Fiber-optic gyroscope ARW variance estimation method based on interval information

Abstract

The invention discloses a fiber-optic gyroscope ARW variance estimation method based on interval information, and relates to the technical field of inertial navigation and fiber-optic gyroscope data processing, and the method comprises the following steps: obtaining IFOG angular rate data; according to the IFOG angular rate data, calculating an ARW standard deviation sequence corresponding to the total data output amount of the gyroscope at the current working moment under different bundling times, and performing confidence interval estimation on an ARW variance value corresponding to the sampling time to obtain multiple pieces of confidence interval information; according to the multiple pieces of confidence interval information, an ARW standard deviation estimation value corresponding to the sampling time is obtained through calculation; and calculating an ARW variance estimation value corresponding to the sampling time according to the ARW standard deviation estimation value corresponding to the sampling time. According to the method, the ARW variance of the fiber-optic gyroscope at the sampling time can be estimated more accurately, so that more reliable prior information is provided for subsequent suppression of IFOG random errors, and the navigation precision of an inertial navigation system is improved.

Description

Technical Field

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

[0002] IFOG (Interferometric Fiber-Optic Gyroscope) is a sensor that uses the principle of interference to measure the angular velocity of rotation and is commonly used in inertial navigation systems. For inertial navigation systems, among the many random errors of IFOG, ARW (Angular Random Walk) is the most important factor affecting its performance indicators. The beam time corresponding to various types of random noise commonly involved in navigation scenarios is generally on the order of 104s or less. Compared with the variance values ​​of other noise items, the variance value corresponding to ARW has a significant order of magnitude advantage. For IFOG, it generally outputs angular rate information, and ARW is the integral result of angular rate white noise. The two are essentially the same type of noise, but there is a difference in the way they are expressed. The modeling and suppression of IFOG random errors are also based on the angular rate data at the sampling time. Therefore, accurately obtaining the ARW variance at the sampling time is important for the subsequent random error modeling and suppression, which can provide more reliable prior information for the subsequent Kalman filter based on the Auto-Regressive Moving Average (ARMA) model, thereby improving the suppression effect of IFOG random errors and further improving the navigation accuracy of the inertial navigation system. Summary of the invention

[0003] The purpose of this application is to provide a fiber optic gyro ARW variance estimation method based on interval information, which can more accurately estimate the ARW variance of the fiber optic gyro at the sampling time, which can provide more reliable prior information for the subsequent suppression of IFOG random errors, thereby improving the navigation accuracy of the inertial navigation system.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] The present application provides a method for estimating the ARW variance of a fiber optic gyroscope based on interval information, and the method for estimating the ARW variance of a fiber optic gyroscope based on interval information comprises the following steps:

[0006] Get IFOG angular rate data.

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

[0008] According to the ARW standard deviation sequence corresponding to the total amount of gyro data output at different clustering times at the current working moment, the confidence interval of the ARW variance value corresponding to the sampling time is estimated to obtain multiple confidence interval information.

[0009] Based on multiple confidence interval information, the estimated value of the ARW standard deviation corresponding to the sampling time is calculated.

[0010] Based on the estimated value of the ARW standard deviation corresponding to the sampling time, calculate the estimated value of the ARW variance corresponding to the sampling time.

[0011] Optionally, according to the IFOG angular rate data, calculating the ARW standard deviation sequence corresponding to the total amount of gyro data output at different beamforming times at the current working moment, specifically includes the following steps:

[0012] According to the IFOG angular velocity data, the relationship between the ARW standard deviation at any beam 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 clustering time and the ARW standard deviation corresponding to the sampling time, the ARW standard deviation at any clustering time is mapped to the ARW standard deviation value corresponding to the sampling time.

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

[0015] Optionally, determining a relationship between an ARW standard deviation at any beamforming time and an ARW standard deviation corresponding to a sampling time according to the IFOG angular velocity data specifically comprises the following steps:

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

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

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

[0019]

[0020] Where τ represents the cluster time, τ 0 represents the sampling time, σ(τ,τ 0 ) represents the ARW standard deviation at any beam time τ mapped to the sampling time τ 0 σ(τ) represents the ARW standard deviation corresponding to any beam time τ.

[0021] Optionally, the following formula is used to calculate the ARW standard deviation at any bundle time and map it to the corresponding ARW standard deviation value at the sampling time:

[0022]

[0023] Where τ represents the cluster time, τ 0 represents the sampling time, σ(τ,τ 0 ) represents the ARW standard deviation at any beam time τ mapped to the sampling time τ 0 σ(τ) represents the ARW standard deviation corresponding to any beam time τ.

