JP7227696B2 - Data processing device, displacement observation system, data processing method, and data processing program - Google Patents

Description

The present invention relates to extraction of periodic noise contained in a plurality of time series analysis data.

Positioning using positioning signals of GNSS (Global Navigation Satellite Systems) such as GPS (Global Positioning System) has been put into practical use. As a highly accurate positioning method, there is positioning (interferometric positioning) using the carrier wave phase of a positioning signal.

For example, Patent Literature 1 describes static positioning, which is a type of positioning using carrier wave phases. Patent document 1 is a system for observing displacement of a slope, and performs smoothing processing on time-series analysis data (for example, displacement data) obtained by static positioning.

Japanese Patent Application Laid-Open No. 2004-144623

However, the analysis data may contain periodic noise. Periodic noise is noise that fluctuates in a predetermined period. For example, when the positioning signal described above is used, the periodic noise is noise due to multipath or diffraction.

Such periodic noise further includes other noise such as observation errors, and it is not easy to accurately extract the periodic noise.

Accordingly, an object of the present invention is to provide a technique for accurately extracting periodic noise contained in time-series data.

A data processing apparatus according to the present invention includes a periodic noise extractor and a periodic noise smoother. The periodic noise extractor extracts periodic noise included in each piece of time-series data. The periodic noise smoothing unit performs smoothing processing for each of a plurality of periodic noises separated by the period of the periodic noise among the periodic noises arranged in time series.

In this configuration, only a plurality of data separated by the period of periodic noise is used for smoothing, so only high-frequency noise is removed while the shape of periodic noise is maintained.

According to the present invention, periodic noise included in time-series data can be accurately extracted.

1 is a block diagram of a data processing device according to an embodiment of the present invention; FIG. FIG. 4 is a block diagram showing a first aspect of the periodic noise smoothing section according to the embodiment of the present invention; FIG. 4 is a diagram for explaining the concept of processing by the periodic noise smoothing unit according to the embodiment of the present invention; FIG. 4 is a block diagram of another aspect of a data processing device according to an embodiment of the present invention; It is a block diagram which shows the structure of the periodic noise suppression part which concerns on embodiment of this invention. FIG. 5 is a block diagram showing a second aspect of the periodic noise smoothing section according to the embodiment of the present invention; 4 is a flow chart showing main processing of the data processing method according to the embodiment of the present invention; 4 is a flow chart showing a first aspect of a periodic noise smoothing method; 7 is a flow chart showing a second aspect of a periodic noise smoothing method; FIG. 8 is a block diagram showing a third aspect of the periodic noise smoothing section according to the embodiment of the present invention; 1 is a functional block diagram of a displacement observation system including a data processing device according to this embodiment; FIG.

A data processing apparatus, a data processing method, and a data processing program according to embodiments of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram of a data processing device according to an embodiment of the invention.

As shown in FIG. 1 , the data processing device 10 includes a periodic noise extractor 11 and a periodic noise smoother 12 . The periodic noise extraction unit 11 and the periodic noise smoothing unit 12 are implemented by hardware such as an IC and programs executed by the hardware, for example.

Time-series analysis data D is input to the periodic noise extraction unit 11 . The time-series analysis data D are obtained sequentially based on the observation period. This time-series analysis data D corresponds to the "time-series data" of the present invention.

The time-series analysis data D is, for example, the amount of displacement of the baseline vector calculated from the carrier phase of the positioning signal (corresponding to the observed value). In this case, the period of the time-series analysis data D is based on the time of the positioning system included in the positioning signal.

Note that the time-series analysis data D is not limited to the amount of displacement of the baseline vector, and may be a value calculated from observation values that are sequentially acquired at predetermined time intervals. Furthermore, the observed values themselves may be used as analysis data.

The periodic noise extractor 11 is implemented by a filter in which the frequency band of the periodic noise is within the passband and the frequency band of the displacement amount is the attenuation band. For example, the periodic noise extractor 11 is realized by a high pass filter (HPF). Specifically, if the periodic noise is noise due to multipath or diffraction, the periodic noise extraction unit 11 passes frequency components with a period of one sidereal day and attenuates frequency components with a period longer than one sidereal day. Realized by a high-pass filter.

