Wind profile radar spectrum peak estimation method based on three-parameter dynamic cost function

Abstract

The invention provides a wind profile radar spectrum peak estimation method based on a three-parameter dynamic cost function, and the method comprises the steps: 1, carrying out the preprocessing of distance and Doppler spectrum data, and carrying out the three-point moving average smoothing; step 2, noise level estimation based on statistics is carried out; step 3, carrying out candidate spectrum peak selection based on space Doppler window smoothing; 4, performing dynamic weight calculation based on covariance matrix eigenvalue decomposition; step 5, constructing a three-parameter cost function of a distance spectrum peak value RSP power item, a continuity item and a spectrum width consistency item, and performing optimization; and step 6, outputting the optimal spectrum peak trajectory to form a complete wind speed profile trajectory. According to the method, the spectrum peak identification precision and stability in a complex environment can be improved, and the u and v wind correlation coefficients of the estimation method are verified to reach 0.880 and 0.879 through a data set disclosed by an atmospheric radiation measurement website.

Description

Technical Field

[0001] This invention belongs to the field of boundary layer wind field inversion, and particularly relates to a method for estimating the radar spectral peak of wind profile based on a three-parameter dynamic cost function. Background Technology

[0002] Wind profiler radar is an important meteorological detection device widely used to detect wind speed and direction information in the atmospheric boundary layer. This type of radar utilizes the scattering effect of electromagnetic waves by clear-sky atmospheric turbulence, obtaining wind speed distributions at different altitudes through the Doppler principle, and possesses continuous detection capabilities with high spatiotemporal resolution. Compared to traditional wind-measuring radar, wind profiler radar achieves real-time, continuous monitoring of the three-dimensional atmospheric wind field, and has broad application value in aviation meteorology, numerical weather prediction, and boundary layer research.

[0003] However, in practical applications, the atmospheric echo signals received by wind profiler radar are usually very weak and susceptible to background noise, ground clutter, and radio frequency interference, leading to signal quality degradation and spectral peak blurring, which in turn affects the accurate extraction of wind speed. To obtain reliable wind field information, accurate spectral peak estimation of the power spectrum in the echo signal is required to identify the corresponding Doppler frequency components. However, under conditions of low signal-to-noise ratio and complex background interference, traditional spectral peak estimation methods are limited by their fixed parameters and static models, making it difficult to effectively adapt to dynamic environments, resulting in a decrease in spectral peak recognition rate and frequency estimation accuracy.

[0004] In recent years, researchers have proposed a series of improved algorithms to enhance the performance of spectral peak estimation. For example, the earliest Vaisala single-peak method directly selects the spectral peak with the largest amplitude in the power spectrum as the atmospheric signal; the maximum entropy method improves estimation accuracy by increasing spectral resolution; the adaptive moment estimation method improves the ability to identify spectral peaks by setting dynamic thresholds; the multi-parameter cost function comprehensively considers both signal power and differential wind shear, but the weighting factors are usually fixed based on empirical settings, and the limited selection of parameters makes it difficult to fully characterize the feature distribution of spectral peaks in complex scenarios, which can easily lead to estimation bias. Summary of the Invention

[0005] Objective of the Invention: The technical problem to be solved by this invention is to address the shortcomings of existing technologies by providing a method for estimating the spectral peak of wind profiler radar based on a three-parameter dynamic cost function, comprising the following steps: Step 1, Preprocessing of range and Doppler spectrum data: The location of ground clutter is determined by finding the local maximum point closest to zero frequency. The signal variation trend is analyzed by differential operation, and the area affected by ground clutter is set to zero and smoothed. Kalman filtering is used to denoise the spectral lines of each range library, and process noise and measurement noise parameters are dynamically set. Finally, three-point moving average smoothing is performed. Step 2, perform statistical analysis. Noise level estimation: Sort the power spectrum of each distance library and calculate the mean of noise samples of different lengths. and variance ,pass The statistic is used to find the value closest to 1, and the corresponding sample length is the optimal number of points for estimating the noise level. Based on the optimal number of points, the noise basis of each distance library is determined. Step 3: Perform candidate spectral peak selection based on spatial Doppler window smoothing; Step 4: Perform dynamic weight calculation based on the eigenvalue decomposition of the covariance matrix; Step 5: Construct a three-parameter cost function for the distance spectral peak RSP power term, continuity term, and spectral width consistency term, and optimize it: Construct the RSP power term by quantizing the spectral peak power intensity based on the exponential decay function of the signal-to-noise ratio. By combining wind shear constraints and smoothness constraints, a continuous term is constructed. The current peak spectral width is calculated using the local second moment, and then compared with the reference spectral widths in the historical and neighborhood distance databases using Gaussian similarity to construct a spectral width consistency term. Dynamic weights are used for the RSP power term. Continuous terms And spectral width consistency term An adaptive weighted summation is performed, and the candidate peak with the highest cost value is selected as the optimal spectral peak in the distance library; Step 6, Output the optimal spectral peak trajectory: Use a group processing strategy with a group size of N1 (usually 3) distance libraries. Process each distance library one by one starting from the first distance library. Use the speed selected in the previous distance library as a reference for the continuity term. Optimize and select the optimal spectral peak for each distance library through a three-parameter cost function to form a complete wind speed profile trajectory.