[0024] Optionally, the following formula is used to calculate the ARW standard deviation sequence corresponding to the total amount of gyro data output at different beamforming times at the current working moment:

[0025]

[0026] Among them, τ 0 represents the sampling time, σ(τ 0 ) represents the sampling time τ 0 The corresponding ARW standard deviation, σ(τ n ,τ 0 ) represents the ARW standard deviation corresponding to the maximum clustering time that can be divided by the FOG output sample capacity at the current moment, m is the number of data blocks corresponding to different clustering times, n = max(m), θ n is the ARW standard deviation series under different bundle times.

[0027] Optionally, the expressions for multiple confidence interval information are:

[0028]

[0029] Among them, [σ d ,σ u ] θn The confidence interval estimated for the ARW standard deviation sequence samples at different times, 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 under different bundle times.

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

[0031] According to the information of multiple confidence intervals, the median of each confidence interval is weighted and summed to calculate the estimated value of the ARW standard deviation corresponding to the sampling time, where the weight coefficient is given by the number of confidence intervals contained in the median of each confidence interval.

[0032] Optionally, the following formula is used to calculate the estimated value of the ARW standard deviation corresponding to the sampling time:

[0033]

[0034] in, represents the sampling time τ 0 The corresponding ARW standard deviation estimate is, is the confidence interval [σ d ,σ u ] θn The median value of thr n Indicates that the confidence interval [σ d ,σ u ] θn Median The total number of other confidence intervals for .

[0035] Optionally, according to the estimated value of the ARW standard deviation corresponding to the sampling time, calculating the estimated value of the ARW variance corresponding to the sampling time specifically includes the following steps:

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

[0037] According to the specific embodiments provided in this application, this 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 ARW standard deviation sequence corresponding to the total data output of the gyroscope at different beam times at the current working moment is calculated, and then a confidence interval estimation method of the ARW variance value is used to obtain multiple confidence interval information. Therefore, on the basis of the confidence interval, the ARW standard deviation estimate corresponding to the sampling time is calculated, and then the corresponding ARW variance estimate is indirectly calculated. Based on the principle that ARW is the integral result of angular rate white noise, the variance information within the complete beam time interval corresponding to the IFOG angular rate white noise is fully utilized, so that the ARW variance of the fiber optic gyroscope at the sampling moment can be more accurately estimated, which can provide more reliable prior information for the subsequent suppression of IFOG random errors, thereby improving the navigation accuracy of the inertial navigation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 An Allan variance double logarithmic curve graph of an IFOG output sequence under room temperature provided in an embodiment of the present application;

[0041] Figure 2 An application environment diagram of a fiber optic gyro ARW variance estimation method based on interval information provided in one embodiment of the present application;

[0042] Figure 3 A schematic flow chart of a method for estimating the ARW variance of a fiber optic gyroscope based on interval information provided in one embodiment of the present application;

[0043] Figure 4 An Allan variance double logarithmic curve graph corresponding to the simulation results provided in an embodiment of the present application;

[0044] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] Figure 1 This is the Allan variance double logarithmic curve of the IFOG output sequence under room temperature. Figure 1 As shown in the figure, the variance at the sampling time corresponding to the beam time always deviates from the tangent with a slope of -1 / 2, which indicates that there are other noises with similar variance values ​​in addition to white noise in the sampling frequency band. It is difficult for the Allan variance method to effectively separate the two types of noise in the same frequency band, resulting in the inability of traditional analysis methods to accurately obtain the ARW variance information at the sampling time. However, the angular rate white noise of IFOG has the characteristics of a wide band. The averaging operation of the Allan variance method at different beam times is equivalent to a bandpass filter, which allows the noise with variance advantages in the medium and low frequency bands to show the characteristics of white noise. Figure 1 The Allan variance double logarithmic curve under medium and long bundle times corresponding to medium sampling time can fit the tangent trend with a slope of -1 / 2 well, which also confirms this analysis conclusion.

[0047] Through the above analysis, it can be seen that the Allan variance of IFOG angular rate broadband white noise in the corresponding medium and long beam time can more 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 beam 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 the subsequent suppression of IFOG random errors, thereby improving the navigation accuracy of the inertial navigation system.

