a signal-to-noise ratio threshold; the echo signal quality analysis module inputs the frequency spectrum characteristic data and the dynamically adjusted signal-to-noise ratio threshold value into an echo quality model and judges whether the current echo signal is valid or not, if yes, interpolation is carried out by adopting a spatial smooth interpolation method, if yes, the dominant frequency of the echo signal is extracted, Doppler frequency shift is calculated, and according to the detection angle and the laying direction, the Doppler frequency shift is calculated; and calculating a radial wind speed, inverting a three-dimensional wind speed vector and carrying out physical consistency correction. According to the correction system, the dynamic adjustment module is introduced, real-time environment parameters and system operation parameters are combined, and a signal-to-noise ratio threshold value required by judgment is dynamically adjusted, so that a judgment basis can be intelligently self-adapted along with a measurement environment, and the accuracy and robustness of echo signal quality judgment are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data correction, in particular to a sound and light radar wind measurement data correction system based on adaptive noise suppression. BACKGROUND

[0002] With the rapid development of atmospheric science, wind energy development and aerospace, etc., the demand for high-precision, high-temporal and spatial resolution wind field detection means is increasing. As an advanced means of non-contact, long-range detection of wind speed and direction, sound and light radar (SODAR, LIDAR, etc.) has the advantages of flexible deployment and high observation accuracy, and has been widely used in wind farm site selection, urban meteorological monitoring, boundary layer research, etc.

[0003] However, in the actual application process, due to the influence of environmental background noise, electromagnetic interference, ground reflection and multipath effect, etc., the original data collected by the sound and light radar often contains a large amount of noise. These noises not only reduce the wind measurement accuracy, but also may cause the omission or misjudgment of key wind field characteristics, affecting the credibility of the wind measurement results. The traditional data filtering and denoising method has the problems of poor adaptability, low processing efficiency and insufficient abnormal value identification ability, etc., and it is difficult to cope with the challenge of superimposed multi-source noise in complex environment.

[0004] The prior art has the following defects:

[0005] In the traditional sound and light radar wind measurement technology, the system often uses a pre-set fixed signal-to-noise ratio threshold to determine whether the echo signal is valid. However, due to the dynamic nature of the measurement environment, such as frequent changes in meteorological conditions, terrain structure, background noise level and other factors, it is difficult for the fixed threshold to accurately adapt to various actual scenes, resulting in frequent misjudgment of valid signals as invalid signals under high noise or boundary conditions, causing wind measurement data loss or abnormality.

[0006] Based on this, the present application proposes a sound and light radar wind measurement data correction system based on adaptive noise suppression. By introducing a dynamic adjustment module, the real-time environmental parameters and system operation parameters are combined to dynamically adjust the required signal-to-noise ratio threshold, so that the judgment basis can be intelligently adapted to the measurement environment, significantly improving the accuracy and robustness of echo signal quality determination. SUMMARY

[0007] The purpose of the present application is to provide a sound and light radar wind measurement data correction system based on adaptive noise suppression to solve the problems in the background art.

[0008] In order to achieve the above purpose, the present application provides the following technical scheme: a sound and light radar wind measurement data correction system based on adaptive noise suppression, comprising a collection module, a dynamic adjustment module, an echo signal quality analysis module, an interpolation module and a wind speed analysis module.

[0009] Acquisition module: collects spectrum characteristic data of acoustic and optical radar;

[0010] Dynamic adjustment module: combines environmental parameters with acoustic and optical radar system parameters to dynamically adjust the signal-to-noise ratio threshold;

[0011] Echo signal quality analysis module: inputs spectrum feature data and dynamically adjusted signal-to-noise ratio threshold into the echo quality model to determine whether the current echo signal is valid;

[0012] Interpolation module: If invalid, spatial smoothing interpolation method is used for interpolation;

[0013] Wind speed analysis module: If valid, the main frequency of the echo signal is extracted and the Doppler frequency shift is calculated. According to the detection angle and deployment direction, the radial wind speed is calculated and the three-dimensional wind speed vector is inverted and physical consistency correction is performed.

[0014] In a preferred embodiment, the echo signal quality analysis module obtains spectrum feature data and a dynamically adjusted signal-to-noise ratio threshold;

[0015] Calculate the instantaneous signal-to-noise ratio and convert the calculated instantaneous signal-to-noise ratio SNR inst and the dynamically adjusted signal-to-noise ratio threshold SNR th For comparison, if SNR inst ≥SNR th , judge whether the main frequency signal in the current spectrum is significant and is regarded as a valid echo signal, otherwise it is judged as an invalid signal.

