CN111830451A - Method for inspecting non-imaging sensor - Google Patents
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Abstract
A method of inspection of a non-imaging sensor, comprising: acquiring detection data of a non-imaging sensor, wherein the detection data comprises data to be analyzed and background data, and preprocessing the acquired data, wherein the data to be analyzed and the background data are detection data of the same region; gridding the preprocessed data to be analyzed and the background data to obtain time sequence data to be analyzed and time sequence background data; filtering and normalizing time sequence data to be analyzed and time sequence background data; calculating the average difference value, standard difference value and correlation coefficient of the normalized time sequence data to be analyzed and the time sequence background data; and checking whether the non-imaging sensor works normally according to the calculation result. The method is simple, efficient and universal, can realize on-orbit detection of the non-imaging sensor under the conditions of insufficient external field conditions and limited standard targets, and can utilize self detection data to perform sensor running state analysis at high frequency.
Description
Technical Field
The invention relates to the field of satellite monitoring, in particular to a non-imaging sensor inspection method.
Background
Electromagnetic monitoring satellites are generally provided with various space electromagnetic detection sensors, and can provide space detection data support for space environment monitoring, earthquake prediction and earth science research. Due to the influence of radiation of the space environment, the working state and the physical performance of the sensor can be changed in the satellite operation period, and the accuracy of the detection data of the sensor and the reliability of the sensor in various applications are further influenced. Therefore, the performance of the periodic tracking sensor is a necessary link for guaranteeing the efficient operation of the detection work of the electromagnetic satellite sensor.
At present, the electromagnetic monitoring satellite is limited by various aspects such as satellite sensors, space, energy consumption, technology and the like, the electromagnetic monitoring satellite does not have on-satellite calibration capability, and a detection method aiming at non-imaging sensors is mainly laboratory detection and is assisted by limited field tests, such as a DEMETER satellite. The former simulates a space environment in a laboratory to carry out simulation detection on the sensor, and cannot transmit a detection result so as to meet the detection requirement of performance change of the non-imaging sensor after the satellite runs. The later utilizes the test field under the outdoor specific set condition to evaluate the running state of the on-satellite sensor through real observation data, can compensate various drift deviations generated in the actual work of the sensor, and improves the accuracy of electromagnetic satellite space observation by establishing a reasonable mathematical model to pre-modify the deviations. However, the field and the measurement process are strictly required by using a test field to carry out inspection, and meanwhile, a large amount of manpower and material resources are consumed for field measurement, so that the requirements of high-frequency detection and analysis of a non-imaging sensor cannot be met.
In recent years, a field-free sensor detection technology, namely cross calibration, is developed for imaging sensors, and the technology can perform on-orbit detection and analysis work between remote sensing imaging sensors by using the same standard target on the ground without a ground test field. However, the electromagnetic monitoring satellite sensor faces to space discrete point observation, has high data sampling frequency, is different from conventional satellite data, and has no imaging characteristic. Therefore, a sensor on-orbit inspection method based on detection data needs to be designed by specially aiming at the detection characteristic of a non-imaging sensor of an electromagnetic monitoring satellite and utilizing the observation characteristic of high-frequency discrete point, so that the periodic and high-frequency performance detection and analysis capability of the electromagnetic detection sensor during the satellite operation period is realized.
Disclosure of Invention
Technical problem to be solved
In view of the above technical problems, the present invention provides a method for inspecting a non-imaging sensor, which at least partially solves the above technical problems.
(II) technical scheme
The invention provides a method for inspecting a non-imaging sensor, which comprises the following steps:
s1: acquiring data to be analyzed and background data detected by a non-imaging sensor, and preprocessing the data to be analyzed and the background data, wherein the data to be analyzed and the background data are detection data of the same region; s2: gridding the preprocessed data to be analyzed and the background data to obtain time sequence data to be analyzed and time sequence background data; s3: filtering and normalizing time sequence data to be analyzed and time sequence background data; s4: calculating the average difference value, standard difference value and correlation coefficient of the normalized time sequence data to be analyzed and the time sequence background data; s5: and checking whether the non-imaging sensor works normally according to the difference average value, the difference standard deviation and the correlation coefficient.
Optionally, the storage format of the data to be analyzed and the background data is a text file format, and in step S1, the preprocessing the data to be analyzed and the background data includes: s1-1: performing read-write processing on data to be analyzed and background data, and extracting key parameters of time, track number, longitude, latitude and detection value to form a pure data file; s1-2; and (4) carrying out abnormal data elimination processing on the data to be analyzed and the background data.