[0006] Step 3 includes: calculating the Doppler velocity window. The corresponding speed difference value Where RR is the radar range resolution and vres is the velocity resolution.

[0007] Step 3 also includes: setting a spatial window for each distance library, extracting all spectral data within the spatial window, and calculating the spatial average power spectrum; selecting the top N1 peaks (usually 5) from the average power spectrum as candidate peaks, ensuring that the distance between peaks is greater than the velocity grid difference value. Record the power value and index position of the candidate spectral peaks.

[0008] Step 4 includes: Perform an inverse transform on the power spectrum data of each distance gate to construct the covariance matrix. : , in, This represents the signal vector of the r-th distance gate in the k-th time window, where K is the number of time windows. This indicates the conjugate transpose.

[0009] Step 4 also includes: By analyzing the covariance matrix Eigenvalue decomposition yields a set of eigenvalues ​​arranged in descending order: , Where the subscript N represents the dimension of the covariance matrix. The largest eigenvalue corresponds to the energy of the principal component of the signal; It is the Nth eigenvalue; By analyzing the intervals between adjacent feature values, a relative gap threshold is used. Automatically divide the signal and noise subspaces and determine the order p of the noise subspace: , in Let k be the k-th eigenvalue, ranging from 1 to N, and be the point where k is the maximum value. This means finding a value within the range of variable k that satisfies the subsequent expression. The specific value of k corresponding to when it is true and reaches its maximum value; Noise power estimation based on eigenvalue distribution : , Where i ranges from 1 to N; Further calculation of linear signal-to-noise ratio : , And convert it to signal-to-noise ratio in decibels. : .

[0010] Step 4 also includes: Based on all distances to the door Distribution, statistical quantiles as dynamic central values ,by For input, through Type function mapping enables dynamic weight allocation: , in, Indicates the weight of the power term. and Here, represents the upper and lower bounds of the constraint boundary, respectively; t is the kurtosis adjustment coefficient; exp is the natural exponential function. This represents the signal-to-noise ratio of the r-th distance gate; The remaining weights are determined by the complementarity relationship: , in The sum of the continuity term and the spectral width consistency term; the allocation ratio of the remaining weights between the continuity term and the spectral width consistency term, while ensuring the continuity and smoothness of the spectral peaks, achieves a comprehensive optimization of spectral width consistency and signal stability, which has been determined through verification using multiple sets of experimental data: , in, , , Representing the power terms respectively Continuous terms And spectral width consistency term The weight.

[0011] Step 5 includes: RSP power term The calculation formula is: , in, The power of the current candidate peak With current distance from the noise power floor The ratio is calculated using the following formula: , Calculate the wind shear term : , in, and These are the wind speeds at the current distance from the reservoir and the wind speed at the previous distance from the reservoir, respectively. Calculate the smoothness term : , in, It is the wind speed at the (i-2)th distance from the reservoir; Continuous terms The weighted sum of the wind shear term and the smoothness term: , Spectral width consistency term The calculation formula is: , in, It is the spectral width of the current peak. It is the reference spectral width. It is the tolerance parameter for spectral width. , where j is the proportionality coefficient.

[0012] Step 5 also includes: The total cost function, TotalCost, is obtained by weighted summation of the power term, continuity term, and spectral width consistency term. , The optimal spectral peak position is determined by selecting the candidate peak with the highest cost value.