[0016] In a preferred embodiment, the wind speed analysis module calculates the Doppler frequency shift: Δf = f peak -f0, where Δf is the Doppler frequency shift, f pcak is the main frequency of the current measuring point, and f0 is the transmitting frequency of the acoustic and optical radar;

[0017] Convert the frequency shift to radial wind speed v r , the calculation formula is: Where: v r is the radial wind speed, c is the propagation speed of sound waves or light waves in the detection medium;

[0018] The radial wind speed v of each beam r1 ,v r2 ,…,v rn and the corresponding beam direction vectors d1, d2, ..., d n Perform joint inversion to construct a three-dimensional wind speed vector

[0019] In a preferred embodiment, the interpolation module determines the spatial position of the current invalid measuring point in the detection grid, and selects a number of measuring points with valid echo signals from the spatial neighborhood of the current measuring point;

[0020] For all the adjacent valid measuring points involved in the interpolation, the corresponding interpolation weights are calculated based on their relative spatial distances, and the dominant frequency values ​​f of the adjacent measuring points are calculated based on the interpolation weights. i and the corresponding weight w i , calculate the main frequency estimation f of the current invalid measurement point interp : k represents the number of all adjacent valid measurement points involved in interpolation.

[0021] In a preferred embodiment, the signal-to-noise ratio SNR inst The calculation formula is: Among them, P peak is the peak power density corresponding to the main frequency in the spectrum, P noise It is the representative value of the average power in a frequency domain interval on both sides of the main frequency.

[0022] In a preferred embodiment, the dynamic adjustment module dynamically adjusts the signal-to-noise ratio threshold according to the average environmental noise power, the degree of environmental noise fluctuation, and the system state offset factor.

[0023] In a preferred embodiment, the dynamically adjusted signal-to-noise ratio threshold output by the dynamic adjustment module is: Where, SNR th is the dynamically adjusted signal-to-noise ratio threshold, is the average noise power of the environment, σ N is the environmental noise fluctuation level, δ sys is the system state offset factor, α, β, γ are weight coefficients, and θ is the initial signal-to-noise ratio threshold.

[0024] In a preferred embodiment, the average ambient noise power is: Where N(t) represents the background spectrum power in the tth second or sampling period, and T is the length of the statistical time window;

[0025] Standard deviation of ambient noise power: is the average noise power of the environment, σ N is the degree of environmental noise fluctuation.

[0026] In a preferred embodiment, the acquisition module receives the echo signal from the acoustic and optical radar system and pre-processes the echo signal;

[0027] Perform fast Fourier transform on the digitized echo signal to extract the spectrum features related to wind speed;

[0028] The spectrum feature data corresponding to the current time, current measurement direction and altitude are packaged to form a standardized spectrum data structure.

[0029] In a preferred embodiment, the acquisition module preprocesses the echo signal, and the preprocessing includes filtering, amplifying and analog-to-digital conversion of the original echo signal to convert the analog echo signal into a digital signal;

[0030] Extract the spectrum features related to wind speed, including the main peak position, peak amplitude, spectrum bandwidth, and background noise energy;

[0031] After forming a standardized spectrum data structure, timestamps, measurement point location information, and operating status information are added. The data structure includes multiple frequency components and their corresponding power values, background noise baselines, and level change trend sets.

[0032] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0033] The present invention combines environmental parameters with acoustic and optical radar system parameters through a dynamic adjustment module to dynamically adjust the signal-to-noise ratio threshold. The echo signal quality analysis module inputs spectral feature data and the dynamically adjusted signal-to-noise ratio threshold into the echo quality model to determine whether the current echo signal is valid. If not, spatial smoothing interpolation is used for interpolation. If valid, the dominant frequency of the echo signal is extracted and the Doppler frequency shift is calculated. Based on the detection angle and deployment direction, the radial wind speed is inferred, the three-dimensional wind speed vector is inverted, and a physical consistency correction is performed. By introducing a dynamic adjustment module, the correction system combines real-time environmental parameters with system operating parameters, dynamically adjusting the signal-to-noise ratio threshold required for judgment, thereby enabling the judgment basis to intelligently adapt to the measurement environment, significantly improving the accuracy and robustness of echo signal quality judgment.

[0034] This invention incorporates an interpolation module into the system architecture. When a measurement point is deemed invalid, it is not simply discarded. Instead, it is compensated using a spatial smoothing interpolation algorithm, leveraging the effective spectral characteristics of adjacent altitude layers or azimuths. This approach not only preserves the continuity of the wind field structure but also suppresses instability caused by short-term data loss, effectively enhancing the system's wind measurement capabilities in areas with low signal quality.

[0035] The present invention also constructs a complete data closed-loop path. The spectral characteristics, dynamic thresholds, and echo validity determination results of the echo signal are jointly input into the wind speed analysis module to ensure that the main frequency extraction and Doppler frequency shift calculation are performed only under the premise that the signal quality is controllable; and the spectral characteristics generated by the interpolation module also participate in the wind speed inversion in an equivalent manner, and combined with the physical consistency verification rules, it ultimately outputs a more reliable three-dimensional wind speed vector. This quality-driven inversion logic solves the error accumulation problem caused by the "measurement and calculation without judgment" in traditional wind measurement systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0037] Figure 1 This is a system architecture diagram of the present invention.

[0038] Figure 2 This is a timing diagram of the correction coefficient of the present invention.

