CN112965069B - Frequency domain ground object suppression method for dual-polarization radar - Google Patents
Frequency domain ground object suppression method for dual-polarization radar Download PDFInfo
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- 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
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- G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
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- G—PHYSICS
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- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/414—Discriminating targets with respect to background clutter
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Abstract
The invention discloses a dual-polarization radar frequency domain ground object suppression method, which comprises the steps of windowing and FFT processing of I/Q data of a horizontal channel and a vertical channel, and calculating a power spectrum and a phase spectrum in a frequency domain; using the frequency domain data, calculating the substrate noise of a horizontal channel and a vertical channel and screening out the noise and effective signal demarcation points; selecting zero frequency and two adjacent power spectrum data in a horizontal channel and a vertical channel, and determining frequency points occupied by ground feature echoes; and respectively filtering the frequency spectrum components determined as the ground object echoes in the horizontal channel and the vertical channel, and fitting and filling the meteorological echoes according to a Gaussian form to ensure that the fitting results of two adjacent times meet the requirements of power difference or radial velocity difference. The method improves the ground feature inhibition capability of the dual-polarization radar, and has important significance for improving the estimation quality of the dual-polarization radar parameters.
Description
Technical Field
The invention relates to a method for suppressing ground objects of a weather radar, in particular to a method for suppressing the ground objects of a frequency domain of a dual-polarization weather radar, and belongs to the field of radar signal processing research.
Background
In the aspect of terrain suppression of weather radar, Notch Filter method (Notch Filter) proposed by Groginsky and Glover (1980) becomes the most widely applied terrain suppression method, and utilizes the characteristic that the Doppler radial velocity of a terrain echo is near a 0 value to perform terrain suppression by using a band-stop Filter method. The notch filtering method is simple and easy to implement, but generates loss on meteorological echoes, especially on the condition that the Doppler radial velocity is close to 0ms-1Of the weather echo. For this reason, Siggia and Passarelli (2004) propose a gaussian Model Adaptive processing GMAP (gaussian Model Adaptive processing) method, and as long as the spectra of the weather echo and the ground object echo on the frequency domain are not overlapped seriously, the GMAP algorithm can separate the ground object well, thereby recovering the weather echo. However, due to the limited number of samples, the frequency domain filtering has a spectrum loss problem, which affects the parameter estimation. Nguyen et al (2008) propose a feature filtering method ptdm (parametric Time domain) of Time domain parameters, which is based on Time domain analysis of signal characteristics, is not affected by spectrum loss, and can still be well filtered under the influence of strong feature echoes, and the main disadvantage is that the calculation speed is slow, which is about one tenth of frequency domain filtering. Li (2013) proposes a BGMAP (Bi-Gaussian Model Adaptive Processing) algorithm, which is a frequency domain ground object filtering method based on a double Gaussian Model. This method performs well in terms of reflectivity factor and spectral width compared to GMAP, but when dealing with echoes near 0 radial velocity, the BGMAP method performs at a reflectivity factor ZHThere is still a significant loss in the estimation.
The domestic research on the aspect of ground feature filtering is advanced earlier, and the work is carried out on the aspects of both the time domain and the frequency domain of echo signals. Early ground clutter suppression mostly adopts a mode of combining software and hardware, for example, Wangxian et al (1986) selects a non-coherent triple-pulse canceller to suppress ground clutter and extract meteorological signals, and tests are carried out on a 713 radar to obtain the results that the ground clutter suppression ratio is greater than 21dB and the meteorological signal loss is less than 5 dB. The method of ground clutter maps is also used earlier, and Wangxian Duty et al (1987) loads the ground clutter maps on the ground clutter suppressors, ground clutter can be effectively suppressed in a clutter area, meteorological information is extracted, and ground clutter suppression processing is not carried out outside the clutter area. Dong gem et al (2006) propose an alternating PRT ground object filtering algorithm for doppler weather radar, filter out spectral components of ground clutter from the frequency domain, and reconstruct the spectrum of the weather echo using amplitude deconvolution and amplitude spectrum correction, then estimate the doppler parameters from the reconstructed spectrum. How to build a new model and the like (2010) develops application research of the adaptive Gaussian filter in the weather radar, analyzes clutter suppression performances of the IIR elliptical filter and the adaptive Gaussian filter, analyzes and compares results, and considers that the clutter suppression performance of the adaptive Gaussian filter without using a clutter map is superior to that of the adaptive Gaussian filter using the clutter map. And (2017) identifying the ground features by using a fuzzy logic algorithm, and directly eliminating the echoes identified as the ground features to realize ground feature suppression.
