Interval information-based fiber-optic gyroscope ARW variance estimation method

Abstract

The application discloses an interval information-based fiber-optic gyroscope ARW variance estimation method, and relates to the technical field of inertial navigation and fiber-optic gyroscope data processing. The method comprises the following steps: acquiring IFOG angular rate data; calculating, according to the IFOG angular rate data, a sequence of ARW standard deviations corresponding to the total amount of data output of the gyroscope at different cluster times at a current working time, and performing confidence interval estimation on an ARW variance value corresponding to a sampling time to obtain a plurality of confidence interval information; calculating, according to the plurality of confidence interval information, an ARW standard deviation estimation value corresponding to the sampling time; and calculating, according to the ARW standard deviation estimation value corresponding to the sampling time, an ARW variance estimation value corresponding to the sampling time. The application can more accurately estimate the ARW variance of the fiber-optic gyroscope at the sampling time, which provides more reliable prior information for subsequent suppression of IFOG random errors, thereby improving the navigation precision of the inertial navigation system.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of inertial navigation and fiber-optic gyroscope data processing, in particular to a fiber-optic gyroscope ARW variance estimation method based on interval information. BACKGROUND

[0002] An IFOG (Interferometric Fiber-Optic Gyroscope) is a sensor for measuring rotation angular velocity by using the interference principle and is commonly used in an inertial navigation system. For the inertial navigation system, among random errors of the IFOG, the ARW (Angular Random Walk) is the most important factor affecting the performance index. The bunching time corresponding to various random noises commonly involved in the navigation scene is generally in the order of 104s or less, and compared with variance values of other noise terms, the variance value corresponding to the ARW has a clear order of magnitude advantage. For the IFOG, the angular rate information is generally output, the ARW is the integral result of the angular rate white noise, and the two are essentially the same kind of noise, only the expression ways are different. The modeling and suppression of the IFOG random error are also based on the angular rate data at the sampling time, therefore, accurately obtaining the ARW variance at the sampling time plays an important role in subsequent random error modeling and suppression, which can provide more reliable prior information for subsequent Kalman filtering based on the ARMA (Auto-Regressive Moving Average) model, thereby improving the suppression effect of the IFOG random error and further improving the navigation precision of the inertial navigation system. SUMMARY

[0003] The application aims to provide a fiber-optic gyroscope ARW variance estimation method based on interval information, which can more accurately estimate the ARW variance of the fiber-optic gyroscope at the sampling time, thereby providing more reliable prior information for subsequent suppression of the IFOG random error and improving the navigation precision of the inertial navigation system.

[0004] To achieve the above-mentioned purpose, the application provides the following scheme.

[0005] The application provides a fiber-optic gyroscope ARW variance estimation method based on interval information, which comprises the following steps:

[0006] Obtaining IFOG angular rate data.

[0007] According to the IFOG angular rate data, the total amount of data output of the gyroscope at the current working time is calculated, and the ARW standard deviation sequence corresponding to different bunching times is calculated.

[0008] According to the ARW standard deviation sequence corresponding to the total data output of the gyroscope at different cluster times at the current working moment, a confidence interval estimation of the ARW variance value corresponding to the sampling time is performed to obtain a plurality of confidence interval information.

[0009] According to the plurality of confidence interval information, an ARW standard deviation estimation value corresponding to the sampling time is calculated.

[0010] According to the ARW standard deviation estimation value corresponding to the sampling time, an ARW variance estimation value corresponding to the sampling time is calculated.

[0011] Optionally, according to the IFOG angular rate data, the ARW standard deviation sequence corresponding to the total data output of the gyroscope at different cluster times at the current working moment is calculated, specifically including the following steps:

[0012] According to the IFOG angular rate data, a relationship between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time is determined.

[0013] According to the relationship between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time, the ARW standard deviation at any cluster time is mapped to the ARW standard deviation value corresponding to the sampling time.

[0014] According to the ARW standard deviation value corresponding to the sampling time, the ARW standard deviation sequence corresponding to the total data output of the gyroscope at different cluster times at the current working moment is calculated.

[0015] Optionally, according to the IFOG angular rate data, the relationship between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time is determined, specifically including the following steps:

[0016] According to the IFOG angular rate data, an Allan variance double logarithmic curve is drawn; the variance at the cluster time corresponding to the sampling time in the Allan variance double logarithmic curve is a tangent line with a deviation slope of -1 / 2.