Optionally, the performing abnormal data elimination processing on the detection data includes: defining a magnetic storm magnetic disturbance condition according to two geomagnetic indexes of the Kp index and the Dst index; and eliminating abnormal data in the data to be analyzed and the background data according to the magnetic storm magnetic interference condition.
Optionally, S2 includes: s2-1: setting a space grid and a time window for the preprocessed data to be analyzed according to the satellite re-returning period and the sampling frequency characteristics of a non-imaging sensor, wherein the time window moves on a time axis in the space grid; s2-2: averaging the data to be analyzed in a time window corresponding to time on a time axis in the spatial grid to obtain the current spatial grid and the average data to be analyzed in the current time period; s2-3: repeating the step S2-2, and sequentially calculating the average value of the data to be analyzed in the time windows corresponding to the times on the time axis for the preprocessed data to be analyzed to obtain a plurality of average data to be analyzed to form time sequence data to be analyzed; s2-4: and repeating the steps S2-1 to S2-3, and processing the preprocessed background data to obtain time sequence background data.
Optionally, S3 includes: s3-1: filtering the time sequence data to be analyzed and the time sequence background data by adopting at least one method of convolution smoothing or pure moving average or linear weighted smoothing or Gaussian filtering smoothing or Butterworth filtering smoothing; s3-2: using mapping functions
Normalizing time sequence data to be analyzed and time sequence background data, wherein x and x*Respectively time sequence data to be analyzed and time sequence background data before and after normalization, xmaxAnd xminThe time sequence data to be analyzed and the time sequence background data correspond to a maximum value and a minimum value respectively.
Optionally, the time-series data to be analyzed and the time-series background data each include electron concentration data and ion concentration data, and S4 includes: s4-1: using formulas
Calculating the normalized time sequence data to be analyzed to obtain the average value mu of the difference values to be analyzedanalysis(ii) a Using formulas
Calculating the normalized time sequence data to be analyzed to obtain a correlation coefficient R to be analyzedanalysis；
S4-2: using formulas
Calculating the normalized time sequence background data to obtain the average value mu of the background differencebg(ii) a Using formulas
Calculating the normalized time sequence background data to obtain the background difference standard deviation sigmabg(ii) a Using formulas
Calculating the normalized time sequence background data to obtain a background correlation coefficient Rbg；
Wherein X, Y represents electron concentration data and ion concentration data, μXYIs the mean value of the difference between the two, σXYIs the standard deviation of the difference between the two, Z is the difference between the two, RXYCov (X, Y) is the covariance of the two, and D (X), D (Y) are the variances of the two, respectively.
Optionally, the average value of the difference values to be analyzed, the correlation coefficient to be analyzed, the average value of the background difference values, the standard deviation of background interpolation, and the background correlation coefficient satisfy the condition:
μbg-σbg≤μanalysis≤μbg+σbg
Ranalysis≥Rbg
and judging that the non-imaging sensor works normally, otherwise, prompting that the non-imaging sensor is abnormal.
(III) advantageous effects
The invention provides a non-imaging sensor inspection method which is simple, efficient and universal, and can realize on-orbit detection of a non-imaging sensor under the conditions of insufficient external field conditions and limited standard targets. Meanwhile, the characteristics of the sensor to be detected are considered, and the running state analysis of the sensor can be carried out by directly utilizing the self detection data at high frequency.
Drawings
FIG. 1 is a schematic diagram of a non-imaging sensor inspection method based on time series data according to an embodiment of the present invention.
FIG. 2 is a flow chart of a non-imaging sensor inspection method based on time series data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
The embodiment of the invention provides a non-imaging sensor on-orbit inspection method based on time sequence data, which only utilizes the self detection data of sensors on a satellite platform with the same space environment quiet period, does not need to construct a ground test field, is not limited by factors such as detection conditions, manpower and material resources and the like, and can carry out detection and analysis at any time and any place. The problems that the layout of an external field is limited, a standard target is lost, and a laboratory detection result cannot be transmitted in the existing non-imaging sensor detection technical scheme are solved. Moreover, the influence of insufficient orbit number and low coverage range overlapping rate of satellites of the same type can be effectively reduced by directly detecting data based on the self time sequence of the sensors on the same satellite platform, and the requirement of practical application is met.