The periodic noise extraction unit 11 filters the time-series analysis data D to generate time-series periodic noise Dn. The periodic noise extraction unit 11 outputs the time-series periodic noise Dn to the periodic noise smoothing unit 12 . This periodic noise Dn includes noise with a shorter period (higher frequency) than the period of the periodic noise Dn.

The periodic noise smoothing unit 12 is implemented by the following configuration. FIG. 2 is a block diagram showing a first aspect of the periodic noise smoothing section according to the embodiment of the present invention.

As shown in FIG. 2 , periodic noise smoothing section 12 includes memory 121 , setting section 122 , and low-pass filter (LPF) 123 .

The memory 121 sequentially stores the periodic noise Dn.

The setting unit 122 sets a plurality of periodic noises Dn to be output to the low-pass filter 123 among the time-series periodic noises Dn stored in the memory 121 . The setting unit 122 includes, for example, an elapsed time counter, a remainder calculator, and an output setting unit.

The elapsed time counter counts the elapsed time from the activation start time of the data processing device 10 . This count is based on the positioning system time when using the positioning signals described above. The remainder calculator performs periodic remainder calculation on the count value. The output setting unit determines a plurality of periodic noises Dn to be output from among the time-series periodic noises Dn stored in the memory 121 using the result of the remainder calculation.

Specifically, the setting unit 122 executes the following processing. FIG. 3 is a diagram for explaining the concept of processing by the periodic noise smoothing unit according to the embodiment of the present invention.

Fixed-point multipaths and diffractions are specific to long-term fixed-point measurements, such as slopes, and depend on topography and structures. And because it depends on the topography, it occurs repeatedly in one sidereal day cycle.

When the latest periodic noise Dn(n) is stored in the memory 121, the setting unit 122 sets a predetermined period K (for example, corresponding to one sidereal day) in the past from the latest periodic noise Dn(n). A plurality of periodic noises Dn spaced apart every period K) are set. That is, as shown in FIGS. 2 and 3, the setting unit 122 sets the periodic noise Dn(n), the periodic noise Dn(n−K), the periodic noise Dn(n−2K) as the plurality of periodic noises Dn to be output. , periodic noise Dn(n−3K), . . . , and periodic noise Dn(n−mK). Note that m is a positive integer. The number of the plurality of periodic noises Dn to be output can be set as appropriate. That is, for example, in the case of FIG. 2, the number of the plurality of periodic noises Dn to be output is m+1, which is 5 or more, but it is sufficient if the number is plural.

Among the time-series periodic noises Dn stored in the memory 121 , a plurality of periodic noises Dn whose outputs are set by the setting unit 122 are input to the low-pass filter 123 . The setting unit 122 executes setting processing for a plurality of periodic noises Dn output from the memory 121 each time the latest periodic noise Dn(n) stored in the memory 121 is updated.

The low-pass filter 123 performs filter processing (smoothing processing) on a plurality of input periodic noises Dn, and outputs smoothed periodic noises Dns. The low-pass filter 123 passes the frequency components of the periodic noise Dn and attenuates frequency components with a shorter period (higher frequency) than the periodic noise Dn.

The low-pass filter 123 performs filter processing each time a plurality of input periodic noises Dn are updated. As a result, the smoothed periodic noise Dns having the same time series as the analysis data D can be obtained.

By using such a configuration and processing, as shown in FIG. 3, the smoothed periodic noise Dns (see the thick solid line in FIG. 3) is included in the periodic noise Dn (see the thin solid line in FIG. 3). Higher frequency noise is suppressed.

As a result, the time-series periodic noise Dn included in the time-series analysis data D can be accurately extracted.

That is, the extracted periodic noise contains random noise. When a low-pass filter is applied to this periodic noise in chronological order according to a general method, data around an arbitrary time is used, so the data around that time is flattened. Therefore, as the degree of smoothing is increased, the extracted periodic noise is gradually flattened, so that the effect of removing the periodic noise decreases.

