CN117368870B - Load characteristic evaluation method, device and equipment for microwave scatterometer - Google Patents
Load characteristic evaluation method, device and equipment for microwave scatterometer Download PDFInfo
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Abstract
The invention discloses a method, a device and equipment for evaluating load characteristics of a microwave scatterometer, relates to the technical field of satellite remote sensing, and is used for solving the problems that in the prior art, the load characteristic evaluation process of the microwave scatterometer is complex and the timeliness is poor. Comprising the following steps: based on the microwave scatterometer observation data of different levels obtained in real time, the calculation and comparison of a series of characteristic parameters including the antenna rotating speed, the gain, the internal calibration, the scatterometer working mode, the MLE mean value, the average deviation and standard deviation compared with a background field, the effective Sigma0 observation quantity, the data acquisition timeliness and the data rejection rate can be realized, so that the load characteristic of the microwave scatterometer is estimated, auxiliary data is not required to be additionally obtained from an external data source, the processing evaluation can be carried out on the multi-level data, the real-time evaluation is carried out on the overall load state of the microwave scatterometer, and the method is simple and easy to realize, high in processing efficiency, reliable in result and good in timeliness.
Description
Technical Field
The invention relates to the technical field of satellite remote sensing, in particular to a method, a device and equipment for evaluating load characteristics of a microwave scatterometer.
Background
As a remote sensing sensor capable of efficiently acquiring a sea surface wind field, the technology for carrying out sea surface wind field inversion by observing provided by a satellite-borne microwave scatterometer has been developed for over 40 years. The microwave scatterometer is a main satellite-borne sensor capable of acquiring all-weather and high-precision global sea surface wind field data in a short time at present. The quality inspection analysis method of the load characteristic of the microwave scatterometer mainly relates to a series of inspection methods of the on-orbit performance based on the load of the microwave scatterometer.
At present, the on-orbit performance inspection of the microwave scatterometer is carried out a series of fine works from the angle of correctness of backward scattering coefficient inversion results, the process is relatively complex, the timeliness is slightly poor, and the performance of the microwave scatterometer cannot be monitored in real time.
Accordingly, there is a need to provide a more reliable load characteristic assessment scheme for a microwave scatterometer.
Disclosure of Invention
The invention aims to provide a method, a device and equipment for evaluating load characteristics of a microwave scatterometer, which are used for solving the problems of complex load characteristic evaluation process and poor timeliness of the microwave scatterometer in the prior art.
In order to achieve the above object, the present invention provides the following technical solutions:
In a first aspect, the present invention provides a method for evaluating load characteristics of a microwave scatterometer, the method comprising:
acquiring microwave scatterometer observation data and determining the data level of the microwave scatterometer observation data; the data level comprises microwave scatterometer L1A observation data, microwave scatterometer L1B observation data, microwave scatterometer L2A observation data and microwave scatterometer L2B observation data; the microwave scatterometer observation data carries a data file; the data file contains threshold values corresponding to the characteristic parameters; the characteristic parameters at least comprise antenna rotating speed, receiver gain information, internal calibration information, working modes, effective Sigma0 observation quantity, MLE mean value, standard deviation and average deviation from background field, data timeliness and data rejection rate;
based on the microwave scatterometer L1A observation data, calculating to obtain the antenna rotating speed, the receiver gain information and the internal calibration information by taking a preset duration as a time interval;
determining the working mode of the microwave scatterometer according to the observed data of the microwave scatterometer L1B;
counting the number of valid Sigma0 observations based on the microwave scatterometer L2A observations;
According to the microwave scatterometer L2B observation data, calculating the MLE mean value, the standard deviation and the average difference of the MLE mean value and the background field, the data timeliness and the data rejection rate;
and comparing the calculated antenna rotating speed, the calculated receiver gain information, the calculated internal calibration information, the calculated working mode, the calculated effective Sigma0 observation quantity, the calculated MLE mean value, the calculated standard deviation and the calculated average deviation of the background field, the calculated data timeliness and the calculated data rejection rate with corresponding thresholds in the data file respectively, and evaluating the load characteristic of the microwave scatterometer according to the comparison result.
Compared with the prior art, the load characteristic evaluation method of the microwave scatterometer provided by the invention is used for evaluating the load characteristic of the microwave scatterometer. Obtaining microwave scatterometer observation data and determining a data level; the data level comprises microwave scatterometer L1A observation data, microwave scatterometer L1B observation data, microwave scatterometer L2A observation data and microwave scatterometer L2B observation data; the microwave scatterometer observation data carries a data file; the data file contains threshold values corresponding to the characteristic parameters; based on the microwave scatterometer L1A observation data, calculating to obtain antenna rotating speed, receiver gain information and internal calibration information by taking preset duration as a time interval; determining the working mode of the microwave scatterometer according to the observed data of the microwave scatterometer L1B; based on the microwave scatterometer L2A observation data, counting the effective Sigma0 observation quantity; according to the microwave scatterometer L2B observation data, calculating to obtain an MLE mean value, a standard deviation and an average deviation of a background field, data timeliness and a data rejection rate; and comparing each calculated characteristic parameter with a corresponding threshold value in the data file, and evaluating the load characteristic of the microwave scatterometer according to the comparison result. The method can realize calculation and comparison of a series of characteristic parameters including antenna rotating speed, gain, internal calibration, scatterometer working mode, MLE mean value, average deviation and standard deviation compared with a background field, effective Sigma0 observation quantity, data acquisition timeliness and data rejection rate based on the microwave scatterometer observation data acquired in real time, so that the load characteristic of the microwave scatterometer is estimated, auxiliary data is not required to be acquired from an external data source, the multi-level data can be processed and estimated, the whole load state of the microwave scatterometer is estimated in real time, and the method is simple and easy to realize in process, high in processing efficiency, reliable in result and good in timeliness.