[0039] Figure 3 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0041] Example 1: Please refer to Figure 1 and Figure 2 As shown, the acoustic and optical radar wind measurement data correction system based on adaptive noise suppression described in this embodiment includes an acquisition module, a dynamic adjustment module, an echo signal quality analysis module, an interpolation module and a wind speed analysis module;

[0042] Acquisition module: collects the spectrum feature data of the acoustic and optical radar, and sends the spectrum feature data to the echo signal quality analysis module;

[0043] Dynamic adjustment module: dynamically adjusts the signal-to-noise ratio threshold by combining environmental parameters with acoustic and optical radar system parameters. The dynamically adjusted signal-to-noise ratio threshold is sent to the echo signal quality analysis module.

[0044] Echo signal quality analysis module: Inputs the spectrum feature data and the dynamically adjusted signal-to-noise ratio threshold into the echo quality model to determine whether the current echo signal is valid. The judgment result is sent to the interpolation module and wind speed analysis module;

[0045] Interpolation module: If invalid, the spatial smoothing interpolation method is used for interpolation, and the interpolated spectrum feature data is sent to the wind speed analysis module;

[0046] Wind speed analysis module: If valid, the main frequency of the echo signal is extracted and the Doppler frequency shift is calculated. According to the detection angle and deployment direction, the radial wind speed is calculated and the three-dimensional wind speed vector is inverted and physical consistency correction is performed.

[0047] This application uses a dynamic adjustment module to combine environmental parameters with acoustic and optical radar system parameters to dynamically adjust the signal-to-noise ratio threshold. The echo signal quality analysis module inputs the spectrum feature data and the dynamically adjusted signal-to-noise ratio threshold into the echo quality model to determine whether the current echo signal is valid. If not, spatial smoothing interpolation is used for interpolation. If valid, the main frequency of the echo signal is extracted and the Doppler frequency shift is calculated. Based on the detection angle and deployment direction, the radial wind speed is calculated, the three-dimensional wind speed vector is inverted, and physical consistency correction is performed. By introducing a dynamic adjustment module, this correction system combines real-time environmental parameters with system operating parameters and dynamically adjusts the signal-to-noise ratio threshold required for judgment, so that the judgment basis can be intelligently adaptive to the measurement environment, significantly improving the accuracy and robustness of the echo signal quality judgment.

[0048] See also Figure 3 As shown in the figure, the specific workflow of the correction system is as follows:

[0049] The correction system collects the spectral characteristic data of the acoustic and optical radar, combines the environmental parameters with the acoustic and optical radar system parameters to dynamically adjust the signal-to-noise ratio threshold, inputs the spectral characteristic data and the dynamically adjusted signal-to-noise ratio threshold into the echo quality model, and determines whether the current echo signal is valid. If not, the spatial smoothing interpolation method is used for interpolation. If valid, the main frequency of the echo signal is extracted and the Doppler frequency shift is calculated. According to the detection angle and layout direction, the radial wind speed is calculated, the three-dimensional wind speed vector is inverted, and physical consistency correction is made.

[0050] Example 2: The acquisition module acquires the spectrum feature data of the acoustic and optical radar, and the spectrum feature data is sent to the echo signal quality analysis module.

[0051] The acquisition module serves as the data input front end of the entire system and is responsible for acquiring spectral feature data from the acoustic and optical radar equipment in real time. Its operation process is not only related to the accuracy of subsequent echo quality analysis, but also directly affects the overall response efficiency and stability of the system. The operation steps of this module can be broken down into the following aspects:

[0052] First, the acquisition module receives echo signals from the acoustic-optical radar system. These signals represent the energy reflected by atmospheric aerosols, particulates, or areas of uneven temperature after the acoustic-optical radar transmits an acoustic beam. To obtain useful spectral characteristics, the acquisition module preprocesses the echo signals, including filtering, amplification, and analog-to-digital conversion of the raw analog signals. This converts the analog echo signals into processable digital signals, preparing them for frequency-domain analysis.

[0053] Next, the acquisition module performs spectrum analysis operations, such as Fast Fourier Transform (FFT), on the digitized echo signal to extract key spectral features related to wind speed. These features primarily include the position of the main peak, peak amplitude, spectral bandwidth, and background noise energy. These features reflect the signal's frequency shift, signal-to-noise ratio, and energy distribution, and are essential parameters for determining signal quality and calculating wind speed.

[0054] In the adaptive noise suppression-based acousto-optic radar wind data correction system, the acquisition module performs spectrum analysis on the digitized echo signal to extract spectral features closely related to wind speed. The entire process includes the following detailed steps, supplemented by code examples to illustrate its logical implementation:

[0055] The acquisition module first receives the echo signal after analog-to-digital conversion, typically represented as a time series. To improve the accuracy of signal analysis, the original signal is first subjected to DC offset processing, that is, the signal's average value is subtracted to suppress the DC component's interference with the spectrum. The time domain signal is converted to the frequency domain using a fast Fourier transform (FFT) to obtain its spectral distribution. The FFT output is a complex vector, and its modulus (amplitude spectrum) can be used to reflect the signal's energy distribution at different frequencies, providing the basis for subsequent dominant frequency identification and feature extraction.