However, by using the configuration and processing of this embodiment, instead of using data near an arbitrary time, only a plurality of data spaced apart by the period of periodic noise is used for smoothing, so the form of periodic noise is maintained while only high frequency noise is removed. Therefore, even if the smoothing period is lengthened, the periodic noise is not flattened, and the shape of the periodic noise can be made more accurate. This makes it possible to enhance the effect of removing periodic noise in subsequent processing.

The periodic noise Dn extracted in this manner is used as follows. FIG. 4 is a block diagram of another aspect of a data processing apparatus according to an embodiment of the invention.

A data processing device 10A shown in FIG. 4 differs from the data processing device 10 shown in FIG. The rest of the configuration of the data processing device 10A is the same as that of the data processing device 10, and the description of the similar parts will be omitted.

The periodic noise suppression unit 13 receives the time-series analysis data D and the smoothed periodic noise Dns.

FIG. 5 is a block diagram showing the configuration of the periodic noise suppressor according to the embodiment of the present invention. As shown in FIG. 5 , the periodic noise suppressor 13 includes a subtractor 131 and a smoothing filter 132 . Note that the smoothing filter 132 may be omitted.

A subtractor 131 subtracts the smoothed periodic noise Dns from the analysis data D. FIG. Thereby, the periodic noise contained in the analysis data D is suppressed.

The smoothing filter 132 smoothes the analysis data after the subtraction, and outputs analysis data Do after suppressing periodic noise. For example, smoothing filter 132 is implemented by, for example, a low-pass filter. As a result, analysis data with further noise suppressed can be obtained. For example, as shown by the dashed line in FIG. 3, analysis data with a displacement of approximately 0 can be obtained for an observation target with a displacement of 0. This makes it possible to obtain observation data that more appropriately indicates the actual state of the observation target.

It should be noted that the periodic noise smoothing unit described above can also be realized with the following configuration. FIG. 6 is a block diagram showing a second aspect of the periodic noise smoothing section according to the embodiment of the present invention.

As shown in FIG. 6, the periodic noise smoothing section 12A includes a setting section 122, a plurality of low-pass filters 124, a switch 125, and a switch 126.

The setting unit 122 has the same configuration as the setting unit 122 shown in FIG. 2, and controls the switches 125 and 126 according to the result of the remainder calculation. Switches 125 and 126 are controlled to select the same low-pass filter 124 among multiple low-pass filters 124 .

The plurality of low-pass filters 124 have the same configuration and are realized by general filter circuits such as first-order IIR filters.

With such a configuration, similar to the configuration and processing described above, since only a plurality of data spaced apart by the period of each periodic noise is used for smoothing, high-frequency noise can be obtained while the shape of the periodic noise is maintained. only removed. Therefore, even if the smoothing period is lengthened, the periodic noise is not flattened, and the shape of the periodic noise can be made more accurate. This makes it possible to enhance the effect of removing periodic noise in subsequent processing.

Also, the plurality of low-pass filters 124 are prepared in a number that divides the period K of the periodic noise Dns into predetermined intervals. The switch 125 and the switch 126 are switched by the setting unit 122 at each switching timing corresponding to the number of divisions of the period K by a predetermined interval. As a result, the smoothed periodic noise Dns consisting of time series is obtained.

In this manner, even when the configuration of the periodic noise smoothing unit 12A is used, the smoothed periodic noise Dns in the time series can be accurately extracted from the analysis data D in the time series.

In the above description, a mode in which the data processing device is realized by a plurality of functional units has been described. It can also be realized. In this case, the data processing device may execute the following data processing method. FIG. 7 is a flow chart showing main processing of the data processing method according to the embodiment of the present invention. Note that specific processing of each processing shown in FIG. 7 has been described above, and the description thereof is omitted here.

As shown in FIG. 7, the data processing device extracts time-series periodic noise Dn from time-series analysis data D (S11). The data processing device smoothes the time-series periodic noise Dn (S12). The data processing device subtracts the time-series smoothed periodic noise Dns from the time-series analysis data D to suppress the periodic noise contained in the analysis data D (S13). The data processing device smoothes the analysis data D after suppressing the periodic noise (S14).