In a second aspect, the present invention provides a load characteristic evaluation device for a microwave scatterometer, the device comprising:
the system comprises a data level determining module of the microwave scatterometer observation data, a data processing module and a data processing module, wherein the data level determining module is used for acquiring the microwave scatterometer observation data and determining the data level of the microwave scatterometer observation data; the data level comprises microwave scatterometer L1A observation data, microwave scatterometer L1B observation data, microwave scatterometer L2A observation data and microwave scatterometer L2B observation data; the microwave scatterometer observation data carries a data file; the data file contains threshold values corresponding to the characteristic parameters; the characteristic parameters at least comprise antenna rotating speed, receiver gain information, internal calibration information, working modes, effective Sigma0 observation quantity, MLE mean value, standard deviation and average deviation from background field, data timeliness and data rejection rate;
the first characteristic parameter calculation module is used for calculating the antenna rotating speed, the receiver gain information and the internal calibration information by taking preset duration as a time interval based on the microwave scatterometer L1A observation data;
the second characteristic parameter calculation module is used for determining the working mode of the microwave scatterometer according to the observed data of the microwave scatterometer L1B;
A third characteristic parameter calculation module, configured to count the effective Sigma0 observation number based on the microwave scatterometer L2A observation data;
the fourth characteristic parameter calculation module is used for calculating the MLE mean value, the standard deviation and the average difference of the MLE mean value and the background field, the data timeliness and the data rejection rate according to the microwave scatterometer L2B observation data;
and the microwave scatterometer load characteristic evaluation module is used for respectively comparing the calculated antenna rotating speed, the receiver gain information, the internal calibration information, the working mode, the effective Sigma0 observation quantity, the MLE mean value, the standard deviation and the average difference between the effective Sigma0 observation quantity and the background field, the data timeliness and the data rejection rate with corresponding thresholds in the data file, and evaluating the microwave scatterometer load characteristic according to the comparison result.
In a third aspect, the present invention provides a load characteristic evaluation apparatus for a microwave scatterometer, the apparatus comprising:
the communication unit/communication interface is used for acquiring the microwave scatterometer observation data and determining the data level of the microwave scatterometer observation data; the data level comprises microwave scatterometer L1A observation data, microwave scatterometer L1B observation data, microwave scatterometer L2A observation data and microwave scatterometer L2B observation data; the microwave scatterometer observation data carries a data file; the data file contains threshold values corresponding to the characteristic parameters; the characteristic parameters at least comprise antenna rotating speed, receiver gain information, internal calibration information, working modes, effective Sigma0 observation quantity, MLE mean value, standard deviation and average deviation from background field, data timeliness and data rejection rate;
The processing unit/processor is used for calculating the antenna rotating speed, the receiver gain information and the internal calibration information by taking preset duration as a time interval based on the microwave scatterometer L1A observation data;
determining the working mode of the microwave scatterometer according to the observed data of the microwave scatterometer L1B;
counting the number of valid Sigma0 observations based on the microwave scatterometer L2A observations;
according to the microwave scatterometer L2B observation data, calculating the MLE mean value, the standard deviation and the average difference of the MLE mean value and the background field, the data timeliness and the data rejection rate;
and comparing the calculated antenna rotating speed, the calculated receiver gain information, the calculated internal calibration information, the calculated working mode, the calculated effective Sigma0 observation quantity, the calculated MLE mean value, the calculated standard deviation and the calculated average deviation of the background field, the calculated data timeliness and the calculated data rejection rate with corresponding thresholds in the data file respectively, and evaluating the load characteristic of the microwave scatterometer according to the comparison result.
In a fourth aspect, the present invention provides a computer storage medium having instructions stored therein, which when executed, implement the above-described method of evaluating load characteristics of a microwave scatterometer.
Technical effects achieved by the apparatus class scheme provided in the second aspect, the device class scheme provided in the third aspect, and the computer storage medium scheme provided in the fourth aspect are the same as those achieved by the method class scheme provided in the first aspect, and are not described herein.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a schematic flow chart of a load characteristic evaluation method of a microwave scatterometer provided by the invention;
FIG. 2 is a schematic diagram of a processing procedure of L1A observation data in a load characteristic evaluation method of a microwave scatterometer according to the present invention;
FIG. 3 is a schematic diagram of a processing procedure of L1B observation data in a load characteristic evaluation method of a microwave scatterometer according to the present invention;
FIG. 4 is a schematic diagram of a processing procedure of L2A observation data in a load characteristic evaluation method of a microwave scatterometer according to the present invention;
FIG. 5 is a schematic diagram of a processing procedure of L2B observation data in a load characteristic evaluation method of a microwave scatterometer according to the present invention;
FIG. 6 is a timing diagram of an antenna rotation speed check;
FIG. 7 is a timing diagram of gain and internal calibration inspection results;
FIG. 8 is a timing diagram of the operation mode checking result;
FIG. 9 is a timing diagram of the effective sigma0 observations;
FIG. 10 is a timing diagram of MLE mean check results;
FIG. 11 is a timing chart of the data timeliness check result;
FIG. 12 is a timing diagram of the result of the data rejection rate check;
FIG. 13 is a schematic diagram of a load characteristic evaluation device for a microwave scatterometer according to the present invention;
fig. 14 is a schematic diagram of a load characteristic evaluation apparatus for a microwave scatterometer according to the present invention.
Detailed Description
In order to clearly describe the technical solution of the embodiments of the present invention, in the embodiments of the present invention, the words "first", "second", etc. are used to distinguish the same item or similar items having substantially the same function and effect. For example, the first threshold and the second threshold are merely for distinguishing between different thresholds, and are not limited in order. It will be appreciated by those of skill in the art that the words "first," "second," and the like do not limit the amount and order of execution, and that the words "first," "second," and the like do not necessarily differ.
In the present invention, the words "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
In the present invention, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a alone, a and B together, and B alone, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, a and b, a and c, b and c, or a, b and c, wherein a, b, c can be single or multiple.
In the prior art, when monitoring the performance of a microwave scatterometer, auxiliary data needs to be acquired from an external data source for comparison and analysis, namely, a large amount of external auxiliary data needs to be additionally acquired besides the acquisition of observation parameters, each observation parameter needs to be additionally acquired for comparison, and long-time series are needed for comparison and matching, so that the process is complex, the efficiency is low, and the timeliness is poor.
Next, the scheme provided by the embodiments of the present specification will be described with reference to the accompanying drawings:
as shown in fig. 1, the process may include the steps of:
step 110: and acquiring microwave scatterometer observation data and determining the data level of the microwave scatterometer observation data.
In the prior art, data processing analysis is only carried out on the L2B-level sea surface wind field products, and the invention carries out data analysis on all product angles, including other levels of data (L1A level, L1B level and L2A level).
The data level includes a microwave scatterometer L1A observation, a microwave scatterometer L1B observation, a microwave scatterometer L2A observation, and a microwave scatterometer L2B observation.
The characteristic parameters at least comprise antenna rotating speed, receiver gain information, internal calibration information, working mode, effective Sigma0 observation quantity, MLE mean value, standard deviation and mean deviation from background field, data timeliness and data rejection rate.