As shown in fig. 1, first, data detected by non-imaging sensors (a plasma analyzer and a langmuir probe) on the same satellite platform, that is, data to be analyzed and background data (basic data) are acquired. In this embodiment, data detected by two sensor data on a demoter (Detection of Electro-Magnetic transmitted from earth seismic Regions, demoter) satellite which has been stably operated and maturely applied is acquired, and then, data to be analyzed and background data are preprocessed, and abnormal data in the data are removed. And thirdly, gridding the data to be analyzed and the background data, and converting the grid point data into gridded time sequence data so as to obtain the time sequence data to be analyzed and the time sequence background data. And then, smoothing and normalizing the time sequence data to be analyzed and the time sequence background data. And finally, on the basis of background field data, carrying out correlation and threshold analysis, and directly detecting the fluctuation rationality of the data to be analyzed through correlation boundary processing, thereby analyzing the on-orbit running performance state of the same-platform sensor.
FIG. 2 is a flow chart of a method for non-imaging sensor inspection based on time series data in accordance with an embodiment of the present invention.
As shown in fig. 2, the non-imaging sensor inspection method may include the steps of:
and S1, acquiring the data to be analyzed and the background data detected by the non-imaging sensor, and preprocessing the data.
In operation S1, selecting current detection data of the same region/point location as data to be analyzed based on two non-imaging sensors, namely, the langmuir probe of the demoter satellite platform and the plasma analyzer; and selecting historical detection data of the same position in the previous year as background data. And according to the data storage characteristics of the non-imaging sensor, selecting the electronic concentration data and the ion concentration data of the data to be analyzed and the background data in the night period.
The obtained data is directly detected by a non-imaging sensor, belongs to Level-1 scientific data and a text file format, and is convenient for subsequent operations, data reading processing is required to form a pure data file, so that data to be analyzed and background data of the pure data are obtained. The specific read operation is: writing a text file reading and writing program by utilizing matlab software, reading directly detected data by using fscaf sentences according to a Level-1 scientific data format, and writing data such as time, track number, longitude and latitude, detection value and the like into a text by using fprintf.
Due to the fact that space environment changes such as magnetic storm and magnetic disturbance can cause space electron and space ion abnormal disturbance, the acquired data to be analyzed and background data or the acquired abnormal data exist, and therefore the abnormal data need to be removed. The embodiment utilizes two geomagnetic indexes, namely a Kp index (Kp index) and a Dst index (Dst index), to remove data in a special space environment. The specific removing condition method comprises the following steps: and defining a magnetic storm magnetic disturbance condition based on Kp more than or equal to 4 and Dst less than or equal to minus 50nT, and rejecting detection data acquired by the sensor in the time period so as to remove the influence of ionospheric disturbance caused by space environment abnormality and analyze the time sequence change characteristics of the ionospheric parameters in the calm period. The geomagnetic index data can be downloaded and obtained through a Japanese world geomagnetic data center website (http:// wdc.kugi.kyoto-u.ac.jp/wdc/Sec3. html).
And S2, respectively carrying out gridding processing on the preprocessed data to be analyzed and the background data to obtain time sequence data to be analyzed and time sequence background data.
It is mentioned in the above operation S1 that the data to be analyzed and the background data are both electron concentration data and ion concentration data of the night period, that is, the data to be analyzed and the background data both include electron concentration data and ion concentration data.
Because the detection data of the non-imaging sensors are acquired in a millisecond mode and stored in a single-track mode, the data sampling frequency is high, and the point sampling frequencies of different sensors are possibly inconsistent and cannot correspond to one another. Therefore, a spatial grid needs to be set to perform gridding processing on the data to be analyzed and the background data.
The gridding method comprises the following steps: according to the characteristics of the satellite re-entry period and the sampling frequency of the sensor, a space grid of N degrees multiplied by M degrees and a time window of X days are set, wherein the time window moves on a time axis in the space grid. Averaging point location detection data (data to be analyzed and background data) in the current time period range of the space grid, and taking the average value as the detection data of the current time period of the grid; moving a time window on a time axis, and continuously calculating the average value of the data in X days of the grid to serve as the detection data of the next time period of the grid; by analogy, the method is adopted to carry out time sequence gridding processing on the data to be analyzed and the background data in the set space grid, and the time sequence data to be analyzed and the time sequence background data are obtained. In this embodiment, a spatial grid of 2 ° × 2 ° is adopted, a time window is set with 30 days (one month) as a time scale, and data is aggregated to obtain time sequence data to be analyzed and time sequence background data.
And S3, filtering and normalizing the time sequence data to be analyzed and the time sequence background data.