Generally, the frequency-domain ground object suppression method is widely applied to Doppler weather radar due to the flexible processing mode and the good effect, and particularly the GMAP method. However, for a dual-polarization weather radar, the existing frequency domain ground object suppression method does not consider the correlation of weather echoes in two channels when processing data of a horizontal channel and a vertical channel, which may cause deviation of the amplitude and phase of a processed signal, and bring about the estimation problem of dual-polarization parameters after ground object processing.
Disclosure of Invention
The technical problem to be solved by the invention is that the frequency domain ground object suppression method aims at the inaccurate result of estimation of dual-polarization parameters caused by deviation of processed signals due to lack of consideration of correlation of a horizontal channel and a vertical channel of a meteorological echo in the dual-polarization weather radar processing process.
In order to solve the technical problem, the method for inhibiting the dual-polarization radar frequency domain ground object comprises the following steps:
i, processing I/Q data of a horizontal channel and a vertical channel by using a Hamming window, transforming the I/Q data of the two channels from a time domain to a frequency domain by using a fast Fourier transform method, and calculating a power spectrum and a phase spectrum in the frequency domain.
And II, sequencing the power spectrum data of the horizontal channel and the vertical channel according to the amplitude, calculating the power mean value of the data with the amplitude of 5% to 40%, subtracting the mean value of the theoretical value of the noise in the corresponding amplitude range to obtain the respective substrate noise of the two channels, comparing all the sequenced power spectrum data with the theoretical value of the corresponding noise, and taking the position where the first power difference value exceeds the threshold as the demarcation point of the noise and the effective signal.
Selecting zero-frequency and two adjacent power spectrum data in the horizontal channel and the vertical channel respectively, and if the mean value of the selected power spectrum data is smaller than the substrate noise of the channel, then the ground object echo does not exist; if the mean value is between one time and two times of the substrate noise of the channel, selecting a zero-frequency position as a ground object echo; and if the mean value is more than twice the background noise of the channel, fitting according to a Gaussian form to determine the position of the ground object echo.
And IV, respectively filtering the frequency spectrum components determined as the ground object echoes in the horizontal channel and the vertical channel, fitting and filling the meteorological echoes according to a Gaussian form, calculating the power and the radial velocity of signals after filling, and comparing the power and the radial velocity with the power and the radial velocity calculated last time, if the power difference is greater than 0.2dB or the radial velocity difference is greater than 1% of the maximum unambiguous velocity in the current mode, fitting and filling again by using the data after filling until the fitting results of two adjacent times meet the requirements of the power difference or the radial velocity difference.
And (3) further optimizing the scheme, calculating the hybrid signal-to-noise ratio of the final fitting result of the horizontal channel and the vertical channel in the step IV, determining to replace the window function used in the step I according to the value of the CSR, and after the window function is replaced, executing the steps I to IV again and using the latest calculation result. When CSR <2.5dB, a rectangular window is selected; when CSR >40dB, choose Blackman window; and when the CSR is more than 20dB and the CSR is less than 40dB, firstly selecting a Blackman window, then processing according to the steps I to IV, calculating the CSR again, selecting the Blackman window when the CSR is more than 25dB, and otherwise, selecting a Hamming window.
In the above technical solution, the threshold in step ii is 1 dB.
In the above technical scheme, in step iii, if the calculation results of the horizontal channel and the vertical channel are not consistent, when there are many ground objects around the radar, the larger value of the two is selected, otherwise the smaller value of the two is selected.
In the above technical solution, when the meteorological echoes are fitted according to the gaussian shape in step iv, the horizontal channel and the vertical channel use the same random phase at the ground object position.
In the above technical solution, the horizontal channel and the vertical channel use the same window function, and the larger value of CSR in the horizontal channel and the vertical channel is taken as the standard.
The method for restraining the dual-polarization radar frequency domain ground object is based on radar I/Q data, and is high in automation degree, convenient and easy to operate. Compared with the prior art, the method improves the ground object processing capacity of the dual-polarization weather radar, and has important significance for improving the estimation precision of the dual-polarization parameters.
Drawings
FIG. 1 is an algorithm flow chart of a dual-polarization radar frequency domain ground object suppression method.