The microwave scatterometer observation data carries a data file; the data file contains threshold values corresponding to the characteristic parameters, for example: the data file comprises a threshold value corresponding to the antenna rotating speed, a threshold value corresponding to the receiver gain information, a threshold value corresponding to the internal calibration information, a threshold value corresponding to the working mode, a threshold value corresponding to the effective Sigma0 observation quantity, a threshold value corresponding to the MLE mean value, a threshold value corresponding to the standard deviation and the mean deviation of the background field, a threshold value corresponding to the data timeliness and a threshold value corresponding to the data rejection rate.
Step 120: based on the microwave scatterometer L1A observation data, calculating to obtain the antenna rotating speed, the receiver gain information and the internal calibration information by taking a preset duration as a time interval.
The preset duration may be set according to the actual scene and the data requirement of the application field, for example: the time interval may be set to 1 minute. The antenna rotation speed, the receiver gain information and the internal calibration information may be calculated based on the observed data of the microwave scatterometer L1A, and when calculating, as shown in fig. 2, the antenna rotation speed is calculated, which specifically includes:
dividing the data into inner and outer beams by using pulse beam identifications in the microwave scatterometer L1A observation data;
according to each frame time information in the microwave scatterometer L1A observation data, calculating the difference value of adjacent pulse azimuth angles of each wave beam in a time interval by taking a preset duration as the time interval;
summing the differences of the adjacent pulse azimuth angles of each wave beam within a preset duration to obtain a summation result;
according to the summation result and the time interval, calculating to obtain the rotation speed of the antenna;
the method for calculating the gain information of the receiver specifically comprises the following steps:
determining a wind field measurement mode by using a working state identifier in the microwave scatterometer L1A observation data;
Acquiring a pulse of the wind field measurement mode and a receiver gain data set of the wind field measurement mode, and determining receiver gain information in the time interval;
calculating internal calibration information, which specifically comprises:
selecting an internal calibration pulse information and an information channel power value data set by using the working state identification of the microwave scatterometer L1A observation data;
and calculating the average signal channel power value of each pulse information, and outputting the average signal channel power value serving as internal calibration information by taking the time interval as a unit.
Step 130: and determining the working mode of the microwave scatterometer according to the observed data of the microwave scatterometer L1B.
The operating modes may include a wind measurement state, an internal calibration state, and a noise measurement state.
In determining the working mode, an external calibration mode needs to be determined, specifically, as shown in fig. 3, the external calibration mode is determined by using a Sigma0 mode identifier of the observed data of the microwave scatterometer L1B; the external calibration mode comprises three working states; the three working states are: wind measurement state, internal calibration state and noise measurement state;
counting the number of data frames in three working states according to the Sigma0 mode identification data set;
the duration of the outer-scaling mode is calculated from the number of data frames for the three operating states.
Step 140: based on the microwave scatterometer L2A observations, the effective Sigma0 observations were counted.
As shown in fig. 4, the counting the effective Sigma0 observations based on the microwave scatterometer L2A observations specifically includes:
dividing the wind vector unit index into inner and outer beams by using a wind unit incidence angle data set in the microwave scatterometer L2A observation data;
and respectively counting the occurrence times of the wind vector units with different cross track distances in the wind vector unit indexes within the time interval, and determining the occurrence times as the observation quantity of the effective Sigma 0.
Step 150: and calculating the MLE mean value, the standard deviation and the average difference of the MLE mean value and the background field, the data timeliness and the data rejection rate according to the microwave scatterometer L2B observation data.
As shown in fig. 5, calculating the MLE mean value may specifically include:
selecting a fuzzy solution index by utilizing a fuzzy solution maximum likelihood value in the microwave scatterometer L2B observation data, filtering and selecting through a KNMI quality identifier in a wind unit quality identifier, and calculating MLE average values of different cross track distances in each data file;
calculating standard deviation and average difference from background field may specifically include:
Dividing the data into different cross track directions by using the wind unit column numbers in the microwave scatterometer L2B observation data;
combining model data in the data files, and calculating average deviation and standard deviation of wind speed and wind direction inverted by the microwave scatterometer with different cross track distances of each data file and the wind speed and wind direction of a background field; the model data includes wind speed and wind direction model data.
Calculating the timeliness of the data can specifically comprise:
calculating the time difference between the data production date and the wind vector unit row in the microwave scatterometer L2B observation data by utilizing the microwave scatterometer L2B observation data in the time interval, and determining the time difference as the data timeliness of the current data file;
the calculating the data rejection rate may specifically include:
and counting wind vector unit identifications influenced by land and sea by utilizing the rejection identification information of KNMI quality control data in the wind vector unit quality marks in the microwave scatterometer L2B observation data, and representing the wind vector unit rejection rate in a percentage form.
Step 160: and comparing the calculated antenna rotating speed, the calculated receiver gain information, the calculated internal calibration information, the calculated working mode, the calculated effective Sigma0 observation quantity, the calculated MLE mean value, the calculated standard deviation and the calculated average deviation of the background field, the calculated data timeliness and the calculated data rejection rate with corresponding thresholds in the data file respectively, and evaluating the load characteristic of the microwave scatterometer according to the comparison result.
The calculated antenna rotating speed, the calculated receiver gain information, the calculated internal calibration information, the calculated working mode, the calculated effective Sigma0 observation quantity, the calculated MLE mean value, the calculated standard deviation and the calculated average deviation of the background field, the calculated data timeliness and the calculated data rejection rate are respectively compared with corresponding thresholds in the data file to obtain comparison results;
if any characteristic parameter in the comparison result exceeds a corresponding threshold value, determining that the load characteristic of the microwave scatterometer is abnormal, wherein the characteristic parameter exceeding the corresponding threshold value is an abnormal item;
and if all the characteristic parameters in the comparison result meet the corresponding threshold values, the load characteristic of the microwave scatterometer is normal.
