CN115356696A - Weather radar signal processing system algorithm calibration method and device - Google Patents

Weather radar signal processing system algorithm calibration method and device Download PDF

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CN115356696A
CN115356696A CN202210911474.6A CN202210911474A CN115356696A CN 115356696 A CN115356696 A CN 115356696A CN 202210911474 A CN202210911474 A CN 202210911474A CN 115356696 A CN115356696 A CN 115356696A
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CN115356696B (en
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步志超
王箫鹏
刘洁
韩旭
陈玉宝
邵楠
李学华
何建新
关宇
代少君
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CMA Meteorological Observation Centre
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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
    • 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
    • G01S7/40—Means for monitoring or calibrating
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention discloses a method and a device for calibrating an algorithm of a weather radar signal processing system, wherein the method comprises the following steps: acquiring a standard data set, transmitting the standard IQ data set to a signal processing system to be calibrated in a radial data stream mode, and receiving base data sent by the signal processing system to be calibrated, wherein the base data is data obtained after the standard IQ data set is processed by the signal processing system to be calibrated; and comparing the base data with the calibration index parameters, and calibrating the algorithm of the signal processing system to be calibrated. The invention can carry out calibration and comparison on the processing results of the signal processor system algorithms of different radar manufacturers, effectively solves the problem that the radar signal processor systems of the prior manufacturers have no unified calibration method and cannot be compared with each other, effectively promotes the improvement of the signal processing algorithms of the different manufacturers and reduces the inconsistency of radar data.

Description

Weather radar signal processing system algorithm calibration method and device
Technical Field
The invention belongs to the technical field of radar calibration, and particularly relates to a method and a device for calibrating an algorithm of a weather radar signal processing system.
Background
At present, signal processor systems produced by various large manufacturers which undertake research, development and production tasks of CINRAD in China are different, hardware structure difference is large, signal processing algorithms are inconsistent, and processing final results are different, so that when weather radars of different manufacturers are networked, data consistency is large in observation of the same weather process, and the application effect of the weather radars in quantitative precipitation estimation is seriously influenced.
At present, the method for achieving the calibration effect of a weather radar signal processor system mainly comprises two methods: the method adopts a signal source-based calibration method, and adopts an actual weather radar echo observation method. The signal source calibration method comprises an internal signal source method and an external signal source method, wherein a signal with known power and frequency deviation is provided by an internal frequency source or an external signal source of a radar system, the signal is injected into a radar signal processor system through a receiver to be processed by an algorithm, then the processed result is compared with a theoretical calculation result through the known signal source quantity, a difference value is calculated, and then the result of signal processing is calibrated. The method can only be limited to the calibration of the signal processor system of a single radar manufacturer, and can not calibrate the consistency of the algorithm processing results of the signal processor systems of different manufacturers; in addition, because the signal generated by the signal source is simple, the signal source can only simply calibrate the reflectivity, the speed, the differential reflectivity and the differential phase, the spectral width and the correlation coefficient cannot be calibrated and compared, and the calibration and comparison cannot be performed on the condition of complex signal amplitude and spectral distribution of the weather echo signal.
And another weather radar signal processor system algorithm calibration based on an actual weather radar echo observation test. A standard weather radar and a radar system provided with a signal processor system to be calibrated are installed at a nearby position, the same weather echo is observed simultaneously, and then the difference of the observation results of the two radars is used for calibrating the radar signal processing algorithm result. The calibration method is characterized in that the whole radar system comprises a transmitter, a receiver and a signal processor algorithm, and when radar data are inconsistent, whether the problem of the signal processing algorithm or the radar hardware problem cannot be determined; at present, the output result of the radar data is inconsistent after the radar hardware is calibrated. In addition, through the calibration of weather echo observation, the difference between the observation area and the observation position of radar beams also exists, so that the processing inconsistency brought by the inconsistency of radar echoes per se is caused.
At present, a direct calibration flow and a direct calibration method for weather radar signal processor system algorithms of different manufacturers are not available, the problem of consistency of processing results of the radar signal processing system algorithms of different manufacturers cannot be directly solved, and data inconsistency and radar data quality when various types of service weather radar are networked are seriously influenced.
Disclosure of Invention
The present invention has been made to solve the above-mentioned problems occurring in the prior art. Therefore, a method and a device for calibrating the weather radar signal processing system algorithm are needed, which can realize calibration of the weather radar signal processor system algorithm of different manufacturers, realize consistency calibration from IQ signals to basic data algorithm processing among the weather radar signal processor systems of different radar manufacturers, and analyze the processing performance.
The technical scheme adopted by the invention is as follows:
according to a first aspect of the invention, a calibration method for a weather radar signal processing system algorithm is provided, the method includes obtaining a standard data set, the standard data set includes a standard IQ data set and a standard basic data set, the standard IQ data includes radar original data, the radar original data includes original orthogonal I and Q signals of a vertical channel and a horizontal channel of each radar distance library, the standard basic data set is a calibration index parameter obtained after the standard IQ data passes through a standard signal processor, and the calibration index parameter at least includes one of reflectivity, speed, spectral width, differential reflectivity, differential phase, correlation coefficient and signal-to-noise ratio; transmitting the standard IQ data set to a signal processing system to be calibrated in a radial data stream mode, and receiving base data sent by the signal processing system to be calibrated, wherein the base data is obtained by processing the standard IQ data set through the signal processing system to be calibrated; and comparing the base data with the calibration index parameters, and calibrating the algorithm of the signal processing system to be calibrated.
Further, the transmitting the standard IQ data set to a signal processing system to be calibrated in a form of a radial data stream, and receiving base data sent from the signal processing system to be calibrated, includes: converting the standard IQ data set into a SWEEP data packet, wherein the SWEEP data packet comprises a plurality of radial SWEEP data, and the radial SWEEP data comprises M groups of SWEEP data; continuously sending SWEEP data packets to the signal processing system to be calibrated according to a pulse sequence at a PRT time interval or more than the PRT time interval, and finally additionally sending an ending identification packet; the continuously sending the SWEEP data packets to the signal processing system to be calibrated comprises sending the 1 st group of SWEEP data, then sending the 2 nd group of SWEEP data until the Mth group of SWEEP data are sent, combining the SWEEP data into a radial SWEEP data, repeating the steps, and then sending the next radial SWEEP data until all the radial SWEEP data are sent to the signal processing system to be calibrated; and receiving feedback data from the signal processing system to be calibrated, storing the base data when the feedback data are the base data and the end marking packet, retransmitting the standard IQ data set to the signal processing system to be calibrated when the feedback data are feedback failure packets, and stopping retransmission when the retransmission times exceed a preset retransmission threshold.
Further, the basic data comprise a weather radar horizontal channel reflectivity factor, a weather radar radial velocity, a spectrum width, a differential reflectivity factor, a differential propagation phase, a zero lag correlation coefficient, a signal-to-noise ratio and a reflectivity factor before dBT filtering; the step of comparing the base data with the calibration index parameters to determine whether the algorithm performance of the signal processing system to be calibrated meets the requirements comprises the following steps: performing radial number matching, distance library number matching and effective value matching on the base data to obtain comparison data, so that the base data of the same elevation layer have the same radial number and distance library number, and meanwhile, each distance library participating in comparison has a one-to-one corresponding relation and is an effective value; comparing the comparison data with a standard basic data set PPI library by library, qualitatively comparing the echo area, the contour and the space structure, comparing a base data PPI difference diagram, and qualitatively judging the deviation of an echo area through color by combining a color code; comparing the comparison data with a profile diagram of any radial or any distance position of a standard basic data set, evaluating consistency by observing whether the comparison data and the profile diagram coincide, and simultaneously comparing the trend of the consistency changing along with the distance position or the azimuth angle; comparing the comparison data with a scatter diagram of any radial direction or any distance position of a standard basic data set, evaluating consistency according to divergence degree of the scatter diagram or performing linear fitting on the scatter diagram, and evaluating consistency according to slope of a fitted straight line; defining an echo region meeting the requirements that SNR is larger than 10dB and W-less than 4m/s as an effective echo region, and defining an echo region meeting the requirements that SNR is larger than 30dB and W-less than 4m/s as a strong echo region; defining an echo region meeting 0dB (zero dB) plus SNR (zero noise ratio) plus 10dB (zero power) plus W (zero power) plus 4m/s as a weak echo region, wherein the SNR is a signal-to-noise ratio; the mean deviation bias, the standard deviation std and the correlation coefficient ρ between the base data in these three regions and the standard base data set are compared, respectively, and are expressed as:
Figure BDA0003774147810000031
Figure BDA0003774147810000032
Figure BDA0003774147810000033
wherein X i Standard base data, Y, representing the ith distance bin i Base data, | X, representing the ith distance after processing by the algorithm to be calibrated i -Y i Where | represents the absolute deviation between the base data and the standard base data set, N is the number of valid samples of data, i is the sample sequence number,
Figure BDA0003774147810000041
for N standard base data X i Is determined by the average value of (a) of (b),
Figure BDA0003774147810000042
for N standard base data Y i Average value of (a).