[0013] The present invention also provides an electronic device, including a processor and a memory, the memory storing program code that, when executed by the processor, causes the processor to perform the steps of the method.

[0014] The present invention also provides a storage medium storing a computer program or instructions that, when the computer program or instructions are run on a computer, execute the steps of the method described.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The method of the present invention optimizes the selection of candidate spectral peaks through spatial Doppler window smoothing, accurately divides the signal and noise subspaces, extracts the signal-to-noise ratio features and maps them to dynamic weight factors of the power term, and constructs a three-parameter cost function by combining the distance spectral peak RSP power term, continuity term, and spectral width consistency term for adaptive dynamic summation, thereby achieving optimal estimation of the spectral peak trajectory and improving the accuracy and stability of spectral peak identification in complex environments. Attached Figure Description

[0016] Figure 1 This is a flowchart of a wind profile radar spectral peak estimation method based on a three-parameter dynamic cost function, provided by an embodiment of the present invention.

[0017] Figure 2 This is a comparison chart of the peak estimation results in this invention.

[0018] Figure 3 This is a comparison chart of the peak estimation results in this invention.

[0019] Figure 4 This invention involves estimating the peak values ​​of 16 sets of power spectrum data and comparing them with GPS radiosonde data. Wind scatter plot.

[0020] Figure 5 This invention involves estimating the peak values ​​of 16 sets of power spectrum data and comparing them with GPS radiosonde data. Wind scatter plot. Detailed Implementation

[0021] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0022] like Figure 1 As shown, this embodiment of the invention provides a method for estimating the spectral peak of a wind profiler radar based on a three-parameter dynamic cost function, including the following steps: Step 1: Range and Doppler spectral data preprocessing. Ground clutter locations are determined by finding local maxima closest to zero frequency. Differential operations are used to analyze signal variation trends, and the clutter-affected areas are zeroed and smoothed. Kalman filtering is employed to denoise the spectral lines of each range library, dynamically setting process noise and measurement noise parameters, and matrix... Set to: , In the formula, Indicates the current distance to the Kudoppler power spectrum The variance, scaled only on the diagonal rather than used directly as a scalar, characterizes the non-stationarity of the turbulence noise at that distance. This is especially relevant when atmospheric turbulence signals are strong. Larger size allows for greater uncertainty in state prediction; in noise-dominated regions Smaller values ​​require more conservative predictions of the state. Scaling factor A value of 0.1 is used to balance the need to preserve the dynamic changes of the signal and suppress excessive prediction jitter.

[0023] Update phase It is the covariance of the observation noise, expressed as: , In the formula, Current distance Kudoppler power spectrum The standard deviation.

[0024] Finally, a three-point moving average smoothing process is performed.

[0025] Step 2, based on statistics Noise level estimation. The power spectrum of each distance library is sorted, and the mean of noise samples of different lengths is calculated. and variance ,pass The statistic is used to find the value closest to 1, and the corresponding sample length is the optimal number of points for estimating the noise level, thereby determining the noise basis of each distance library.

[0026] Step 3: Candidate spectral peak selection based on spatial Doppler window smoothing. Calculate the Doppler velocity window. , For radar range resolution, in units The corresponding speed difference value , For velocity resolution, units .

[0027] For the current distance database to be processed Based on the preset window size Define a spatial window that contains the current distance database and its neighboring distance databases. The starting index of this spatial window is denoted as . The end distance of the library index is denoted as The number of distance libraries contained in the window is: , From the preprocessed Doppler velocity spectrum matrix Extracting the index range The spectral data corresponding to all distances within the database constitute a spatial window data set: , in, This represents the power spectral value on the r-th distance cell and the m-th Doppler cell, where M is the total number of velocity cells.

[0028] Based on this, the average power spectrum of set X along the distance dimension is obtained after spatial smoothing. : , in, This represents the value of the average power spectrum after spatial smoothing in the m-th velocity element.

[0029] After obtaining the spatially smoothed average power spectrum, the power spectrum can be further sorted and filtered. The top five peaks in the average power spectrum are selected as candidate peaks, ensuring that the distance between the peaks is greater than the Doppler window constraint. Record the power value and index position of the candidate spectral peaks.

