JP2019105562A - Data processing device, displacement observation system, data processing method, and data processing program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To effectively suppress noise including non-regular noise and to improve followability to data fluctuation. A data processing device includes a forward smoothing processing unit 11, a reverse smoothing processing unit 12, and a smoothing method determining unit 13. The forward smoothing processing unit 11 calculates the forward smoothing time-series data by sequentially smoothing the observed time-series data before processing and the time-series data from the past to the present time. The reverse smoothing processing unit 12 calculates the reverse smoothing time-series data by sequentially smoothing the forward-smoothing time-series data from the present time to the past. The smoothing method determination unit 13 determines the integrated value of the difference value between the time-series data of the backward smoothing and the time-series data before machining or the time-series data of the forward smoothing within a predetermined period, or these time-series data. The responsiveness of the forward smoothing processing unit and the backward smoothing processing unit is determined according to the temporal difference value of any one of the above. [Selection diagram] Fig. 1

Description

  The present invention relates to a technique of smoothing processing on a plurality of analysis data consisting of time series.

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

  For example, Patent Document 1 describes static positioning, which is a type of positioning using carrier wave phase. Patent Document 1 is a system for observing the displacement of a slope, and performs a smoothing process on analysis data (displacement data) obtained by static positioning. Thereby, the variation included in the analysis data (displacement data) is suppressed.

  As one type of such smoothing processing, there is smoothing processing using weighting coefficients. Roughly speaking, weighting factors for smoothing processing are set respectively for the analysis data of this time and the analysis data of the past, and the smoothing values are weighted with the analysis data of this time and the pasted weight. Calculated using the analysis data of

  Traditionally, in many cases the weighting factor was determined by the residual between the smoothed value and the analytical data. More specifically, the weighting factors have been adaptively processed to minimize residuals.

JP, 2004-144623, A

  However, in the adaptive processing for minimizing the residual, although there is a suppression effect of normal noise, the weighting coefficient at this time is set to follow non-normal noise such as multipath. Therefore, the suppression effect of non-normal noise is reduced.

  On the other hand, in the smoothing process, if weighting to the past analysis data is increased, non-normal noise as well as normal noise can be suppressed, but the follow-up property to fluctuation of the analysis data is degraded.

  Therefore, an object of the present invention is to provide a data processing technique that effectively suppresses noise including non-normal noise and has high tracking ability to fluctuations in time-series data.

  A data processing apparatus according to the present invention includes a forward smoothing processor, a reverse smoothing processor, and a smoothing method determination unit. The forward smoothing processing unit sequentially smoothes the observed time-series data before processing toward the current time from the past to calculate time-series data of smoothing. The backward smoothing processing unit sequentially smoothes forward-smoothing time-series data from the present time to the past to calculate backward-smoothing time-series data. The smoothing method determination unit includes an integral value within a predetermined period of a difference value between the time-series data of reverse smoothing and the time-series data before processing, the time-series data of reverse smoothing and the time-series data of forward smoothing. Integral value within a predetermined period of differential value, temporal differential value of time series data of reverse smoothing, temporal differential value of time series data before processing, temporal differential value of time series data of forward smoothing According to at least one of the above, the responsiveness of the forward smoothing processor and the reverse smoothing processor is determined.

  In this configuration, the responsiveness of the smoothing process is adjusted in accordance with the amount of change in time-series data, that is, the magnitude, speed, acceleration, or the like of the change.

  According to the present invention, noise can be effectively suppressed, and the tracking ability to the fluctuation of time-series data can be enhanced.

