L-band radar precipitation particle signal recognition and extraction method

Abstract

The application relates to an L-band radar precipitation particle signal recognition and extraction method, which mainly comprises the following steps: firstly, pre-processing the collected L-band radar data, sequentially completing DC removal, three-point smoothing, noise level estimation and spectrum moment calculation, so as to suppress non-target signal interference and provide reliable initial reference; then, based on multi-beam radial velocity analysis, automatically judging whether there is a precipitation signal, and only starting subsequent processing when it is confirmed that there is a precipitation signal; carrying out double-Gaussian or single-Gaussian model fitting on the power spectrum data of the precipitation signal, and extracting parameters such as amplitude, expected value and standard deviation of the signal; finally, judging the signal attribute through signal reconstruction and characteristic parameter analysis, combining the radial velocity mean value and standard deviation, extracting the precipitation signal and recording the parameters. The application can accurately extract the precipitation signal under the conditions of multiple signals and superposition, reduce redundant calculation, and improve the processing efficiency and signal recognition accuracy.

Description

Technical Field

[0001] The present invention belongs to the field of radar signal processing, and in particular relates to an L-band radar precipitation particle signal recognition and extraction method based on power spectrum data, which is used to accurately recognize and extract precipitation particle signals in the case where multiple echo signals and precipitation signals are superimposed. Background Art

[0002] Heavy precipitation is currently a crucial type of meteorological disaster. Observation systems for vertical variation of precipitation particles provide crucial data support for operational and scientific research, including detection, early warning, and forecasting of meteorological disasters. Among meteorological radar systems, L-band radar possesses strong detection capabilities, providing rich echo information for monitoring precipitation particles. In particular, it offers potential advantages in detecting parameters such as falling velocity and echo intensity. However, despite these technical advantages for precipitation particle detection, current practical applications focus more on extracting clear-air atmospheric turbulence signals, while insufficient attention has been paid to processing precipitation signals.

[0003] Existing L-band radar processing systems typically perform simple classification by distinguishing the characteristics of clear-sky and precipitation signals. However, in practice, precipitation signals often overlap with multi-echo signals, and the differences in characteristics between the signals are not obvious. This simple classification method cannot meet the actual needs under complex weather conditions. For example, during heavy rainfall or complex airflow environments, the overlap between precipitation particle signals and other interfering signals increases significantly, resulting in the inability of existing methods to accurately extract precipitation particle information, thereby reducing the reliability of radar data inversion results. In addition, most current processing processes lack a mechanism to automatically determine whether precipitation signal recognition is necessary, which increases operational complexity and the risk of misidentification, limiting the application value of L-band radar in precipitation monitoring.

[0004] Compared with other precipitation signal extraction algorithms, the present invention proposes an optimized precipitation signal identification and extraction method. During the preprocessing stage, this method can automatically determine whether a precipitation signal exists, avoiding invalid processing. By adopting a double-Gaussian and single-Gaussian mixture model, it can effectively cope with the superposition of precipitation signals and multi-echo signals. Before fitting the precipitation signal, this method calculates the initial values ​​of relevant parameters, improving fitting efficiency and effectively avoiding overfitting. In addition, by introducing FFT resampling and signal reconstruction technology, while accurately extracting precipitation signals, the amount of information processed is significantly reduced and the calculation speed is improved. This comprehensive signal processing method significantly enhances the practicality and reliability of L-band radar in precipitation signal extraction. Summary of the Invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention proposes a method for identifying and extracting precipitation particle signals from an L-band radar, comprising the following steps:

[0006] Step 1: Preprocess the collected L-band radar data, including DC removal, three-point smoothing, noise level estimation, and spectral moment calculation.