[0048] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0049] The fiber optic gyro ARW variance estimation method based on interval information provided in the embodiment of the present application can be applied to Figure 2In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the IFOG angular rate data to the server 104. After the server 104 receives the IFOG angular rate data, for the IFOG angular rate data, the server 104 calculates the ARW standard deviation sequence corresponding to the total data output of the gyroscope at the current working moment at different beam times; according to the ARW standard deviation sequence corresponding to the total data output of the gyroscope at the current working moment at different beam times, the ARW variance value corresponding to the sampling time is estimated by the confidence interval to obtain multiple confidence interval information; according to the multiple confidence interval information, the ARW standard deviation estimate corresponding to the sampling time is calculated; according to the ARW standard deviation estimate corresponding to the sampling time, the ARW variance estimate corresponding to the sampling time is calculated. The server 104 can feed back the obtained ARW variance estimate corresponding to the sampling time to the terminal 102. In addition, in some embodiments, the fiber optic gyroscope ARW variance estimation method based on interval information can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly process the IFOG angular velocity data and estimate the ARW variance, or the server 104 can obtain the IFOG angular velocity data from the data storage system and process the IFOG angular velocity data and estimate the ARW variance.

[0050] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0051] In an exemplary embodiment, Figure 2 As shown, a method for estimating the ARW variance of a fiber optic gyroscope based on interval information is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 2 The server 104 in FIG. 1 is used as an example to illustrate. Figure 3 As shown, the specific steps include:

[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 acquiring the IFOG angular rate data and its IFOG angular rate white noise data, the variance information in the complete beam time interval corresponding to the IFOG angular rate white noise can be obtained, so that the variance information in the complete beam time interval corresponding to the IFOG angular rate white noise can be fully utilized to make a more accurate estimate of the ARW variance at the sampling time, thereby improving the accuracy of the estimated ARW variance.

[0054] Step S2: Calculate the ARW standard deviation sequence corresponding to the total amount of gyro data output at different beamforming times at the current working moment according to the IFOG angular rate data.

[0055] In this embodiment, step S2 calculates the ARW standard deviation sequence corresponding to the total amount of gyro data output at different beamforming times at the current working moment according to the IFOG angular rate data, and specifically includes the following steps:

[0056] Step S21: Determine, based on the IFOG angular velocity data, a relationship between the ARW standard deviation at any beamforming time and the ARW standard deviation corresponding to the sampling time.

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

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

[0059] Step S212: derive the correlation between the ARW standard deviation at any clustering time and the ARW standard deviation corresponding to the sampling time according to the Allan variance double logarithmic curve, and establish a relationship between the ARW standard deviation at any clustering time and the ARW standard deviation corresponding to the sampling time.

[0060] Step S22: Calculate the ARW standard deviation at any clustering time and map it to the ARW standard deviation value at the sampling time according to the relationship between the ARW standard deviation at any clustering time and the ARW standard deviation corresponding to the sampling time.

[0061] Step S23, according to the ARW standard deviation at any beam time mapped to the ARW standard deviation value corresponding to the sampling time, calculate the ARW standard deviation sequence corresponding to the total amount of gyro data output at different beam times at the current working moment.

[0062] Step S3: according to the ARW standard deviation sequence corresponding to the total amount of gyro data output at different bunching times at the current working moment, the confidence interval of the ARW variance value corresponding to the sampling time is estimated to obtain multiple confidence interval information.

[0063] Step S4: Calculate the estimated value of the ARW standard deviation corresponding to the sampling time according to the multiple confidence interval information.

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

[0065] Based on the information of multiple confidence intervals, the median of each confidence interval is weighted and summed to calculate the estimated standard deviation of ARW corresponding to the sampling time. The weight coefficient is given by the number of confidence intervals contained in the median of each confidence interval.

[0066] Step S5: Calculate the estimated value of the ARW variance corresponding to the sampling time according to the estimated value of the ARW standard deviation corresponding to the sampling time.

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

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

[0069] This embodiment starts from the perspective of confidence interval. By calculating the ARW standard deviation sequence corresponding to the total data output of the gyroscope at different beam times at the current working moment, and then adopting the confidence interval estimation method of the ARW variance value to obtain multiple confidence interval information, the ARW standard deviation estimate corresponding to the sampling time is calculated on the basis of the confidence interval information, and then the corresponding ARW variance estimate is indirectly calculated. Based on the principle that ARW is the integral result of angular rate white noise, the variance information within the complete beam time interval corresponding to the IFOG angular rate white noise is fully utilized, so that the ARW variance of the fiber optic gyroscope at the sampling moment can be more accurately estimated, which can provide more reliable prior information for the subsequent suppression of IFOG random errors, thereby improving the navigation accuracy of the inertial navigation system.