In order to reduce the influence of random fluctuation of data on the analysis and inspection result, the time sequence data to be analyzed and the time sequence background data need to be subjected to smoothing filtering respectively. The time series data to be analyzed and the time series background data may be filtered using at least one of convolution smoothing or pure moving average or linear weighted smoothing or gaussian or butterworth smoothing. In the embodiment, a Savitzky-Golay filtering algorithm is adopted, and the method can be used for smoothing time sequence data to be analyzed and time sequence background data under the condition that the variation trend and the characteristics of the time sequence data are not changed. The concrete formula is as follows:
wherein,
as fitting value, Xj+iAs original value, CiThe coefficient of the ith data in the time-sequence background data (time-sequence data to be analyzed), m is a half filtering window, N is the length of a filter, and N is less than or equal to 2m + 1; t iskIs the sequence fit result index after the kth iteration,
the data is the ith data in the time sequence data to be analyzed (time sequence background data) in the sequence after the non-iteration and the k-th iteration.
Because the magnitude difference of the detection data of the electromagnetic detection non-imaging sensor is large, in order to make the data analysis have stronger contrast, the electronic concentration data and the ion concentration data of the time sequence data to be analyzed and the time sequence background data need to be normalized, and the numerical values are uniformly normalized to be between [0, 1 ]. The linear mapping function is:
wherein, x and x*Respectively time sequence data to be analyzed and time sequence background data before and after normalization, xmaxAnd xminThe time sequence data to be analyzed and the time sequence background data correspond to a maximum value and a minimum value respectively.
S4, calculating the difference average value and the correlation coefficient of the normalized time sequence data to be analyzed to obtain the difference average value and the correlation coefficient to be analyzed; and calculating the average difference value, the standard difference value and the correlation coefficient of the normalized time sequence background data to obtain the average background difference value, the standard difference value and the correlation coefficient of the background.
In particular, using formulas
Calculating the average value of the difference values to be analyzed of the time sequence data to be analyzed and the average value of the background difference values of the time sequence background data;
using formulas
Calculating the background difference standard deviation of the time sequence background data;
using Pearson correlation coefficient (Pearson correlation coefficient) expression
Calculating a correlation coefficient of time sequence data to be analyzed and a correlation coefficient of time sequence background data;
wherein X, Y represents the electron concentration data and ion concentration data, μ, of the time-series data to be analyzed and the time-series background data, respectivelyXYIs the mean value of the difference between the electron concentration data and the ion concentration data, σXYIs the standard deviation of the difference between the two, Z is the difference between the two, RXYIs the correlation coefficient of the two, and Cov (X, Y) is the covariance of the twoD (X), D (Y) are the variance of the two.
And S5, checking whether the non-imaging sensor works normally according to the average value of the difference values to be analyzed, the correlation coefficient to be analyzed, the average value of the background difference values, the standard deviation of the background difference values and the background correlation coefficient.
Specifically, if the condition is satisfied:
μbg-σbg≤μanalysis≤μbg+σbg
Ranalysis≥Rbg
and judging that the performance working state of the non-imaging sensor is good, otherwise, prompting that the non-imaging sensor is abnormal, and further analyzing the performance of the non-imaging sensor in detail. Wherein, mubgAs background mean of difference, σbgAs standard deviation of background difference, RbgAs background correlation coefficient, μanalysisFor the mean value of the differences, R, to be analyzedanalysisIs the correlation coefficient to be analyzed.
In addition, the above definitions of steps and methods are not limited to the specific formulas or modes mentioned in the examples, and those skilled in the art can easily modify or replace them, for example:
1) in the aspect of data preprocessing, in consideration of the difference of the in-orbit running time of different satellites, when the method is applied to other non-imaging sensors, the background time sequence data can also adopt the data of the previous month or the previous two or three years; the reading and writing of the data file can also be realized by other programming languages such as Python, C + +, and the like;
2) in the data gridding process, time sequence data in the same grid can be acquired by adopting different time windows such as ten-day average, week average and the like according to the change of data quantity;
3) the time series data smoothing can also adopt simple moving average, linear weighted smoothing, Gaussian filtering smoothing, Butterworth filtering smoothing and the like to carry out data smoothing processing.