FIG. 8 is a flow chart showing a first aspect of the periodic noise smoothing method. Note that specific processing of each processing shown in FIG. 8 has been described above, and description thereof will be omitted here.

As shown in FIG. 8, the data processing device sequentially stores the periodic noise Dn (S21). The data processing device selects a periodic noise Dn with a period K (for example, a period of one sidereal day) from the stored time-series periodic noises Dn (S22). The data processing device smoothes the selected periodic noise Dn (S23). These processes are performed each time the latest periodic noise Dn is input.

FIG. 9 is a flow chart showing a second aspect of the periodic noise smoothing method. Note that specific processing of each processing shown in FIG. 9 has been described above, and description thereof will be omitted here.

As shown in FIG. 9, the data processing device receives an input of periodic noise (S31). The data processing device selects a filter according to time (S32). The data processing device uses the selected filter to smooth the periodic noise Dn (S33). These processes are performed while switching filters each time the latest periodic noise Dn is input.

In the above description, the periodic noise smoothing unit 12 selects the periodic noise Dn spaced apart at the period K and performs filter processing. However, when the periodic noise Dn is missing at the time position of the period K, the periodic noise smoothing unit filters the missing periodic noise Dn using the periodic noise Dn before and after the time axis. processing may be performed. FIG. 10 is a block diagram showing a third aspect of the periodic noise smoothing section according to the embodiment of the present invention.

As shown in FIG. 10, periodic noise smoothing section 12B includes memory 121, setting section 122, and low-pass filter 123B. The memory 121 of the periodic noise smoothing unit 12B is the same as the memory 121 described above, and the description thereof is omitted.

When the latest periodic noise Dn(n) is stored in the memory 121, the setting unit 122 sets a predetermined period K (for example, corresponding to one sidereal day) in the past from the latest periodic noise Dn(n). A plurality of periodic noises Dn spaced apart every period K) are set. Further, when the periodic noise Dn is missing at the time position of the period K, the setting unit 122 sets a plurality of periodic noises before and after the plurality of periodic noises Dn spaced every period K on the time axis. set. For example, in the case of FIG. 10, the setting unit 122 sets one periodic noise Dn before and after each period K on the time axis (for the latest periodic noise, Only the previous (past) periodic noise Dn) is selected.

That is, in the case of the example of FIG. 10, the setting unit 122 outputs a plurality of periodic noises Dn. Set Dn(n-2K+1) and periodic noise Dn(n-2K-1). Then, the setting unit 122 calculates the periodic noise Dn(n-2K) from the average value of the periodic noise Dn(n-2K+1) and the periodic noise Dn(n-2K-1).

Note that, here, a mode in which two pieces of periodic noise are used before and after each of the missing periodic noises in the time series is shown. However, this number is not limited to two, and may be four or more. When a large number of pixels are used, it is preferable to perform interpolation by a method different from smoothing using a low-pass filter, such as curve fitting.

Also, either the periodic noise before or after the missing periodic noise may be used, or the missing periodic noise may be replaced with the periodic noise before or after an integral multiple of K in the time series.

Also, here, the case where the periodic noise is missing is shown as an example, but if the past periodic noise is not complete at the initial stage, for example, for the periodic noise to be replaced, an integer multiple of K in the time series The replacement may be performed using a pre- or post-periodic noise consisting of .

The low-pass filter 123B performs filter processing (smoothing processing) on a plurality of periodic noises Dn output from the memory 121 by the setting unit 122, and outputs smoothed periodic noises Dns.

Even with such a configuration and processing, it is possible to accurately extract the time-series periodic noise contained in the time-series analysis data, and to accurately suppress the period noise contained in the analysis data.

In the above description, the period of the periodic noise is one sidereal day. However, the period of the periodic noise may be appropriately set according to the observation system to be applied and the periodic noise included in the time-series data.