It is a block diagram of an analysis data processing device concerning an embodiment of the present invention. (A) is a block diagram which shows the 1st aspect of the smoothing method determination part which concerns on the 1st Embodiment of this invention, (B) is the smoothing method determination which concerns on the 1st Embodiment of this invention. It is a block diagram which shows the 2nd aspect of a part. It is a graph for demonstrating the concept of determination of the smoothing coefficient which concerns on the 1st Embodiment of this invention. (A), (B) is a figure which shows transition of the smoothing value by the analysis data processor which concerns on the 1st Embodiment of this invention, and the smoothing value of a comparison structure, and (C) is this invention It is a figure which shows transition of the filter coefficient of the analysis data processing apparatus which concerns on 1st Embodiment. It is a flow chart which shows the main processing of the analysis result processing method concerning a 1st embodiment of the present invention. (A) is a block diagram which shows the 3rd aspect of the smoothing method determination part, (B) is a block diagram which shows the 4th aspect of the smoothing method determination part. It is a graph for demonstrating the concept of determination of the filter coefficient in the smoothing method determination part of a structure shown to FIG. 6 (A) and FIG. 6 (B), and a function. It is a functional block diagram of an observation system containing an analysis data processing device concerning this embodiment.

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

  As shown in FIG. 1, the analysis data processing apparatus 10 includes a forward smoothing processing unit 11, a backward smoothing processing unit 12, and a smoothing method determination unit 13. The forward smoothing processing unit 11, the backward smoothing processing unit 12, and the smoothing method determination unit 13 are realized by, for example, hardware such as an arithmetic unit and a program executed in the hardware. The analysis data processing apparatus 10 corresponds to the "data processing apparatus" of the present invention. The forward smoothing processing unit 11 and the backward smoothing processing unit 12 correspond to the “smoothing processing unit” in the present invention.

  A plurality of analysis data D composed of time series are input to the forward smoothing processing unit 11, the backward smoothing processing unit 12, and the smoothing method determination unit 13. The analysis data D is, for example, an amount of displacement of a baseline vector calculated from the carrier wave phase (corresponding to the observed value) of the positioning signal. The analysis data D is not limited to the displacement amount of the baseline vector, but may be a value calculated from observed values sequentially acquired at predetermined time intervals. Furthermore, observation values themselves may be used as analysis data. The analysis data D corresponds to the "time-series data before processing" of the present invention.

  The forward smoothing processing unit 11 executes forward smoothing processing of a plurality of analysis data D arranged in time series. The forward smoothing process is a process of calculating the current forward smoothed value Dsa using the past forward smoothed value and the analysis data of this time. The forward smoothing processing is realized by weighted averaging processing, Kalman filtering processing, and the like. The forward smoothing value Dsa corresponds to "time-sequential data of forward smoothing" of the present invention.

  At this time, the forward smoothing processing unit 11 performs forward smoothing processing using the filter coefficient Ga or the function fa. The filter coefficient Ga or the filter function fa is determined by the smoothing method determination unit 13 by the process described later.

  The forward smoothing processor 11 outputs the forward smoothing value Dsa to the backward smoothing processor 12.

  The backward smoothing processing unit 12 performs backward smoothing processing of a plurality of forward smoothing values Dsa arranged in time series. The backward smoothing process is performed using the time of the latest forward smoothing process as a reference time, and using the forward smoothed value Dsa past this time. In the reverse smoothing process, the forward smoothing value Dsa is sequentially smoothed in the past direction to sequentially calculate the reverse smoothing value Dsb. The backward smoothing process is realized by a weighted averaging process, a Kalman filter process, etc., as in the forward smoothing process. The backward smoothing value Dsb corresponds to "time-lapse data of reverse smoothing" of the present invention.

  At this time, the backward smoothing processing unit 12 performs forward smoothing processing using the filter coefficient Gb or the function fb. The filter coefficient Ga or the filter function fa is determined by the smoothing method determination unit 13 by the process described later.

  The backward smoothing processing unit 12 outputs the backward smoothing value Dsb to the outside as output data of the analysis data processing device 10 and also to the smoothing method determination unit 13.

  The smoothing method determination unit 13 determines the responsiveness of each smoothing process, that is, the filter coefficients Ga and Gb or the functions fa and fb, using the change value of the analysis data. When setting smoothing processing using filter coefficients, the smoothing method determination unit 13 outputs the filter coefficient Ga to the forward smoothing processing unit 11 and outputs the filter coefficient Gb to the backward smoothing processing unit 12. When setting smoothing processing using a function, the smoothing method determination unit 13 outputs the function fa to the forward smoothing processing unit 11 and outputs the function fb to the backward smoothing processing unit 12. The smoothing method determination unit 13 may output both the filter coefficient and the function to the forward smoothing processing unit 11 and the backward smoothing processing unit 12. Specific processing in the smoothing method determination unit 13 will be described later.