FIG. 2 is a comparison between the present invention and the estimation result of polarization parameter processed by the existing frequency domain feature suppression method. Data were collected by Nanjing university C-band dual polarization radar (NJU-CPOL) 43 minutes (UTC) at 08 o 6, 15/2014.
Detailed Description
For polarization parameters, the calculation uses both horizontal and vertical channel data, e.g. differential reflectivity ZDRAnd correlation coefficient ρhvThe conventional estimation methods of (1) and (2) are shown in the following equations, wherein ShAnd SvRespectively represent the levelSignal power of the channel and the vertical channel, Chv(0) Represents the zeroth order cross correlation function and a' represents the estimate.
As can be seen from the calculation formula, under the condition that the consistency of a horizontal channel and a vertical channel is not particularly considered, when the existing frequency domain feature suppression method processes the feature, the difference of the characteristics of two channel filters can cause additional deviation on signal power estimation, thereby causing the feature position ZDRAn increase in the standard deviation is estimated. In addition, the current method for processing the phase when fitting the filtered meteorological echoes can further reduce the correlation of the meteorological echoes of the horizontal and vertical channels, and cause rhohvThe reduction of the estimated value leads to the obvious reduction of the parameter estimation quality of the dual-polarization radar.
Referring to fig. 1, the scheme of the present invention is further explained by taking an NJU-CPOL radar as an example.
Windowing is carried out on the I/Q data of the horizontal channel and the vertical channel, Hamming windows are simultaneously used for the two channels, the I/Q data of the two channels are converted from a time domain to a frequency domain by using a Fast Fourier Transform (FFT) method, and a power spectrum and a phase spectrum in the frequency domain are calculated. Since the best window function cannot be obtained initially, the Hamming window is used first, and then a suitable window function is selected according to the actual processing result.
And II, sequencing the power spectrum data of the horizontal channel and the vertical channel according to the amplitude by using the frequency domain data, calculating the power mean value of the data with the amplitude between 5% and 40%, and subtracting the mean value of the theoretical value of the noise in the corresponding amplitude range to obtain the respective substrate noise of the two channels. And comparing all sequenced power spectrum data with corresponding noise theoretical values, and taking the position of which the first power difference value exceeds 1dB as a demarcation point of noise and effective signals. The noise theoretical value conforms to the poisson distribution. The step outputs the respective substrate noise of the two channels, and the frequency points occupied by the noise and the effective signals.
And III, selecting the zero frequency and the adjacent frequency spectrums in the horizontal channel and the vertical channel to count 3 power spectrum data, and determining the frequency points occupied by the ground feature echoes. If the mean value of the selected power spectrum data is smaller than the substrate noise of the channel, the ground object echo does not exist; if the mean value is larger than the substrate noise and smaller than twice the substrate noise, selecting a zero-frequency position as a ground object echo; otherwise, fitting according to a Gaussian model to determine the position of the ground feature. The feature of the ground object target on the horizontal channel and the feature of the ground object target on the vertical channel are different, so that the difference of ground object frequency points identified by the two channels can be caused.
And IV, respectively filtering the frequency spectrum components determined as the ground object echoes in the horizontal channel and the vertical channel, and fitting and filling the meteorological echoes according to a Gaussian form. And recalculating the power and the radial velocity of the filled signal, comparing the recalculated power and the calculated radial velocity with the last calculated power and the last calculated radial velocity, and if the power difference is greater than 0.2dB or the radial velocity difference is greater than 1% of the maximum unambiguous velocity in the current mode, fitting and filling again by using the filled data until the two adjacent fitting results meet the requirement of the power difference or the radial velocity difference. The advantage of performing multiple fits is that when the clutter and the weather echo overlap in the spectrum, the result of a single fit is significantly less in power than the weather echo in adjacent frequency bins. When the meteorological echo signals are fitted, the horizontal channel and the vertical channel use the same random phase at the ground object position, otherwise, the correlation of the two channels is reduced.
And V, calculating a hybrid signal-to-noise ratio (CSR) of the final fitting result of the horizontal channel and the vertical channel in the step IV, and determining whether the window function used in the step I needs to be replaced according to the value of the CSR so as to reduce the influence of the window function on parameter estimation errors. After replacing the window function, steps i to v need to be executed again, and the latest calculation result is used. The losses caused by different window functions to the meteorological echo are not consistent, generally, when the terrain echo is weak, the window function with weak side lobe suppression is selected, and when the terrain echo is strong, the window function with strong side lobe suppression is selected, so that the meteorological echo can be recovered better. The side lobe suppression capability of a typical window function is: a rectangular window of 12 dB; hamming window 40 dB; blackman window 55 dB.