The method of fig. 1, by acquiring microwave scatterometer observations and determining a data level; the data level comprises microwave scatterometer L1A observation data, microwave scatterometer L1B observation data, microwave scatterometer L2A observation data and microwave scatterometer L2B observation data; the microwave scatterometer observation data carries a data file; the data file contains threshold values corresponding to the characteristic parameters; based on the microwave scatterometer L1A observation data, calculating to obtain antenna rotating speed, receiver gain information and internal calibration information by taking preset duration as a time interval; determining the working mode of the microwave scatterometer according to the observed data of the microwave scatterometer L1B; based on the microwave scatterometer L2A observation data, counting the effective Sigma0 observation quantity; according to the microwave scatterometer L2B observation data, calculating to obtain an MLE mean value, a standard deviation and an average deviation of a background field, data timeliness and a data rejection rate; and comparing each calculated characteristic parameter with a corresponding threshold value in the data file, and evaluating the load characteristic of the microwave scatterometer according to the comparison result. The method can realize calculation and comparison of a series of characteristic parameters including antenna rotating speed, gain, internal calibration, scatterometer working mode, MLE mean value, average deviation and standard deviation compared with a background field, effective Sigma0 observation quantity, data acquisition timeliness and data rejection rate based on the microwave scatterometer observation data acquired in real time, so that the load characteristic of the microwave scatterometer is estimated, auxiliary data is not required to be acquired from an external data source, the multi-level data can be processed and estimated, the whole load state of the microwave scatterometer is estimated in real time, and the method is simple and easy to realize in process, high in processing efficiency, reliable in result and good in timeliness.
Based on the method of fig. 1, the examples of the present specification also provide some specific implementations of the method, as described below.
Next, taking 1 minute as an example, the steps 110 to 160 in fig. 1 will be described:
1) In step 120, the antenna rotation speed is calculated, and the antenna rotation speed check is performed based on the antenna rotation speed, specifically:
the data is divided into inner and outer beams by using pulse beam identification in the microwave scatterometer L1A observation data. According to the time information of each frame of the data, the difference of the adjacent pulse azimuth angles of each wave beam in each 1 minute is calculated by taking 1 minute as a time interval, the difference is summed, and the rotating speed of the antenna is calculated according to the sum value and the time interval (1 minute). And comparing the antenna rotating speed obtained by calculation with a threshold value, wherein the antenna rotating speed is abnormal when the antenna rotating speed exceeds the threshold value range.
As shown in fig. 6, the last frame time of the 1 minute time interval is taken as the horizontal axis, the antenna rotation speed is taken as the vertical axis, the time sequence diagram of the antenna rotation speed under the inner and outer beams is drawn, the threshold line is drawn, and the real-time monitoring result of the antenna rotation speed of the microwave scatterometer is expressed in an intuitive form.
2) In step 120, receiver gain information is calculated and gain checking is performed based on the receiver gain information:
The pulse of the wind field measurement mode is selected and the receiver gain data set of the wind field measurement mode is acquired by using the operation state identification information in the observed data of the microwave scatterometer L1A, and the receiver gain information of each 1 minute interval is output at 1 minute intervals. And comparing the gain machine information at 1 minute intervals with a threshold value, and if the gain machine information exceeds the threshold value range, judging that the gain machine information is abnormal.
Calculating internal calibration information, and performing internal calibration checking based on the internal calibration information: and selecting a data set of internal calibration pulse information and an information channel power value by using a working state identification data set of the observed data of the microwave scatterometer L1A, calculating an average signal channel power value of each pulse, and outputting the signals by taking 1 minute intervals as units. And comparing the average signal channel power value at 1 minute intervals with a threshold value, and if the average signal channel power value exceeds the threshold value range, determining that the average signal channel power value is abnormal. As shown in fig. 7, the time is taken as the horizontal axis, the gain machine information is taken as the vertical axis, the gain time sequence diagram of the microwave scatterometer is drawn, meanwhile, the threshold line is drawn, and the real-time monitoring result of the gain of the microwave scatterometer is displayed in an intuitive form.
And drawing a calibration checking time sequence diagram in the microwave scatterometer by taking time as a horizontal axis and a pulse power average value as a vertical axis, drawing a threshold line, and displaying a real-time monitoring result of the calibration in the microwave scatterometer in an intuitive form.
3) In step 130, after determining the operation mode, further operation mode checking is performed:
the external calibration mode is obtained from Sigma0 mode identification data of the microwave scatterometer L1B observation data, and has three working states: wind measurement state (128), internal calibration state (144), and noise measurement state (160). The duration of the outer-scaling mode is calculated from the Sigma0 mode identification dataset by counting the number of data frames for these three operating states. The duration of the off-scale mode is compared to a threshold range, and if the duration exceeds the threshold range, the off-scale mode is abnormal.
As shown in fig. 8, the time sequence diagram of the external calibration duration of the microwave scatterometer is drawn by taking the time of the microwave scatterometer as a horizontal axis and the external calibration duration as a vertical axis, and meanwhile, a threshold line is drawn, so that the real-time monitoring result of the external calibration mode duration of the microwave scatterometer is displayed in an intuitive form.
4) In step 140, after calculating the effective Sigma0 observed quantity, an effective Sigma0 observed quantity check is performed:
the wind vector unit index is divided into inner and outer beams by utilizing a wind unit incidence angle data set in the microwave scatterometer L2A observation data, and the times of occurrence of wind vector units with different cross track distances in the wind vector unit index within 1 minute interval are counted respectively, namely the observation quantity of effective Sigma0 (note: invalid value does not participate in operation). And comparing the counted effective Sigma0 observation quantity with a threshold value, wherein the condition that the number exceeds the threshold value range is abnormal.
As shown in fig. 9, a time-series chart of the effective Sigma0 observation number of the microwave scatterometer is drawn by taking time as a horizontal axis and the effective Sigma0 observation number as a vertical axis, and meanwhile, a threshold line is drawn, so that the real-time monitoring result of the effective Sigma0 observation number of the microwave scatterometer is displayed in an intuitive form.
5) In step 150, after the MLE mean is calculated, an MLE mean check is performed:
and selecting a fuzzy solution index by using a fuzzy solution maximum likelihood value (Maximum Likelihood Estimation, MLE) in the microwave scatterometer L2B observation data, filtering and selecting by using a KNMI quality identifier in the wind unit quality identifier, and calculating MLE average values of different cross track distances in each data file. Comparing the obtained MLE mean value with a threshold value, wherein the condition that the MLE mean value exceeds the threshold value is abnormal.
As shown in fig. 10, time is taken as a horizontal axis, an MLE average result is taken as a vertical axis, time charts of MLE average inspection results under different cross tracks are drawn, meanwhile, threshold lines are drawn, and the results of real-time monitoring of the MLE average of the microwave scatterometer are displayed in an intuitive form.
6) In step 150, the standard deviation and the average deviation from the background field:
dividing the data into different cross directions by using the wind unit column numbers in the microwave scatterometer L2B data, and calculating the average deviation and standard deviation of the inverted wind speed and direction of the microwave scatterometer with different cross direction distances of each data file and the wind speed and direction of the background field by combining the model data (wind speed and wind direction model data) in the data file. And comparing the average deviation with the standard deviation and a threshold value, wherein the deviation exceeding the threshold value range is abnormal.