Dividing the base data from small to large, and respectively calculating the average deviation of an effective echo region, a strong echo region and a weak echo region; and measuring whether the original power and the noise of the signal processing system to be calibrated are accurately calculated according to the standard signal-to-noise ratio.
Further, the step of performing radial number matching, distance library number matching and effective value matching on the base data to obtain comparison data includes: the radial number matching includes: processing the base data as a two-dimensional array, wherein the rows of the array represent the radial direction, the columns of the array represent the distance position, and the base data are radially filled into corresponding rows of the blank array according to an azimuth angle obtained from a radial head block when the base data are read by the following method:
i. when only one radial direction exists after the azimuth angle zero is rounded, filling the current radial direction into a corresponding row of the blank array;
ii, after azimuth angle zero rounding, a plurality of radial directions exist, and the next radial direction is filled into a corresponding row of the blank array;
iii, after azimuth angle zero rounding, azimuth angle missing exists, and the previous line is radially filled into a corresponding line of the blank array;
the distance library numberThe matching comprises the following steps: calculating the reference distance bin number B by the following formula max ：
Figure BDA0003774147810000043
Wherein τ is the pulse width and PRF is the pulse repetition frequency;
if the maximum distance library number of the base data is larger than the reference, deleting the part of the base data larger than the reference; if the maximum distance base number of the base data is smaller than the reference, the maximum distance base number is equal to the reference distance base number.
The valid value matching includes: if a certain distance library in the standard basic data or the base data is an invalid value, the corresponding distance library in other base data is also assigned as the invalid value.
Further, the measuring whether the original power and the noise of the signal processing system to be calibrated are accurately calculated according to the standard signal-to-noise ratio includes: .
When the average deviation of a typical calibration area is less than 0.5dB, the standard deviation is less than 1dB, and the correlation coefficient is greater than 0.99, the original power and noise of the signal processing system to be calibrated are accurate;
under the condition of the same radar calibration constant, pulse accumulation number and atmospheric constant, the average deviation of the reflectivity factor dBT and the reflectivity factor Z before filtering inhibition is less than 0.2dB, the standard deviation is less than 1dB, and the correlation coefficient is greater than 0.99, which indicates that the original power and noise of the signal processing system to be calibrated are accurate;
differential reflectivity Z dr The average deviation of the signal processing system to be calibrated is less than 0.1dB, the standard deviation of the signal processing system to be calibrated is less than 0.2dB, and the correlation coefficient of the signal processing system to be calibrated is greater than 0.99, which indicates that the original power and the noise of the signal processing system to be calibrated are accurate.
Further, the comparing the base data with the calibration index parameter to determine whether the performance of the algorithm of the signal processing system to be calibrated meets the requirement includes:
performing radial number matching, distance library number matching and effective value matching on the base data to obtain comparison data, so that the base data of the same elevation layer have the same radial number and distance library number, and meanwhile, each distance library participating in comparison has a one-to-one corresponding relation and is an effective value;
and judging the ground clutter and the weather echo region, and performing qualitative comparison and quantitative comparison on the base data of the ground clutter and the weather echo region respectively according to the judgment result.
Further, the judging the ground clutter and the weather echo region comprises:
for the base data subjected to ground clutter suppression, if the reflectivity factor dBT before filtering suppression and the reflectivity factor dBZ after suppression of a certain distance library are effective values and meet the condition that dBT-dBZ is more than Thr, the distance library can be judged to be the ground clutter, wherein Thr is a judgment threshold;
for the base data without ground clutter suppression, dBT = dBZ and Thr is set Z And Thr V Respectively as the judgment threshold of the reflectivity factor and the radial velocity, if the reflectivity factor dBZ and the radial velocity V before filtering and inhibiting of a certain distance library are both effective values and satisfy the dBZ>Thr Z ,abs(V)<Thr V Then, the distance library is determined to be the ground clutter.
Further, the judging the ground clutter and the weather echo region, and the qualitative comparison and the quantitative comparison of the base data of the ground clutter and the weather echo region according to the judging result respectively comprise:
the measure of the degree of change TDBZ between the reflectivity factor and the bin is calculated according to the following formula:
Figure BDA0003774147810000051
wherein Z is a reflectivity factor, i is the index number of the selected radial direction, j is the index number of the initial distance library, and M is the number of the selected distance libraries in the TDBZ calculation;
if the adjacent range bin reflectivity factor satisfies the following two conditions:
sign{Z i,j -Z i,j-1 }＝-sign{Z i,j+1 -Z i,j }
Figure BDA0003774147810000061
the measure SPIN of the gradient sign change frequency of the reflectivity factor in a certain radial direction of the distance library is 1, the number of distance libraries meeting the SPIN of 1 in the M distance libraries is counted, and the percentage is calculated;
calculating clutter phase suppression CPA according to the following formula:
Figure BDA0003774147810000062
wherein x n The nth sampling I/Q sequence of a certain distance library;
utilizing membership function to measure TDBZ of variation degree between reflectivity factor and distance library, measure SPIN of variation frequency of reflectivity factor gradient sign in certain radial direction of distance library, clutter phase rejection CPA and differential reflectivity texture sigma ZDRn Differential phase texture sigma φDPn The weight converted to 0-1 is:
Figure BDA0003774147810000063
Figure BDA0003774147810000064
Figure BDA0003774147810000065
Figure BDA0003774147810000066
Figure BDA0003774147810000067
wherein TDBZ n Scattering Rate texture, SPIN, for the nth range bin n Scatter gradient rotation change for nth range bin, CPA n Clutter phase suppression for the nth range bin;
calculating clutter probability according to the following formula:
Figure BDA0003774147810000071
wherein the value of CP is between 0-1, if CP > 0.5, regard this distance storehouse as the clutter of the ground object;
according to the judgment result of the ground clutter, independently retaining the reflectivity factors of the ground clutter areas and the weather echo areas before and after the inhibition of different algorithms; respectively calculating a PPI difference map of the base data for the ground clutter region and the weather echo region, and directly analyzing the residual ground clutter condition and the loss condition of the weather echo;
calculating a ground clutter suppression quantification index: the method comprises the following steps of calculating a ground Clutter rejection Ratio (CFSR) and an Echo power Loss Ratio (WELR) of different normalized spectral widths and radial velocity changes respectively, comparing curves, and defining the ground Clutter rejection Ratio (CFSR) and the Echo power Loss Ratio (WELR) as:
CFSR＝10log 10 (P c /P cf )＝dBZ c -dBZ cf
WELR＝10log 10 (P w /P wf )＝dBZ w -dBZ wf
wherein P is c Ground clutter power, P, before suppression for individual ground clutter regions cf For ground clutter power, P, after suppression of individual ground clutter regions w Weather echo power, P, before suppression for individual weather echo regions wf The suppressed weather echo power for the individual weather echo region;
and if the CFSR is more than 50dB and the WELR is less than 1dB, determining that the signal processing system to be calibrated meets the standard requirement.
According to a second aspect of the invention, there is provided a calibration device for a weather radar signal processing system algorithm, the device comprising a signal processing calibration system and a signal processing system to be calibrated:
the signal processing calibration system is configured to obtain a standard data set, the standard data set comprises a standard IQ data set and a standard basic data set, the standard IQ data comprises radar original data, the radar original data comprises I and Q signals of a vertical channel and a horizontal channel of each radar distance library, the standard basic data set is a calibration index parameter obtained after the standard IQ data passes through a standard signal processor, and the calibration index parameter at least comprises one of reflectivity, speed, spectral width, differential reflectivity, differential phase, correlation coefficient and signal-to-noise ratio; transmitting the standard IQ data set to a signal processing system to be calibrated in a radial data stream mode, and receiving base data sent by the signal processing system to be calibrated; comparing the base data with the calibration index parameters, and calibrating a signal processing system algorithm to be calibrated;
the signal processing system to be calibrated is configured to receive a standard I1 data set from the signal processing calibration system, process the standard IQ data set to obtain base data, and transmit the base data to the signal processing calibration system.