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

[0071] First, according to Figure 1The linear mapping relationship with a slope of -1 / 2 in the Allan variance double logarithmic curve in the above formula can be used to deduce the ARW standard deviation and sampling time τ under any beam time τ. 0 The relationship between the corresponding ARW standard deviation, that is, the ARW standard deviation at any bunching time τ and the sampling time τ 0 The corresponding ARW standard deviation relationship is:

[0072]

[0073] Where τ represents the cluster time, τ 0 represents the sampling time, σ(τ,τ 0 ) is the ARW standard deviation at any beam time τ mapped to the sampling time τ 0 σ(τ) is the ARW standard deviation corresponding to any beam time τ.

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

[0075]

[0076] According to formula (2), the corresponding sampling time τ can be calculated at different times 0 The corresponding ARW standard deviation sequence is expressed as formula (3):

[0077]

[0078] Among them, τ 0 represents the sampling time, σ(τ 0 ) represents the sampling time τ 0 The corresponding ARW standard deviation, σ(τ n ,τ 0 ) represents the ARW standard deviation corresponding to the maximum clustering time that can be divided by the FOG output sample capacity at the current moment, where n = max(m). m is the number of data blocks corresponding to different clustering times, specifically the number of data blocks corresponding to different clustering times when calculating the Allan variance. θ n is the ARW standard deviation series under different bundle times.

[0079] In this embodiment, after obtaining each sampling time τ 0 The corresponding ARW standard deviation sequence, based on each set of ARW standard deviation sequences mentioned above, can be used to calculate the sampling time τ 0 The corresponding ARW variance value is estimated by confidence interval, thereby obtaining multiple confidence intervals with a confidence level of 1-α, which can be expressed as formula (4):

[0080]

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

[0082] Furthermore, the overlapping intervals of these confidence intervals are counted, and the medians of each overlapping interval are weighted summed to obtain the final sampling time τ 0 The corresponding ARW standard deviation estimate. The weight coefficient can be given according to the number of confidence intervals involved in each overlapping interval, and is defined as is the confidence interval The median value of thr n Indicates that the confidence interval is included Median The total number of other confidence intervals. From this, the sampling time τ can be calculated 0 The corresponding ARW standard deviation estimate is expressed as:

[0083]

[0084] in, represents the sampling time τ 0 The corresponding ARW standard deviation estimate is, is the confidence interval The median value of thr n Indicates that the confidence interval is included Median The total number of other confidence intervals for .

[0085] In this embodiment, the sampling time τ is calculated. 0 After the corresponding ARW standard deviation is estimated, 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 based on the ARW standard deviation. Therefore, when the sampling time τ is obtained, 0 The corresponding ARW standard deviation estimate Afterwards, the formula Calculate the sampling time τ 0 The corresponding ARW variance estimation value is used as the final ARW variance estimation result, thereby completing the precise estimation of ARW variance and obtaining accurate and reliable ARW variance results.

[0086] This embodiment uses a fiber optic gyro ARW variance estimation method based on interval information to perform simulation experiments on 10-minute short-term noise data and 5-hour ideal data. The simulation results are as follows: Figure 4As shown, Figure 4 The Allan variance curve of the 10-minute short-term noise data and the Allan variance curve of the 5-hour ideal data are shown, as well as a partial enlargement of the vertical coordinate position. Among them, the Allan variance curve of the 5-hour ideal data can be used as a benchmark for the accuracy of variance estimation. Figure 4 It can be seen from the simulation results in that, under the method proposed in this application, the Allan variance curve of the 10-min short-term noise data is close to the Allan variance curve of the 5-h ideal data, and the estimated ARW variance result is more accurate and reliable. Therefore, the method proposed in this application can effectively improve the sampling time τ 0 The estimation accuracy of the corresponding ARW variance.

[0087] In an exemplary embodiment, a fiber optic gyro ARW variance estimation system based on interval information is provided. The system may be a computer device, which may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a 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 capabilities. 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 data required for fiber optic gyroscope ARW variance estimation such as IFOG angular rate data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a fiber optic gyroscope ARW variance estimation method based on interval information is implemented.

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

[0089] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0090] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for estimating the variance of an optical fiber gyro ARW based on interval information, characterized in that: The fiber optic gyro ARW variance estimation method based on interval information includes: Get IFOG angular rate data; According to the IFOG angular rate data, the ARW standard deviation sequence corresponding to the total amount of gyro data output at the current working moment at different beamforming times is calculated; According to the ARW standard deviation sequence corresponding to the total amount of gyro data output at different beam times at the current working moment, the ARW variance value corresponding to the sampling time is estimated with confidence intervals to obtain multiple confidence interval information; Based on multiple confidence interval information, the estimated value of the ARW standard deviation corresponding to the sampling time is calculated; Based on the estimated value of the ARW standard deviation corresponding to the sampling time, calculate the estimated value of the ARW variance corresponding to the sampling time.

