CN110749871A - Parameter estimation method of dual-polarization weather radar - Google Patents
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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. The early weather radar detection parameters are few, echo data are only qualitatively analyzed, the Doppler technology is developed in the 60 th 20 th century, and the weather radar has the capability of quantitatively detecting the atmospheric flow field structure. Doppler weather radar WSR-88D was developed in the United states in 1988 and was deployed in its entirety and continuously improved in the 90's generation. China develops a new Generation weather alert radar CIRAD (Chinese Next Generation Raar) 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 showed that the dual-linear polarization radar has a higher accuracy of measuring the intensity of rain than the conventional weather radar. With the development of dual-polarization technology, single-transmission dual-reception, dual-transmission dual-reception and alternate transmission modes become the main three working modes of the current dual-polarization radar. By utilizing the dual-polarization radar data, not only can more accurate precipitation estimation products be obtained, but also the researches on the aspects of precipitation particle phase recognition, precipitation micro-physical structures and the like can be carried out.
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 Visala (VAISALA) RVP900, comprises 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 RVP902rAnd spectral width σvIso-normal parameters and differential reflectance ZDRCorrelation coefficient rhohvEqual 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 presence of ground objects can cause the radar to overestimate the reflectivity factor of the precipitation echo, the estimation of the radial speed is shifted towards the 0 value, and the influence on the spectral width estimation isThe method is complex (such as spectral width, signal-to-noise ratio (SNR), radial velocity and the like of precipitation particles), and has influence on estimation of each polarization parameter to different degrees (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 and2006) the algorithm estimates various parameters with an autocorrelation function (ACF)/Cross Correlation Function (CCF) of order 0 or 1. 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 (signal-to-noise ratio) and more influenced by the spectral width of the detected meteorological target, because the correlation order which can be used for parameter estimation is influenced. 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 <20dB), 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 10m/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 materials.
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,
⑴ receive I/Q data for the radar horizontal and vertical channels.
⑵ use fuzzy logic algorithm or Bayesian classification algorithm to identify the feature of the I/Q data.
⑶ perform feature suppression by a Gaussian adaptive filter on the range bins identified as features.
⑷ estimating SNR and spectral width sigma according to conventional algorithm for I/Q data after feature identification and feature suppressionv。
⑸ judging according to SNR threshold, if SNR of current distance bank is greater than or equal to SNR designated threshold, calculating other parameters by using conventional algorithm, if SNR of current distance bank is less than SNR designated thresholdDetermining the threshold, and further judging the spectrum width sigmavIf the fruit spectrum width σ is widevIf 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 sigmavAnd other parameters.
⑹ storing the estimated parameters in radial direction and performing primary quality control to form the basic data product.
In the further optimization of the technical scheme, step 4 is to estimate the radial velocity v according to the traditional algorithmrWhile calculating the standard deviation std (v) of the radial velocity from bin to bin in the radial directionr)。
In the further optimization of the above technical solution, if the SNR of the current distance library is smaller than the SNR specified threshold in step 5, std (v) needs to be further determinedr) When spectral width σvGreater 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 0.6 m/s.
According to the technical scheme, the following beneficial effects can be realized:
the method of the invention combines 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 radar and conventional Doppler weather radar. 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 <20dB), and has important significance for the subsequent application of radar parameters.
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FIG. 1 is an algorithmic flow chart of one embodiment of the present invention.
Fig. 2 is a flow chart of the algorithm 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 applied to dual-polarization weather radar after being expanded. The conventional algorithm estimates various parameters with an autocorrelation function (ACF)/cross-correlation function (CCF) of 0 order or 1 order, which may vary in the estimation formula of a specific parameter, but is typically characterized by using an ACF/CCF of no more than 1 order, and particularly, the estimation of signal power is performed by an ACF of 0 order, i.e., S ^ (h, v) ═ R ^ (h, v) (0) -N ^ (h, v), where S denotes signal power, h and v denote horizontal and vertical channels, N denotes noise power, superscript ^ denotes estimated values, superscript "-" denotes mean values, and R (0) denotes an ACF of 0 order. 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 at lower signal-to-noise ratios (SNRs).
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. And (4) estimating SNR and spectral width sigmav according to a traditional algorithm for the I/Q data after the surface feature identification and surface feature suppression processing.
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 spectral width is smaller than the specified threshold, the spectral width σ v is further judged. 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 (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. And (3) estimating SNR, spectral width sigmav and radial velocity vr according to a traditional algorithm on the I/Q data subjected to the ground object identification and ground object suppression processing, and calculating standard deviation std (vr) of the radial velocity from library to library according to the radial direction.
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 it is less than its specified threshold, the spectral widths σ v and std (vr) are further determined. If the spectral width and std (vr) are both greater than the respective specified thresholds, then the conventional algorithm is still used to estimate the other parameters, otherwise the multi-order correlation algorithm is used to re-estimate 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.