  FIG. 2A is a block diagram showing a first aspect of the smoothing method determination unit according to the first embodiment of the present invention, and FIG. 2B is a block diagram according to the first embodiment of the present invention It is a block diagram which shows the 2nd aspect of the smoothing method determination part.

  FIG. 3 is a graph for explaining the concept of smoothing coefficient determination according to the first embodiment of the present invention. In FIG. 3, the horizontal axis is the elapsed time (observation time), and the vertical axis is the displacement. In FIG. 3, thin solid lines are analysis data. The thick solid line is the forward smoothed value Dsa, and the dashed line is the backward smoothed value Dsb.

  In the aspect illustrated in FIG. 2A, the smoothing method determination unit 13 includes a difference value calculation unit 131, an integrated value calculation unit 132, and a coefficient setting unit 133. In the aspect illustrated in FIG. 2B, the smoothing method determination unit 13 includes a difference value calculation unit 131, an integrated value calculation unit 132, and a function setting unit 134. In the aspect shown in FIG. 2A and the aspect shown in FIG. 2B, the difference value calculation unit 131 and the integrated value calculation unit 132 have the same configuration.

  The difference value calculation unit 131 receives the forward smoothed value Dsa and the backward smoothed value Dsb. Analysis data D may be input to the difference value calculation unit 131 as shown in FIGS. 2 (A) and 2 (B).

  The difference value calculation unit 131 calculates a difference value ΔD between the forward smoothed value Dsa and the backward smoothed value Dsb. At this time, as shown in FIG. 3, the difference value calculation unit 131 sets a time ts (m) past a predetermined time with respect to the latest time ts (i), and starts from time ts (m) to time ts (i). At each time in the period Pts up to), the difference value ΔD is calculated.

  Note that, in a mode in which the analysis data D is input, the difference value calculation unit 131 may calculate the difference value ΔD between the analysis data D and the backward smoothed value Dsb instead of the forward smoothed value Dsa. In this case, it is preferable to smooth the difference value ΔD.

  The difference value calculation unit 131 outputs the difference value ΔD of each time to the integrated value calculation unit 132.

  The integrated value calculation unit 132 integrates the difference value ΔD of each time with respect to the period Pts from the time ts (m) to the time ts (i) to calculate an integrated value Sts. The integrated value Sts corresponds to the "change value" of the present invention.

  The calculation process of the difference value ΔD in the difference value calculation unit 131 and the calculation process of the integration value Sts in the integration value calculation unit 132 are sequentially performed at time intervals for changing the setting of the smoothing coefficient. Although the time interval for setting change of the smoothing coefficient is generally longer than the acquisition interval of the plurality of analysis data D, it may be the same.

  The coefficient setting unit 133 sets the filter coefficients Ga and Gb in accordance with the integrated value Sts. The filter coefficients Ga and Gb are coefficients for setting the weight of the analysis data D with respect to the pre-smoothed values Dsa and Dsb.

  For example, when the filter coefficients Ga and Gb are designed to be large, if the filter coefficients Ga and Gb are designed to increase the filter time constant, the weight of the analysis data D becomes relatively large and the response becomes faster. The degree of smoothing is reduced. On the other hand, if the filter coefficients Ga and Gb are small, the weight of the analysis data D becomes relatively small, the weight of the smoothing values Dsa and Dsb in advance becomes relatively large, and the response becomes slow and the smoothing degree becomes growing.

  If the integrated value Sts is large, the coefficient setting unit 133 sets the filter coefficients Ga and Gb to large values. If the integrated value Sts is small, the coefficient setting unit 133 sets the filter coefficients Ga and Gb to small values. That is, when the forward smoothing value Dsa and the backward smoothing value Dsb largely deviate, the filter coefficients Ga and Gb are set to large values. On the other hand, when the forward smoothed value Dsa and the backward smoothed value Dsb are close to each other, the filter coefficients Ga and Gb are set to small values.