[0007] Step 2: Based on the pre-processed data, determine whether there is a precipitation signal through a comprehensive judgment method based on multi-beam radial velocity analysis;

[0008] Step 3: Perform power spectrum fitting on the data with precipitation signals to obtain relevant signal parameters;

[0009] Step 4: Based on the signal parameters obtained by fitting, perform the following processing:

[0010] Step 4.1: Reconstruct the power spectrum data of the precipitation signal echo1 and the non-precipitation signal echo2 based on the fitted signal parameters and noise level values;

[0011] Step 4.2: Analyze the reconstructed power spectrum data and calculate the key parameters of each signal, including the mean and standard deviation of the radial velocity;

[0012] Step 4.3: Extract precipitation signal;

[0013] Step 4.4: Repeat step 4.3 to process all beams and all range library data in sequence: If the data of all beams and range libraries have been processed, save all precipitation signal parameters; otherwise, continue processing the next beam or range library until all calculations are completed.

[0014] Furthermore, in step 1, the collected L-band radar data is preprocessed, including DC removal, three-point smoothing, noise level estimation, and spectral moment calculation operations. Specifically, the collected L-band radar data is first subjected to DC removal processing to suppress the influence of non-target signals such as zero-frequency interference; then three-point smoothing processing is performed to further reduce signal noise; then the noise level is estimated to provide an initial value reference for subsequent precipitation signal extraction; finally, spectral moment calculation is performed to calculate the Doppler velocity for subsequent precipitation signal judgment.

[0015] Furthermore, in step 2, based on the pre-processed data, a comprehensive judgment method based on multi-beam radial velocity analysis is used to determine whether a precipitation signal exists, specifically:

[0016] Step 2.1: Obtain the power spectrum data of each beam of the pre-processed radar data;

[0017] Step 2.2: For each beam's power spectrum data, estimate the radial velocity for all range bins;

[0018] Step 2.3: In each beam, calculate the mean and variance of the radial velocity for all range bins within the range of 2-4 km;

[0019] Step 2.4: Determine the calculation results of each beam;

[0020] Step 2.5: Decision is made on the signal containing multiple beams.

[0021] Furthermore, in step 3, power spectrum fitting is performed on the data containing precipitation signals to obtain relevant signal parameters, specifically using a double Gaussian or single Gaussian model for signal fitting;

[0022] Furthermore, in step 3, power spectrum fitting is performed on the data containing precipitation signals to obtain relevant signal parameters. The specific steps are as follows:

[0023] Step 3.1: For each distance bin power spectrum data that is judged to contain precipitation signals, subtract the noise level value of the distance bin to obtain the denoised power spectrum data;

[0024] Step 3.2: Resample the denoised power spectrum data with equal intervals, and set the FFT points of the resampled power spectrum data to 256;

[0025] Step 3.3: Use the double Gaussian model to fit the equally spaced resampled power spectrum data;

[0026] Step 3.4: Classify the fitted signal parameters;

[0027] Step 3.5: Save the fitted parameters of the precipitation signal echo1 and the non-precipitation signal echo2.

[0028] Furthermore, the step 2.4: judging the calculation result of each beam is specifically as follows:

[0029] If v < v t or σ v >σ t , where v t and σ t If the threshold is determined based on historical radar data, it is determined that there is no precipitation signal in the beam, the information is recorded and saved to the database, and the next beam is processed;

[0030] If v≥v t And σ v ≤σ t , it is determined that there is a precipitation signal in the beam and the beam is marked as a precipitation signal beam.

[0031] Furthermore, the step 2.5: judging the signal containing multiple beams is specifically as follows:

[0032] If at least one beam is marked as a precipitation signal beam, it is determined that there is a precipitation signal as a whole, and the relevant information is recorded; if all beams are not marked as precipitation signal beams, it is determined that there is no precipitation signal, and the information is recorded and saved in the database.

[0033] Furthermore, the step 3.3: using a double Gaussian model to fit the power spectrum data after equal spacing resampling is specifically as follows:

[0034] If the double Gaussian model converges after fitting, record the amplitudes a1, a2, expected values ​​b1, b2 and standard deviations c1, c2 of the two Gaussian signals g1 and g2;

[0035] If the double Gaussian model fitting does not converge, a single Gaussian model is used to fit the power spectrum data, and the amplitude a, expected value b, and standard deviation c of the single Gaussian signal are recorded.

