CN110749871B - Parameter estimation method of dual-polarization weather radar - Google Patents
Parameter estimation method of dual-polarization weather radar Download PDFInfo
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- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/95—Lidar systems specially adapted for specific applications for meteorological use
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Abstract
The invention discloses a parameter estimation method of a dual-polarization weather radar, which comprises the steps of receiving I/Q data of a horizontal channel and a vertical channel of the radar; recognizing the ground features of the I/Q data by adopting a fuzzy logic algorithm or a Bayesian classification algorithm; performing ground object suppression on the distance library identified as the ground object; estimating SNR and spectral width sigmav by using a traditional algorithm for the processed I/Q data; if the SNR of the current distance library is more than or equal to the specified threshold, calculating other parameters by using a traditional algorithm, if the SNR of the current distance library is less than the specified threshold, further judging the spectral width sigmav, if the spectral width sigmav is more than the specified threshold, estimating other parameters by still using the traditional algorithm, and otherwise re-estimating the spectral width and other parameters by using a multi-order correlation algorithm; and storing the estimated parameters in a radial direction, and performing primary quality control to form a base data product. The method can effectively improve the data quality of parameter estimation under low signal-to-noise ratio and expand the data application range.
Description
Technical Field
The invention relates to a parameter estimation method of a weather radar, in particular to a parameter estimation method of a dual-polarization weather radar, and belongs to the field of radar signal processing research.
Background
Weather radar is an important tool in monitoring, early warning and research of disastrous weather. Early weather radar detection parameters are few, echo data are only qualitatively analyzed, the Doppler technology is developed in the 60's 20 th century, and the weather radar has the capability of quantitatively detecting the atmospheric flow field structure. In 1988, doppler weather radar WSR-88D was developed in the United states and was deployed in its entirety and continuously improved in the 90 s. China develops a new Generation weather alert Radar CINRAD (China New Next Generation radio) on the basis of WSR-88D, and completes national network deployment. At present, the Doppler weather radar is widely applied to research on thermal and dynamic processes of the disastrous weather, and plays a great role in mechanism cognition of the disastrous weather. At the end of the 20 th century 70 s, the american scientist Seliga et al (1976) proposed the concept of dual-linear polarization weather radar, which measures the intensity of precipitation by alternately transmitting horizontally and vertically polarized waves orthogonal to each other between pulses, and has shown that the accuracy of the rain intensity of the dual-linear polarization radar is higher than that of the conventional weather radar. With the development of dual-polarization technology, single-transmitting dual-receiving, dual-transmitting dual-receiving and alternate-transmitting modes become three main working modes of the current dual-polarization radar. By utilizing the dual-polarization radar data, the method can obtain more accurate precipitation estimation products, and can also be used for researching the aspects of precipitation particle phase recognition, precipitation micro physical structure and the like.
The signal processing system is an important component of the weather radar, and the signal processing level also determines the technical level of the radar to a certain extent. The signal processing system comprises two parts, namely hardware and software (algorithm), wherein the hardware is responsible for controlling and collecting radar signals, and the software is responsible for estimating detection parameters. A typical signal processing system, such as the Visalal (VAISALA) RVP900, includes two parts, RVP901 and RVP 902. The I/Q data collected by the RVP901 can obtain various parameters detected by the radar, such as a reflectivity factor dBZ and a radial velocity v through an algorithm of the RVP902 r And spectral width σ v Iso-ordinary parameters and differential reflectivity Z DR Correlation coefficient rho hv Equal dual polarization parameters. In terms of estimation of radar parameters, terrain and noise are two important aspects that affect the quality of the parameter estimation (Cao et al, 2012). The existence of the ground objects can cause the radar to overestimate the reflectivity factor of the precipitation echo, the estimation of the radial velocity shifts towards a 0 value, the influence on the estimation of the spectral width is complex (such as the spectral width, the signal-to-noise ratio (SNR) and the radial velocity of the precipitation particles), and the estimation of each polarization parameter has different degrees of influence (Li and the like, 2013); the existence of noise affects the detection capability of the radar on weak echoes, and the estimation deviation of radar parameters at low signal-to-noise ratio is increased.