Further, the signal processing calibration system is further configured to:
converting the standard IQ data set into a SWEEP data packet, wherein the SWEEP data packet comprises a plurality of radial SWEEP data, and the radial SWEEP data comprises M groups of SWEEP data;
continuously sending SWEEP data packets to the signal processing system to be calibrated according to a pulse sequence at a PRT time interval or more than the PRT time interval, and finally additionally sending an end identification packet; the continuously sending the SWEEP data packets to the signal processing system to be calibrated comprises sending the 1 st group of SWEEP data, then sending the 2 nd group of SWEEP data until the Mth group of SWEEP data are sent, combining the SWEEP data into a radial SWEEP data, repeating the steps, and then sending the next radial SWEEP data until all the radial SWEEP data are sent to the signal processing system to be calibrated;
and receiving feedback data from the signal processing system to be calibrated, storing the base data when the feedback data are the base data and the end marking packet, retransmitting the standard IQ data set to the signal processing system to be calibrated when the feedback data are feedback failure packets, and stopping retransmission when the retransmission times exceed a preset retransmission threshold.
The weather radar signal processing system algorithm calibration method and device according to the various schemes of the invention at least have the following technical effects:
(1) The method can be used for calibrating and comparing the processing results of the signal processor system algorithms of different radar manufacturers, effectively solves the problem that the radar signal processor systems of the existing manufacturers cannot be compared with each other due to the fact that no unified calibration method exists, effectively promotes the improvement of the signal processing algorithms of the different manufacturers, and reduces the inconsistency of radar data.
(2) The method can carry out comprehensive calibration and comparison on basic parameter estimation and ground clutter suppression algorithms in a weather radar signal processor system algorithm, effectively solves the problems that the existing signal source calibration method and the like can only carry out calibration and comparison on individual algorithms in a limited and simple manner and can not carry out detailed and detailed calibration and comparison on the advantages and disadvantages of the signal processing system algorithm, and provides a calibration and comparison means for a 'black box' of the weather radar signal processor.
Drawings
FIG. 1 is a schematic diagram of calibration and comparison between signal processing systems of different manufacturers.
FIG. 2 is a flow chart of standard data set creation.
FIG. 3 is a flow chart of a method for calibrating a base data signal processing algorithm.
Fig. 4 is a flow chart of an azimuth matching method.
FIG. 5 is a flowchart of a calibration method of a weather radar ground clutter suppression algorithm.
Fig. 6 is a flow chart of a weather radar base data signal processing algorithm and ground clutter suppression signal processing calibration.
Fig. 7a is a graph of reflectance factor versus PPI for a standard base data file implementing the primary base data signal processing calibration process of example 1.
Fig. 7b is a plot of the reflectivity factor ratio PPI of the signal processing system 1 to be calibrated in the primary base data signal processing calibration process of embodiment 1.
Fig. 7c is a plot of the reflectivity factor ratio PPI of the signal processing system 2 to be calibrated in the primary base data signal processing calibration process of embodiment 1.
Fig. 8a is a graph of the reflectivity difference corresponding to each distance bin between the standard base data and the signal processing system 1 to be calibrated in embodiment 1.
Fig. 8b is a graph of the reflectivity difference corresponding to each distance bin between the standard base data of embodiment 1 and the signal processing system 2 to be calibrated.
Fig. 9a is a comparison graph of reflectivity scattergrams corresponding to each distance bin between the standard base data of the embodiment 1 and the signal processing system 1 to be calibrated.
Fig. 9b is a comparison graph of the reflectivity scatter diagram corresponding to each distance bin between the standard base data of the embodiment 1 and the signal processing system 2 to be calibrated.
Fig. 10a is a PPI chart of the standard base data clutter region reflectron of the calibration process of the primary ground clutter suppression algorithm in embodiment 2.
Fig. 10b is a PPI chart of the clutter region reflectron of the signal processing system 1 to be calibrated in the calibration process of the primary clutter suppression algorithm of the embodiment 2.
Fig. 10c is a PPI chart of the clutter region reflectron of the signal processing system 2 to be calibrated implementing the calibration process of the primary clutter suppression algorithm of the ground clutter 2.
Fig. 11a is a diagram of the reflectivity difference corresponding to each distance bin between the standard base data of the embodiment 2 and the signal processing system 1 to be calibrated, wherein the right lower ellipse part is the weather echo region.
Fig. 11b is a diagram of the reflectivity difference corresponding to each distance bin between the standard base data of embodiment 2 and the signal processing system 2 to be calibrated, wherein the right lower ellipse part is the weather echo region.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following detailed description of embodiments of the invention is provided in connection with the accompanying drawings and the detailed description of embodiments of the invention, but is not intended to limit the invention. The order in which the various steps described herein are described as examples should not be construed as a limitation if there is no requirement for a contextual relationship between each other, and one skilled in the art would know that sequential adjustments may be made without destroying the logical relationship between each other, rendering the overall process impractical.
The embodiment of the invention provides an algorithm calibration method for a weather radar signal processing system. The algorithm calibration flow between radar signal processor systems of different manufacturers mainly provides a calibration flow which is based on a standard signal processor or standard IQ data and basic data, injects the same IQ data and system parameters into radar signal processor systems of different weathers through a network protocol interface, cooperatively controls different signal processors to perform algorithm processing work, and receives data for comparison; the calibration and comparison methods of different algorithms for processing weather radar signals provide processing requirements for a base data signal processing method and a ground clutter suppression method in signal processing, and different calibration methods and procedures are adopted.
Fig. 1 is a schematic diagram of an application scenario according to an embodiment of the present invention. The signal processing algorithm calibration system performs data interaction with multiple signal processor systems to be evaluated, for example, two signal processor systems to be evaluated as shown in fig. 1 may be used, the signal processor systems to be evaluated are configured to process a received standard IQ data set according to a configured algorithm thereof, transmit processed base data to the signal processing algorithm calibration system, compare the base data with the standard data, evaluate consistency, and determine whether the signal processor systems to be evaluated meet requirements.
Specifically, the algorithm calibration flow and method for different weather radar signal processing systems are mainly characterized in that the algorithm calibration flow and method are based on the same standard IQ data and are sent to different signal processing systems to be calibrated in an Ethernet communication mode to perform corresponding algorithm processing, processing results are returned, then standard base data are used for being compared and calibrated with the processing results of the different signal processing systems to be calibrated, so that the accuracy and performance of the signal processing algorithms to be calibrated are measured, the signal processing systems are fed back for algorithm improvement, and finally the processing performance of the algorithms of the different signal processing systems to be calibrated is improved and improved after multiple comparison and calibration, so that the output consistency meets the service requirements.
The algorithm calibration flow and the method of the radar signal processing system for different weather are divided into three major parts, namely standard data set establishment, standard protocol interface establishment and algorithm calibration flow.
The standard data set comprises a standard IQ data set and a standard basic data set; the standard IQ data are radar original data and partial radar system parameters related to signal processing, wherein the radar original data comprise I and Q signals which are originally orthogonal to a vertical channel and a horizontal channel of each radar distance library, and the standard basic data are basic data such as reflectivity, speed, spectrum width, differential reflectivity, differential phase, correlation coefficient, signal-to-noise ratio and the like obtained after the IQ data pass through a standard signal processor; the standard IQ data and the standard base data are in one-to-one correspondence.
And establishing different standard IQ data sets and basic data sets according to different algorithms to be calibrated. For calibration of a base data signal processing algorithm, the establishment of a standard data set requires selection of echoes with different SNRs and different spectral widths. For calibration of a ground clutter suppression algorithm, the establishment of a standard data set needs to select independent space positions of ground clutter and weather echoes, and select weather echoes under different conditions such as aliasing of the ground clutter and the weather echoes.