FIG. 2 is a comparison of the polarization parameter estimation results after the feature echo in one PPI scan is processed by the method of the present invention and the current frequency domain feature suppression method, where FIG. 2a and FIG. 2b are the Z estimated after the prior art methodDRAnd ρhvFIGS. 2c and 2d show the Z values estimated after processing by the method of the present inventionDRAnd ρhvThe abscissa in the figure is still the distance, in km. As can be seen from the figure, Z after the prior art method treatmentDRThe estimated standard deviation at the feature position significantly increases, resulting in numerical jitter, and ρ at the same positionhvThere was also a significant drop in the estimates. Z of feature position using the method of the inventionDREstimate p at a position where the standard deviation is significantly reducedhvThe data quality is obviously improved.
Claims (7)
1. The method for restraining the ground object in the frequency domain of the dual-polarization radar is characterized by comprising the following steps: comprises the following steps of,
i, processing I/Q data of a horizontal channel and a vertical channel by using a Hamming window, transforming the I/Q data of the two channels from a time domain to a frequency domain by using a fast Fourier transform method, and calculating a power spectrum and a phase spectrum in the frequency domain;
sorting the power spectrum data of the horizontal channel and the vertical channel according to the amplitude, calculating the power mean value of the data with the amplitude between 5% and 40%, subtracting the mean value of the noise theoretical value of the corresponding amplitude range to obtain the respective substrate noise of the two channels, comparing all the sorted power spectrum data with the corresponding noise theoretical value, and taking the position where the first power difference value exceeds the threshold as the demarcation point of the noise and the effective signal;
selecting zero-frequency and two adjacent power spectrum data in the horizontal channel and the vertical channel respectively, and if the mean value of the selected power spectrum data is smaller than the substrate noise of the channel, then the ground object echo does not exist; if the mean value is between one time and two times of the substrate noise of the channel, selecting a zero-frequency position as a ground object echo; if the mean value is more than twice of the substrate noise of the channel, fitting according to a Gaussian form to determine the position of the ground object echo;
and IV, respectively filtering the frequency spectrum components determined as the ground object echoes in the horizontal channel and the vertical channel, fitting and filling the meteorological echoes according to a Gaussian form, calculating the power and the radial velocity of signals after filling, and comparing the power and the radial velocity with the power and the radial velocity calculated last time, if the power difference is greater than 0.2dB or the radial velocity difference is greater than 1% of the maximum unambiguous velocity in the current mode, fitting and filling again by using the data after filling until the fitting results of two adjacent times meet the requirements of the power difference or the radial velocity difference.
2. The dual-polarization radar frequency-domain feature suppression method of claim 1, wherein: and D, calculating the clutter to signal ratio of the final fitting result of the horizontal channel and the vertical channel in the step IV, determining and replacing the window function used in the step I according to the CSR value, and after the window function is replaced, executing the steps I to IV again and using the latest calculation result.
3. The dual-polarization radar frequency-domain feature suppression method of claim 1 or 2, wherein: the threshold in step II is 1 dB.
4. The dual-polarization radar frequency-domain feature suppression method of claim 1 or 2, wherein: and step III, if the calculation results of the horizontal channel and the vertical channel are inconsistent, selecting the larger value of the two when the ground objects around the radar are more, and otherwise, selecting the smaller value of the two.
5. The dual-polarization radar frequency-domain feature suppression method of claim 1 or 2, wherein: and IV, when the meteorological echoes are fitted according to the Gaussian form, the horizontal channel and the vertical channel use the same random phase at the position of the ground object.
6. The dual-polarization radar frequency-domain feature suppression method of claim 2, wherein: when CSR <2.5dB, a rectangular window is selected; when CSR >40dB, choose Blackman window; and when the CSR is more than 20dB and the CSR is less than 40dB, firstly selecting a Blackman window, then processing according to the steps I to IV, calculating the CSR again, selecting the Blackman window when the CSR is more than 25dB, and otherwise, selecting a Hamming window.
7. The dual-polarization radar frequency-domain feature suppression method of claim 2, wherein: the horizontal channel and the vertical channel use the same window function, subject to the larger value of CSR in the horizontal channel and the vertical channel.
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