7) In step 150, after determining the validity of the data, a data timeliness check is performed:
and taking 1 minute as an interval, and acquiring the time difference between the data production date of the microwave scatterometer L2B observation data and the wind vector unit row by using the two pieces of information, wherein the time difference is the data timeliness of the current data file. And comparing the data aging result with a threshold value, wherein the condition that the data aging result exceeds the threshold value range is abnormal.
As shown in fig. 11, the time is taken as the horizontal axis, the data timeliness checking result is taken as the vertical axis, the time sequence diagram of the data timeliness checking result of the microwave scatterometer is drawn, meanwhile, the threshold line is drawn, and the data timeliness real-time monitoring result of the microwave scatterometer is displayed in an intuitive form.
8) In step 150, after determining the data rejection rate, a data rejection rate check is performed
And counting wind vector unit identifications influenced by land, sea and the like by utilizing the rejection identification information of KNMI quality control data in the wind vector unit quality marks in the microwave scatterometer L2B observation data, and representing the wind vector unit rejection rate in a percentage form. And comparing the rejection rate of the wind vector unit with a threshold value, wherein the wind vector unit is abnormal when the rejection rate exceeds the threshold value range.
As shown in fig. 12, a time sequence diagram of the check result of the data rejection rate of the microwave scatterometer is drawn by taking time as a horizontal axis and the data rejection rate as a vertical axis, and meanwhile, a threshold line is drawn, so that the real-time monitoring result of the data rejection rate of the microwave scatterometer is displayed in an intuitive form.
In order to monitor the load characteristic of the microwave scatterometer in real time, the invention provides a method for evaluating the load characteristic of the microwave scatterometer, which develops antenna rotating speed, gain, internal calibration, scatterometer working mode, MLE average value, average deviation and standard deviation compared with background field, effective Sigma0 observation quantity, data acquisition timeliness and data rejection rate for real-time data of the microwave scatterometer.
Based on the same thought, the invention also provides a load characteristic evaluation device of the microwave scatterometer, as shown in fig. 13, the device can comprise:
a data level determining module 131 for acquiring the observed data of the microwave scatterometer and determining the data level of the observed data of the microwave scatterometer; the data level comprises microwave scatterometer L1A observation data, microwave scatterometer L1B observation data, microwave scatterometer L2A observation data and microwave scatterometer L2B observation data; the microwave scatterometer observation data carries a data file; the data file contains threshold values corresponding to the characteristic parameters; the characteristic parameters at least comprise antenna rotating speed, receiver gain information, internal calibration information, working modes, effective Sigma0 observation quantity, MLE mean value, standard deviation and average deviation from background field, data timeliness and data rejection rate;
A first characteristic parameter calculation module 132, configured to calculate, based on the observed data of the microwave scatterometer L1A, the antenna rotation speed, the receiver gain information, and the internal calibration information at a time interval of a preset duration;
a second characteristic parameter calculation module 133, configured to determine the operation mode of the microwave scatterometer according to the observed data of the microwave scatterometer L1B;
a third characteristic parameter calculation module 134, configured to count the effective Sigma0 observation number based on the microwave scatterometer L2A observation data;
a fourth characteristic parameter calculation module 135, configured to calculate the MLE mean value, standard deviation from a background field, average difference, the data timeliness, and the data rejection rate according to the microwave scatterometer L2B observation data;
and the microwave scatterometer load characteristic evaluation module 136 is configured to compare the calculated antenna rotation speed, the receiver gain information, the internal calibration information, the working mode, the effective Sigma0 observation number, the MLE average value, the standard deviation from background field and the average difference, the data timeliness and the data rejection rate with corresponding thresholds in the data file, and evaluate the microwave scatterometer load characteristic according to the comparison result.
Based on the apparatus in fig. 13, some specific implementation units may also be included:
optionally, the antenna rotation speed calculating unit in the first characteristic parameter calculating module 132 is specifically configured to:
dividing the data into inner and outer beams by using pulse beam identifications in the microwave scatterometer L1A observation data;
according to each frame time information in the microwave scatterometer L1A observation data, calculating the difference value of adjacent pulse azimuth angles of each wave beam in a time interval by taking a preset duration as the time interval;
summing the differences of the adjacent pulse azimuth angles of each wave beam within a preset duration to obtain a summation result;
according to the summation result and the time interval, calculating to obtain the rotation speed of the antenna;
optionally, the receiver gain information calculating unit in the first characteristic parameter calculating module 132 is specifically configured to:
determining a wind field measurement mode by using a working state identifier in the microwave scatterometer L1A observation data;
acquiring a pulse of the wind field measurement mode and a receiver gain data set of the wind field measurement mode, and determining receiver gain information in the time interval;
optionally, the internal calibration information calculating unit in the first characteristic parameter calculating module 132 is specifically configured to:
Selecting an internal calibration pulse information and an information channel power value data set by using the working state identification of the microwave scatterometer L1A observation data;
and calculating the average signal channel power value of each pulse information, and outputting the average signal channel power value serving as internal calibration information by taking the time interval as a unit.
Optionally, the second characteristic parameter calculating module 133 includes an operation mode determining unit, specifically configured to:
determining an external calibration mode by using a Sigma0 mode identifier of the microwave scatterometer L1B observation data; the external calibration mode comprises three working states; the three working states are: wind measurement state, internal calibration state and noise measurement state;
counting the number of data frames in three working states according to the Sigma0 mode identification data set;
the duration of the outer-scaling mode is calculated from the number of data frames for the three operating states.
Optionally, the effective Sigma0 observed quantity calculating unit in the third characteristic parameter calculating module 134 is specifically configured to:
dividing the wind vector unit index into inner and outer beams by using a wind unit incidence angle data set in the microwave scatterometer L2A observation data;
and respectively counting the occurrence times of the wind vector units with different cross track distances in the wind vector unit indexes within the time interval, and determining the occurrence times as the observation quantity of the effective Sigma 0.
Optionally, the MLE mean value calculation unit in the fourth characteristic parameter calculation module 135 is specifically configured to:
selecting a fuzzy solution index by utilizing a fuzzy solution maximum likelihood value in the microwave scatterometer L2B observation data, filtering and selecting through a KNMI quality identifier in a wind unit quality identifier, and calculating MLE average values of different cross track distances in each data file;
optionally, the fourth characteristic parameter calculating unit in the fourth characteristic parameter calculating module 135 is specifically configured to:
dividing the data into different cross track directions by using the wind unit column numbers in the microwave scatterometer L2B observation data;
combining model data in the data files, and calculating average deviation and standard deviation of wind speed and wind direction inverted by the microwave scatterometer with different cross track distances of each data file and the wind speed and wind direction of a background field; the model data includes wind speed and wind direction model data.