The standard data set is established as shown in fig. 2. The standard IQ data set is obtained through actual radar acquisition or echo simulation or through radar simulation. The radar used for collecting the IQ data selects a radar with stable and reliable long-term performance so as to ensure the data quality of the IQ data set. The standard basis set is usually obtained by IQ data processing using domestic and foreign signal processing systems with superior performance, such as the output of the currently domestic and foreign approved RVP900 weather radar signal processing system, to ensure the accuracy and referential of the basis data set.
The standard protocol interface establishment means that an alignment communication interface protocol is established between the signal processing alignment system and the signal processing system to be aligned, and a data communication flow in algorithm alignment is completed. In communication, the signal processing calibration system is used as a client, the signal processing system to be calibrated is used as a server, the interface adopts TCP/IP as a communication protocol, and communication is carried out in TLV format (type/length/value). The specific data protocol steps are as follows:
1) The signal processing calibration system continuously sends SWEEP data packets according to the pulse sequence at a PRT time interval or more than the PRT time interval; after sending out the 1 st group of SWEEP data, then sending out the 2 nd group of SWEEP data, and so on until sending out the M th group of SWEEP data, and combining the data into 1 radial SWEEP data; and in turn, similarly, the signal processing calibration system sends the next radial SWEEP data until all data in the IQ file are sent, and finally, an end identification packet is additionally sent, and the sending is stopped.
2) And the signal processing system to be calibrated collects the last radial pulse, returns a radial basic data flow return data packet after a signal processing flow is carried out, and returns the processed basic data to the algorithm evaluation platform. After all the base data are sent, an end identification packet is added. And if the processing fails, the signal processing system to be calibrated feeds back a failure packet. And if the signal processing calibration system receives wrong feedback information. Retransmitting the radial data; if the retransmission exceeds three times, the connection is disconnected, the task fails, and the reason of the failure is recorded.
3) The signal processing calibration system communicates with the signal processing system to be calibrated in a long connection mode, after connection is established, the signal processing calibration system and the signal processing system to be calibrated adopt a keep-alive mechanism, and if the network is disconnected, the algorithm evaluation platform informs and displays disconnection information.
4) And the signal processing calibration system sends the last data stream in a PPI IQ file and resends the 1 st to 4 th radial data streams, then returns the base data stream to be stored as a base data file, and simultaneously performs corresponding algorithm calibration processing flow with the standard data to output a calibration result. And simultaneously, the connection is disconnected. And finishing the data transmission process of the file.
The algorithm calibration flow is a process of realizing calibration control flow by a signal processing calibration system and signal processing to be calibrated on the basis of standard data set establishment and standard communication protocol establishment, and comprises the following specific steps:
1) And the signal processing and calibration system is connected with different signal processing systems to be calibrated through Ethernet, and the signal processing and calibration system takes out standard IQ files of a base data signal processing algorithm from the standard data set, reads the standard IQ data and performs protocol packet mode.
2) The signal processing calibration system sends IQ data and signal processing parameters to different signal processing systems to be calibrated in a radial data stream mode, then the signal processing system is calibrated, analyzes the data and the parameters according to the signal processing parameters, carries out basic data signal processing algorithm processing on the IQ data, and feeds back the IQ data to the signal processing calibration system in a basic data stream mode after the IQ data and the signal processing parameters are processed.
3) After the signal processing calibration system receives the base data stream, carrying out protocol analysis on the base data, taking out standard base data corresponding to the standard IQ data from the standard data set, and then carrying out base data preprocessing;
4) And the signal processing calibration system calibrates according to an algorithm. The algorithm calibration system executes the algorithm calibration method and the flow for the processing results of different signal processing systems to be calibrated. If the output indexes of calibration are all smaller than a certain range, if the standard deviation of the reflectivity factor is smaller than 1dB, the standard deviation meets the requirement, the consistency of the signal processing systems to be calibrated of different manufacturers is better, and if the standard deviation is not smaller than 1dB, the algorithm improvement and the parameter modification are carried out on the signal processing systems with the differences not meeting the requirement, so that the index requirement is met.
The following embodiments of the present invention will describe the calibration method of the base data signal processing algorithm and the calibration method of the ground clutter suppression signal processing algorithm in detail.
Please refer to fig. 3, which is a flowchart of a calibration method of a base data signal processing algorithm. The calibration method of the base data signal processing algorithm comprises the following steps: firstly, selecting standard I/Q data as input of signal processing algorithms of base data of different signal processing systems, comparing the processed base data with the standard base data, and mainly spreading the processed base data and the standard base data around the consistency of the processed base data of different algorithms and the standard base data, wherein the algorithm performance with better consistency is better. The method specifically comprises the following steps:
1) Firstly, reading standard base data and base data processed by a signal processing system to be calibrated, and preprocessing the base data. The basic data comprises a weather radar horizontal channel reflectivity factor with a unit of dBZ; the weather radar radial velocity is in m/s; the spectrum width is m/s; differential reflectivity factor in dB; differential propagation phase in °; the zero lag correlation coefficient, signal-to-noise ratio, reflectivity factor before dBT filtering, is in dBZ.
The base data preprocessing comprises radial number matching, distance library number matching and effective value matching, and aims to enable the base data of the same elevation layer to have the same radial number and distance library number, and meanwhile, each distance library participating in comparison has a one-to-one corresponding relation and is an effective value.
Azimuth matching: because different algorithms are different in processing manner of azimuth angles in actually acquired I/Q data, the obtained base data has a condition of radial misalignment (misalignment) or radial inconsistency, in which case, azimuth angle matching needs to be performed on the base data, and the azimuth angle matching method is shown in fig. 4.
Each base datum may be processed as a two-dimensional array, with the rows of the array representing radial directions and the columns of the array representing distance positions. For volume sweep data of one circle of rotation of the radar antenna, the theoretical maximum azimuth angle cannot exceed 360 degrees, and because the beam width (angular resolution) set in the simulation is 1 degree, the maximum radial number is 360 degrees, the newly-built blank two-dimensional array is 360 rows, and the number of the columns of the array needs to be set according to the specific distance library number. And radially filling the corresponding rows of the blank array according to rules according to the azimuth angle obtained from the radial head block when the base data is read, wherein the filling rules are as follows:
i. after the azimuth angle zero rounding, only one radial direction exists, and at the moment, the radial direction is only required to be filled into a corresponding row of a blank array. For example, the azimuth angle of the current radial direction is 80.5 °, the azimuth angle of the subsequent radial direction is 81.5 °, after rounding, the two radial directions are respectively 80 ° and 81 °, and the two radial directions are respectively filled into the 80 th row and the 81 th row of the blank array;
and ii, after azimuth zero rounding, a plurality of radial directions exist, and the next radial direction is filled into a corresponding row of the blank array. For example, if the current radial azimuth angle is 100.1 °, the next radial azimuth angle is 100.9 °, and the last radial azimuth angle is 100 ° after rounding, the next radial azimuth angle is taken and filled into the 100 th row of the blank array;
and iii, after azimuth angle zero rounding, azimuth angle missing exists, and the previous line is radially filled into a corresponding row of the blank array. For example, if the azimuth angle of the current radial direction is 120.9 °, the azimuth angle of the subsequent radial direction is 122.1 °, the current radial direction is 120 ° and 122 ° after rounding, respectively, and the azimuth angle 121 ° is missing, the current radial direction is taken to fill in the 121 th row of the blank array.
Matching distance library numbers: maximum distance bin number B to be calculated by parameters read from reference base data max As the number of reference distance bins, the calculation formula is:
Figure BDA0003774147810000131
where τ is the pulse width and PRF is the pulse repetition frequency. If the maximum distance library number of certain base data is larger than the reference, deleting the part of the base data larger than the reference; if the maximum distance library number of certain base data is smaller than the reference, an invalid value region needs to be added, so that the maximum distance library number is equal to the reference.
And (3) matching the effective value: if a certain distance library in the reference base data and the base data processed by different algorithms is an invalid value, corresponding distance libraries in other base data are also assigned with invalid values and do not participate in subsequent comparison.
2) And comparing the base data processed by the base data signal processing algorithm of different signal processing systems with standard base data PPI one by one, and qualitatively comparing the echo area, the contour and the spatial structure. Then comparing the PPI difference image of the basic data, and qualitatively judging which echo areas have larger deviation through color by combining a color code.
3) Comparing the basic data processed by the basic data signal processing algorithm of different signal processing systems with the profile map of a certain radial or certain distance position of the standard basic data, judging whether the consistency is good or bad by observing whether the basic data and the profile map coincide with each other, and simultaneously comparing the trend of the consistency changing along with the distance position or the azimuth angle.