  By using such settings of the filter coefficients Ga and Gb, as shown in FIG. 3, the forward smoothed value Dsa and the backward smoothed value Dsb are set to a plurality of stable values in the past in a period in which the change in analysis data is small. We are greatly influenced by analysis data. Therefore, forward smoothed value Dsa and backward smoothed value Dsb are stabilized by the influence of noise including non-normal noise.

  On the other hand, as shown in FIG. 3, the forward smoothing value Dsa and the reverse smoothing value Dsb change so as to be largely influenced by the analysis data of each time in a period in which the analysis data fluctuates. Therefore, the followability of the forward smoothed value Dsa and the backward smoothed value Dsb to the fluctuation of analysis data is improved.

  In addition, it is better to set the period in which the change of the analysis data D is small to a long time constant that can suppress the period of non-periodic noise, and to fix the time constant in the period. This makes it possible to obtain the smoothed value more stably in a period of little change.

  The function setting unit 134 sets the functions fa and fb according to the integrated value Sts. The functions fa and fb are for setting the relationship between the weight of the smoothing values Dsa and Dsb in advance and the weight of the analysis data D. The functions fa and fb are set by, for example, a linear function, a quadratic function or the like. By appropriately setting the functions fa and fb, it is possible to make the weight of the analysis data D relatively larger or smaller than the weight of the smoothing values Dsa and Dsb after the smoothing processing in advance. The relationship between the integrated value Sts and the functions fa and fb is stored, for example, by a correspondence table.

  If the integrated value Sts is large, the function setting unit 134 sets a function that reduces the weight of the smoothing values Dsa and Dsb in advance and increases the weight of the analysis data D. If the integrated value Sts is small, the coefficient setting unit 133 sets a function that increases the weight of the smoothing values Dsa and Dsb in advance and decreases the weight of the analysis data D. That is, when the forward smoothed value Dsa and the backward smoothed value Dsb largely deviate from each other, a function is set which decreases the weight of the smoothed values Dsa and Dsb in advance and increases the weight of the analysis data D. On the other hand, when the forward smoothing value Dsa and the backward smoothing value Dsb are close to each other, a function is set to increase the weight of the smoothing values Dsa and Dsb in advance and reduce the weight of the analysis data D.

  By using such settings of the functions fa and fb, as shown in FIG. 3, the forward smoothed value Dsa and the backward smoothed value Dsb are used for multiple stable analyzes in the past in a period in which the change in analysis data is small. We are greatly affected by the data. Therefore, forward smoothed value Dsa and backward smoothed value Dsb are stabilized by the influence of noise including non-normal noise.

  On the other hand, as shown in FIG. 3, the forward smoothing value Dsa and the reverse smoothing value Dsb change so as to be largely influenced by the analysis data of each time in a period in which the analysis data fluctuates. Therefore, the followability of the forward smoothed value Dsa and the backward smoothed value Dsb to the fluctuation of analysis data is improved.

  FIG. 4A and FIG. 4B are diagrams showing transition of the smoothed value and the smoothed value of the comparison configuration by the analysis data processing device according to the first embodiment of the present invention. The horizontal axis of FIG. 4 (A) and FIG. 4 (B) is an elapsed time, and the vertical axis of FIG. 4 (A) and FIG. 4 (B) is a displacement. In FIGS. 4A and 4B, a thin solid line is analysis data, a thick solid line is a smoothed value of the present application, and a broken line is a smoothed value of the comparison configuration. The comparison configuration A shown in FIG. 4A is a configuration set so that the influence of past analysis data becomes greater than that of the present analysis data. The comparison configuration B shown in FIG. 4B is a configuration set so that the influence of the analysis data of this time is larger than that of the analysis data of the past.

  FIG. 4C is a diagram showing the transition of the filter coefficient of the analysis data processing apparatus according to the first embodiment of the present invention. In FIG. 4C, the horizontal axis is the elapsed time, and the vertical axis is the filter coefficient. Moreover, the function employ | adopted in each time is described in the figure of FIG.4 (C). The position of each time of the horizontal axis of FIG. 4 (A) and FIG. 4 (B) and the position of each time of the horizontal axis of FIG. 4 (C) are the same.