2. The method for estimating the ARW variance of an optical fiber gyro based on interval information according to claim 1, characterized in that: According to the IFOG angular rate data, the ARW standard deviation sequence corresponding to the total amount of gyro data output at different beam times at the current working moment is calculated, specifically including: Determine the relationship between the ARW standard deviation at any beam time and the ARW standard deviation corresponding to the sampling time according to the IFOG angular velocity data; 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 calculated and mapped to the ARW standard deviation value corresponding to the sampling time; According to the mapping of the ARW standard deviation at any beam time to the corresponding ARW standard deviation value at the sampling time, the ARW standard deviation sequence corresponding to the total data output of the gyroscope at different beam times at the current working moment is calculated.

3. The method for estimating the ARW variance of an optical fiber gyro based on interval information according to claim 2, characterized in that: According to the IFOG angular velocity data, the relationship between the ARW standard deviation at any beam time and the ARW standard deviation corresponding to the sampling time is determined, specifically including: According to the IFOG angular velocity data, an Allan variance double logarithmic curve graph is drawn; the variance of the cluster time corresponding to the sampling time in the Allan variance double logarithmic curve graph 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 clustering time and the ARW standard deviation corresponding to the sampling time is derived, and a relationship between the ARW standard deviation at any clustering time and the ARW standard deviation corresponding to the sampling time is established.

4. The method for estimating the ARW variance of an optical fiber gyro based on interval information according to claim 2, characterized in that: The relationship between the ARW standard deviation at any cluster time and the ARW standard deviation corresponding to the sampling time is: Among them, τ represents the cluster time, τ0 represents the sampling time, σ(τ,τ0) represents 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) represents the ARW standard deviation corresponding to any cluster time τ.

5. The method for estimating the ARW variance of a fiber optic gyroscope based on interval information according to claim 2, characterized in that: The following formula is used to calculate the ARW standard deviation at any bundle time and map it to the corresponding ARW standard deviation at the sampling time: Among them, τ represents the cluster time, τ0 represents the sampling time, σ(τ,τ0) represents 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) represents the ARW standard deviation corresponding to any cluster time τ.

6. The method for estimating the ARW variance of a fiber optic gyroscope based on interval information according to claim 2, characterized in that: The following formula is used to calculate the ARW standard deviation sequence corresponding to the total amount of gyro data output at the current working moment at different beam times: Where 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 clustering time that can be divided by the FOG output sample capacity at the current moment, m is the number of data blocks corresponding to different clustering times, n=max(m), θ n is the ARW standard deviation series under different bundle times.

7. The method for estimating the ARW variance of an optical fiber gyroscope based on interval information according to claim 1, characterized in that: The expressions for multiple confidence interval information are: in, The confidence interval estimated for the ARW standard deviation sequence samples at different times, 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 under different bundle times.

8. The method for estimating the ARW variance of a fiber optic gyroscope based on interval information according to claim 1, characterized in that: Based on multiple confidence interval information, the estimated value of the ARW standard deviation corresponding to the sampling time is calculated, including: According to the information of multiple confidence intervals, the median of each confidence interval is weighted and summed to calculate the estimated value of the ARW standard deviation corresponding to the sampling time, where the weight coefficient is given by the number of confidence intervals contained in the median of each confidence interval.

9. The method for estimating the ARW variance of a fiber optic gyroscope based on interval information according to claim 8, characterized in that: The estimated value of the ARW standard deviation corresponding to the sampling time is calculated using the following formula: in, represents the estimated value of the ARW standard deviation corresponding to the sampling time τ0, is the confidence interval The median value of thr n Indicates that the confidence interval is included Median The total number of other confidence intervals for .

10. The method for estimating the ARW variance of a fiber optic gyroscope based on interval information according to claim 1, characterized in that: According to the estimated value of the ARW standard deviation corresponding to the sampling time, the estimated value of the ARW variance corresponding to the sampling time is calculated, including: According to the estimated value of the ARW standard deviation corresponding to the sampling time, the square of the estimated value of the ARW standard deviation corresponding to the sampling time is calculated to obtain the estimated value of the ARW variance corresponding to the sampling time.

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