Current weather radar parameter estimation methods fall into two broad categories in general. One class is called the traditional algorithms (Conventional estimators, doviak and
2006 The algorithm estimates various kinds of autocorrelation function (ACF)/Cross Correlation Function (CCF) of 0 th order or 1 st orderA parameter. For example, the signal power is estimated using an autocorrelation function of order 0, the spectral width is estimated using autocorrelation functions of order 0 and 1, the correlation coefficient is calculated using an autocorrelation function of order 0 and a cross-correlation function, and the like. Another class is known as multi-order correlation algorithms (Lei et al, 2012), which do not use autocorrelation functions of order 0, but use higher order autocorrelation functions to estimate the parameters, and when using cross-correlation functions, still use cross-correlations of order 0. The deviation of the traditional method for estimating parameters mainly comes from the deviation of noise estimation, and is greatly influenced when the signal-to-noise ratio is low; the multi-order correlation algorithm is less influenced by SNR and more influenced by the spectral width of the detected meteorological target, because the correlation order can be used for parameter estimation. Lei et al (2012) find that, under the condition that the application condition is satisfied, the estimation quality of parameters such as the spectral width and the correlation coefficient can be significantly improved by the multi-order correlation algorithm, and even if the SNR is greater than 20dB, the multi-order correlation algorithm still has a smaller standard deviation.
For traffic weather radar, the currently used parametric estimation method is the traditional algorithm, at the end of each volume sweep, the transmitter is turned off at the highest elevation angle of the volume sweep, an on-line measurement of the noise power is made, and this noise value is used as the noise power for the next volume sweep. The noise power in each radial direction as the radar actually scans is affected by various factors, such as ground object radiation, rainstorms or lightning, other radiation, and possible thermal noise variations of the radar itself, and thus varies with azimuth and elevation (Melnikov et al, 2007). This variation in noise power can introduce variations in the parametric estimation, especially where the signal-to-noise ratio is small (SNR <20 dB), and continues to deteriorate as the SNR decreases. The multi-order correlation algorithm uses higher-order ACF and CCF for parameter estimation, so that the influence caused by noise estimation errors can be avoided, but the order which can be used for parameter estimation is influenced by the spectral width of a detected target and radar scanning parameters, and the order cannot be used when the spectral width is large (specific numerical values are determined according to the radar scanning parameters). In the actual weather process detection, the situation that the spectrum width value of convection precipitation is more than 4m/s is easy to occur, and the situation that the spectrum width value is high (the maximum value of the spectrum width can exceed 10 m/s) is also easy to occur in the situation that the stratospheric precipitation has wind cut. In this case, the multi-order correlation algorithm often does not satisfy the application condition, and the parameter estimation using the method brings a large error.
Disclosure of Invention
The invention aims to solve the technical problem of providing a novel dual-polarization radar parameter estimation method aiming at the defects of the traditional algorithm of weather radar parameters, which can effectively improve the estimation quality of the dual-polarization radar parameters under the condition of low SNR and expand the application range of radar data.
In order to solve the above technical problem, the parameter estimation method of dual-polarization weather radar of the present invention comprises the following steps,
the method includes receiving I/Q data of a radar horizontal channel and a vertical channel.
And recognizing the ground objects of the I/Q data by adopting a fuzzy logic algorithm or a Bayesian classification algorithm.
Performing ground object suppression of the Gaussian adaptive filter on the distance library identified as the ground object.