4) The comparison of the scatter diagrams of the base data processed by the signal processing algorithms of the base data of different signal processing systems and the scatter diagrams of the standard base data at a certain radial or certain distance position is characterized in that the point-to-point direct comparison of the two base data is performed, the consistency of the base data can be seen from the divergence degree of the scatter diagrams, the linear fitting can be performed on the scatter diagrams, and the consistency evaluation can be performed through the slope of a straight line after the fitting.
5) Because the performance of the base data signal processing algorithm is greatly influenced by the signal-to-noise ratio, the processing precision of strong echoes and weak echoes is often different, and calibration and analysis need to be separately carried out. Since the signal-to-noise ratio is too low and the probability that the echo with too large spectral width is a stray point is high, the part of the distance library needs to be ignored in the analysis. Therefore, designing and defining an echo region meeting SNR & gt 10dB and W & lt 4m/s as an effective echo region, and defining an echo region meeting SNR & gt 30dB and W & lt 4m/s as a strong echo region on the basis; an echo region satisfying 0 dB-less SNR-less 10dB and W-less 4m/s is defined as a weak echo region. By drawing a table, respectively comparing the average deviation bias, the standard deviation std and the correlation coefficient rho between the processed base data and the reference base data of different algorithms in the three regions, wherein the three statistics are respectively defined as:
Figure BDA0003774147810000141
Figure BDA0003774147810000142
Figure BDA0003774147810000151
wherein X i Standard base data, Y, representing the ith distance bin i Base data, | X, representing the ith distance after processing by the algorithm to be calibrated i -Y i I represents the absolute deviation between the base data and the standard base data set, N is the number of valid samples of data, i is the sample sequence number,
Figure BDA0003774147810000152
for N standard base data X i Is determined by the average value of (a),
Figure BDA0003774147810000153
for N standard base data Y i Average value of (a).
6) And (3) counting the distribution of the average deviation of the base data processed by different algorithms along with the numerical value of the base data by drawing a histogram, namely dividing the base data from small to large according to intervals, and respectively calculating the average deviation of each interval.
7) And measuring whether the calculation of the original power and the noise of the signal processor system to be calibrated is accurate or not by the average deviation of the SNR of the standard data. When the average deviation of SNR of a typical calibration area is less than 0.5dB, the standard deviation is less than 1dB, and the correlation coefficient is greater than 0.99, it indicates that the original power and noise of the signal processor to be calibrated are accurate, otherwise, the signal processor to be calibrated needs to adjust the noise and power. Then, on the basis of SNR calibration, detecting the dBT and Z before filtering, and under the conditions of the same radar calibration constant, pulse accumulation number, atmospheric constant and the like, detecting that the average deviation of the dBT and Z is less than 0.2dB, the standard deviation is less than 1dB, and the correlation coefficient is greater than 0.99, otherwise, adjusting a data sampling mode, a calculation constant and an algorithm formula in the dBT and Z algorithm is needed, so that the errors of the dBT and Z meet the requirements. On the premise of accurate SNR, dBT and Z calibrationChecking the differential reflectivity Z dr Has an average deviation of less than 0.1dB, a standard deviation of less than 0.2dB and a correlation coefficient of greater than 0.99, otherwise Z needs to be adjusted dr Offset constant offset in algorithm and algorithm formula, make Z dr The error of (2) meets the requirement. Velocity v, w and differential phase
Figure BDA0003774147810000154
Is a basic parameter related to radar phase, the average deviation of the velocity v and the velocity w needs to be less than 0.5m/s, the standard deviation is less than 1m/s, the correlation coefficient is more than 0.99, and the differential phase is
Figure BDA0003774147810000155
Less than 1 degree, standard deviation less than 3 degrees and correlation coefficient more than 0.99 are needed, otherwise, the data sampling mode and the calculation method in the algorithm need to be adjusted.
Please refer to fig. 5, which is a flowchart illustrating a calibration method of a weather radar ground clutter suppression algorithm. The ground clutter suppression algorithm calibration method comprises the following steps: firstly, selecting standard I/Q data containing weather echoes and ground clutter echoes as input of a ground clutter suppression processing algorithm of a signal processing system to be calibrated, comparing the base data after suppression processing with standard base data, and mainly developing the ground clutter suppression capability of the base data after suppression processing and the standard base data of the ground clutter to be calibrated, the loss of the ground clutter to the weather echoes and the like. The method specifically comprises the following steps:
1) Reading base data files before and after the inhibition of different algorithms, and preprocessing the base data. The preprocessing flow is the same as the basic data signal processing algorithm flow.
2) And judging the ground clutter and the weather echo area, and performing qualitative comparison and quantitative comparison on the base data of the two areas according to the judgment result. The judgment method comprises a simple judgment method and a CMD identification judgment method, wherein the simple judgment method is independent of ground clutter and weather echoes.
i. Simple judgment method for ground clutter area
The method is used for judging by using the difference between the reflectivity factors before and after ground clutter suppression and the ground clutter characteristics.
For the base data subjected to ground clutter suppression, if the reflectivity factor dBT before filtering suppression and the reflectivity factor Z after suppression of a certain distance library are both effective values, and the effective values satisfy the following conditions:
dBT-Z>Thr
the distance library can be judged to be the clutter of the ground objects, wherein Thr is the judgment threshold. When the ground clutter suppression degree is better, thr can be set to be larger so as to avoid that more weather echoes are mistakenly identified.
For the base data which has not been subjected to the ground clutter suppression, dBT = Z at this time, the identification of the ground clutter can be realized by using the characteristics that the ground clutter reflectivity factor is large and the speed is close to zero. And setting ThrZ and ThrV as the judgment thresholds of the reflectivity factor and the radial velocity respectively, and if Z and V of a certain distance library are both effective values and satisfy the following conditions:
dBZ>Thr Z ,abs(V)<Thr V
the distance library can be judged as the ground clutter.
The simple judging method for the ground clutter area is easy to realize, but the judging accuracy is poor for base data with small ground clutter power suppression degree and large weather echo power loss.
CMD determination method
The principle of the CMD judgment method can be briefly summarized as follows: and converting various characteristic fields into weights from 0 to 1 by using a membership function, then integrating various weights by using a decision standard and weighting, finally normalizing the result to be between 0 and 1, and using a threshold as a boundary for ground clutter decision. The characteristic field is divided into a single polarization characteristic field and a double polarization characteristic field, and mainly comprises the following components:
A. reflectivity Texture (TDBZ)
Compared with the weather echo region, the difference of the reflectivity factors between the ground clutter region distance library is large, and the TDBZ is the measurement of the variation degree between the reflectivity factor distance libraries and is defined as:
Figure BDA0003774147810000171
wherein Z is the reflectivity factor, i is the index number of the selected radial, j is the index number of the initial distance library, and M is the number of the selected distance libraries when calculating TDBZ.
B. Gradient reflectivity rotation variation (SPIN)
SPIN is a measure of how frequently the sign of the reflectivity factor gradient changes in a certain radial direction, if the reflectivity factors of adjacent distance bins satisfy the following two conditions:
sign{Z i,j -Z i,j-1 }＝-sign{Z i,j+1 -Z i,j }
Figure BDA0003774147810000172
then the SPIN of the range bin is 1, count how many of the M range bins meet the range bin with SPIN of 1, and calculate the percentage.
C. Clutter phase suppression (CPA)
CPA is defined by:
Figure BDA0003774147810000173
wherein x is n Is the nth sample I/Q sequence of a range bin. Because the ground clutter target basically does not move and the distance relative to the radar is certain, the phase is basically unchanged for different sampling pulses; the phase between the meteorological target pulses is changed regularly along with the radial speed; the phase between the noise pulses is random. Based on the above characteristics, the CPA of clutter is generally large, and the CPA of meteorological targets and noise is close to 0. Since the zero-speed narrow-spectrum weather echo CPA is high and may be confused with the CPA of ground clutter, median filtering is generally adopted to solve the problem.
D. Differential reflectivity texture (σ) ZDRn ) And differential phase texture (σ) φDPn )
In clutter, ZDR and phidp are very noisy, with large differences between range bins.