Such an analysis data processing device can be applied to the following system using static positioning. FIG. 11 is a functional block diagram of an observation system including a data processing device according to this embodiment. As shown in FIG. 11 , the observation system 1 includes an observation station 21 , an observation station 22 , a reference station 30 , a line concentrator 40 , a server 50 and an analysis device 60 . The observation system 1 corresponds to the "displacement observation system" of the present invention.

In FIG. 11, there are two observation stations and one reference station, but this is not restrictive. The number of observation stations and the number of reference stations are appropriately determined according to the locations to be observed. Moreover, although FIG. 11 shows an embodiment using GPS, other positioning systems of GNSS can also be used.

Observation station 21, observation station 22, and reference station 30 are connected to line concentrator 40 by wire or wirelessly. Line concentrator 40 , server 50 and analysis device 60 are connected via network 500 .

The observation station 21 has a GPS antenna 211 and a GPS receiver 212 . GPS antenna 211 receives positioning signals (GPS signals) transmitted from each of a plurality of positioning satellites SAT1, SAT2, SAT3, and SAT4, and outputs the signals to GPS receiver 212 . The GPS receiver 212 observes carrier phases of positioning signals from a plurality of positioning satellites SAT1, SAT2, SAT3, and SAT4 respectively. In addition, the GPS receiver 212 measures its own position by independent positioning using code phase or the like. GPS receiver 212 transmits these carrier phases and positioning results to line concentrator 40 .

The observation station 22 has a GPS antenna 221 and a GPS receiver 222 . GPS antenna 221 receives positioning signals (GPS signals) transmitted from each of a plurality of positioning satellites SAT1, SAT2, SAT3, and SAT4, and outputs the signals to GPS receiver 222 . The GPS receiver 222 observes carrier phases of positioning signals from a plurality of positioning satellites SAT1, SAT2, SAT3, and SAT4 respectively. In addition, the GPS receiver 222 measures its own position by independent positioning using code phase or the like. GPS receiver 222 transmits these carrier phases and positioning results to line concentrator 40 .

Reference station 30 comprises a GPS antenna 301 and a GPS receiver 302 . GPS antenna 301 receives positioning signals (GPS signals) transmitted from each of a plurality of positioning satellites SAT1, SAT2, SAT3, and SAT4, and outputs them to GPS receiver 302 . The GPS receiver 302 observes carrier phases of positioning signals from a plurality of positioning satellites SAT1, SAT2, SAT3, and SAT4 respectively. Also, the GPS receiver 302 measures its own position by independent positioning or the like using code phase. GPS receiver 302 transmits these carrier phases and positioning results to line concentrator 40 .

Line concentrator 40 transmits carrier phases and positioning results from observation station 21 , observation station 22 , and reference station 30 to server 50 . The server 50 is implemented by, for example, an FTP server.

The analysis device 60 includes a calculation section 61 and a storage section 62 . The calculation unit 61 has the functions of the data processing device 10 described above. Further, the calculation unit 61 acquires from the server 50 carrier wave phases and positioning results from each of the observation station 21, the observation station 22, and the reference station 30, and calculates a baseline vector connecting each station. The calculation unit 61 sequentially calculates the baseline vector at intervals of preset baseline vector observation time. Furthermore, the calculation unit 61 sequentially calculates the displacement amount of this base line vector. For example, the amount of displacement of this baseline vector corresponds to the "time-series data" of the present invention.

For example, in the case of a landslide observation system, this corresponds to the amount of ground movement at the positions where the observation stations 21 and 22 are installed. Such a ground fluctuation amount does not change stably unless there is a ground fluctuation caused by an earthquake, heavy rain, or the like. On the other hand, when ground changes occur, changes occur over a wide range depending on the factors involved. Therefore, by using the carrier wave phase, such a change can be observed with high accuracy. Further, by using the configuration of the data processing device 10 of the present invention, the effects of periodic noise due to multipath, diffraction, etc. can be effectively reduced. can be suppressed.

In the above description, an aspect using analysis data by static positioning is shown, but the above-described configuration and processing are also applied to analysis data by kinematic positioning such as RTK (real-time kinematic) or precision point positioning (PPP). can be applied.