Fourth, I/Q data after ground object recognition and ground object suppression processing are estimated according to a traditional algorithm for SNR and spectral width sigma v 。
Fifthly, judging according to the SNR threshold, if the SNR of the current distance library is greater than or equal to the SNR designated threshold, calculating other parameters by using a traditional algorithm, and if the SNR of the current distance library is less than the SNR designated threshold, further judging the spectral width sigma v If the spectral width σ is v If the spectral width is larger than the specified threshold of the spectral width, the traditional algorithm is still used for estimating other parameters, otherwise, the multi-order correlation algorithm is used for re-estimating the spectral width sigma v And other parameters.
Sixthly, storing the estimated parameters in a radial direction, and performing primary quality control to form a base data product.
In the further optimization of the technical scheme, step 4 is to estimate the radial velocity v according to the traditional algorithm r While calculating the standard deviation std (v) of the radial velocity from bin to bin in radial direction r )。
The technical scheme is further optimized in step 5 asIf SNR of current distance library is less than SNR designated threshold, std (v) needs to be further judged r ) When spectral width σ v Greater than a spectral width specified threshold while std (v) r ) If the standard deviation is larger than the specified threshold, the traditional algorithm is still used for estimating other parameters, otherwise, the multi-order correlation algorithm is used for re-estimating the spectrum width and other parameters.
In the further optimization of the technical scheme, the SNR specified threshold is 20dB, and the standard deviation specified threshold is 0.6m/s.
According to the technical scheme, the following beneficial effects can be realized:
the method of the invention has the advantages of the current traditional algorithm and the multi-order correlation algorithm, has high automation degree, is convenient and easy to operate, and can be used for dual-polarization Doppler weather radars and conventional Doppler weather radars. Compared with the prior art, the method improves the quality of parameter estimation of the radar under the condition of low signal-to-noise ratio (SNR <20 dB), and has important significance for the subsequent application of radar parameters.
Drawings
FIG. 1 is an algorithmic flow chart of one embodiment of the present invention.
Fig. 2 is an algorithm flow chart of the preferred embodiment of the present invention.
Fig. 3 is a calculation result of main parameters obtained by 1.5-degree elevation angle PPI scanning for 10-minute (UTC) long feng field observation test at 7, 30, 14 days in 2014.
Fig. 4 is a comparison of the performance estimated by the optimal embodiment algorithm of the present invention and the conventional algorithm for 1.5 degree elevation PPI scanning for 10 minutes (UTC) long-feng field observation test of anhui at 7, 30, 14 days in 2014.
Detailed Description
In the current weather radar parameter estimation method, conventional algorithms (contextual estimators, doviak and Doviak)
1993；Doviak and
2006; bringi and Chandrasekar, 2001) are the most widely used methods. The method is firstly applied to Doppler weather radar and then is expanded to be applied to dual-polarization weather radar. The conventional algorithm estimates various parameters by using 0 order or 1 order autocorrelation function (ACF)/Cross Correlation Function (CCF), which may be different in the estimation formula of a specific parameter, but is typically characterized by using no more than 1 order ACF/CCF, especially the estimation of signal power is performed by using 0 order ACF, that is, the estimation of signal power is performed by using 0 order ACF
Where S represents signal power, h and v represent horizontal and vertical channels, N represents noise power, superscript "^" represents estimated values, superscript "-" represents average values, and R (0) represents a 0 th order ACF. Specifically, the signal power is estimated using a 0 th order ACF, the spectral width is estimated using 0 th and 1 st order ACFs, and the correlation coefficient is estimated using the 0 th order ACF and the CCF. The signal power estimation mode of the traditional algorithm determines that the estimation precision is influenced by the noise estimation precision, so that all radar parameter estimation related to the signal power is influenced by the noise. This effect is more pronounced in cases where the signal-to-noise ratio (SNR) is relatively low.
Referring to fig. 1, a first embodiment of the present invention.
1. And receiving I/Q data of a radar horizontal channel and a radar vertical channel.
2. And (3) carrying out surface feature identification on the I/Q data, wherein a fuzzy logic algorithm can be used, and a Bayesian classification algorithm can also be used. The invention uses a frequency domain ground object identification method based on Bayesian classification.