[0056] The position of the main peak of the spectrum reflects the frequency shift center of the echo signal and is proportional to the radial velocity of the target object relative to the radar. The main peak can be obtained by finding the frequency corresponding to the maximum amplitude point, which is the basis for calculating the Doppler shift. The amplitude of the main peak represents the energy intensity of the signal and is an important indicator for evaluating the effectiveness of the signal. The spectrum bandwidth reflects the degree of signal expansion in the frequency domain. The wider the bandwidth, the wider the velocity distribution contained in the signal. It may also indicate that the signal contains more noise or multipath components. After removing the influence of the main peak, the background noise level is estimated by calculating the average or median energy of the remaining frequency regions. This value is used for subsequent signal-to-noise ratio calculations and dynamic threshold adjustment, affecting the signal quality judgment. The corresponding code example (Python) is as follows:

[0057]

[0058]

[0059]

[0060] Code Explanation:

[0061] De-DC offset: Removes the signal average value to prevent DC from affecting FFT analysis;

[0062] FFT analysis: Use np.fft.rfft to calculate the spectrum of the real signal and take its amplitude spectrum for energy analysis;

[0063] Main frequency extraction: the main frequency corresponds to the frequency point with the maximum amplitude;

[0064] Bandwidth estimation: Find the width of the range in the spectrum that exceeds half the energy of the main peak;

[0065] Noise estimation: Avoid the main frequency area and calculate the average energy of the remaining frequency bands as the background noise indicator.

[0066] After extracting spectral features, the acquisition module packages the spectral feature data corresponding to the current time, measurement direction, and altitude according to a predefined format, creating a standardized spectral data structure. It also adds necessary timestamps, measurement point location information, and system operating status information to facilitate precise alignment and spatiotemporal analysis in subsequent modules. This data structure typically includes a collection of parameters such as multiple frequency components and their corresponding power values, background noise baselines, and level change trends.

[0067] Finally, the acquisition module transmits this standardized spectral signature data to the echo signal quality analysis module via a high-speed bus or internal shared memory channel. To enhance real-time performance and system robustness, this module is equipped with a cache management mechanism and packet loss detection logic. This ensures that even in high-frequency measurement scenarios, spectral data is stably and completely transmitted to subsequent processing stages, preventing data loss or delays from impacting judgment results.

[0068] Through the orderly execution of the above steps, the acquisition module not only completes the task of converting the original signal of the acoustic and optical radar into spectral characteristics, but also provides solid and controllable basic data support for subsequent signal quality judgment and wind speed inversion.

[0069] The dynamic adjustment module dynamically adjusts the signal-to-noise ratio threshold by combining environmental parameters with the acoustic-optical radar system parameters, and sends the dynamically adjusted signal-to-noise ratio threshold to the echo signal quality analysis module.

[0070] The core function of the dynamic adjustment module is to dynamically adjust the signal-to-noise ratio (SNR) threshold in real time under different measurement environments and system operating conditions, combining external environmental parameters and acousto-optic radar system parameters to adapt to changing noise background and signal conditions, thereby improving the accuracy and robustness of echo signal quality judgment. The calculation expression for the dynamically adjusted SNR threshold is:

[0071] The signal-to-noise ratio threshold SNR output by the dynamic adjustment module th is determined by the following formula:

[0072] In the formula, SNR th is the dynamic adjusted signal-to-noise ratio threshold, is the average ambient noise power, which represents the average value of the background spectrum power collected in a period of time. It reflects the external environmental background noise level, σ N is the degree of fluctuation of ambient noise, the standard deviation of ambient noise power, which reflects the instability of noise, δ sys is the system state offset factor, a correction coefficient obtained according to the system operating state (such as transmit power, receive sensitivity, temperature control state), α, β, γ are weight coefficients, empirical parameters or obtained by machine learning training, which determine the weight of each factor on the threshold, θ is the initial signal-to-noise ratio threshold.

[0073] The average ambient noise power is: where N(t) represents the background spectrum power in the tth second or sampling period, T is the length of the time window for statistics (such as 5 seconds, 10 seconds), represents the dynamically estimated average ambient noise power.

[0074] The standard deviation of ambient noise power is: This parameter represents the degree of fluctuation of noise power in a short period of time. The more intense the fluctuation of noise, the more unstable the environment, and the signal-to-noise ratio threshold should be increased accordingly to prevent false judgments.

[0075] The system state offset factor is:

[0076]

[0077] where: P tx represents the current transmit power, P tx,nom represents the nominal transmit power, G rx represents the current receive sensitivity, G rx,nom represents the nominal receive sensitivity, T radar represents the current device operating temperature, T nom represents the nominal operating temperature, ω1, ω2, ω3 represent the weight parameters of the system offset term, which are configured according to the system characteristics. When the transmit power decreases, the receive sensitivity decreases, or the device temperature is abnormal, the overall system signal quality decreases, and the threshold should be increased accordingly.

[0078] The above dynamic signal-to-noise ratio threshold formula realizes comprehensive perception and response to real-time environmental noise characteristics and system operating state changes. It has the following functional characteristics:

[0079] Environmental perception capability: By calculating the average noise power and noise fluctuation in real time, it effectively reflects the impact of external noise background on signal judgment.