Also, in the above description, a mode in which the line concentrator 40 is provided has been shown, but this can be omitted. In this case, the observation stations 21 and 22 and the reference station 30 should output the carrier wave phase and the positioning result to the server 50 . Also, the server 50 can be omitted. In this case, the line aggregator 40 or the observation stations 21 and 22 and the reference station 30 should output the carrier wave phase and the positioning result to the analysis device 60 . The analysis device 60 stores these in the storage unit 62 and uses them for the above-described processing.

Further, the carrier phase and the positioning result may be aggregated by one of the observation stations 21, 22 and the reference station 30 to calculate the baseline vector and the amount of displacement of the baseline vector. In this case, the station that has calculated the amount of displacement of the baseline vector should output the amount of displacement of the baseline vector to the server 50 .

Furthermore, in the above description, a mode in which the analysis device 60 is provided separately from the observation stations 21, 22 and the reference station 30 has been described. may be built into the

1: Observation system 10, 10A: Data processing device 11: Periodic noise extractor 12, 12A, 12B: Periodic noise smoother 13: Periodic noise suppressor 21, 22: Observation station 30: Reference station 40: Line concentrator 50: Server 60: Analysis device 61: Calculation unit 62: Storage unit 121: Memory 122: Setting units 123, 123B, 124: Low-pass filters 125, 126: Switch 131: Subtractor 132: Smoothing filters 211, 221, 301: GPS antennas 212, 222, 302: GPS receiver 500: Network D: Analysis data Dn: Periodic noise Dns: Periodic noise after smoothing SAT1, SAT2, SAT3, SAT4: Positioning satellites

Claims (14)