[0030] Step 4: Dynamic weight calculation based on eigenvalue decomposition of the covariance matrix. The Doppler power spectrum data for each range gate is inversely transformed into a time-domain signal, the covariance matrix is ​​constructed, and eigenvalue decomposition is performed: , in, This represents the signal vector of the r-th distance gate in the k-th time window, where K is the number of time windows. This indicates the conjugate transpose.

[0031] By analyzing the covariance matrix Eigenvalue decomposition yields a set of eigenvalues ​​arranged in descending order: , Where the subscript N represents the dimension of the covariance matrix. The largest eigenvalue corresponds to the energy of the principal component of the signal.

[0032] By analyzing the intervals between adjacent feature values, a relative gap threshold is used. Automatically divide the signal and noise subspaces and determine the order p of the noise subspace: , In the formula, The k-th eigenvalue takes values ​​from 1 to N. Based on eigenvalue variation analysis and multiple sets of data tests to obtain the optimal solution of 0.3, good results can be achieved in signal and noise separation.

[0033] Noise power estimation based on eigenvalue distribution : , The threshold position p is determined by the relative gap between adjacent feature values.

[0034] Further calculation of linear signal-to-noise ratio : , And convert it to signal-to-noise ratio in decibels. : , Based on all distances to the door The distribution was analyzed, and its quantiles (the 70th percentile was used for experimental verification) were determined as the dynamic center value. .by For input, through Type function mapping enables dynamic weight allocation: , in, Indicates the weight of the power term. and To constrain the boundaries, the values ​​are 0.8 and 0.2 respectively. To adjust the steepness coefficient, This represents the signal-to-noise ratio of the r-th distance gate.

[0035] The remaining weights are determined by the complementarity relationship: , in This is the sum of the continuity term and the spectral width consistency term. The remaining weights are allocated between the continuity term and the spectral width consistency term to achieve a comprehensive optimization of spectral width consistency and signal stability while ensuring the continuity and smoothness of spectral peaks. This was determined through verification using multiple sets of experimental data. , in, , , These represent the weights of the power constraint, continuity constraint, and spectral width constraint, respectively.

[0036] Step 5: Construction and optimization of the three-parameter cost function for the distance spectral peak RSP power term, continuity term, and spectral width consistency term. The RSP power term is constructed by quantizing the spectral peak power intensity based on the exponential decay function of the signal-to-noise ratio. Power Term The power intensity of the spectral peaks was quantified. The formula is as follows: , in, The ratio of the power of the current candidate spectral peak to the power floor of the current distance noise level is used to measure the significance of the spectral peak relative to the noise. The formula is: , By using the exponential decay function, spectral peaks with higher SNR values ​​will be given higher weights, ensuring peak priority for strong signals.

[0037] By combining wind shear constraints and smoothness constraints, a continuity term is constructed to ensure the physical rationality of wind speeds between adjacent reservoirs. The continuation term consists of two parts: a wind shear term and a smoothness term. Large differences in wind speed lead to large wind shear, expressed as: , in, and These are the wind speeds at the current distance and the previous distance, respectively. The Doppler velocity window controls the penalty intensity of the wind shear term when there are large differences in wind speed.

[0038] To further smooth out wind speed variations, curvature is used to calculate smoothness constraints: , in, , , These are the current wind speed and the wind speed at the two nearest points to the reservoir. This is the Doppler velocity window.

[0039] The final continuity term The weighted sum of the wind shear term and the smoothness term: , Here, 0.7 and 0.3 are the weighting coefficients for the wind shear term and the smoothness term, respectively, indicating that wind shear contributes significantly to the continuity constraint. Experiments have verified that the wind shear term has a more significant impact on wind speed continuity, and therefore it is given a higher weight, while the smoothness term is used to further smooth the speed changes between adjacent distances, and therefore it is given a lower weight.

[0040] Spectral width consistency term Based on local second-order moment calculations, this term ensures that the spectral width of the current peak is consistent with the spectral widths of historical peaks and the neighboring distance library. The calculation method is as follows: , in, It is the spectral width of the current peak. It is a reference spectral width, obtained by combining the spectral widths of previously selected peaks in the current distance database and the spectral widths of the strongest peaks in the neighboring distance databases, and then filtering it with the median. This allows it to adaptively reflect the spectral width characteristics of local meteorological targets. It is the tolerance parameter for spectral width, set to the reference spectral width. A fixed proportionality coefficient, namely The proportionality coefficient Preferably .