[0080] System self-checking capability: Utilize fluctuations in system operating parameters to adjust judgment strategies in a timely manner to prevent misjudgments caused by equipment aging and power drift.

[0081] Dynamic robustness adjustment: In harsh environments or poor device conditions, the signal-to-noise ratio threshold is raised to reduce false reception; in ideal conditions, the threshold is appropriately lowered to ensure that signals are not lost.

[0082] Adapt to multi-scenario measurement requirements: Especially suitable for areas with significant day-night changes, complex terrain, and changeable weather, improving the versatility and reliability of wind measurement radar systems.

[0083] Through this mechanism, the dynamic adjustment module achieves the following functional features: it can perceive the ambient noise level and fluctuations in real time, dynamically detect operational deviations of the radar system, and comprehensively generate the most appropriate signal-to-noise ratio threshold, thus avoiding false or missed detections caused by fixed thresholds. The resulting dynamic threshold is directly applied to the echo signal quality analysis module, guiding it in determining whether the measured spectrum data is valid for wind speed extraction. This approach effectively improves the system's wind measurement accuracy and stability in complex environments.

[0084] The echo signal quality analysis module inputs the spectrum feature data and the dynamically adjusted signal-to-noise ratio threshold into the echo quality model to determine whether the current echo signal is valid, and sends the judgment result to the interpolation module and wind speed analysis module.

[0085] The core task of the echo signal quality analysis module is to receive spectral signature data and a dynamically adjusted signal-to-noise ratio threshold, input this information into the echo quality model, and comprehensively determine whether the current echo signal is sufficiently reliable for wind speed calculation. If the signal is judged invalid, the interpolation module performs replacement processing; if it is judged valid, the wind speed analysis process proceeds directly. The specific judgment process can be broken down into the following detailed steps:

[0086] The echo signal quality analysis module processes spectral feature data measured by the acoustic and optical radar and dynamically adjusts the real-time signal-to-noise ratio threshold output by the module. Spectral feature data includes, but is not limited to, power spectrum distribution, dominant frequency peak, and spectral width. During preprocessing, the module normalizes, smoothes, and reduces noise on the spectral curve to eliminate unstable components caused by transient interference or system jitter, ensuring greater stability in subsequent analysis.

[0087] In the pre-processed spectrum, the system will identify the power intensity P corresponding to the peak of the main frequency. peak , and calculate the average background noise power P in the non-main frequency area noiseThe signal-to-noise ratio calculation formula is as follows:

[0088] wherein P peak is the power density peak value corresponding to the main frequency in the spectrum, and is usually the highest single frequency point, and P noise is the representative value of the average power in a certain frequency domain interval on both sides of the main frequency, used to represent the background noise level.

[0089] The echo quality model compares the instantaneous signal-to-noise ratio SNR inst calculated above with the threshold SNR th output by the dynamic adjustment module. If SNR inst ≥ SNR th , it is considered that the main frequency signal in the current spectrum has sufficient prominence and can be regarded as an effective echo signal. Otherwise, it is determined to be an invalid signal, which will be processed by the interpolation module subsequently. This judgment criterion can ensure that the system dynamically adapts to different background noise and device states, and improves the overall robustness.

[0090] In some key measurement stages (such as scenes with dramatic changes in the boundary layer), the module also introduces two auxiliary judgment indicators to enhance the discrimination accuracy: 1. Spectrum peak sharpness indicator: used to analyze the steepness of the main peak and its adjacent region of the spectrum, to identify whether it is a typical Doppler peak shape. 2. Spectrum limitation indicator: used to judge whether the current spectrum peak is too wide or not concentrated due to interference, to avoid misidentifying wideband noise as the main frequency peak. Only when the main judgment condition is met (SNR is greater than the threshold), and the auxiliary indicators are within a reasonable range, the system finally determines it to be effective.

[0091] Once the effectiveness of the echo signal is confirmed or denied, the module encodes the judgment result and sends it to two subsequent processing modules:

[0092] If it is an effective signal, it is sent to the wind speed analysis module, along with the spectrum main frequency and its related features;

[0093] If it is an invalid signal, it is sent to the interpolation module, along with the geographical information of the current measurement point, the time index, and the spectrum reference information of the adjacent points, for the interpolation module to make spatial smoothing substitution.

[0094] If it is invalid, the interpolation module adopts spatial smoothing interpolation method for interpolation, and the interpolated spectrum feature data is sent to the wind speed analysis module.

[0095] When the echo signal quality analysis module determines that the spectrum signal of the current measurement point is invalid, the interpolation module will use the spatial smoothing interpolation method to estimate and complete the spectrum characteristic data of the measurement point to ensure that the wind speed analysis module can still obtain reasonable spectrum data input at the invalid measurement point. This spatial interpolation process not only considers the spatial distribution of adjacent measurement points, but also combines the physical continuity of the spectrum main frequency to ensure the physical consistency of the interpolation results. Specifically, it includes the following steps:

[0096] The interpolation module first determines the spatial location of the currently invalid measurement point within the detection grid, typically indexed by altitude layer, azimuth, and range gate. It then selects several measurement points with valid echo signals within the spatial neighborhood of the current measurement point and extracts their corresponding spectral signature data, including parameters such as dominant frequency, power spectrum shape, and spectral width.