[0017] According to the Allan variance double logarithmic curve, a correlation between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time is derived, and a relationship between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time is established.

[0018] Optionally, the relationship between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time is:

[0019]

[0020] wherein τ represents the bundle time, τ0 represents the sampling time, σ(τ, τ0) represents the ARW standard deviation value corresponding to the sampling time τ0 of the ARW standard deviation at any bundle time τ, and σ(τ) represents the ARW standard deviation at any bundle time τ.

[0021] Optionally, the ARW standard deviation value corresponding to the sampling time τ0 of the ARW standard deviation at any bundle time τ is calculated by the following formula:

[0022]

[0023] wherein τ represents the bundle time, τ0 represents the sampling time, σ(τ, τ0) represents the ARW standard deviation value corresponding to the sampling time τ0 of the ARW standard deviation at any bundle time τ, and σ(τ) represents the ARW standard deviation at any bundle time τ.

[0024] Optionally, the total amount of data output of the gyroscope at the current working time is calculated by the following formula:

[0025]

[0026] wherein τ0 represents the sampling time, σ(τ0) represents the ARW standard deviation at the sampling time τ0, σ(τ n , τ0) represents the ARW standard deviation corresponding to the maximum bundle time divided by the FOG output sample capacity at the current time, m represents the data block quantity corresponding to different bundle times, n = max(m), and θ n represents the ARW standard deviation sequence at different bundle times.

[0027] Optionally, the expression of the plurality of confidence interval information is as follows:

[0028]

[0029] wherein [σ d , σ u ] θn represents the confidence interval estimated by the ARW standard deviation sequence sample at different times, σ d represents the lower limit of the confidence interval, σ u represents the upper limit of the confidence interval, and θ n represents the ARW standard deviation sequence at different bundle times.

[0030] Optionally, the ARW standard deviation estimation value corresponding to the sampling time is calculated according to the plurality of confidence interval information, and specifically includes the following steps:

[0031] According to the plurality of confidence interval information, a weighted sum of each confidence interval median value is calculated to obtain the ARW standard deviation estimation value corresponding to the sampling time, wherein the weight coefficient is given by the number of confidence intervals contained in each confidence interval median value.

[0032] Optionally, the ARW standard deviation estimation value corresponding to the sampling time is calculated by the following formula:

[0033]

[0034] wherein, represents the ARW standard deviation estimation value corresponding to the sampling time τ0, is the median value of the confidence interval [σ d ,σ u ] θn , thr n represents the total number of other confidence intervals containing the confidence interval [σ d ,σ u ] θn median value .

[0035] Optionally, according to the ARW standard deviation estimation value corresponding to the sampling time, the ARW variance estimation value corresponding to the sampling time is calculated, specifically including the following steps:

[0036] According to the ARW standard deviation estimation value corresponding to the sampling time, the square of the ARW standard deviation estimation value corresponding to the sampling time is calculated to obtain the ARW variance estimation value corresponding to the sampling time.

[0037] According to the specific embodiments provided in the present application, the present application has the following technical effects:

[0038] The present application provides a fiber-optic gyroscope ARW variance estimation method based on interval information. From the perspective of confidence interval, the total amount of data output of the gyroscope at the current working time is calculated under different cluster times to obtain a sequence of ARW standard deviation corresponding to the sampling time. Then, a plurality of confidence interval information is obtained by using the confidence interval estimation method of the ARW variance value. Thus, on the basis of the confidence interval, the ARW standard deviation estimation value corresponding to the sampling time is calculated, and then the corresponding ARW variance estimation value is indirectly calculated. Based on the principle that the ARW is the integral result of the angular rate white noise, the variance information in the complete cluster time interval corresponding to the IFOG angular rate white noise is fully utilized, and the ARW variance of the fiber-optic gyroscope at the sampling time can be more accurately estimated. This can provide more reliable prior information for the subsequent suppression of IFOG random error, thereby improving the navigation accuracy of the inertial navigation system. BRIEF DESCRIPTION OF DRAWINGS

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 Allan variance double logarithmic curve of IFOG output sequence under room temperature conditions provided in an embodiment of this application;

[0041] Figure 2 An application environment diagram of an ARW variance estimation method for fiber optic gyroscopes based on interval information provided in an embodiment of this application;

[0042] Figure 3 A flowchart illustrating an ARW variance estimation method for fiber optic gyroscopes based on interval information, provided as an embodiment of this application;

[0043] Figure 4 This is an Allan variance double logarithmic curve plot corresponding to the simulation results provided in one embodiment of this application;