Periodic noise that is included in the amount of displacement of the baseline vector of the time series and that repeats with a period of one sidereal day is extracted by filtering in which the frequency band of the periodic noise is within the passband and the frequency band of the amount of displacement is the attenuation area. a periodic noise extractor;
a periodic noise smoothing unit that selects a plurality of periodic noises spaced apart for each period and smoothes the displacement amount of the selected plurality of periodic noises;
with
The periodic noise smoothing unit is
a storage unit that stores displacement amounts corresponding to the plurality of periodic noises arranged in time series;
With K as the period, for the periodic noise Dn(n), the periodic noise Dn(n−K), the periodic noise Dn(n−2K), the periodic noise Dn(n−3K), . a setting unit that selects periodic noise Dn (n−mK);
With respect to the periodic noise Dn(n), periodic noise Dn(n−K), periodic noise Dn(n−2K), periodic noise Dn(n−3K), . -mK), a smoothing filter that performs smoothing processing by filtering the frequency of the periodic noise within the passband;
A data processing device comprising: Periodic noise that is included in the amount of displacement of the baseline vector of the time series and that repeats with a period of one sidereal day is extracted by filtering in which the frequency band of the periodic noise is within the passband and the frequency band of the amount of displacement is the attenuation area. a periodic noise extractor;
a periodic noise smoothing unit that selects a plurality of periodic noises spaced apart for each period and smoothes displacement amounts corresponding to the selected plurality of periodic noises;
with
The periodic noise smoothing unit is
a plurality of smoothing filters whose number is based on the period;
a switch that selects the plurality of smoothing filters;
a setting unit that switches the switch using the period;
A data processing device comprising: The data processing device according to claim 1 or claim 2,
The setting unit
an elapsed time counter that counts the elapsed time from the boot start time of the device;
a remainder calculation unit that performs remainder calculation according to the period for the count value;
an output setting unit that determines periodic noise to be selected using the result of the remainder calculation;
comprising
Data processing equipment. The data processing device according to any one of claims 1 to 3,
The periodic noise smoothing unit is
If the periodic noise is missing at the time position set by the period, smoothing is performed by interpolating the displacement amount of the periodic noise at the time position where the periodic noise is missing.
Data processing equipment. The data processing device according to claim 4,
The periodic noise smoothing unit is
Performing the interpolation using at least one of the periodic noise extracted from the observation data before and after the time position where the periodic noise is missing,
Data processing equipment. The data processing device according to claim 4,
The periodic noise smoothing unit is
The interpolation is performed by the average value of periodic noise extracted from a plurality of observation data before and after the time position where the periodic noise is missing or by curve fitting.
Data processing equipment. The data processing device according to claim 4,
The periodic noise smoothing unit is
Performing the interpolation using periodic noise at a time position separated by an integral multiple of the period from the time position where the periodic noise is missing;
Data processing equipment. The data processing device according to any one of claims 1 to 7,
Further comprising a periodic noise suppression unit having a subtraction unit that subtracts the smoothed periodic noise from the time-series observed data,
Data processing equipment. The data processing device according to claim 8,
The periodic noise suppression unit is
Further comprising a smoothing filter that smoothes after subtracting the smoothed periodic noise from the time-series observed data,
Data processing equipment. A configuration of the data processing device according to claim 8 or claim 9;
a plurality of stations connected to the data processing device, each receiving GNSS positioning signals and each observing carrier phase;
with
The data processing device obtains a displacement amount of the baseline vector obtained from the carrier wave phase.
Displacement observation system. Periodic noise that is included in the displacement of the baseline vector of the time series and that repeats in one sidereal day cycle is extracted by filtering in which the frequency band of the periodic noise is within the passband and the frequency band of the displacement is the attenuation band. ,
Selecting a plurality of periodic noises spaced apart for each period, smoothing displacement amounts corresponding to the selected plurality of periodic noises,
The smoothing process is
storing displacement amounts corresponding to the plurality of periodic noises arranged in time series;
With K as the period, for the periodic noise Dn(n), the periodic noise Dn(n−K), the periodic noise Dn(n−2K), the periodic noise Dn(n−3K), . Select the periodic noise Dn(n−mK),
With respect to the periodic noise Dn(n), periodic noise Dn(n−K), periodic noise Dn(n−2K), periodic noise Dn(n−3K), . -mK) is smoothed by filtering with the frequency of the periodic noise within the passband.
Data processing method. Periodic noise that is included in the displacement of the baseline vector of the time series and that repeats in one sidereal day cycle is extracted by filtering in which the frequency band of the periodic noise is within the passband and the frequency band of the displacement is the attenuation band. ,
Selecting a plurality of periodic noises spaced apart for each period, smoothing displacement amounts corresponding to the selected plurality of periodic noises,
The smoothing process is
setting a plurality of smoothing filters whose number is based on the period;
setting a switch that selects the plurality of smoothing filters;
using the period to toggle the switch;
Data processing method. Periodic noise that is included in the amount of displacement of the baseline vector of the time series and that repeats with a period of one sidereal day is extracted by filtering in which the frequency band of the periodic noise is within the passband and the frequency band of the amount of displacement is the attenuation area. processing;
A process of selecting a plurality of periodic noises spaced apart for each period and smoothing displacement amounts corresponding to the selected plurality of periodic noises;
is executed by the information processing device,
The smoothing process includes
a process of storing displacement amounts corresponding to the plurality of periodic noises arranged in time series;
With K as the period, for the periodic noise Dn(n), the periodic noise Dn(n−K), the periodic noise Dn(n−2K), the periodic noise Dn(n−3K), . A process of selecting periodic noise Dn (n−mK);
With respect to the periodic noise Dn(n), periodic noise Dn(n−K), periodic noise Dn(n−2K), periodic noise Dn(n−3K), . -mK) is smoothed by filtering with the frequency of the periodic noise within the passband;
A data processing program that causes an information processing device to execute. Periodic noise that is included in the amount of displacement of the baseline vector of the time series and that repeats with a period of one sidereal day is extracted by filtering in which the frequency band of the periodic noise is within the passband and the frequency band of the amount of displacement is the attenuation area. processing;
A process of selecting a plurality of periodic noises spaced apart for each period and smoothing displacement amounts corresponding to the selected plurality of periodic noises;
is executed by the information processing device,
The smoothing process includes
A process of setting a plurality of smoothing filters whose number is based on the period;
a process of setting a switch that selects the plurality of smoothing filters;
a process of switching the switch using the period;
A data processing program that causes an information processing device to execute.

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