[0097] In principle, a 3D spherical neighborhood is preferred for neighborhood selection. If data is missing in a particular direction, it can be degraded to a 2D plane or linear interpolation. Common neighborhood scales include ±12 grid cells in the vertical direction.

[0098] For all nearby valid measurement points involved in interpolation, the module will calculate the corresponding interpolation weights based on their relative spatial distances. Typical weight calculation methods are Gaussian weighting function or inverse distance weighting (IDW). Take inverse distance weighting as an example: Among them, w i represents the weight of the i-th neighboring measurement point, d i is the spatial Euclidean distance from the measurement point to the currently invalid measurement point, and p is a weighted exponent, usually ranging from 1.5 to 2.5. All weights are normalized to satisfy: This step ensures that the effective measurement points with closer distances have a greater impact on the interpolation results, which is consistent with the spatial continuity law of the wind field.

[0099] Based on the dominant frequency value f of the adjacent measuring points i and the corresponding weight w i , the interpolation module calculates the main frequency estimation of the current invalid measurement point: In addition, to maintain the rationality of the spectrum shape, the module also performs similar interpolation processing on the spectrum width Δf and the power spectrum shape. For example, the spectrum width interpolation is:

[0100] Finally, based on the main frequency, spectral width and amplitude obtained by interpolation, a simulated spectrum that conforms to the typical Gaussian spectrum or empirical spectrum model is constructed as the reconstructed spectrum feature of the current measurement point.

[0101] After the interpolated spectrum is initially generated, the module also introduces a spectral morphology correction mechanism to ensure physical continuity with surrounding measurement points in terms of dominant frequency position, energy distribution, and spectral steepness. If the interpolated spectrum differs from the spectrum of adjacent measurement points, such as frequency jumps, unusually sharp peaks, or broadening, fine-tuning is performed by adjusting the peak smoothing factor and power normalization constant to prevent it from being misidentified as an interference signal during the wind speed analysis phase.

[0102] After interpolation, the module sends the reconstructed spectral signature data to the wind speed analysis module. The interpolated result is structurally identical to the original effective spectrum, including information such as the dominant frequency position, spectral width, and power density sequence, and is also identified as an interpolated result. The wind speed analysis module extracts the dominant frequency according to conventional procedures for Doppler shift calculation and wind vector inversion. The interpolated data can also be flagged for later quality control or statistical processing.

[0103] The interpolation module achieves high-fidelity compensation for invalid echo measurement points, which not only maintains the integrity of the three-dimensional wind field in terms of data continuity, but also improves the reliability of the overall wind speed inversion results in boundary areas and data-missing areas, ensuring that the system has anti-interference and anti-obstruction capabilities.

[0104] If valid, the wind speed analysis module extracts the main frequency of the echo signal and calculates the Doppler frequency shift. Based on the detection angle and deployment direction, the radial wind speed is estimated and the three-dimensional wind speed vector is inverted and physical consistency correction is performed.

[0105] When the echo signal quality analysis module determines that the echo signal of the measurement point is valid, the wind speed analysis module takes the spectrum characteristics of the measurement point as input and sequentially performs main frequency extraction, Doppler frequency shift calculation, radial wind speed calculation, three-dimensional wind speed vector inversion and physical consistency correction to ensure that the output wind field information is complete and accurate in terms of spatial structure and physical laws. The specific analysis process includes the following detailed steps:

[0106] The wind speed analysis module first extracts the dominant frequency from the input spectral signature data. This process typically locates the peak location—the frequency point with the highest power—based on the power spectrum curve. If the power spectrum contains multiple local peaks, the system uses a combination of peak height, peak width, and background noise ratio to identify the frequency component corresponding to the primary reflected wave, ensuring that the extracted frequency component is the primary reflected wave, rather than secondary interference or ghosting echoes.

[0107] After the main frequency is extracted, the system calculates the Doppler frequency shift according to the following formula: Δf = f peak -f0, where Δf is the Doppler frequency shift, f pcak is the main frequency of the current measurement point, and f0 is the transmit frequency of the acoustic and optical radar. This frequency shift directly reflects the relative velocity projection of the target air mass with respect to the radar system and is the core physical quantity for subsequent wind speed estimation.

[0108] According to the principle of classical Doppler effect, the system converts the frequency shift into the radial wind speed v r , the calculation formula is as follows: Where: v r is the radial wind speed, c is the propagation speed of sound or light wave in the detection medium (such as sound wave in air is about 343 m / s, light wave is about 3 x 10 8 m / s), other parameters are the same as above. This step outputs the projection value of wind speed in the direction of radar beam, i.e. the component speed of target air mass on the detection path.