Optionally, the data timeliness calculation unit in the fourth characteristic parameter calculation module 135 is specifically configured to:
calculating the time difference between the data production date and the wind vector unit row in the microwave scatterometer L2B observation data by utilizing the microwave scatterometer L2B observation data in the time interval, and determining the time difference as the data timeliness of the current data file;
Optionally, the data rejection rate calculation unit in the fourth characteristic parameter calculation module 135 is specifically configured to:
and counting wind vector unit identifications influenced by land and sea by utilizing the rejection identification information of KNMI quality control data in the wind vector unit quality marks in the microwave scatterometer L2B observation data, and representing the wind vector unit rejection rate in a percentage form.
Optionally, the microwave scatterometer load characteristic evaluation module 136 may specifically include:
the comparison unit is used for respectively comparing the calculated antenna rotating speed, the calculated receiver gain information, the calculated internal calibration information, the calculated working mode, the calculated effective Sigma0 observation quantity, the calculated MLE mean value, the calculated standard deviation and the calculated average deviation of the background field, the calculated data timeliness and the calculated data rejection rate with corresponding thresholds in the data file to obtain comparison results;
the load characteristic abnormality determining unit is used for determining that the load characteristic of the microwave scatterometer is abnormal and the characteristic parameter exceeding the corresponding threshold value is abnormal if any characteristic parameter exceeding the corresponding threshold value exists in the comparison result;
and the normal load characteristic determining unit of the microwave scatterometer is used for determining that the load characteristic of the microwave scatterometer is normal if all the characteristic parameters in the comparison result meet the corresponding threshold values.
Based on the same thought, the embodiment of the specification also provides a load characteristic evaluation device of the microwave scatterometer. As shown in fig. 14, may include:
the communication unit/communication interface is used for acquiring the microwave scatterometer observation data and determining the data level of the microwave scatterometer observation data; the data level comprises microwave scatterometer L1A observation data, microwave scatterometer L1B observation data, microwave scatterometer L2A observation data and microwave scatterometer L2B observation data; the microwave scatterometer observation data carries a data file; the data file contains threshold values corresponding to the characteristic parameters; the characteristic parameters at least comprise antenna rotating speed, receiver gain information, internal calibration information, working modes, effective Sigma0 observation quantity, MLE mean value, standard deviation and average deviation from background field, data timeliness and data rejection rate;
the processing unit/processor is used for calculating the antenna rotating speed, the receiver gain information and the internal calibration information by taking preset duration as a time interval based on the microwave scatterometer L1A observation data;
determining the working mode of the microwave scatterometer according to the observed data of the microwave scatterometer L1B;
Counting the number of valid Sigma0 observations based on the microwave scatterometer L2A observations;
according to the microwave scatterometer L2B observation data, calculating the MLE mean value, the standard deviation and the average difference of the MLE mean value and the background field, the data timeliness and the data rejection rate;
and comparing the calculated antenna rotating speed, the calculated receiver gain information, the calculated internal calibration information, the calculated working mode, the calculated effective Sigma0 observation quantity, the calculated MLE mean value, the calculated standard deviation and the calculated average deviation of the background field, the calculated data timeliness and the calculated data rejection rate with corresponding thresholds in the data file respectively, and evaluating the load characteristic of the microwave scatterometer according to the comparison result.
As shown in fig. 14, the terminal device may further include a communication line. The communication line may include a pathway to communicate information between the aforementioned components.
Optionally, as shown in fig. 14, the terminal device may further include a memory. The memory is used for storing computer-executable instructions for executing the scheme of the invention, and the processor is used for controlling the execution. The processor is configured to execute computer-executable instructions stored in the memory, thereby implementing the method provided by the embodiment of the invention.
In a specific implementation, as one embodiment, as shown in FIG. 14, the processor may include one or more CPUs, such as CPU0 and CPU1 in FIG. 14.
In a specific implementation, as an embodiment, as shown in fig. 14, the terminal device may include a plurality of processors, such as the processor in fig. 14. Each of these processors may be a single-core processor or a multi-core processor.
Based on the same thought, the embodiments of the present disclosure further provide a computer storage medium corresponding to the above embodiments, where instructions are stored, and when the instructions are executed, the method in the above embodiments is implemented.
The above description has been presented mainly in terms of interaction between the modules, and the solution provided by the embodiment of the present invention is described. It is understood that each module, in order to implement the above-mentioned functions, includes a corresponding hardware structure and/or software unit for performing each function. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The embodiment of the invention can divide the functional modules according to the method example, for example, each functional module can be divided corresponding to each function, or two or more functions can be integrated in one processing module. The integrated modules may be implemented in hardware or in software functional modules. It should be noted that, in the embodiment of the present invention, the division of the modules is schematic, which is merely a logic function division, and other division manners may be implemented in actual implementation.
Although the invention has been described in connection with specific features and embodiments thereof, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the invention. Accordingly, the specification and drawings are merely exemplary illustrations of the present invention as defined in the appended claims and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the invention. It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. A method for evaluating load characteristics of a microwave scatterometer, the method comprising:
acquiring microwave scatterometer observation data and determining the data level of the microwave scatterometer observation data; the data level comprises microwave scatterometer L1A observation data, microwave scatterometer L1B observation data, microwave scatterometer L2A observation data and microwave scatterometer L2B observation data; the microwave scatterometer observation data carries a data file; the data file contains threshold values corresponding to the characteristic parameters; the characteristic parameters at least comprise antenna rotating speed, receiver gain information, internal calibration information, working modes, effective Sigma0 observation quantity, MLE mean value, standard deviation and average deviation from background field, data timeliness and data rejection rate;
based on the microwave scatterometer L1A observation data, calculating to obtain the antenna rotating speed, the receiver gain information and the internal calibration information by taking a preset duration as a time interval;
determining the working mode of the microwave scatterometer according to the observed data of the microwave scatterometer L1B;
counting the number of valid Sigma0 observations based on the microwave scatterometer L2A observations;
according to the microwave scatterometer L2B observation data, calculating the MLE mean value, the standard deviation and the average difference of the MLE mean value and the background field, the data timeliness and the data rejection rate;
Comparing the calculated antenna rotating speed, the calculated receiver gain information, the calculated internal calibration information, the calculated working mode, the calculated effective Sigma0 observation quantity, the calculated MLE mean value, the calculated standard deviation and the calculated average deviation of the background field, the calculated data timeliness and the calculated data rejection rate with corresponding thresholds in the data file respectively, and evaluating the load characteristic of the microwave scatterometer according to the comparison result;
the determining the working mode of the microwave scatterometer according to the observed data of the microwave scatterometer L1B specifically comprises the following steps:
determining an external calibration mode by using a Sigma0 mode identifier of the microwave scatterometer L1B observation data; the external calibration mode comprises three working states; the three working states are: wind measurement state, internal calibration state and noise measurement state;
counting the number of data frames in three working states according to the Sigma0 mode identification data set;
the duration of the outer-scaling mode is calculated from the number of data frames for the three operating states.