[0041] The total cost function is obtained by weighting and summing the power term, continuity term, and spectral width consistency term: , The optimal spectral peak position is determined by selecting the candidate peak with the highest cost value.

[0042] Step 6: Output the optimal spectral peak trajectory. A grouping strategy is used, with each group consisting of three distance libraries. Processing begins with the first distance library and proceeds sequentially. The velocity selected in the previous distance library is used as a reference for the continuity term. A three-parameter cost function is used to optimize and select the optimal spectral peak for each distance library, forming a complete wind speed profile trajectory.

[0043] The experiment used data from the RWP915 wind profiler radar and radiosonde provided on the ARM official website. The radiosonde data only included the three-dimensional wind components u and v. Therefore, the vertical beam at 18:39:40 on August 31, the north beam at 42 seconds, and the east beam at 48 seconds were selected from the RWP915 radar data set. After data processing and spectral peak estimation by the method of this invention, the three beams were synthesized and decomposed into the three-dimensional wind components u and v. Figure 2This is a comparison chart of the peak estimation results of the u-wind in this invention (the u and v wind speed components are synthesized by three beams, and the radiosonde data can be used as the true value of the atmospheric wind speed at the time of collection. The higher the degree of agreement between the two, the better the algorithm of this invention). Figure 3 This is a comparison chart of the wind speed estimation results of the spectral peak in this invention. It can be seen that the invention has stronger robustness and constraints, and has better performance in spectral peak detection and wind speed estimation. The obtained wind profile has a high degree of agreement with the measured data.

[0044] To improve the scientific rigor and physical adaptability of the spectral peak estimation algorithm in this invention, and to examine its comprehensive performance under different weather scenarios, 16 sets of RWP915 wind profiler radar echo data (August 2008) were selected. Figure 4 This is a scatter plot of the U-wind data obtained by estimating the peak of 16 sets of power spectrum data in this invention and comparing it with GPS radiosonde data (the closer the scatter points are to the diagonal, the more accurate the algorithm's peak identification and the smaller the wind speed error). Figure 5 This is a scatter plot showing the wind component inversion results of the present invention on the overall validation sample, comparing the peak estimation of 16 sets of power spectrum data with GPS radiosonde data. The closer the scatter plot is to the diagonal, the more accurate the peak identification of the algorithm and the smaller the wind speed error, reflecting its adaptability and robustness under different meteorological conditions. The results show that the present invention can identify atmospheric peaks more accurately in multiple sets of experiments. Its wind speed estimation results are in high agreement with the radiosonde data, with small errors and good stability. The scatter plots have a high degree of matching with the reference slope, and the correlation coefficients of u and v winds are 0.88 and 0.879, respectively.

[0045] This invention provides a method for estimating the spectral peak of wind profiler radar based on a three-parameter dynamic cost function. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A method for estimating the spectral peak of wind profile radar based on a three-parameter dynamic cost function, characterized in that, Includes the following steps: Step 1, Preprocessing of range and Doppler spectrum data: The location of ground clutter is determined by finding the local maximum point closest to zero frequency. The signal variation trend is analyzed by differential operation, and the area affected by ground clutter is set to zero and smoothed. Kalman filtering is used to denoise the spectral lines of each range library, and process noise and measurement noise parameters are dynamically set. Finally, three-point moving average smoothing is performed. Step 2, perform statistical analysis. Noise level estimation: Sort the power spectrum of each distance library and calculate the mean of noise samples of different lengths. and variance ,pass The statistic is used to find the value closest to 1, and the corresponding sample length is the optimal number of points for estimating the noise level. Based on the optimal number of points, the noise basis of each distance library is determined. Step 3: Perform candidate spectral peak selection based on spatial Doppler window smoothing; Step 4: Perform dynamic weight calculation based on the eigenvalue decomposition of the covariance matrix; Step 5: Construct a three-parameter cost function for the distance spectral peak RSP power term, continuity term, and spectral width consistency term, and optimize it: Construct the RSP power term by quantizing the spectral peak power intensity based on the exponential decay function of the signal-to-noise ratio. By combining wind shear constraints and smoothness constraints, a continuous term is constructed. The current peak spectral width is calculated using the local second moment, and then compared with the reference spectral widths in the historical and neighborhood distance databases using Gaussian similarity to construct a spectral width consistency term. Dynamic weights are used for the RSP power term. Continuous terms And spectral width consistency term An adaptive weighted summation is performed, and the candidate peak with the highest cost value is selected as the optimal spectral peak in the distance library; Step 6, Output the optimal spectral peak trajectory: Use a group processing strategy with a group size of N1 distance libraries. Process each distance library one by one starting from the first distance library. Use the speed selected in the previous distance library as a reference for the continuity term. Optimize and select the optimal spectral peak for each distance library through a three-parameter cost function to form a complete wind speed profile trajectory.