Converting the information of the characteristic field into a weight value from 0 to 1 by using a membership function, wherein the weight value comprises the following components:
Figure BDA0003774147810000174
Figure BDA0003774147810000181
Figure BDA0003774147810000182
Figure BDA0003774147810000183
Figure BDA0003774147810000184
E. clutter Probability (CP)
The Clutter Probability (CP) expression is:
Figure BDA0003774147810000185
CP values between 0 and 1, and it is generally considered that when CP > 0.5, the range bin is considered as a clutter.
3) And according to the judgment result of the ground clutter region, independently retaining the reflectivity factors of the ground clutter region and the weather echo region before and after the suppression of different algorithms. And respectively calculating a PPI difference diagram of the base data for the ground clutter area and the weather echo area, and directly analyzing the residual ground clutter condition and the weather echo loss condition.
4) Calculating a ground clutter suppression quantification index: ground Clutter Suppression Ratio (CFSR) and Echo power Loss Ratio (WELR), and CFSR and WELR of different normalized spectral widths and radial velocity variation are respectively calculated, and comparison between curves is carried out, wherein the two are respectively defined as:
CFSR＝10log 10 (P c /P cf )＝dBZ c -dBZ cf
WELR＝10log 10 (P w /P wf )＝dBZ w -dBZ wf
wherein P is c Ground clutter power, P, before suppression for individual ground clutter regions cf Ground clutter power after suppression for individual ground clutter regions, P w Weather echo power, P, before suppression for individual weather echo zones wf The suppressed weather echo power for the individual weather echo region. dBZ is usually used to determine the clutter suppression effect c Regions greater than 60dBZ are selected.
5) The ground clutter suppression algorithm generally has a large influence on the zero-velocity weather echo, so that the weather echo region is further divided into a zero-velocity region and a non-zero-velocity region, and the average deviation bias, the standard deviation std and the correlation coefficient rho of the front and back basic data for suppression by different algorithms in the two regions are respectively calculated.
6) The advantages and disadvantages of the ground clutter suppression algorithm are measured by the ground clutter suppression ratio CFSR, generally the CFSR is more than 50dB, the loss ratio of the ground clutter suppression algorithm to the weather echo is less than 1dB, and the calibration target is basically achieved.
The following examples of the present invention will further illustrate the feasibility and progress of the present invention in conjunction with specific embodiments thereof.
Example 1:
this embodiment is a method and a flow for calibrating signal processing of a base data algorithm between signal processing systems of different manufacturers, and fig. 1 is a method for calibrating an algorithm of different signal processing systems. Fig. 6 is a calibration process of signal processing systems of different manufacturers, which includes the following steps:
(1) The algorithm calibration method of different signal processing systems comprises the following steps: the signal processing calibration system is connected with the signal processing system 1 to be calibrated and the signal processing system 2 to be calibrated through the Ethernet; the signal processing calibration system takes out the standard IQ file of the base data signal processing algorithm from the standard data set, reads the standard IQ data, and performs protocol packet mode
(2) The signal processing calibration system sends IQ data and signal processing parameters to be calibrated to the signal processing systems 1 and 2 in a radial data stream mode, then the calibration signal processing systems 1 and 2 analyze the data and the parameters according to the signal processing parameters, carry out basic data signal processing algorithm processing on the IQ data, and after the processing is finished, the IQ data is fed back to the signal processing calibration system in a basic data stream mode.
(3) After the signal processing calibration system receives the base data stream, carrying out protocol analysis on the base data, taking out standard base data corresponding to the standard IQ data from the standard data set, and then carrying out base data preprocessing;
(4) And the signal processing calibration system calibrates the flow according to a base data signal processing algorithm. And the algorithm calibration system compares and calibrates the results processed by the signal processing system to be calibrated 1 and the signal processing system to be calibrated 2 with the standard base data respectively, and the difference between the signal processing system to be calibrated 1 and the signal processing system to be calibrated 2 and the standard data set is calibrated. If the difference between the two signal processing systems is smaller than a certain range, if the standard deviation of the reflectivity factor is smaller than 1dB, the requirement is met, the consistency of the signal processing systems to be calibrated of different manufacturers is better, otherwise, the signal processing systems with the difference not meeting the requirement are required to be subjected to algorithm improvement and parameter modification so as to meet the index requirement.
Fig. 7a-c are PPI plots showing reflectivity factor ratio for one calibration process of base data signal processing, where fig. 7a, fig. 7b, and fig. 7c are PPI plots of a standard base data file, a signal processing system 1 to be calibrated, and a signal processing system 2 to be calibrated, respectively. As can be seen from the comparison of the figures, the echo structures and the profiles of the three figures are consistent, and the basic processing results of the calibration signal processing system 1 and the signal processing system to be calibrated 2 are correct.
Fig. 8a and 8b are graphs of reflectivity difference corresponding to each distance bin between the standard base data and the signal processing system to be calibrated 1 and between the standard base data and the signal processing system to be calibrated 2, respectively. It can be seen from the figure that the reflectivity difference between the signal processing system 2 to be calibrated and the standard base data is within 0.5dB, which meets the business requirement of 1dB, while the reflectivity difference between the signal processing system 1 to be calibrated and the standard data set fluctuates between 1-3dB, which indicates that the signal processing system 1 to be calibrated needs to further adjust parameters to meet the requirement.
Fig. 9a and 9b are reflectance scatter diagram comparisons corresponding to each distance bin between the standard base data and the signal processing system to be calibrated 1 and between the standard base data and the signal processing system to be calibrated 2, respectively. As can be seen from the figure, the difference of the reflectivity of the signal processing system 2 to be calibrated and the standard base data is small; the difference between the reflectivity of the signal processing system 1 to be calibrated and the reflectivity of the standard data set is large.
The table 1 shows the standard base data and the signal processing system 1 to be calibrated, the standard base data and the signal processing system 2 to be calibrated have the calculated values of the mean deviation bias, the standard deviation std and the correlation coefficient rho of the standard deviation refractive index factors in the strong echo region and the weak echo region, and as can be seen from the table, the mean deviation and the standard deviation of the signal processing system 2 to be calibrated and the standard coefficient are both 0.005dB and are completely consistent with the standard data; the standard deviation of the signal processing system 1 to be calibrated reaches more than 2dB and does not meet the requirement within 1dB, and a processing algorithm needs to be further improved.
TABLE 1 bias, std and rho contrast of reflectivity factors processed by two different signal processing system base data signal processing algorithms
Figure BDA0003774147810000201
Example 2:
the embodiment is a signal processing calibration method and a signal processing calibration flow of a ground clutter suppression algorithm between signal processing systems of different manufacturers, and the algorithm calibration method of the signal processing systems of different manufacturers is shown in fig. 1. Referring to fig. 2, a calibration process of signal processing systems of different manufacturers includes the following steps:
(1) The algorithm calibration method of different signal processing systems comprises the following steps: the signal processing calibration system is connected with the signal processing system 1 to be calibrated and the signal processing system 2 to be calibrated through the Ethernet; and the signal processing calibration system takes out a standard IQ file of a ground clutter suppression signal processing algorithm from the standard data set, reads the standard IQ data and performs a protocol packet mode.
(2) The signal processing calibration system sends IQ data and signal processing parameters to be calibrated to the signal processing systems 1 and 2 in a radial data stream mode, then the calibration signal processing systems 1 and 2 analyze the data and the parameters according to the signal processing parameters, carry out ground clutter suppression algorithm processing on the IQ data, and after the processing is finished, the IQ data is fed back to the signal processing calibration system in a base data stream mode.
(3) And after the signal processing calibration system receives the base data stream, carrying out protocol analysis on the base data, taking out standard base data corresponding to the ground clutter calibration standard IQ data from the standard data set, and then carrying out base data preprocessing.
(4) And the signal processing calibration system calibrates the flow according to a ground clutter suppression algorithm. The algorithm calibration system calibrates and compares the results processed by the signal processing system 1 to be calibrated and the signal processing system 2 to be calibrated according to two different conditions of a clutter area and a weather echo area, and respectively calculates a clutter suppression ratio and a weather echo loss ratio. If the clutter suppression ratio is more than 50dB, the clutter suppression capability meets the requirement; the smaller the weather return loss ratio, the better, the system index requirement is within 1dB, and the design requirement can be met.