2. The method for evaluating load characteristics of a microwave scatterometer according to claim 1, wherein calculating the rotation speed of the antenna specifically comprises:
dividing the data into inner and outer beams by using pulse beam identifications in the microwave scatterometer L1A observation data;
According to each frame time information in the microwave scatterometer L1A observation data, calculating the difference value of adjacent pulse azimuth angles of each wave beam in a time interval by taking a preset duration as the time interval;
summing the differences of the adjacent pulse azimuth angles of each wave beam within a preset duration to obtain a summation result;
according to the summation result and the time interval, calculating to obtain the rotation speed of the antenna;
the method for calculating the gain information of the receiver specifically comprises the following steps:
determining a wind field measurement mode by using a working state identifier in the microwave scatterometer L1A observation data;
acquiring a pulse of the wind field measurement mode and a receiver gain data set of the wind field measurement mode, and determining receiver gain information in the time interval;
calculating internal calibration information, which specifically comprises:
selecting an internal calibration pulse information and an information channel power value data set by using the working state identification of the microwave scatterometer L1A observation data;
and calculating the average signal channel power value of each pulse information, and outputting the average signal channel power value serving as internal calibration information by taking the time interval as a unit.
3. The method for evaluating load characteristics of a microwave scatterometer according to claim 1, wherein the counting the effective Sigma0 observed quantity based on the microwave scatterometer L2A observed data specifically comprises:
Dividing the wind vector unit index into inner and outer beams by using a wind unit incidence angle data set in the microwave scatterometer L2A observation data;
and respectively counting the occurrence times of the wind vector units with different cross track distances in the wind vector unit indexes within the time interval, and determining the occurrence times as the observation quantity of the effective Sigma 0.
4. The method for evaluating load characteristics of a microwave scatterometer according to claim 1, wherein calculating the MLE average value comprises:
selecting a fuzzy solution index by utilizing a fuzzy solution maximum likelihood value in the microwave scatterometer L2B observation data, filtering and selecting through a KNMI quality identifier in a wind unit quality identifier, and calculating MLE average values of different cross track distances in each data file;
calculating standard deviation and average difference of the background field, specifically comprising:
dividing the data into different cross track directions by using the wind unit column numbers in the microwave scatterometer L2B observation data;
combining model data in the data files, and calculating average deviation and standard deviation of wind speed and wind direction inverted by the microwave scatterometer with different cross track distances of each data file and the wind speed and wind direction of a background field; the model data includes wind speed and wind direction model data.
5. The method for evaluating load characteristics of a microwave scatterometer according to claim 1, wherein calculating the timeliness of the data comprises:
calculating the time difference between the data production date and the wind vector unit row in the microwave scatterometer L2B observation data by utilizing the microwave scatterometer L2B observation data in the time interval, and determining the time difference as the data timeliness of the current data file;
the method for calculating the data rejection rate specifically comprises the following steps:
and counting wind vector unit identifications influenced by land and sea by utilizing the rejection identification information of KNMI quality control data in the wind vector unit quality marks in the microwave scatterometer L2B observation data, and representing the wind vector unit rejection rate in a percentage form.
6. The method according to claim 1, wherein the comparing the calculated antenna rotation speed, the receiver gain information, the internal calibration information, the operation mode, the effective Sigma0 observed quantity, the MLE mean value, the standard deviation from background field and the average deviation, the data timeliness and the data rejection rate with corresponding threshold values in the data file, and evaluating the microwave scatterometer load characteristic according to the comparison result, comprises:
The calculated antenna rotating speed, the calculated receiver gain information, the calculated internal calibration information, the calculated working mode, the calculated effective Sigma0 observation quantity, the calculated MLE mean value, the calculated standard deviation and the calculated average deviation of the background field, the calculated data timeliness and the calculated data rejection rate are respectively compared with corresponding thresholds in the data file to obtain comparison results;
if any characteristic parameter in the comparison result exceeds a corresponding threshold value, determining that the load characteristic of the microwave scatterometer is abnormal, wherein the characteristic parameter exceeding the corresponding threshold value is an abnormal item;
and if all the characteristic parameters in the comparison result meet the corresponding threshold values, the load characteristic of the microwave scatterometer is normal.
7. A load characteristic evaluation device for a microwave scatterometer, the device comprising:
the system comprises a data level determining module of the microwave scatterometer observation data, a data processing module and a data processing module, wherein the data level determining module is used for acquiring the microwave scatterometer observation data and determining the data level of the microwave scatterometer observation data; the data level comprises microwave scatterometer L1A observation data, microwave scatterometer L1B observation data, microwave scatterometer L2A observation data and microwave scatterometer L2B observation data; the microwave scatterometer observation data carries a data file; the data file contains threshold values corresponding to the characteristic parameters; the characteristic parameters at least comprise antenna rotating speed, receiver gain information, internal calibration information, working modes, effective Sigma0 observation quantity, MLE mean value, standard deviation and average deviation from background field, data timeliness and data rejection rate;
The first characteristic parameter calculation module is used for calculating the antenna rotating speed, the receiver gain information and the internal calibration information by taking preset duration as a time interval based on the microwave scatterometer L1A observation data;
the second characteristic parameter calculation module is used for determining the working mode of the microwave scatterometer according to the observed data of the microwave scatterometer L1B;
a third characteristic parameter calculation module, configured to count the effective Sigma0 observation number based on the microwave scatterometer L2A observation data;
the fourth characteristic parameter calculation module is used for calculating the MLE mean value, the standard deviation and the average difference of the MLE mean value and the background field, the data timeliness and the data rejection rate according to the microwave scatterometer L2B observation data;
the microwave scatterometer load characteristic evaluation module is used for respectively comparing the calculated antenna rotating speed, the receiver gain information, the internal calibration information, the working mode, the effective Sigma0 observation quantity, the MLE mean value, the standard deviation and the average difference between the average value and the background field, the data timeliness and the data rejection rate with corresponding thresholds in the data file, and evaluating the microwave scatterometer load characteristic according to the comparison result;
The second characteristic parameter calculation module comprises a working mode determination unit, and is specifically configured to:
determining an external calibration mode by using a Sigma0 mode identifier of the microwave scatterometer L1B observation data; the external calibration mode comprises three working states; the three working states are: wind measurement state, internal calibration state and noise measurement state;
counting the number of data frames in three working states according to the Sigma0 mode identification data set;
the duration of the outer-scaling mode is calculated from the number of data frames for the three operating states.