  As shown in FIGS. 4A and 4B, in the analysis data processing apparatus 10 according to the present embodiment, noise including non-normal noise in analysis data is suppressed, and followability to fluctuation of analysis data is improved. high. On the other hand, in the comparison configuration A shown in FIG. 4A, although the noise of the analysis data is suppressed, the followability to the fluctuation of the analysis data is low. Further, in the comparison configuration B shown in FIG. 4B, non-normal noise may be followed.

  Further, as shown in FIG. 4C, the analysis data processing apparatus 10 according to the present embodiment can appropriately set the smoothing coefficient and the function according to the fluctuation of the analysis data.

  In the above description, the aspect in which the analysis data processing apparatus 10 is configured by a plurality of functional units has been shown. However, the processing performed by the plurality of functional units described above may be performed by one arithmetic processing unit. In this case, the arithmetic processing unit programs and stores the functions performed by the plurality of functional units described above. The arithmetic processing unit reads out and executes this program.

  FIG. 5 is a flowchart showing main processing of the analysis result processing method according to the first embodiment of the present invention.

  The arithmetic processing unit acquires a plurality of time series analysis data (S101). The arithmetic processing unit determines the smoothing method as described above according to the change values of the plurality of analysis data (S102). For example, in the above-described example, the arithmetic processing unit determines the filter coefficients Ga and Gb or the functions fa and fb using the integrated value of the analysis data D or the difference value between the forward smoothed value Dsa and the backward smoothed value Dsb. Do. The arithmetic processing unit executes the smoothing process of the analysis data using this smoothing method (S103).

  In the above description, the aspect using the integrated value of the difference value between the analysis data D or the forward smoothing value Dsa and the backward smoothing value Dsb is shown, but the analysis data D, the forward smoothing value Dsa, or the reverse It is also possible to determine the smoothing method by any temporal difference value (differential value) of the smoothing value Dsb.

  The smoothing method determination unit of the analysis data processing apparatus can also be realized by the configuration of each embodiment shown below. FIG. 6A is a block diagram showing a third aspect of the smoothing method determination unit. FIG. 6B is a block diagram showing a fourth aspect of the smoothing method determination unit. FIG. 7 is a graph for explaining the concept of the determination of the filter coefficient and the function in the smoothing method determination unit having the configuration shown in FIGS. 6 (A) and 6 (B). In FIG. 7, the horizontal axis is the elapsed time (observation time), and the vertical axis is the displacement. In FIG. 7, thin solid lines are analysis data. The thick solid line is the forward smoothed value Dsa, and the dashed line is the backward smoothed value Dsb.

  The smoothing method determination unit shown in FIG. 6A differs from the smoothing method determination unit shown in FIG. 2A in that the difference value calculation unit 131 and the integration value calculation unit 132 are omitted, and the differential value calculation unit 135 have been added. Further, in the smoothing method determination unit shown in FIG. 6B, the difference value calculation unit 131 and the integration value calculation unit 132 are omitted from the smoothing method determination unit shown in FIG. A calculating unit 135 is added. Due to these changes, the processing of the coefficient setting unit 133 and the processing of the function setting unit 134 are different.

  The differential value calculation unit 135 calculates a differential value at the latest time (corresponding to ts (i) in FIG. 7) at which the forward smoothing value Dsa is calculated. For example, when the differential value calculation unit 135 calculates a differential value with respect to the forward smoothing value Dsa, using the forward smoothing value Dsa at the latest time and the forward smoothing value Dsa past this time, these differences are calculated. A differential value dDsa / dt is calculated by calculating a value and dividing by a time difference. At this time, the differential value calculation unit 135 uses the forward smoothed value Dsa (i) at the latest time ts (i) and the past forward smoothed value Dsa (i-N) to obtain the differential value dDsa (i) / It is preferable to calculate dt. N is an integer, and ts (i-N) means N times before the latest time ts (i). N is a value that can be set as appropriate, and by reducing N, it is possible to reduce the delay from the analysis data of the smoothed value when the change of the analysis data is large.