[0044] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] Figure 1 This is a double logarithmic plot of the Allan variance of the IFOG output sequence at room temperature. Figure 1 As shown, the variance at the sampling time corresponding to the set time always deviates from the tangent line with a slope of -1 / 2. This indicates that there are other noises besides white noise with similar variance values ​​in the sampling frequency band. The Allan variance method has difficulty effectively separating the two types of noise in the same frequency band, causing traditional analysis methods to fail to accurately obtain ARW variance information at the sampling time. However, IFOG's angular rate white noise has a wide bandwidth. The averaging operation of the Allan variance method at different set times is equivalent to a bandpass filter, which allows noise with variance advantage in the mid- and low-frequency bands to exhibit the characteristics of white noise. Figure 1The Allan variance double logarithmic curve under the medium and long cluster time corresponding to the medium sampling time can be well fitted to the trend of the tangent with a slope of -1 / 2, which also verifies the analysis conclusion.

[0047] Through the above analysis, it can be known that the Allan variance of the IFOG angular rate wideband white noise in the corresponding medium and long cluster time can accurately reflect the characteristics of the noise. Based on this, the present application aims to make full use of the variance information in the complete cluster time interval corresponding to the IFOG angular rate white noise to more accurately estimate the ARW variance of the fiber optic gyroscope at the sampling time, which can provide more reliable prior information for subsequent suppression of the random error of the IFOG, thereby improving the navigation accuracy of the inertial navigation system.

[0048] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below in combination with the drawings and specific embodiments.

[0049] The interval information-based fiber optic gyroscope ARW variance estimation method provided by the embodiments of the present application can be applied in an application environment as shown in Figure 2 The terminal 102 communicates with the server 104 through a network. The data storage system can store the data required to be processed by the server 104. The data storage system can be separately arranged, can be integrated on the server 104, or can be placed on the cloud or other servers. The terminal 102 can send the IFOG angular rate data to the server 104. After receiving the IFOG angular rate data, the server 104 calculates the ARW standard deviation sequence corresponding to the total data output of the gyroscope at different cluster times at the current working time for the IFOG angular rate data; performs confidence interval estimation on the ARW variance value corresponding to the sampling time according to the ARW standard deviation sequence corresponding to the total data output of the gyroscope at different cluster times at the current working time, to obtain a plurality of confidence interval information; calculates the ARW standard deviation estimation value corresponding to the sampling time according to the plurality of confidence interval information; and calculates the ARW variance estimation value corresponding to the sampling time according to the ARW standard deviation estimation value corresponding to the sampling time. The server 104 can feed back the ARW variance estimation value corresponding to the sampling time obtained to the terminal 102. In addition, in some embodiments, the interval information-based fiber optic gyroscope ARW variance estimation method can also be realized by the server 104 or the terminal 102 alone, for example, the terminal 102 can directly process and estimate the ARW variance for the IFOG angular rate data, or the server 104 can obtain the IFOG angular rate data from the data storage system and process and estimate the ARW variance for the IFOG angular rate data.

[0050] The terminal 102 can be, but is not limited to, various desktop computers, notebook computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things device can be a smart speaker, a smart television, a smart air conditioner, a smart vehicle device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The server 104 can be implemented by a single server or a server cluster composed of multiple servers, and can also be a cloud server.

[0051] In an exemplary embodiment, as shown in Figure 2 , a fiber-optic gyroscope ARW variance estimation method based on interval information is provided, which is executed by a computer device, specifically, can be executed by a terminal or a server, or both. In the embodiments of the present application, the method is applied to the server 104 in Figure 2 , as shown in Figure 3 , specifically includes the following steps:

[0052] Step S1, obtaining IFOG angular rate data.

[0053] In this embodiment, the IFOG angular rate data refers to the angular rate data output by the IFOG at each sampling time, including IFOG angular rate white noise data, etc. By obtaining the IFOG angular rate data and its IFOG angular rate white noise data, the variance information in the complete cluster time interval corresponding to the IFOG angular rate white noise can be obtained, so that the ARW variance at the sampling time can be more accurately estimated, and the accuracy of estimating the ARW variance can be improved.

[0054] Step S2, calculating the ARW standard deviation sequence corresponding to the total data output of the gyroscope at different cluster times at the current working time according to the IFOG angular rate data.