2. The method according to claim 1, characterized in that, Step 3 includes: calculating the Doppler velocity window. The corresponding speed difference value Where RR is the radar range resolution and vres is the velocity resolution.

3. The method according to claim 2, characterized in that, Step 3 also includes: setting a spatial window for each distance library, extracting all spectral data within the spatial window, and calculating the spatial average power spectrum; selecting the top N1 peaks in the average power spectrum as candidate peaks, ensuring that the distance between peaks is greater than the velocity grid difference value. Record the power value and index position of the candidate spectral peaks.

4. The method according to claim 3, characterized in that, Step 4 includes: Perform an inverse transform on the power spectrum data of each distance gate to construct the covariance matrix. : ， in, This represents the signal vector of the r-th distance gate in the k-th time window, where K is the number of time windows. This indicates the conjugate transpose.

5. The method according to claim 4, characterized in that, Step 4 also includes: By analyzing the covariance matrix Eigenvalue decomposition yields a set of eigenvalues ​​arranged in descending order: ， Where the subscript N represents the dimension of the covariance matrix. The largest eigenvalue corresponds to the energy of the principal component of the signal; It is the Nth eigenvalue; By analyzing the intervals between adjacent feature values, a relative gap threshold is used. Automatically divide the signal and noise subspaces and determine the order p of the noise subspace: ， in Let k be the k-th eigenvalue, ranging from 1 to N, and be the point where k is the maximum value. This means finding a value within the range of variable k that satisfies the subsequent expression. The specific value of k corresponding to when it is true and reaches its maximum value; Noise power estimation based on eigenvalue distribution : ， Where i ranges from 1 to N; Further calculation of linear signal-to-noise ratio : ， And convert it to signal-to-noise ratio in decibels. : 。 6. The method according to claim 5, characterized in that, Step 4 also includes: Based on all distances to the door Distribution, statistical quantiles as dynamic central values ,by For input, through Type function mapping enables dynamic weight allocation: ， in, Indicates the weight of the power term. and Here, represents the upper and lower bounds of the constraint boundary, respectively; t is the kurtosis adjustment coefficient; exp is the natural exponential function. This represents the signal-to-noise ratio of the r-th distance gate; The remaining weights are determined by the complementarity relationship: ， in The sum of the continuity term and the spectral width consistency term; the allocation ratio of the remaining weights between the continuity term and the spectral width consistency term, while ensuring the continuity and smoothness of the spectral peaks, achieves a comprehensive optimization of spectral width consistency and signal stability, which has been determined through verification using multiple sets of experimental data: ， in, , , Representing the power terms respectively Continuous terms And spectral width consistency term The weight.

7. The method according to claim 6, characterized in that, Step 5 includes: RSP power term The calculation formula is: ， in, The power of the current candidate peak With current distance from the noise power floor The ratio is calculated using the following formula: ， Calculate the wind shear term : ， in, and These are the wind speeds at the current distance from the reservoir and the wind speed at the previous distance from the reservoir, respectively. Calculate the smoothness term : ， in, It is the wind speed at the (i-2)th distance from the reservoir; Continuous terms The weighted sum of the wind shear term and the smoothness term: ， Spectral width consistency term The calculation formula is: ， in, It is the spectral width of the current peak. It is the reference spectral width. It is the tolerance parameter for spectral width. , where j is the proportionality coefficient.

8. The method according to claim 7, characterized in that, Step 5 also includes: The total cost function, TotalCost, is obtained by weighted summation of the power term, continuity term, and spectral width consistency term. ， The optimal spectral peak position is determined by selecting the candidate peak with the highest cost value.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing program code that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 8.

10. A storage medium, characterized in that, It stores a computer program or instructions that, when run on a computer, perform the steps of the method as described in any one of claims 1 to 8.

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