[0109] The acoustic-optic radar system usually adopts multi-angle multi-beam arrangement, i.e. scanning the same volume region from different azimuth and elevation angles to obtain radial wind speed in multiple directions. The wind speed analysis module jointly inverts the radial wind speed v r1 ,v r2 ,…,v rn and the corresponding beam direction vector d1, d2, …, d n to construct the three-dimensional wind speed vector Specifically, the least square method is used to solve the following equation group: Where: u, v, w are the east-west, north-south and vertical wind speed components, d i is the unit direction vector of the i-th beam, n is the number of beams, usually not less than 3. This step outputs the complete three-dimensional vector information of wind speed, which is used to describe the real motion of air mass in space.

[0110] The following is a code example (written in Python) to implement the five steps of "if valid, the wind speed analysis module extracts the main frequency of the echo signal and calculates the Doppler frequency shift, according to the detection angle and arrangement direction, calculates the radial wind speed and inverts the three-dimensional wind speed vector and makes physical consistency correction", with detailed explanation:

[0111]

[0112] "Extract main frequency"

[0113] peaks,_=find_peaks(spectrum_power)

[0114] iflen(peaks)==0:

[0115] return None

[0116] main_peak=peaks[np.argmax(spectrum_power[peaks])]

[0117] return spectrum_freq[main_peak]

[0118] def compute_doppler_shift(f_peak,f0):

[0119] """Calculate Doppler shift"""

[0120] return f_peak - f0

[0121] def compute_radial_velocity(delta_f,f0,c):

[0122] """Calculate radial wind speed"""

[0123] return(c*delta_f) / (2*f0)

[0124] definvert_3d_wind(radial_velocities,beam_dirs):

[0125] """Inversion of 3D wind speed vector"""

[0126] D = np.array(beam_dirs) # beam direction matrix

[0127] v_r=np.array(radial_velocities).reshape(-1,1)

[0128] V,_,_,_=lstsq(D,v_r)

[0129] return V.flatten()#[u,v,w]

[0130] def smooth_velocity_field(current_vector,neighbor_vectors,weight=0.5):

[0131] """Smoothing correction: weighted average of surrounding wind speed vectors"""

[0132] avg_neighbor=np.mean(neighbor_vectors,axis=0)

[0133] return weight*current_vector+(1-weight)*avg_neighbor

[0134] #Sample data (actually provided by spectrum analysis and radar system)

[0135]

[0136] print("Original wind speed vector:",wind_vector)

[0137] print("Corrected wind speed vector:",corrected_wind_vector)

[0138] else:

[0139] print("Failed to identify the effective main frequency")

[0140] Code Explanation:

[0141] Step 1: Extract the main frequency

[0142] Use scipy.signal.find_peaks to find all peaks in the power spectrum; select the frequency corresponding to the peak with the largest power value as the main frequency f_peak.

[0143] Step 2: Calculate the Doppler shift

[0144] The Doppler shift Δf is the difference between the main frequency and the transmission frequency: Δf = f_peak - f0; if the target approaches the radar, the frequency shift is positive; if it moves away, it is negative.

[0145] Step 3: Calculate radial wind speed

[0146] Use the standard Doppler formula v_r = (c*Δf) / (2*f); this is the target's velocity along the radar beam.

[0147] Step 4: Invert 3D wind speed vector

[0148] A linear equation system is established for the radial wind speed measured in each beam; the three-dimensional wind speed [u, v, w], i.e., the components of the three-dimensional wind speed, is solved using the least squares method.

[0149] Step 5: Physical consistency correction

[0150] This example uses the neighboring point average smoothing method to correct wind speed to avoid sudden changes; it can be expanded to more complex physical constraint models such as boundary constraints and continuity corrections.

[0151] The three-dimensional wind speed vector obtained from the initial inversion may not conform to physical laws due to local measurement errors or inappropriate inversion algorithms, such as sudden wind direction reversal, sudden wind speed changes, abnormal vertical speed, etc. Therefore, the wind speed analysis module also includes the following physical consistency correction steps:

[0152] Smoothing correction: spatially smooth the wind speed at the current measuring point and the adjacent measuring points to reduce isolated outliers;

[0153] Boundary constraint: Boundary shape adjustment is made to the wind speed vector in combination with a meteorological model or boundary condition (e.g. near-surface vertical wind speed approaching zero);

[0154] Continuity constraint: Ensure that the wind field change meets the conservation of mass, avoid the emergence of unpowered or no-flow sudden change of air flow at the radar shear layer;

[0155] Historical trend constraint: In the continuous observation mode, the system will also use the time series trend model to evaluate whether the current wind speed is consistent with the previous time wind field trend, and if the deviation is too large, it will trigger the correction.

[0156] In the description of the present specification, the description referring to the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0157] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details and limit the application to the specific embodiments. Obviously, many modifications and changes can be made according to the content of the present specification. The present specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and utilize the application. The application is limited only by the claims and their full scope and equivalents.