  The calculation process of the differential value dDsa (i) / dt in the differential value calculation unit 135 is sequentially performed at time intervals for changing the setting of the smoothing coefficient. Although the time interval for setting change of the smoothing coefficient is generally longer than the acquisition interval of the plurality of analysis data D, it may be the same.

  In the configuration of FIG. 6A, the differential value calculation unit 135 outputs the differential value dDsa / dt of the forward smoothed value Dsa to the coefficient setting unit 133. The differential value dDsa / dt corresponds to the "change value" of the present invention.

  The coefficient setting unit 133 sets the filter coefficients Ga and Gb according to the differential value dDsa / dt.

  For example, if the differential value dDsa / dt is large, the coefficient setting unit 133 sets the filter coefficients Ga and Gb to large values. In other words, the time constant of filtering for the input is set short. If the differential value dDsa / dt is small, the coefficient setting unit 133 sets the filter coefficients Ga and Gb to small values. In other words, the time constant of filtering for the input is set long. That is, when the change of the forward smoothing value Dsa is large, the filter coefficients Ga and Gb are set to large values. On the other hand, when the change of the forward smoothing value Dsa is small, the filter coefficients Ga and Gb are set to small values.

  By using such settings of the filter coefficients Ga and Gb, the forward smoothing value Dsa and the backward smoothing value Dsb are greatly influenced by a plurality of past stable analysis data in a period during which a change in the analysis data is small. . Therefore, forward smoothed value Dsa and backward smoothed value Dsb are stabilized by the influence of noise including non-normal noise.

  On the other hand, in the period in which the analysis data fluctuates, the forward smoothing value Dsa and the backward smoothing value Dsb change so as to be greatly influenced by the analysis data of each time. Therefore, the followability of the forward smoothed value Dsa and the backward smoothed value Dsb to the fluctuation of analysis data is improved.

  In the configuration of FIG. 6B, the differential value calculation unit 135 outputs the differential value dDsa / dt of the forward smoothed value Dsa to the function setting unit 134. The differential value dDsa / dt corresponds to the "change value" of the present invention.

  The function setting unit 134 sets the functions fa and fb according to the differential value dDsa / dt.

  For example, if the differential value dDsa / dt is large, the function setting unit 134 sets a function to reduce the weight of the smoothing values Dsa and Dsb in advance and to increase the weight of the analysis data D. In other words, set a short time constant for the input of the function. If the integrated value Sts is small, the coefficient setting unit 133 sets a function that increases the weight of the smoothing values Dsa and Dsb in advance and decreases the weight of the analysis data D. In other words, set a long time constant for the input of the function. When the change of the analysis data D is large, a function is set to reduce the weight of the smoothing values Dsa and Dsb in advance and increase the weight of the analysis data D. On the other hand, when the change in the analysis data D is small, a function is set to increase the weight of the smoothing values Dsa and Dsb in advance and reduce the weight of the analysis data D.

  By using such settings of the functions fa and fb, the forward smoothed value Dsa and the backward smoothed value Dsb are greatly influenced by a plurality of past stable analyzed data in a period during which the change in the analyzed data is small. Therefore, forward smoothed value Dsa and backward smoothed value Dsb are stabilized by the influence of noise including non-normal noise.

  On the other hand, in the period in which the analysis data fluctuates, the forward smoothing value Dsa and the backward smoothing value Dsb change so as to be greatly influenced by the analysis data of each time. Therefore, the followability of the forward smoothed value Dsa and the backward smoothed value Dsb to the fluctuation of analysis data is improved.

  In the above description, the first order differential value of the forward smoothing value Dsa is used, but an nth order differential value (n is an integer of 2 or more) of the forward smoothing value Dsa may be used. Alternatively, the m-th order differential value (m is an integer of 1 or more) of the backward smoothed value Dsb may be used (for example, the first order differential value dDsb / dt of the backward smoothed value Dsb as shown in FIG. 7). Furthermore, the mth-order differential value (m is an integer of 1 or more) of the analysis data D may be used (for example, first-order differential value dD / dt of the analysis data D as shown in FIG. 7). Furthermore, a smoothed value such as a simple moving average of differential values may be used.