[0036] Furthermore, the step 3.4: classifying the signal parameters obtained by fitting is specifically as follows:

[0037] If b1 ≥ b2 and c1 ≥ c2, the parameters of Gaussian signals g1 and g2 are stored in precipitation signal echo1 and non-precipitation signal echo2 respectively;

[0038] If only a single Gaussian model is used for fitting, the fitting parameters are stored in the precipitation signal echo1 or the non-precipitation signal echo2.

[0039] The further step 4.3: extracting precipitation signal, specifically comprises:

[0040] Step 4.3.1: Read the data of the reconstructed signals echo1 and echo2;

[0041] Step 4.3.2: For each beam i signal echo1, calculate its radial velocity mean v within the range below 8 km i and standard deviation d;

[0042] Step 4.3.3: For each range bin j of each beam, calculate the radial velocity v1 of the range bin signal echo1 ij and the mean radial velocity v i The difference dv1,

[0043] Step 4.3.4: If dv1≤1.5×d, mark the distance library signal as a precipitation signal and record the precipitation signal parameters;

[0044] Step 4.3.5: If dv1 > 1.5 × d, calculate the radial velocity v2 of the range library signal echo2 ij and the mean radial velocity The difference dv2,

[0045] Step 4.3.6: If dv2≤1.5×d, mark the distance library signal as a precipitation signal and record the precipitation signal parameters;

[0046] Step 4.3.7: If dv2>1.5×d, the distance library signal is marked as missing.

[0047] Compared with the traditional method, the present invention has the following advantages, thereby solving the corresponding technical problems:

[0048] 1. This invention can independently determine the presence of precipitation signals, avoiding the drawbacks of traditional methods that require human intervention or unnecessary processing. By determining the mean and variance of multi-beam radial velocities and combining them with historical thresholds, it automatically determines precipitation signals. This creates a beam-by-beam, range-by-range data screening mechanism, and uses the combined results of multiple beams to determine the overall signal status. This method avoids the limitations of traditional single-beam or single-threshold determination, improves the accuracy and robustness of precipitation signal determination, effectively reduces the processing time for invalid data, and enhances the efficiency and reliability of real-time processing.

[0049] 2. The present invention proposes a precipitation signal extraction method based on reconstructed power spectrum. First, the power spectrum data is fitted by a double Gaussian model to accurately separate the characteristic parameters of precipitation signals and non-precipitation signals; when the double Gaussian model fitting does not converge or the signal complexity is low, it automatically switches to a single Gaussian model, which improves the stability and computational efficiency of the fitting. Based on the signal parameters and noise level values ​​obtained by fitting, the power spectrum data of the precipitation signal and the non-precipitation signal are reconstructed to ensure the accuracy and completeness of the signal separation; at the same time, focusing on the range below 8 kilometers, the radial velocity mean and standard deviation of the signal are calculated, and the difference between the radial velocity and the mean is used as the judgment condition. Through cyclic processing of beam-by-beam and distance library-by-distance library, dynamic classification and precise extraction of precipitation signals are achieved. This method effectively copes with the complex situation of multiple signals and superimposed signals, solves the problems of signal fitting failure, computational redundancy and misjudgment in traditional methods, and ensures the comprehensiveness, accuracy and efficiency of precipitation signal extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flowchart for automatic judgment of precipitation signals;

[0051] Figure 2 This is a schematic diagram of precipitation signal extraction results; DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solution in the embodiment of the present invention in conjunction with the accompanying drawings in the embodiment of the present invention. The method includes the following steps:

[0053] Step 1: Preprocess the collected L-band radar data, including DC removal, three-point smoothing, noise level estimation, and spectral moment calculation.