Fig. 10a-c are PPI graphs showing reflectivity factor ratios of clutter regions in the primary ground clutter suppression algorithm calibration process, where fig. 10a, fig. 10b, and fig. 10c are PPI graphs of standard base data clutter region reflectivities, and PPI graphs of clutter region reflectivities of the signal processing system to be calibrated 1 and the signal processing system to be calibrated 2, respectively. As can be seen from comparison in the figure, the calibration signal processing system 1 and the signal processing system to be calibrated 2 both have certain clutter suppression capability. However, the signal processing system 2 to be calibrated has relatively poor suppression capability and more detail residual ground object echoes.
The calculation results of the ground clutter rejection ratio of the standard base data and the signal processing system to be calibrated 1 are given in the table 2, the rejection ratio of the signal processing system to be calibrated 1 is higher than 50dB, and the service requirements are met. And the ground feature suppression ratio of the calibration signal processing system 2 is about 40dB, which does not meet the service requirement, and the ground feature suppression algorithm needs to be improved.
TABLE 2 clutter rejection ratio CFSR of ground clutter algorithms for two different signal processing systems
System 1 System 2
Clutter rejection ratio CFSR (dB) 54.87 40.08
Fig. 11a and 11b are graphs of reflectivity difference corresponding to each distance bin between the standard base data and the signal processing system to be calibrated 1 and between the standard base data and the signal processing system to be calibrated 2, respectively. The lower right ellipse part is a weather echo region, and it can be seen from the figure that the signal processing system 1 to be calibrated and the standard data have small weather echo loss before and after inhibition, and the signal processing system 2 to be calibrated and the standard data have large weather echo loss before and after inhibition.
And a calculation result of the weather echo loss ratio of the standard base data and the signal processing system to be calibrated 1 and the weather echo loss ratio of the signal processing system to be calibrated 2 is given in the table 3, and the suppression ratio of the signal processing system to be calibrated 1 is lower than 1dB, so that the service requirement is met. And the ground feature suppression ratio of the calibration signal processing system 2 is more than 2dB, which does not meet the requirements of services, and the ground feature suppression algorithm needs to be improved.
TABLE 3 weather echo ratio WELR of ground clutter algorithm of two different signal processing systems
System 1 System 2
Weather echo loss ratio WELR (dB) 0.02 2.34
Moreover, although exemplary embodiments have been described herein, the scope thereof includes any and all embodiments based on the invention with equivalent elements, modifications, omissions, combinations (e.g., of various embodiments across), adaptations or alterations. The elements of the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described in the present specification or during the prosecution of the application, which examples are to be construed as non-exclusive. It is intended, therefore, that the specification and examples be considered as exemplary only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.
The above description is intended to be illustrative and not restrictive. For example, the above-described examples (or one or more versions thereof) may be used in combination with each other. For example, other embodiments may be used by those of ordinary skill in the art upon reading the above description. In addition, in the foregoing detailed description, various features may be grouped together to streamline the disclosure. This should not be interpreted as an intention that features of an unclaimed invention be essential to any of the claims. Rather, inventive subject matter may lie in less than all features of a particular inventive embodiment. Thus, the following claims are hereby incorporated into the detailed description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that these embodiments may be combined with each other in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims (10)

1. A weather radar signal processing system algorithm calibration method is characterized by comprising the following steps:
acquiring a standard data set, wherein the standard data set comprises a standard IQ data set and a standard basic data set, the standard IQ data comprises radar original data, the radar original data comprises I and Q signals of a vertical channel and a horizontal channel of each radar distance library, the standard basic data set is a calibration index parameter obtained after the standard IQ data passes through a standard signal processor, and the calibration index parameter at least comprises one of reflectivity, speed, spectrum width, differential reflectivity, differential phase, correlation coefficient and signal-to-noise ratio;
transmitting the standard IQ data set to a signal processing system to be calibrated in a radial data stream mode, and receiving base data sent by the signal processing system to be calibrated, wherein the base data is obtained by processing the standard IQ data set through the signal processing system to be calibrated;
and comparing the base data with the calibration index parameters, and calibrating the algorithm of the signal processing system to be calibrated.
2. The method of claim 1, wherein said transmitting the standard IQ data set in a radial data stream to a signal processing system to be calibrated and receiving base data from the signal processing system to be calibrated comprises:
converting the standard IQ data set into a SWEEP data packet, wherein the SWEEP data packet comprises a plurality of radial SWEEP data, and the radial SWEEP data comprises M groups of SWEEP data;
continuously sending SWEEP data packets to the signal processing system to be calibrated according to a pulse sequence at a PRT time interval or more than the PRT time interval, and finally additionally sending an ending identification packet; the continuously sending the SWEEP data packets to the signal processing system to be calibrated comprises the steps of sending 1 st group of SWEEP data, then sending 2 nd group of SWEEP data until the Mth group of SWEEP data is sent, combining the SWEEP data into one radial SWEEP data, repeating the steps in the same order, and then sending the next radial SWEEP data until all the radial SWEEP data are sent to the signal processing system to be calibrated;
and receiving feedback data from the signal processing system to be calibrated, storing the base data when the feedback data are the base data and the end marking packet, retransmitting the standard IQ data set to the signal processing system to be calibrated when the feedback data are feedback failure packets, and stopping retransmission when the retransmission times exceed a preset retransmission threshold.
3. The method of claim 1, wherein the base data comprises a weather radar level channel reflectivity factor, a weather radar radial velocity, a spectral width, a differential reflectivity factor, a differential propagation phase, a zero lag correlation coefficient, a signal-to-noise ratio, a reflectivity factor before dBT filtering; the step of comparing the base data with the calibration index parameters to determine whether the algorithm performance of the signal processing system to be calibrated meets the requirements comprises the following steps:
performing radial number matching, distance library number matching and effective value matching on the base data to obtain comparison data, so that the base data of the same elevation layer have the same radial number and distance library number, and meanwhile, each distance library participating in comparison has a one-to-one corresponding relation and is an effective value;
comparing the comparison data with a standard basic data set PPI library by library, qualitatively comparing the echo area, the contour and the space structure, comparing a base data PPI difference diagram, and qualitatively judging the deviation of an echo area through color by combining a color code;
comparing the comparison data with a profile diagram of any radial or any distance position of a standard basic data set, evaluating consistency by observing whether the comparison data and the profile diagram coincide, and simultaneously comparing the trend of the consistency changing along with the distance position or the azimuth angle;
comparing the comparison data with a scatter diagram of any radial direction or any distance position of a standard basic data set, evaluating consistency according to divergence degree of the scatter diagram or performing linear fitting on the scatter diagram, and evaluating consistency according to slope of a fitted straight line;
defining an echo region meeting the conditions that SNR is larger than 10dB and W-P4 m/s as an effective echo region, and defining an echo region meeting the conditions that SNR is larger than 30dB and W-P4 m/s as a strong echo region; defining an echo region which meets 0 dB-to-0 SNR (noise ratio) 10dB and W-to-4 m/s as a weak echo region, wherein the SNR is a signal-to-noise ratio; the mean deviation bias, the standard deviation std and the correlation coefficient ρ between the base data in these three regions and the standard base data set are compared, respectively, and are expressed as:
Figure FDA0003774147800000021
Figure FDA0003774147800000022
Figure FDA0003774147800000023
wherein X i Standard base data, Y, representing the ith distance bin i Base data, | X, representing the ith distance after processing by the algorithm to be calibrated i -Y i Where | represents the absolute deviation between the base data and the standard base data set, N is the number of valid samples of data, i is the sample sequence number,
Figure FDA0003774147800000031
for N standard base data X i Is determined by the average value of (a) of (b),
Figure FDA0003774147800000032
for N standard base data Y i Average value of (a).
Dividing the base data from small to large, and respectively calculating the average deviation of an effective echo region, a strong echo region and a weak echo region;
and measuring whether the original power and the noise of the signal processing system to be calibrated are accurately calculated according to the standard signal-to-noise ratio.