3. Feature suppression by a Gaussian adaptive filter (GMAP) is performed on the range bins identified as features. And if the weather echo is identified, the feature suppression is not carried out.
4. Estimating SNR and spectral width sigma according to a traditional algorithm for the I/Q data after the ground object identification and the ground object suppression processing v 。
5. According to SNR thresholdJudging, if the SNR of the current distance library is more than or equal to the specified threshold, calculating other parameters by using a traditional algorithm; if the spectrum width is less than the specified threshold, the spectrum width sigma is further judged v . If the spectral width is larger than the specified threshold, estimating other parameters by using a traditional algorithm, otherwise, re-estimating the spectral width and other parameters by using a multi-order correlation algorithm;
6. and storing the estimated parameters in a radial direction, and performing primary quality control to form a base data product.
Referring to fig. 2, embodiment two of the present invention.
1. And receiving I/Q data of a radar horizontal channel and a radar vertical channel.
2. And (4) performing feature recognition on the I/Q data by using a fuzzy logic algorithm or a Bayesian classification algorithm. The invention uses a frequency domain ground object identification method based on Bayesian classification.
3. Feature suppression by a Gaussian adaptive filter (GMAP) is performed on the range bins identified as features. And if the weather echo is identified, the feature suppression is not carried out.
4. Estimating SNR and spectral width sigma according to a traditional algorithm for the I/Q data after the ground object identification and the ground object suppression processing v And radial velocity v r While calculating the standard deviation std (v) of the radial velocity from bin to bin in radial direction r )。
5. Judging according to the SNR threshold, and if the SNR of the current distance library is more than or equal to the specified threshold, calculating other parameters by using a traditional algorithm; if the spectrum width is less than the specified threshold, the spectrum width sigma is further judged v And std (v) r ). If the spectral width sum std (v) r ) And if the estimated spectral width is larger than the respective specified threshold, the traditional algorithm is still used for estimating other parameters, otherwise, the multi-order correlation algorithm is used for re-estimating the spectral width and other parameters.
6. And storing the estimated parameters in a radial direction, and performing primary quality control to form a base data product.
Scheme one uses two decision parameters SNR and sigma v The method is suitable for the situation that the radar noise estimation error is small, and the calculated amount is small;scheme two uses three judgment parameters SNR and sigma v And std (v) r ) The method is suitable for general situations, and has large calculation amount but good universality.
Fig. 3-4 show the implementation process and algorithm performance of the algorithm of the present invention, using a C-band dual-polarization doppler weather radar of the university of nanjing, gazette, 10 minutes (UTC) at 30, 7, 30, 14, 7, 2014 as an example. The difference between scheme one and scheme two is in steps 4 and 5. The above scheme is further explained below.
1. And receiving I/Q data of a horizontal channel and a vertical channel sent by the radar digital intermediate frequency receiver.
2. And carrying out surface feature recognition on the I/Q data of the two channels, wherein the recognition algorithm used in the invention is a frequency domain surface feature recognition algorithm and is based on a naive Bayes classifier.
3. And transforming the I/Q data into a frequency domain by using a Fast Fourier Transform (FFT) method, performing terrain suppression on radar echoes identified as terrain by using a Gaussian adaptive filter (GMAP), and not processing positions identified as meteorological echoes. After the processing in the frequency domain is completed, the I/Q data is transformed to the time domain using an inverse fast fourier transform IFFT method.
4. For the first scheme, the signal-to-noise ratio SNR and the spectral width sigma are estimated for the echo subjected to the feature processing by using the traditional algorithm v . For the second scheme, a traditional algorithm is used for estimating the signal-to-noise ratio SNR and the spectral width sigma of the echo subjected to the ground object processing v Radial velocity v r And std (v) r ). Wherein the signal power of the conventional algorithm is calculated according to equation (1), where subscripts h and v denote horizontal and vertical channels, superscript ^ denotes estimated values, superscript-denotes mean values, R denotes autocorrelation function, and N denotes noise power. Calculating the spectrum width according to the formula (2); the radial velocity is calculated according to equation (3). std (v) r ) According to the radial library-by-library calculation, the current point and the distance points before and after the current point are calculated, and in general, 5 distance points in total are selected for calculation.