[0054] Step 2: Based on the pre-processed data, determine whether there is a precipitation signal through a comprehensive judgment method based on multi-beam radial velocity analysis;

[0055] Step 3: Perform power spectrum fitting on the data with precipitation signals to obtain relevant signal parameters;

[0056] Step 4: Based on the signal parameters obtained by fitting, perform the following processing:

[0057] Step 4.1: Reconstruct the power spectrum data of the precipitation signal echo1 and the non-precipitation signal echo2 based on the fitted signal parameters and noise level values;

[0058] Step 4.2: Analyze the reconstructed power spectrum data and calculate the key parameters of each signal, including the mean and standard deviation of the radial velocity;

[0059] Step 4.3: Extract precipitation signal;

[0060] Step 4.4: Repeat step 4.3 to process all beams and all range library data in sequence: If the data of all beams and range libraries have been processed, save all precipitation signal parameters; otherwise, continue processing the next beam or range library until all calculations are completed.

[0061] Furthermore, in step 1, the collected L-band radar data is preprocessed, including DC removal, three-point smoothing, noise level estimation, and spectral moment calculation operations. Specifically, the collected L-band radar data is first subjected to DC removal processing to suppress the influence of non-target signals such as zero-frequency interference; then three-point smoothing processing is performed to further reduce signal noise; then the noise level is estimated to provide an initial value reference for subsequent precipitation signal extraction; finally, spectral moment calculation is performed to calculate the Doppler velocity for subsequent precipitation signal judgment.

[0062] Furthermore, in step 2, based on the pre-processed data, a comprehensive judgment method based on multi-beam radial velocity analysis is used to determine whether a precipitation signal exists, specifically:

[0063] Step 2.1: Obtain the power spectrum data of each beam of the pre-processed radar data;

[0064] Step 2.2: For each beam's power spectrum data, estimate the radial velocity for all range bins;

[0065] Step 2.3: In each beam, calculate the mean and variance of the radial velocity for all range bins within the range of 2-4 km;

[0066] Step 2.4: Determine the calculation results of each beam;

[0067] Step 2.5: Decision is made on the signal containing multiple beams.

[0068] Furthermore, in step 3, power spectrum fitting is performed on the data containing precipitation signals to obtain relevant signal parameters, specifically using a double Gaussian or single Gaussian model for signal fitting;

[0069] Furthermore, in step 3, power spectrum fitting is performed on the data containing precipitation signals to obtain relevant signal parameters. The specific steps are as follows:

[0070] Step 3.1: For each distance bin power spectrum data that is judged to contain precipitation signals, subtract the noise level value of the distance bin to obtain the denoised power spectrum data;

[0071] Step 3.2: Resample the denoised power spectrum data with equal intervals, and set the FFT points of the resampled power spectrum data to 256;

[0072] Step 3.3: Use the double Gaussian model to fit the equally spaced resampled power spectrum data;

[0073] Step 3.4: Classify the fitted signal parameters;

[0074] Step 3.5: Save the fitted parameters of the precipitation signal echo1 and the non-precipitation signal echo2.

[0075] Furthermore, the step 2.4: judging the calculation result of each beam is specifically as follows:

[0076] like or σ v >σ t , where v t and σ t If the threshold is determined based on historical radar data, it is determined that there is no precipitation signal in the beam, the information is recorded and saved to the database, and the next beam is processed;

[0077] like And σ v ≤σ t , it is determined that there is a precipitation signal in the beam and the beam is marked as a precipitation signal beam.

[0078] Furthermore, the step 2.5: judging the signal containing multiple beams is specifically as follows:

[0079] If at least one beam is marked as a precipitation signal beam, it is determined that there is a precipitation signal as a whole, and the relevant information is recorded; if all beams are not marked as precipitation signal beams, it is determined that there is no precipitation signal, and the information is recorded and saved in the database.