  Such an analysis data processing apparatus can be applied to a system using static positioning described below. FIG. 8 is a functional block diagram of an observation system including an analysis data processing apparatus according to the present embodiment. As shown in FIG. 8, 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.

Although there are two observation stations and one reference station in FIG. 8, the present invention is not limited to this. The number of observation stations and the number of reference stations are appropriately determined according to the location to be observed. Moreover, although the aspect using GPS is shown in FIG. 8, the other positioning system of GNSS can also be used.

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

  The observation station 21 includes a GPS antenna 211 and a GPS receiver 212. The GPS antenna 211 receives positioning signals (GPS signals) transmitted from each of the plurality of positioning satellites SAT 1, SAT 2, SAT 3 and SAT 4, and outputs the positioning signals to the 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. Also, the GPS receiver 212 measures its own device position by single positioning or the like using a code phase. The GPS receiver 212 transmits the carrier phase and the positioning result to the line concentrator 40.

  The observation station 22 includes a GPS antenna 221 and a GPS receiver 222. The GPS antenna 221 receives positioning signals (GPS signals) transmitted from the plurality of positioning satellites SAT 1, SAT 2, SAT 3, and SAT 4, and outputs the positioning signals to the 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. Also, the GPS receiver 222 measures its own device position by single positioning or the like using a code phase. The GPS receiver 222 transmits the carrier phase and the positioning result to the line aggregator 40.

  The reference station 30 comprises a GPS antenna 301 and a GPS receiver 302. The GPS antenna 301 receives positioning signals (GPS signals) transmitted from the plurality of positioning satellites SAT 1, SAT 2, SAT 3, and SAT 4, and outputs the positioning signals to the 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 device position by single positioning or the like using a code phase. The GPS receiver 302 transmits the carrier phase and the positioning result to the line aggregator 40.

  The line concentrator 40 transmits, to the server 50, the carrier phase and the positioning result from each of the observation station 21, the observation station 22, and the reference station 30. The server 50 is realized by, for example, an FTP server.

  The analysis device 60 includes an arithmetic unit 61 and a storage unit 62. The calculation unit 61 has the function of the analysis data processing apparatus 10 described above. In addition, the calculation unit 61 obtains carrier phase and positioning result from each of the observation station 21, the observation station 22 and the reference station 30 from the server 50 and calculates a baseline vector connecting the stations. The calculation unit 61 sequentially calculates baseline vectors at preset intervals of observation time of baseline vectors. Furthermore, the calculation unit 61 sequentially calculates the amount of displacement of the base line vector. For example, the displacement of this baseline vector corresponds to the "analysis data" of the present invention.

  For example, in the case of a landslide observation system, this corresponds to the fluctuation amount of the ground at the position where the observation station 21 and the observation station 22 are installed. Such ground fluctuation does not change stably unless there is ground fluctuation due to an earthquake, heavy rainfall, or the like. On the other hand, when a change in ground occurs, a change occurs in a wide range according to the factor. Therefore, by using the carrier wave phase, such a change can be observed with high accuracy, and further, by using the configuration of the analysis data processing apparatus 10 of the present invention, the influence of noise can be effectively suppressed and fluctuation can be obtained. It is possible to realize high followability of observation data against Furthermore, when using a positioning signal, non-normal noise such as a multipath error occurs, but such non-normal noise can also be suppressed by using the configuration of the analysis data processing apparatus 10 of the present invention.

  In the above description, although an aspect using analysis data by static positioning is shown, the above configuration and processing are also applied to analysis data by kinematic positioning such as RTK (real-time kinematic) or precise single positioning (PPP etc) Can be applied.