In summary, the embodiments of the present invention provide a non-imaging sensor inspection method based on time series data, which performs filtering and normalization after gridding processing is performed on self-detection data of a non-imaging sensor, performs correlation calculation on the normalized time series data, and inspects a working state of the non-imaging sensor according to a correlation calculation result. The method is simple, efficient and universal, and can realize on-orbit detection of the non-imaging sensor under the conditions of insufficient external field conditions and limited standard targets. Meanwhile, the characteristics of the sensor to be detected are considered, and the running state analysis of the sensor can be carried out by directly utilizing the self detection data at high frequency.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A method of inspecting a non-imaging sensor, comprising:
s1: acquiring data to be analyzed and background data detected by the non-imaging sensor, and preprocessing the data to be analyzed and the background data, wherein the data to be analyzed and the background data are detection data of the same region;
s2: gridding the preprocessed data to be analyzed and the background data to obtain time sequence data to be analyzed and time sequence background data;
s3: filtering and normalizing the time sequence data to be analyzed and the time sequence background data;
s4: calculating the average difference value, standard difference value and correlation coefficient of the normalized time sequence data to be analyzed and the time sequence background data;
s5: and checking whether the non-imaging sensor works normally or not according to the difference average value, the difference standard deviation and the correlation coefficient.
2. The method for checking performance of a non-imaging sensor according to claim 1, wherein the storage format of the data to be analyzed and the background data is a text file format, and the preprocessing of the data to be analyzed and the background data in step S1 includes:
s1-1: reading and writing the data to be analyzed and the background data, extracting key parameters of time, track number, longitude, latitude and detection value, and forming a pure data file;
s1-2; and carrying out abnormal data elimination processing on the data to be analyzed and the background data.
3. The method for testing performance of a non-imaging sensor as claimed in claim 2, wherein said performing abnormal data rejection processing on said detection data comprises:
defining a magnetic storm magnetic disturbance condition according to two geomagnetic indexes of the Kp index and the Dst index;
and rejecting abnormal data in the data to be analyzed and the background data according to the magnetic storm magnetic interference condition.
4. The method for verifying performance of a non-imaging sensor according to claim 1, wherein the step S2 includes:
s2-1: setting a space grid and a time window for the preprocessed data to be analyzed according to the satellite re-circulation period and the sampling frequency characteristics of a non-imaging sensor, wherein the time window moves on a time axis in the space grid;
s2-2: averaging the data to be analyzed in the time window corresponding to one time on the time axis in the space grid to obtain the current space grid and the average data to be analyzed in the current time period;
s2-3: repeating the step S2-2, and sequentially calculating the average value of the to-be-analyzed data in the time windows corresponding to the times on the time axis for the preprocessed to-be-analyzed data to obtain a plurality of average to-be-analyzed data to form time sequence to-be-analyzed data;
s2-4: and repeating the steps S2-1 to S2-3, and processing the preprocessed background data to obtain time sequence background data.
5. The method for verifying performance of a non-imaging sensor according to claim 1, wherein the step S3 includes:
s3-1: filtering the time sequence data to be analyzed and the time sequence background data by adopting at least one method of convolution smoothing or pure moving average or linear weighted smoothing or Gaussian filtering smoothing or Butterworth filtering smoothing;
s3-2: using mapping functions
Normalizing the time sequence data to be analyzed and the time sequence background data, wherein x and x*The time sequence data to be analyzed and the time sequence background data x before and after normalization respectivelymaxAnd xminAnd respectively corresponding to the maximum value and the minimum value of the time sequence data to be analyzed and the time sequence background data.
6. The method for checking performance of a non-imaging sensor according to claim 1, wherein the time-series data to be analyzed and the time-series background data each include electron concentration data and ion concentration data, and the step S4 includes:
s4-1: using formulas
Calculating the normalized time sequence data to be analyzed to obtain the average value mu of the difference values to be analyzedanalysis(ii) a Using formulas
Calculating the normalized time sequence data to be analyzed to obtain a correlation coefficient R to be analyzedanalysis；
S4-2: using formulas
Calculating the normalized time sequence background data to obtain the average value of background difference valuesμbg(ii) a Using formulas
Calculating the normalized time sequence background data to obtain a background difference value standard deviation sigmabg(ii) a Using formulas
Calculating the normalized time sequence background data to obtain a background correlation coefficient Rbg；
Wherein X, Y represents the electron concentration data and the ion concentration data, μ, respectivelyXYIs the mean value of the difference between the two, σXYIs the standard deviation of the difference between the two, Z is the difference between the two, RXYCov (X, Y) is the covariance of the two, and D (X), D (Y) are the variances of the two, respectively.
7. The method for testing performance of a non-imaging sensor according to claim 6, wherein the average value of the differences to be analyzed, the correlation coefficient to be analyzed, the average value of the background differences, the standard deviation of the background interpolation, and the background correlation coefficient satisfy the following conditions:
μbg-σbg≤μanalysis≤μbg+σbg
Ranalysis≥Rbg
and judging that the non-imaging sensor works normally, otherwise, prompting that the non-imaging sensor is abnormal.
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