4. The method according to claim 3, wherein the performing radial number matching, distance bin number matching and valid value matching on the base data to obtain comparison data comprises:
the radial number matching includes: processing the base data as a two-dimensional array, wherein the rows of the array represent the radial direction, the columns of the array represent the distance position, and the base data are radially filled into corresponding rows of the blank array according to an azimuth angle obtained from a radial head block when the base data are read by the following method:
i. when only one radial direction exists after the azimuth angle zero rounding, filling the current radial direction into the corresponding row of the blank array;
ii, after azimuth zero rounding, a plurality of radial directions exist, and the next radial direction is filled into a corresponding row of the blank array;
iii, after azimuth angle zero rounding, azimuth angle missing exists, and the previous line is radially filled into a corresponding line of the blank array;
the distance bin number matching comprises: calculating the reference distance bin number B by the following formula max ：
Figure FDA0003774147800000033
Wherein τ is the pulse width and PRF is the pulse repetition frequency;
if the maximum distance library number of the base data is larger than the reference distance library number, deleting the part of the base data larger than the reference distance library number; if the maximum distance base number of the base data is smaller than the reference distance base number, the maximum distance base number is equal to the reference distance base number;
the valid value matching includes: if a certain distance library in the standard basic data or the base data is an invalid value, the corresponding distance library in other base data is also assigned as the invalid value.
5. The method of claim 3, wherein the measuring the accuracy of the calculation of the raw power and the noise of the signal processing system to be calibrated according to the standard signal-to-noise ratio comprises:
when the average deviation of the typical calibration area is less than 0.5dB, the standard deviation is less than 1dB, and the correlation coefficient is greater than 0.99, the original power and noise of the signal processing system to be calibrated are accurate;
under the condition of the same radar calibration constant, pulse accumulation number and atmospheric constant, the average deviation of the reflectivity factor dBT before filtering inhibition and the reflectivity factor Z after inhibition is less than 0.2dB, the standard deviation is less than 1dB, and the correlation coefficient is greater than 0.99, which indicates that the original power and noise of the signal processing system to be calibrated are accurate;
differential reflectivity Z dr The average deviation of the signal processing system to be calibrated is less than 0.1dB, the standard deviation of the signal processing system to be calibrated is less than 0.2dB, and the correlation coefficient of the signal processing system to be calibrated is more than 0.99, which indicates that the original power and the noise of the signal processing system to be calibrated are accurate.
6. The method of claim 1, wherein comparing the base data with the calibration index parameters to determine whether performance of an algorithm of a signal processing system to be calibrated meets requirements comprises:
performing radial number matching, distance library number matching and effective value matching on the base data to obtain comparison data, so that the base data of the same elevation layer have the same radial number and distance library number, and meanwhile, each distance library participating in comparison has a one-to-one corresponding relation and is an effective value;
and judging the ground clutter and the weather echo region, and performing qualitative comparison and quantitative comparison on the base data of the ground clutter and the weather echo region respectively according to the judgment result.
7. The method of claim 6, wherein determining the ground clutter and the weather echo region comprises:
for the base data subjected to ground clutter suppression, if the reflectivity factor dBT before filtering suppression and the reflectivity factor Z after suppression of a certain distance library are both effective values and meet dBT-Z > Thr, judging that the distance library is the ground clutter, wherein Thr is a judgment threshold;
for the base data without ground clutter suppression, dBT = Z, thr is set Z And Thr V Respectively as the judgment threshold of the reflectivity factor and the radial velocity, if the reflectivity factor Z and the radial velocity V after the inhibition of a certain distance library are both effective values and satisfy Z>Thr Z ,abs(V)<Thr V Then, the distance library can be judged as the ground clutter.
8. The method according to claim 6, wherein the determining the ground clutter and the weather echo region, and performing the qualitative comparison and the quantitative comparison on the base data of the ground clutter and the weather echo region according to the determination result comprises:
the measured scatter texture TDBZ of the degree of variation between the reflectivity factor distance bins is calculated according to the following formula:
Figure FDA0003774147800000041
wherein Z is a reflectivity factor, i is the index number of the selected radial direction, j is the index number of the initial distance library, and M is the number of the selected distance libraries when the TDBZ is calculated;
if the adjacent distance bin reflectivity factor satisfies the following two conditions:
sign{Z i,j -Z i,j-1 }＝-sign{Z i,j+1 -Z i,j }
Figure FDA0003774147800000051
the measure SPIN of the gradient sign change frequency of the reflectivity factor in a certain radial direction of the distance library is 1, the number of the distance libraries meeting the SPIN of 1 in the M distance libraries is counted, and the percentage is calculated;
calculating a clutter phase suppression CPA according to the following formula:
Figure FDA0003774147800000052
wherein x is n An I/Q sequence of an nth range bin of a range bin;
measuring scattering rate texture TDBZ of variation degree between reflectivity factor distance libraries, measuring scattering rate gradient rotation variation SPIN of variation frequency of reflectivity factor gradient sign on a certain radial direction of the distance libraries, clutter phase suppression CPA, and differential reflectivity texture sigma ZDRn Differential phase texture sigma φDPn The weight converted to 0-1 is:
Figure FDA0003774147800000053
Figure FDA0003774147800000054
Figure FDA0003774147800000055
Figure FDA0003774147800000056
Figure FDA0003774147800000061
wherein TDBZ n Scattering Rate texture for the nth Range library, SPIN n Scattering rate gradient rotation change for nth range bin, CPA n Clutter phase suppression for the nth range bin;
the clutter probability is calculated according to the following formula:
Figure FDA0003774147800000062
wherein the value of CP is between 0-1, if CP > 0.5, regard this distance library as the clutter of ground object;
according to the judgment result of the ground clutter, independently retaining the reflectivity factors of the ground clutter areas and the weather echo areas before and after the inhibition of different algorithms; respectively calculating a PPI difference diagram of the base data for the ground clutter area and the weather echo area, and directly analyzing the residual ground clutter condition and the weather echo loss condition;
respectively calculating ground clutter suppression ratio CFSR and echo power loss ratio WELR of different normalized spectral widths and radial velocity changes, and comparing curves, wherein the ground clutter suppression ratio CFSR and the echo power loss ratio WELR are respectively defined as:
CFSR＝10log 10 (P c /P cf )＝dBZ c -dBZ cf
WELR＝10log 10 (P w /P wf )＝dBZ w -dBZ wf
wherein P is c Ground clutter power, P, before suppression for individual ground clutter regions cf For ground clutter power, P, after suppression of individual ground clutter regions w Weather echo power, P, before suppression for individual weather echo zones wf The weather echo power after the suppression is carried out on the independent weather echo area;
and if the CFSR is more than 50dB and the WELR is less than 1dB, determining that the signal processing system to be calibrated meets the standard requirement.
9. The weather radar signal processing system algorithm calibration device is characterized by comprising a signal processing calibration system and a signal processing system to be calibrated:
the signal processing calibration system is configured to obtain a standard data set, the standard data set comprises a standard IQ data set and a standard basic data set, the standard IQ data comprises radar original data, the radar original data comprises I and Q signals of a vertical channel and a horizontal channel of each radar distance library, the standard basic data set is a calibration index parameter obtained after the standard IQ data passes through a standard signal processor, and the calibration index parameter at least comprises one of reflectivity, speed, spectral width, differential reflectivity, differential phase, correlation coefficient and signal-to-noise ratio; transmitting the standard IQ data set to a signal processing system to be calibrated in a radial data stream mode, and receiving base data sent by the signal processing system to be calibrated; comparing the base data with the calibration index parameters, and calibrating a signal processing system algorithm to be calibrated;
the signal processing system to be calibrated is configured to receive a standard I1 data set from the signal processing calibration system, process the standard IQ data set to obtain base data, and transmit the base data to the signal processing calibration system.
10. The apparatus of claim 9, wherein the signal processing calibration system is further configured to:
converting the standard IQ data set into a SWEEP data packet, wherein the SWEEP data packet comprises a plurality of radial SWEEP data, and the radial SWEEP data comprises M groups of SWEEP data;
continuously sending SWEEP data packets to the signal processing system to be calibrated according to a pulse sequence at a PRT time interval or more than the PRT time interval, and finally additionally sending an end identification packet; the continuously sending the SWEEP data packets to the signal processing system to be calibrated comprises the steps of sending 1 st group of SWEEP data, then sending 2 nd group of SWEEP data until the Mth group of SWEEP data is sent, combining the SWEEP data into one radial SWEEP data, repeating the steps in the same order, and then sending the next radial SWEEP data until all the radial SWEEP data are sent to the signal processing system to be calibrated;
receiving feedback data from the signal processing system to be calibrated, storing the base data when the feedback data is a base data and an end marking packet, retransmitting a standard IQ data set to the signal processing system to be calibrated when the feedback data is a feedback failure packet, and stopping retransmission when the retransmission times exceed a preset retransmission threshold.
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