[0055] In this embodiment, step S2 calculates the ARW standard deviation sequence corresponding to the total data output of the gyroscope at different cluster times at the current working time according to the IFOG angular rate data, specifically including the following steps:

[0056] Step S21, determining the relationship between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time according to the IFOG angular rate data.

[0057] In this embodiment, step S21 determines the relationship between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time according to the IFOG angular rate data, specifically including the following steps:

[0058] Step S211, according to the IFOG angular rate data, an Allan variance double logarithmic curve is drawn; the variance of the cluster time corresponding to the sampling time in the Allan variance double logarithmic curve is a tangent with a deviation slope of-1 / 2.

[0059] Step S212, according to the Allan variance double logarithmic curve, a correlation between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time is derived, and a relationship between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time is established.

[0060] Step S22, according to the relationship between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time, the ARW standard deviation at any cluster time is mapped to the ARW standard deviation value corresponding to the sampling time.

[0061] Step S23, according to the ARW standard deviation at any cluster time mapped to the ARW standard deviation value corresponding to the sampling time, the data output total amount of the gyroscope at the current working time is calculated to correspond to the ARW standard deviation sequence at different cluster times.

[0062] Step S3, according to the ARW standard deviation sequence corresponding to the data output total amount of the gyroscope at the current working time at different cluster times, confidence interval estimation is performed on the ARW variance value corresponding to the sampling time, and a plurality of confidence interval information is obtained.

[0063] Step S4, according to the plurality of confidence interval information, an ARW standard deviation estimation value corresponding to the sampling time is calculated.

[0064] In this embodiment, step S4 calculates the ARW standard deviation estimation value corresponding to the sampling time according to the plurality of confidence interval information, and specifically includes the following steps:

[0065] According to the plurality of confidence interval information, weighted summation is performed on the median value of each confidence interval, and the ARW standard deviation estimation value corresponding to the sampling time is calculated. The weight coefficient is given by the number of confidence intervals contained in the median value of each confidence interval.

[0066] Step S5, according to the ARW standard deviation estimation value corresponding to the sampling time, an ARW variance estimation value corresponding to the sampling time is calculated.

[0067] In this embodiment, step S5 calculates the ARW variance estimation value corresponding to the sampling time according to the ARW standard deviation estimation value corresponding to the sampling time, and specifically includes the following steps:

[0068] According to the ARW standard deviation estimation value corresponding to the sampling time, the square of the ARW standard deviation estimation value corresponding to the sampling time is calculated to obtain the ARW variance estimation value corresponding to the sampling time.

[0069] The embodiment calculates the ARW standard deviation sequence corresponding to the total amount of data output of the gyroscope at different cluster times at the current working time, and then obtains multiple confidence interval information by using the confidence interval estimation method of the ARW variance value, so as to calculate the ARW standard deviation estimation value corresponding to the sampling time on the basis of the confidence interval information, and then indirectly calculate the corresponding ARW variance estimation value. Based on the principle that the ARW is the integral result of the angular rate white noise, the variance information in the complete cluster time interval corresponding to the IFOG angular rate white noise is fully utilized, the ARW variance of the fiber-optic gyroscope at the sampling time can be more accurately estimated, which can provide more reliable prior information for subsequent suppression of the random error of the IFOG, and thus the navigation precision of the inertial navigation system is improved.

[0070] In order to make the technical scheme of the embodiment clearer, the specific implementation process of the technical scheme of the embodiment is described in the form of examples below.

[0071] Firstly, according to the linear mapping relationship of the slope-1 / 2 in the Allan variance double logarithmic curve in formula (1), the relationship between the ARW standard deviation under any cluster time τ and the ARW standard deviation corresponding to the sampling time τ0 can be derived, that is, the relationship between the ARW standard deviation under any cluster time τ and the ARW standard deviation corresponding to the sampling time τ0 is as follows: Figure 1

[0072]

[0073] Wherein, τ represents the cluster time, τ0 represents the sampling time, σ(τ, τ0) is the ARW standard deviation under any cluster time τ mapped to the ARW standard deviation value corresponding to the sampling time τ0, and σ(τ) is the ARW standard deviation corresponding to any cluster time τ.

[0074] Further, formula (2) can be derived:

[0075]

[0076] According to formula (2), the ARW standard deviation sequence corresponding to the sampling time τ0 at different times can be calculated, which is represented as formula (3):

[0077]

[0078] Wherein, τ0 represents the sampling time, σ(τ0) represents the ARW standard deviation corresponding to the sampling time τ0, and σ(τ​n τ0) represents the ARW standard deviation corresponding to the maximum bundle time that the FOG output sample capacity can divide at the current moment, where n = max(m). m is the number of data blocks corresponding to different bundle times, specifically the number of data blocks corresponding to different bundle times when calculating the Allan variance. θ n The standard deviation sequences of ARW at different bundle times are given.