5. For the first scheme, the signal-to-noise ratio is judged, and if the signal-to-noise ratio exceeds a specified SNR threshold, other parameters are estimated by using a traditional algorithm; if not, further judging the spectrum width, if the spectrum width exceeds a specified threshold, still using the traditional algorithm, otherwise, using a multi-order correlation algorithm. For the second scheme, the signal-to-noise ratio is judged, and if the signal-to-noise ratio exceeds the specified SNR threshold, other parameters are estimated by using a traditional algorithm; if not, further judging the spectral width and std (v) r ). If the spectral width exceeds a specified threshold and std (v) r ) And if the specified threshold is also exceeded, the conventional algorithm is still used, otherwise, the multi-order correlation algorithm is used. The SNR threshold is generally selected to be below 20dB, std (v) r ) The threshold is selected to be 0.6m/s, the spectral width threshold is selected according to specific radar parameters, and the calculation formula is sigma v _Thr＝λ/(4T s πw n ) Where λ is the radar wavelength in meters, T s Is the radar work repetition period with the unit of second, w n Is the order available, which is at least equal to or greater than 2 for the multi-order correlation algorithm. From these parameters, the spectral width threshold σ can be calculated v And (iv) Thr. The calculation formula of each main parameter is shown below, where h and v represent the horizontal and vertical channels, R represents the autocorrelation function, C represents the cross-correlation function, n represents the correlation order, M represents the number of pulses of the correlation pulse group, S represents the signal power, σ represents the spectral width, and Z represents the spectral width DR Representing differential reflectivity, p hv The correlation coefficient is represented, N represents noise power, superscripts (1) and (2) represent formulas for calculating parameters by using 1-order correlation and 2-order correlation, no superscript represents a traditional algorithm, a represents an estimated value, an average value, V represents an I/Q voltage signal collected by a radar, and is represented by a complex number, and x represents conjugation.
6. After the radar calculates each parameter by the radial database, the obtained data is subjected to preliminary quality control including abnormal point filtering, threshold filtering (SNR threshold, CCOR threshold and the like) and the like to form radial basic data.
And storing the radial basic data according to a specified format, such as a standard format specified by the China weather service or other standard formats, to form a basic data product.
Claims (4)
1. The parameter estimation method of the dual-polarization weather radar is characterized by comprising the following steps: comprises the following steps of,
receiving I/Q data of a horizontal channel and a vertical channel of a radar;
secondly, recognizing the ground objects of the I/Q data by adopting a fuzzy logic algorithm or a Bayesian classification algorithm;
performing ground object suppression of the Gaussian adaptive filter on the distance library identified as the ground object;
fourthly, estimating SNR and spectral width sigma according to a traditional algorithm for the I/Q data after being subjected to ground object identification and ground object suppression treatment v ；
Fifthly, judging according to the SNR threshold, and if the SNR of the current distance library is larger than or equal to the SNR specified threshold, calculating the radial velocity v by using a traditional algorithm r Differential reflectivity Z DR Correlation coefficient rho hv Signal power S, if SNR of current distance library is less than SNR designated threshold, then further judging spectral width sigma v If the spectral width σ is v Above a spectral width specified threshold, the radial velocity v is still estimated using conventional algorithms r Differential reflectivity Z DR Correlation coefficient rho hv Signal power S, otherwise, spectral width σ is re-estimated using a multi-order correlation algorithm v And radial velocity v r Differential reflectivity Z DR Correlation coefficient rho hv Signal power S;
sixthly, storing the estimated parameters in a radial direction, and performing primary quality control to form a base data product.