Claims

1. An acoustic and optical radar wind measurement data correction system based on adaptive noise suppression is characterized by: It includes acquisition module, dynamic adjustment module, echo signal quality analysis module, interpolation module and wind speed analysis module; Acquisition module: collects spectrum characteristic data of acoustic and optical radar; Dynamic adjustment module: combines environmental parameters with acoustic and optical radar system parameters to dynamically adjust the signal-to-noise ratio threshold; Echo signal quality analysis module: inputs spectrum feature data and dynamically adjusted signal-to-noise ratio threshold into the echo quality model to determine whether the current echo signal is valid; Interpolation module: If invalid, spatial smoothing interpolation method is used for interpolation; Wind speed analysis module: If valid, the main frequency of the echo signal is extracted and the Doppler frequency shift is calculated. According to the detection angle and deployment direction, the radial wind speed is calculated and the three-dimensional wind speed vector is inverted and physical consistency correction is performed.

2. The adaptive noise suppression-based acousto-optic radar wind measurement data correction system according to claim 1, characterized in that: The echo signal quality analysis module obtains spectrum feature data and a dynamically adjusted signal-to-noise ratio threshold; Calculate the instantaneous signal-to-noise ratio and convert the calculated instantaneous signal-to-noise ratio SNR inst and the dynamically adjusted signal-to-noise ratio threshold SNR th For comparison, if SNR inst ≥SNR th , judge whether the main frequency signal in the current spectrum is significant and is regarded as a valid echo signal, otherwise it is judged as an invalid signal.

3. The adaptive noise suppression-based acousto-optic radar wind measurement data correction system according to claim 2, characterized in that: The wind speed analysis module calculates the Doppler frequency shift: Δf = f peak -f0, where Δf is the Doppler frequency shift, f pcak is the main frequency of the current measuring point, and f0 is the transmitting frequency of the acoustic and optical radar; Convert the frequency shift to radial wind speed v r , the calculation formula is: Where: v r is the radial wind speed, c is the propagation speed of sound waves or light waves in the detection medium; The radial wind speed v of each beam r1 ,v r2 ,…,v rn and the corresponding beam direction vectors d1, d2, ..., d n Perform joint inversion to construct a three-dimensional wind speed vector V = [u, v, w] T .

4. The adaptive noise suppression-based acousto-optic radar wind measurement data correction system according to claim 3, characterized in that: The interpolation module determines the spatial position of the current invalid measuring point in the detection grid, and selects a number of measuring points with valid echo signals from the spatial neighborhood of the current measuring point; For all the adjacent valid measuring points involved in the interpolation, the corresponding interpolation weights are calculated based on their relative spatial distances, and the dominant frequency values ​​f of the adjacent measuring points are calculated based on the interpolation weights. i and the corresponding weight w i , calculate the main frequency estimation f of the current invalid measurement point interp : k represents the number of all adjacent valid measurement points involved in interpolation.

5. The adaptive noise suppression-based acousto-optic radar wind measurement data correction system according to claim 4, characterized in that: Signal-to-noise ratio (SNR) inst The calculation formula is: Among them, P peak is the peak power density corresponding to the main frequency in the spectrum, P noise It is the representative value of the average power in a frequency domain interval on both sides of the main frequency.

6. The adaptive noise suppression-based acousto-optic radar wind measurement data correction system according to claim 5, characterized in that: The dynamic adjustment module dynamically adjusts the signal-to-noise ratio threshold according to the average environmental noise power, the environmental noise fluctuation level and the system state offset factor.

7. The adaptive noise suppression-based acousto-optic radar wind measurement data correction system according to claim 6, characterized in that: The dynamically adjusted signal-to-noise ratio threshold output by the dynamic adjustment module is: Where, SNR th is the dynamically adjusted signal-to-noise ratio threshold, is the average noise power of the environment, σ N is the environmental noise fluctuation level, δ sys is the system state offset factor, α, β, γ are weight coefficients, and θ is the initial signal-to-noise ratio threshold.

8. The adaptive noise suppression-based acousto-optic radar wind measurement data correction system according to claim 7, characterized in that: Average ambient noise power: Where N(t) represents the background spectrum power in the tth second or sampling period, and T is the length of the statistical time window; Standard deviation of ambient noise power: is the average noise power of the environment, σ N is the degree of environmental noise fluctuation.

9. The adaptive noise suppression-based acousto-optic radar wind measurement data correction system according to claim 8, characterized in that: The acquisition module receives the echo signal from the acoustic and optical radar system and pre-processes the echo signal; Perform fast Fourier transform on the digitized echo signal to extract the spectrum features related to wind speed; The spectrum feature data corresponding to the current time, current measurement direction and altitude are packaged to form a standardized spectrum data structure.

10. The adaptive noise suppression-based acousto-optic radar wind measurement data correction system according to claim 9, characterized in that: The acquisition module pre-processes the echo signal, and the pre-processing includes filtering, amplifying and analog-to-digital conversion of the original echo signal, and converting the analog echo signal into a digital signal; Extract the spectrum features related to wind speed, including the main peak position, peak amplitude, spectrum bandwidth, and background noise energy; After forming a standardized spectrum data structure, timestamps, measurement point location information, and operating status information are added. The data structure includes multiple frequency components and their corresponding power values, background noise baselines, and level change trend sets.

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