[0080] Furthermore, the step 3.3: using a double Gaussian model to fit the power spectrum data after equal spacing resampling is specifically as follows:

[0081] If the double Gaussian model converges after fitting, record the amplitudes a1, a2, expected values ​​b1, b2 and standard deviations c1, c2 of the two Gaussian signals g1 and g2;

[0082] If the double Gaussian model fitting does not converge, a single Gaussian model is used to fit the power spectrum data, and the amplitude a, expected value b, and standard deviation c of the single Gaussian signal are recorded.

[0083] Furthermore, the step 3.4: classifying the signal parameters obtained by fitting is specifically as follows:

[0084] If b1 ≥ b2 and c1 ≥ c2, the parameters of Gaussian signals g1 and g2 are stored in precipitation signal echo1 and non-precipitation signal echo2 respectively;

[0085] If only a single Gaussian model is used for fitting, the fitting parameters are stored in the precipitation signal echo1 or the non-precipitation signal echo2.

[0086] The further step 4.3: extracting precipitation signal, specifically comprises:

[0087] Step 4.3.1: Read the data of the reconstructed signals echo1 and echo2;

[0088] Step 4.3.2: For each beam i signal echo1, calculate its radial velocity mean v within the range below 8 km i and standard deviation d;

[0089] Step 4.3.3: For each range bin j of each beam, calculate the radial velocity v1 of the range bin signal echo1 ij and the mean radial velocity v i The difference dv1,

[0090] Step 4.3.4: If dv1≤1.5×d, mark the distance library signal as a precipitation signal and record the precipitation signal parameters;

[0091] Step 4.3.5: If dv1 > 1.5 × d, calculate the radial velocity v2 of the range library signal echo2 ij and the mean radial velocity The difference dv2,

[0092] Step 4.3.6: If dv2≤1.5×d, mark the distance library signal as a precipitation signal and record the precipitation signal parameters;

[0093] Step 4.3.7: If dv2>1.5×d, the distance library signal is marked as missing.

[0094] Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by ordinary technicians in this field without making creative work are all within the scope of protection of the present invention.

Claims

1. A method for identifying and extracting precipitation particle signals from an L-band radar, comprising the following steps: Step 1: Preprocess the collected L-band radar data, including DC removal, three-point smoothing, noise level estimation, and spectral moment calculation. Step 2: Based on the pre-processed data, determine whether there is a precipitation signal through a comprehensive judgment method based on multi-beam radial velocity analysis; Step 3: Perform power spectrum fitting on the data with precipitation signals to obtain relevant signal parameters; Step 4: Based on the signal parameters obtained by fitting, perform the following processing: Step 4.1: Reconstruct the power spectrum data of the precipitation signal echo1 and the non-precipitation signal echo2 based on the fitted signal parameters and noise level values; Step 4.2: Analyze the reconstructed power spectrum data and calculate the key parameters of each signal, including the mean and standard deviation of the radial velocity; Step 4.3: Extract precipitation signal; Step 4.4: Repeat step 4.3 to process all beams and all range library data in sequence: If the data of all beams and range libraries have been processed, save all precipitation signal parameters; otherwise, continue processing the next beam or range library until all calculations are completed.

2. The method for identifying and extracting L-band radar precipitation particle signals according to claim 1, wherein: In step 1, the collected L-band radar data is preprocessed, including DC removal, three-point smoothing, noise level estimation, and spectral moment calculation operations. Specifically, the collected L-band radar data is first subjected to DC removal processing to suppress the influence of zero-frequency interference non-target signals; then, three-point smoothing processing is performed to further reduce signal noise; then, the noise level is estimated to provide an initial value reference for subsequent precipitation signal extraction; and finally, spectral moment calculation is performed to calculate the Doppler velocity for subsequent precipitation signal judgment.