The first scheme uses two judgment parameters SNR and sigmav, is suitable for the situation that the radar noise estimation error is small, and has small calculation amount; the second scheme uses three judgment parameters SNR, σ v and std (vr), is suitable for general situations, has larger calculation amount and 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 lies 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 to 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, a traditional algorithm is used for estimating the signal-to-noise ratio SNR and the spectral width sigmav of the echo subjected to the feature processing. For scenario two, the signal-to-noise ratio SNR, spectral width σ v, radial velocity vr, and std (vr) are estimated for the clutter processed using conventional algorithms. 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 (vr) is calculated by radial library by library, and the current point and the distance point before and after the current point are calculated, and the current distance point and the distance point before and after the current point are selected to be calculated by 5 distance points in total.
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 a specified SNR threshold, other parameters are estimated by using a traditional algorithm; if not, the spectral width and std (vr) are further judged. If the spectral width exceeds a specified threshold and std (vr) also exceeds a specified threshold, the conventional algorithm is still used, otherwise the multi-order correlation algorithm is used. The SNR threshold is generally selected to be less than 20dB, the std (vr) threshold is selected to be 0.6m/s, the spectral width threshold is selected according to specific radar parameters, and the calculation formula is σ v _ Thr ═ λ/(4Ts π wn), wherein λ is the radar wavelength and is expressed in meters, Ts is the radar work repetition period and is expressed in seconds, wn is the available order, and the value is at least greater than or equal to 2 for the multi-order correlation algorithm. From these parameters, a threshold σ v — Thr of the spectral width can be calculated. The calculation formula of each main parameter is as follows, wherein h and V represent a horizontal channel and a vertical channel, R represents an autocorrelation function, C represents a cross-correlation function, N represents a correlation order, M represents the number of pulses of a correlation pulse group, S represents a signal power, σ represents a spectral width, ZDR represents a differential reflectivity, ρ hv represents a correlation coefficient, N represents a noise power, superscripts (1) and (2) represent formulas for calculating parameters using correlation of order 1 and 2, superscripts do not represent a conventional algorithm, and ^ represents an estimated value, -represents an average value, V represents an I/Q voltage signal collected by a radar, and represents a conjugate by a complex number.
6. After the radar calculates each parameter by the radial database, the obtained data is subjected to preliminary quality control including outlier filtering, threshold filtering (SNR threshold, CCOR threshold, etc.), and the like, so as 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,
⑴ receiving I/Q data of the radar horizontal channel and the vertical channel;
⑵ adopting fuzzy logic algorithm or Bayesian classification algorithm to identify the terrain of the I/Q data;
⑶ performing feature suppression by a Gaussian adaptive filter on the distance library identified as the feature;
⑷ estimating SNR and spectral width sigma according to conventional algorithm for I/Q data after feature identification and feature suppressionv；
⑸ judging according to SNR threshold, if SNR of current distance library is greater than or equal to SNR designated threshold, calculating other parameters by using traditional algorithm, if SNR of current distance library is less than SNR designated threshold, further judging spectral width sigmavIf the spectral width σ isvIf 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 sigmavAnd other parameters;
⑹ storing the estimated parameters in radial direction and performing primary quality control to form the basic data product.
2. Method of dual polarization weather radar according to claim 1A parameter estimation method, characterized by: in step 4, the radial velocity v needs to be estimated according to a traditional algorithmrWhile calculating the standard deviation std (v) of the radial velocity from bin to bin in radial directionr)。
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 judgedr) When spectral width σvGreater 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.
4. A parametric estimation method for dual polarization weather radar as claimed in claim 3, wherein: the SNR specified threshold is 20dB, and the standard deviation specified threshold is 0.6 m/s.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112731402A (en) * | 2020-12-17 | 2021-04-30 | 南京大学 | Multi-order correlation-based real-time estimation method for noise of weather radar |
| CN112965069A (en) * | 2021-03-21 | 2021-06-15 | 南京大学 | Frequency domain ground object suppression method for dual-polarization radar |
| CN113777573A (en) * | 2021-08-30 | 2021-12-10 | 中船重工鹏力（南京）大气海洋信息系统有限公司 | Dual-polarization radar secondary echo identification method based on naive Bayes classifier |
| CN114895381A (en) * | 2022-07-11 | 2022-08-12 | 南京气象科技创新研究院 | Ground flash grading early warning method based on double-linear polarization radar |
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 |
|---|
| YUE-XIA WANG等: "A Novel Spectral Parameters Estimation Approach for Weather Echoes Based on Fitting", 《2010 6TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS NETWORKING AND MOBILE COMPUTING》 * |
| 赵普洋: "双偏振天气雷达探测能力改善方法的设计", 《信息与电脑》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112731402A (en) * | 2020-12-17 | 2021-04-30 | 南京大学 | Multi-order correlation-based real-time estimation method for noise of weather radar |
| CN112731402B (en) * | 2020-12-17 | 2023-08-04 | 南京大学 | Weather radar noise real-time estimation method based on multi-order correlation |
| CN112965069A (en) * | 2021-03-21 | 2021-06-15 | 南京大学 | Frequency domain ground object suppression method for dual-polarization radar |
| CN113777573A (en) * | 2021-08-30 | 2021-12-10 | 中船重工鹏力（南京）大气海洋信息系统有限公司 | Dual-polarization radar secondary echo identification method based on naive Bayes classifier |
| 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 |
| CN114895381A (en) * | 2022-07-11 | 2022-08-12 | 南京气象科技创新研究院 | Ground flash grading early warning method based on double-linear polarization radar |
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