2. The parametric estimation method for dual polarization weather radar as recited in claim 1, wherein: in step 4, the radial velocity v needs to be estimated according to a traditional algorithm r While calculating the standard deviation std (v) of the radial velocity from bin to bin in radial direction r )。
3. The parametric estimation method for dual polarization weather radar as recited in claim 2, wherein: in step 5, if the SNR of the current distance library is less than the SNR specified threshold, the std (v) is further judged r ) When spectral width σ v Greater than a spectral width specified threshold while std (v) r ) If the radial velocity is greater than the specified threshold of the standard deviation, the radial velocity v is estimated by using the traditional algorithm r Differential reflectivity Z DR Correlation coefficient rho hv Signal power S, otherwise spectral width and radial velocity v are re-estimated using a multi-order correlation algorithm r Differential reflectivity Z DR Correlation coefficient rho hv Signal power S.
4. The parametric estimation method for a dual polarization weather radar of claim 3, wherein: the SNR specified threshold is 20dB, and the standard deviation specified threshold is 0.6m/s.
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| CN113777573B (en) * | 2021-08-30 | 2023-12-01 | 中船鹏力(南京)大气海洋信息系统有限公司 | Double-polarization radar secondary echo identification method based on naive Bayes classifier |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5247303A (en) * | 1992-07-20 | 1993-09-21 | University Corporation For Atmospheric Research | Data quality and ambiguity resolution in a doppler radar system |
| CN103336274A (en) * | 2013-06-27 | 2013-10-02 | 成都信息工程学院 | Two-way multi-order related detection method for dual-polarization weather radar |
| CN105137407A (en) * | 2015-10-08 | 2015-12-09 | 南京信息工程大学 | ZDR on-line calibration method of dual-polarization weather radar and apparatus thereof |
| CN107238826A (en) * | 2017-06-09 | 2017-10-10 | 杨波 | The method being distributed using Dual-Polarized Doppler Weather Radar echo inverting thunderstorm charge |
| CN107843884A (en) * | 2017-09-13 | 2018-03-27 | 成都信息工程大学 | The method for improving the Thunderstorm Weather early-warning and predicting degree of accuracy is observed based on dual polarization radar |
| CN107942305A (en) * | 2017-10-11 | 2018-04-20 | 南京大学 | The online calibration method of dual polarization radar system initial differential phase |
-
2019
- 2019-11-05 CN CN201911071135.6A patent/CN110749871B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5247303A (en) * | 1992-07-20 | 1993-09-21 | University Corporation For Atmospheric Research | Data quality and ambiguity resolution in a doppler radar system |
| CN103336274A (en) * | 2013-06-27 | 2013-10-02 | 成都信息工程学院 | Two-way multi-order related detection method for dual-polarization weather radar |
| CN105137407A (en) * | 2015-10-08 | 2015-12-09 | 南京信息工程大学 | ZDR on-line calibration method of dual-polarization weather radar and apparatus thereof |
| CN107238826A (en) * | 2017-06-09 | 2017-10-10 | 杨波 | The method being distributed using Dual-Polarized Doppler Weather Radar echo inverting thunderstorm charge |
| CN107843884A (en) * | 2017-09-13 | 2018-03-27 | 成都信息工程大学 | The method for improving the Thunderstorm Weather early-warning and predicting degree of accuracy is observed based on dual polarization radar |
| CN107942305A (en) * | 2017-10-11 | 2018-04-20 | 南京大学 | The online calibration method of dual polarization radar system initial differential phase |
Non-Patent Citations (2)
| Title |
|---|
| A Novel Spectral Parameters Estimation Approach for Weather Echoes Based on Fitting;Yue-Xia Wang等;《2010 6th International Conference on Wireless Communications Networking and Mobile Computing》;IEEE;20101231;正文1-4页 * |
| 双偏振天气雷达探测能力改善方法的设计;赵普洋;《信息与电脑》;20120930;29-30 * |
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