[0079] In this embodiment, after obtaining the ARW standard deviation sequence corresponding to each sampling time τ0, based on the above ARW standard deviation sequence, the confidence interval of the ARW variance value corresponding to the sampling time τ0 can be estimated, thereby obtaining multiple confidence intervals with a confidence level of 1-α, expressed as Equation (4):

[0080]

[0081] Among them, [σ d ,σ u ] θn σ is the confidence interval estimated for ARW standard deviation sequence samples at different time points. d σ is the lower limit of the confidence interval. u This represents the upper limit of the confidence interval.

[0082] Furthermore, the overlapping intervals of these confidence intervals are statistically analyzed, and the medians of each overlapping interval are weighted and summed to obtain the final ARW standard deviation estimate corresponding to the sampling time τ0. The weighting coefficients can be given based on the number of confidence intervals involved in each overlapping interval, defined as follows: Confidence interval The median, thr n Indicates a confidence interval Median The total number of other confidence intervals. Therefore, the ARW standard deviation estimate corresponding to the sampling time τ0 can be calculated as follows:

[0083]

[0084] in, This represents the ARW standard deviation estimate corresponding to the sampling time τ0. Confidence interval The median, thr n Indicates a confidence interval Median The total number of other confidence intervals.

[0085] In this embodiment, after the ARW standard deviation estimation value corresponding to the sampling time τ0 is calculated, since the standard deviation is the arithmetic square root of the variance, that is, the variance is equal to the square of the standard deviation. Therefore, the above relationship between the variance and the standard deviation can be used to further calculate the ARW variance according to the ARW standard deviation, and thus, after the ARW standard deviation estimation value corresponding to the sampling time τ0 is obtained The ARW variance estimation value corresponding to the sampling time τ0 can be calculated by using the formula as the final ARW variance estimation result. Thus, the accurate estimation of the ARW variance is completed, and the accurate and reliable ARW variance result is obtained.

[0086] In this embodiment, a fiber-optic gyroscope ARW variance estimation method based on interval information is used to perform simulation experiments on 10min short-time noise-added data and 5h ideal data, and the simulation results are shown in Figure 4 Figure 4 The Allan variance curves of the 10min short-time noise-added data and the 5h ideal data and the local enlarged view of the ordinate position are shown. The Allan variance curve of the 5h ideal data can be used as a measurement benchmark for the variance estimation accuracy, and according to the simulation results in Figure 4 It can be seen from the simulation results in that under the method proposed in the present application, the trend of the Allan variance curve of the 10min short-time noise-added data is close to that of the 5h ideal data, and the estimated ARW variance result is relatively accurate and reliable. Therefore, the method proposed in the present application can effectively improve the estimation accuracy of the ARW variance corresponding to the sampling time τ0.

[0087] In an exemplary embodiment, a fiber-optic gyroscope ARW variance estimation system based on interval information is provided, which can be a computer device, which can be a server or a terminal, and the internal structure diagram thereof can be as shown in Figure 5 ​As shown in the figure. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store various fiber-optic gyroscope ARW variance estimation required data such as IFOG angular rate data. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with the terminal outside through the network connection. The computer program is executed by the processor to realize a fiber-optic gyroscope ARW variance estimation method based on interval information.

[0088] Those skilled in the art can understand that, Figure 5 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement. In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to realize the steps in the above-mentioned fiber-optic gyroscope ARW variance estimation method based on interval information.