  Moreover, although the aspect provided with the line concentrator 40 was shown in the above-mentioned description, this can also be abbreviate | omitted. In this case, the observation stations 21 and 22 and the reference station 30 may 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 concentrator 40 or the observation stations 21 and 22 and the reference station 30 may 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 any of the observation stations 21 and 22 and the reference station 30, and the displacement amounts of the base line vector and the base line vector may be calculated. In this case, the station that has calculated the displacement amount of the baseline vector may output the displacement amount of the baseline vector to the server 50.

  Furthermore, in the above description, the aspect in which the analysis device 60 is provided separately from the observation stations 21 and 22 and the reference station 30 has been described. However, the analysis device 60 is not limited to any one of the observation stations 21 and 22 and the reference station 30. It may be built in.

1: Observation system 10: Analysis data processing unit 11: Forward smoothing processing unit 12: Reverse smoothing processing unit 13: Smoothing method determination unit 21, 22: Observation station 30: Reference station 40: Line aggregation unit 50: Server 60: Analysis device 61: Calculation unit 62: Storage unit 131: Difference value calculation unit 132: Integration value calculation unit 133: Coefficient setting unit 134: Function setting unit 135: Differential value calculation unit 211, 221, 301: GPS antenna 212, 222, 302: GPS receiver 500: Network

Claims (7)

A forward smoothing processing unit that sequentially smoothes observed time-series data before processing toward the current time from the past, and calculates time-series data of forward smoothing;
A backward smoothing processing unit that sequentially smoothes forward-smoothing time-series data from the present time to the past to calculate backward-smoothing time-series data;
An integral value within a predetermined period of a difference value between the time-series data of reverse smoothing and the time-series data before processing, a difference value of time series data of reverse smoothing and time-series data of forward smoothing Integral value within a predetermined period, temporal difference value of time series data of the reverse smoothing, temporal difference value of time series data before the processing, temporal difference value of time series data of the forward smoothing And a smoothing method determination unit that determines responsiveness of the forward smoothing processing unit and the backward smoothing processing unit according to at least one of
, A data processing device. The data processing apparatus according to claim 1, wherein
The smoothing method determination unit
When the amount of change in the time series data before the processing is large, the followability of the time series data in the forward smoothing to the time series data before the processing is enhanced, and the amount of change in the time series data before the processing is If smaller, determine the responsiveness to increase the stability of the forward smoothing time series data,
Data processing unit. The data processing apparatus according to claim 1 or 2, wherein
The responsiveness is set by a filter coefficient or a function
Data processing unit. The data processing apparatus according to any one of claims 1 to 3, wherein
The above time series data before processing is
It is the displacement of the baseline vector of the coordinates based on the carrier wave phase of the positioning signal,
Data processing unit. A configuration of the data processing device according to claim 4;
A plurality of stations connected to the data processing device, each receiving the positioning signal and observing the carrier wave phase respectively;
Displacement observation system. The observed time series data before processing is sequentially smoothed from the past to the current time to calculate forward smoothing time series data,
The forward smoothing time series data is sequentially smoothed from the present time to the past to calculate backward smoothing time series data;
An integral value within a predetermined period of a difference value between the time-series data of reverse smoothing and the time-series data before processing, a difference value of time series data of reverse smoothing and time-series data of forward smoothing Integral value within a predetermined period, temporal difference value of time series data of the reverse smoothing, temporal difference value of time series data before the processing, temporal difference value of time series data of the forward smoothing Determine the responsiveness of the forward smoothing process and the backward smoothing process according to at least one of
Data processing method. A process of sequentially smoothing the observed time-series data before processing toward the current time from the past, and calculating time-series data of forward smoothing;
Processing for sequentially smoothing the time-series data of the forward smoothing from the present time to the past, and calculating time-series data of backward smoothing;
An integral value within a predetermined period of a difference value between the time-series data of reverse smoothing and the time-series data before processing, a difference value of time series data of reverse smoothing and time-series data of forward smoothing Integral value within a predetermined period, temporal difference value of time series data of the reverse smoothing, temporal difference value of time series data before the processing, temporal difference value of time series data of the forward smoothing A process of determining responsiveness of the forward smoothing process and the backward smoothing process according to at least one of
A data processing program that causes an arithmetic processing unit to execute

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