8. A load characteristic evaluation apparatus of a microwave scatterometer, characterized in that the apparatus comprises:
the communication unit/communication interface is used for acquiring the microwave scatterometer observation data and determining the data level of the microwave scatterometer observation data; the data level comprises microwave scatterometer L1A observation data, microwave scatterometer L1B observation data, microwave scatterometer L2A observation data and microwave scatterometer L2B observation data; the microwave scatterometer observation data carries a data file; the data file contains threshold values corresponding to the characteristic parameters; the characteristic parameters at least comprise antenna rotating speed, receiver gain information, internal calibration information, working modes, effective Sigma0 observation quantity, MLE mean value, standard deviation and average deviation from background field, data timeliness and data rejection rate;
The processing unit/processor is used for calculating the antenna rotating speed, the receiver gain information and the internal calibration information by taking preset duration as a time interval based on the microwave scatterometer L1A observation data;
determining the working mode of the microwave scatterometer according to the observed data of the microwave scatterometer L1B;
counting the number of valid Sigma0 observations based on the microwave scatterometer L2A observations;
according to the microwave scatterometer L2B observation data, calculating the MLE mean value, the standard deviation and the average difference of the MLE mean value and the background field, the data timeliness and the data rejection rate;
comparing the calculated antenna rotating speed, the calculated receiver gain information, the calculated internal calibration information, the calculated working mode, the calculated effective Sigma0 observation quantity, the calculated MLE mean value, the calculated standard deviation and the calculated average deviation of the background field, the calculated data timeliness and the calculated data rejection rate with corresponding thresholds in the data file respectively, and evaluating the load characteristic of the microwave scatterometer according to the comparison result;
the determining the working mode of the microwave scatterometer according to the observed data of the microwave scatterometer L1B specifically comprises the following steps:
determining an external calibration mode by using a Sigma0 mode identifier of the microwave scatterometer L1B observation data; the external calibration mode comprises three working states; the three working states are: wind measurement state, internal calibration state and noise measurement state;
Counting the number of data frames in three working states according to the Sigma0 mode identification data set;
the duration of the outer-scaling mode is calculated from the number of data frames for the three operating states.
9. A computer storage medium having instructions stored therein which, when executed, implement the method of evaluating load characteristics of a microwave scatterometer according to any one of claims 1 to 6.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013171565A2 (en) * | 2012-05-16 | 2013-11-21 | LICENZIATI, Arturo, Pino, Giuseppe, Antonio | Method and system for evaluating molecules in biological samples using microarray derived images |
| CN103698750A (en) * | 2014-01-07 | 2014-04-02 | 国家卫星海洋应用中心 | HY-2 satellite scatterometer sea surface wind field retrieval method and device |
| CN107300561A (en) * | 2016-04-15 | 2017-10-27 | 北京空间飞行器总体设计部 | Ocean Salinity satellite based on many remote sensor combined detections |
| CN112798013A (en) * | 2019-11-13 | 2021-05-14 | 中国科学院光电研究院 | Method for verifying on-orbit absolute radiation calibration result of optical load |
| CN112966656A (en) * | 2021-03-29 | 2021-06-15 | 国家卫星海洋应用中心 | Data processing method and device |
| WO2021159987A1 (en) * | 2020-02-11 | 2021-08-19 | Oppo广东移动通信有限公司 | Method and device for predicting operating state of vehicle, terminal, and storage medium |
| CN114442057A (en) * | 2022-01-25 | 2022-05-06 | 国家卫星海洋应用中心 | Microwave scatterometer load fault positioning method and system based on HY2 satellite |
| CN114779192A (en) * | 2022-06-17 | 2022-07-22 | 中国科学院空天信息创新研究院 | SAR (synthetic aperture radar) field-free calibration method and device, electronic equipment and storage medium |
| CN115718226A (en) * | 2022-10-31 | 2023-02-28 | 航天东方红卫星有限公司 | Sea wind and sea wave microwave load remote sensing satellite initial sample radio frequency compatibility test verification method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220413166A1 (en) * | 2021-05-18 | 2022-12-29 | Luminated Glazings, Llc | Scattering fields in a medium to redirect wave energy onto surfaces in shadow |
-
2023
- 2023-12-08 CN CN202311677450.XA patent/CN117368870B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013171565A2 (en) * | 2012-05-16 | 2013-11-21 | LICENZIATI, Arturo, Pino, Giuseppe, Antonio | Method and system for evaluating molecules in biological samples using microarray derived images |
| CN103698750A (en) * | 2014-01-07 | 2014-04-02 | 国家卫星海洋应用中心 | HY-2 satellite scatterometer sea surface wind field retrieval method and device |
| CN107300561A (en) * | 2016-04-15 | 2017-10-27 | 北京空间飞行器总体设计部 | Ocean Salinity satellite based on many remote sensor combined detections |
| CN112798013A (en) * | 2019-11-13 | 2021-05-14 | 中国科学院光电研究院 | Method for verifying on-orbit absolute radiation calibration result of optical load |
| WO2021159987A1 (en) * | 2020-02-11 | 2021-08-19 | Oppo广东移动通信有限公司 | Method and device for predicting operating state of vehicle, terminal, and storage medium |
| CN112966656A (en) * | 2021-03-29 | 2021-06-15 | 国家卫星海洋应用中心 | Data processing method and device |
| CN114442057A (en) * | 2022-01-25 | 2022-05-06 | 国家卫星海洋应用中心 | Microwave scatterometer load fault positioning method and system based on HY2 satellite |
| CN114779192A (en) * | 2022-06-17 | 2022-07-22 | 中国科学院空天信息创新研究院 | SAR (synthetic aperture radar) field-free calibration method and device, electronic equipment and storage medium |
| CN115718226A (en) * | 2022-10-31 | 2023-02-28 | 航天东方红卫星有限公司 | Sea wind and sea wave microwave load remote sensing satellite initial sample radio frequency compatibility test verification method |
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