[0089] The technical features of the above embodiments can be combined in any way. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0090] The principles and implementation modes of the present application are described by applying specific examples herein, and the above-mentioned embodiments are only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A method for estimating the ARW variance of a fiber-optic gyroscope based on interval information, characterized in that, The interval information-based fiber-optic gyroscope ARW variance estimation method comprises: Obtaining IFOG angular rate data; According to the IFOG angular rate data, the total amount of data output of the gyroscope at the current working time corresponding to the ARW standard deviation sequence at different cluster times is calculated; According to the ARW standard deviation sequence corresponding to the total amount of data output of the gyroscope at the current working time at different cluster times, the confidence interval estimation of the ARW variance value corresponding to the sampling time is performed to obtain a plurality of confidence interval information; According to the plurality of confidence interval information, the ARW standard deviation estimation value corresponding to the sampling time is calculated; According to the ARW standard deviation estimation value corresponding to the sampling time, the ARW variance estimation value corresponding to the sampling time is calculated. 2.The interval information based fiber optic gyroscope ARW variance estimation method of claim 1, wherein, According to the IFOG angular rate data, the total amount of data output of the gyroscope at the current working time corresponding to the ARW standard deviation sequence at different cluster times is calculated, specifically comprising: According to the IFOG angular rate data, the relationship between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time is determined; According to the relationship between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time, the ARW standard deviation value corresponding to the sampling time is calculated by mapping the ARW standard deviation at any cluster time; According to the ARW standard deviation value corresponding to the sampling time by mapping the ARW standard deviation at any cluster time, the ARW standard deviation sequence corresponding to the total amount of data output of the gyroscope at the current working time at different cluster times is calculated. 3.The interval information based FOG ARW variance estimation method according to claim 2, characterized in that, According to the IFOG angular rate data, the relationship between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time is determined, specifically comprising: According to the IFOG angular rate data, an Allan variance double logarithmic curve is drawn; the variance at the cluster time corresponding to the sampling time in the Allan variance double logarithmic curve is a tangent line with a deviation slope of-1 / 2; According to the Allan variance double logarithmic curve, the correlation between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time is deduced, and a relationship between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time is established. 4.The interval information based FOG ARW variance estimation method according to claim 2, wherein, The relationship between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time is: Wherein, τ represents the cluster time, τ0 represents the sampling time, σ(τ, τ0) represents the ARW standard deviation value corresponding to the sampling time τ0 by mapping the ARW standard deviation at any cluster time τ, and σ(τ) represents the ARW standard deviation corresponding to any cluster time τ. 5.The interval information based fiber optic gyroscope ARW variance estimation method of claim 2, wherein, The ARW standard deviation value corresponding to the sampling time is calculated by mapping the ARW standard deviation at any cluster time as follows: Wherein, τ represents the cluster time, τ0 represents the sampling time, σ(τ, τ0) represents the ARW standard deviation value corresponding to the sampling time τ0 by mapping the ARW standard deviation at any cluster time τ, and σ(τ) represents the ARW standard deviation corresponding to any cluster time τ. 6.The interval information based fiber optic gyroscope ARW variance estimation method of claim 2, wherein, The total amount of data output of the gyroscope at the current working moment is calculated according to the following formula: wherein τ0represents the sampling time, σ(τ0) represents the ARW standard deviation corresponding to the sampling time τ0, σ(τ n 0) represents the ARW standard deviation corresponding to the maximum cluster time that can be divided by the FOG output sample capacity at the current time, m is the data block number corresponding to different cluster times, n = max(m), θ n is the ARW standard deviation sequence at different cluster times. 7.The interval information based fiber optic gyroscope ARW variance estimation method of claim 1, wherein, The expression of the plurality of confidence interval information is: wherein, is the lower limit of the confidence interval, σ d is the lower limit of the confidence interval, σ u is the upper limit of the confidence interval, θ n is the ARW standard deviation series for different cluster times. 8.The interval information based fiber optic gyroscope ARW variance estimation method of claim 1, wherein, According to the plurality of confidence interval information, the ARW standard deviation estimation value corresponding to the sampling time is calculated, specifically including: According to the plurality of confidence interval information, the weighted sum of the median of each confidence interval is calculated to obtain the ARW standard deviation estimation value corresponding to the sampling time, wherein the weight coefficient is given by the number of confidence intervals contained in each confidence interval. 9.The interval information based fiber optic gyroscope ARW variance estimation method of claim 8, wherein, The ARW standard deviation estimation value corresponding to the sampling time is calculated according to the following formula: wherein, denotes the ARW standard deviation estimate corresponding to the sampling time τ0, is the median of the confidence interval , thr n denotes the total number of other confidence intervals containing the median of the confidence interval the median of the confidence interval​ 10.The interval information based fiber optic gyroscope ARW variance estimation method of claim 1, wherein, According to the ARW standard deviation estimation value corresponding to the sampling time, the ARW variance estimation value corresponding to the sampling time is calculated, specifically including: According to the ARW standard deviation estimation value corresponding to the sampling time, the square of the ARW standard deviation estimation value corresponding to the sampling time is calculated to obtain the ARW variance estimation value corresponding to the sampling time.

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