3. The method for identifying and extracting L-band radar precipitation particle signals according to claim 1, wherein: In step 2, based on the pre-processed data, a comprehensive judgment method based on multi-beam radial velocity analysis is used to determine whether a precipitation signal exists, specifically: Step 2.1: Obtain the power spectrum data of each beam of the pre-processed radar data; Step 2.2: Estimate the radial velocity for all range bins using the power spectrum data for each beam ; Step 2.3: In each beam, calculate the average radial velocity for all range bins in the range 2-4 km. and variance ; Step 2.4: Determine the calculation results of each beam; Step 2.5: Decision is made on the signal containing multiple beams.

4. The method for identifying and extracting L-band radar precipitation particle signals according to claim 1, wherein: In step 3, power spectrum fitting is performed on the data containing precipitation signals to obtain relevant signal parameters, specifically using a double Gaussian or single Gaussian model to perform signal fitting.

5. The method for identifying and extracting L-band radar precipitation particle signals according to claim 4, wherein: In step 3, power spectrum fitting is performed on the data containing precipitation signals to obtain relevant signal parameters. The specific steps are as follows: Step 3.1: For each distance bin power spectrum data that is judged to contain precipitation signals, subtract the noise level value of the distance bin to obtain the denoised power spectrum data; Step 3.2: Resample the denoised power spectrum data with equal intervals, and set the FFT points of the resampled power spectrum data to 256; Step 3.3: Use the double Gaussian model to fit the equally spaced resampled power spectrum data; Step 3.4: Classify the fitted signal parameters; Step 3.5: Save the fitted parameters of the precipitation signal echo1 and the non-precipitation signal echo2.

6. The method for identifying and extracting L-band radar precipitation particle signals according to claim 2, wherein: The step 2.4: judging the calculation result of each beam is as follows: like or ,in, and If the threshold is determined based on historical radar data, it is determined that there is no precipitation signal in the beam, the information is recorded and saved to the database, and the next beam is processed; like and , it is determined that there is a precipitation signal in the beam and the beam is marked as a precipitation signal beam.

7. The method for identifying and extracting L-band radar precipitation particle signals according to claim 2, wherein: The step 2.5: judging the signal containing multiple beams is specifically as follows: If at least one beam is marked as a precipitation signal beam, it is determined that there is a precipitation signal as a whole, and the relevant information is recorded; if all beams are not marked as precipitation signal beams, it is determined that there is no precipitation signal, and the information is recorded and saved in the database.

8. The method for identifying and extracting L-band radar precipitation particle signals according to claim 4, wherein: Step 3.3: Use the double Gaussian model to fit the power spectrum data after equal spacing resampling, specifically: If the double Gaussian model converges after fitting, record two Gaussian signals and Amplitude , , expected value , and standard deviation , ; If the double Gaussian model fitting does not converge, use the single Gaussian model to fit the power spectrum data and record the amplitude of the single Gaussian signal. , expected value , and standard deviation .

9. The method for identifying and extracting L-band radar precipitation particle signals according to claim 7, wherein: The step 3.4: classifying the signal parameters obtained by fitting, specifically: like and , then the Gaussian signal and The parameters are stored in precipitation signal echo1 and non-precipitation signal echo2 respectively; If only a single Gaussian model is used for fitting, the fitting parameters are stored in the precipitation signal echo1 or the non-precipitation signal echo2.

10. The method for identifying and extracting L-band radar precipitation particle signals according to claim 1, wherein: The step 4.3: extracting precipitation signals, specifically comprises: Step 4.3.1: Read the data of the reconstructed signals echo1 and echo2; Step 4.3.2: For each beam Calculate the radial velocity mean of the signal echo1 within the range below 8 km. and standard deviation ; Step 4.3.3: For each range bin of each beam , calculate the radial velocity of the distance library signal echo1 and the mean radial velocity difference , ; Step 4.3.4: If , then mark the distance library signal as a precipitation signal and record the precipitation signal parameters; Step 4.3.5: If , calculate the radial velocity of the distance library signal echo2 and the mean radial velocity difference , ; Step 4.3.6: If , then mark the distance library signal as a precipitation signal and record the precipitation signal parameters; Step 4.3.7: If , then the distance library signal is marked as missing.

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