Single-axis rotation inertial navigation all-zero bias estimation method, device and medium

Abstract

The present application relates to the technical field of inertial navigation, in particular to a single-axis rotation inertial navigation all-zero bias estimation method, device and medium, the method comprising the following steps: installing two sets of single-axis rotation inertial navigation systems on a carrier, the first single-axis rotation inertial navigation system is installed on a base, and the second single-axis rotation inertial navigation system is installed on a platform; after the inertial navigation system is started and stabilized, the two sets of single-axis rotation inertial navigation systems are rotated according to different rotation strategies, angular velocity and specific force information output by the two sets of single-axis rotation inertial navigation systems are collected, and the angular velocity and specific force information are demodulated; a measurement equation is constructed; a state equation is constructed; and all-zero biases of the two sets of single-axis rotation inertial navigation systems are estimated in real time through Kalman filtering algorithm according to the measurement equation and the state equation. The present application improves the autonomous navigation performance of single-axis rotation inertial navigation, and solves the problem that the existing single-axis rotation inertial navigation system is difficult to estimate and compensate the z-axis zero bias of inertial devices.

Description

Technical Field

[0001] This invention relates to the field of inertial navigation technology, specifically to a method, device, and medium for estimating all zero biases in a single-axis rotating inertial navigation system. Background Technology

[0002] Inertial navigation systems (INS) are widely used in navigation on various launch platforms due to their advantages of strong autonomy, good continuity, and high stealth. However, errors in the gyroscopes and accelerometers (hereinafter referred to as inertial elements) of the inertial measurement unit (IMU) in INS can cause autonomous navigation errors to accumulate over time. In single-axis rotating inertial navigation systems (hereinafter referred to as single-axis rotating INS), the inertial measurement unit rotates around the z-axis, allowing the influence of the zero-bias error of the inertial element in the horizontal direction to be canceled by rotation modulation, thus improving the accuracy of autonomous navigation. However, the error of the inertial element in the rotation axis (z-axis) direction of the single-axis rotating INS cannot be modulated by rotation, and the error of the z-axis inertial element will affect the autonomous navigation performance of the single-axis rotating INS. To accurately calibrate the errors of the inertial elements, INS needs to undergo sufficient laboratory calibration before use. However, the constant zero bias of the gyroscope and accelerometer can change with factors such as the working environment, usage time, and startup method.

[0003] The disadvantages of existing technologies are as follows:

[0004] (1) Limitations of Supporting Auxiliary Equipment. Existing methods for estimating all zero biases in inertial navigation systems (INS) typically require a high-precision three-axis turntable or INS modulated by dual-axis rotation. Since high-precision three-axis turntables are bulky and require precise leveling at the mounting point before use, INS zero bias estimation can only be performed in a laboratory setting and cannot be conducted on a mobile platform, such as during ship navigation. Compared to single-axis rotational INS, dual-axis rotational INS is more expensive and has a more complex system design, thus single-axis rotational INS is more widely used. If a ship does not have a dual-axis rotational INS, existing technologies cannot estimate zero biases in all directions.

[0005] (2) Limitations of applicable working environment. Existing inertial navigation zero bias estimation methods are usually performed under static base conditions. The zero bias of inertial components will change with factors such as working environment, usage time, inertial navigation startup, and fault abnormalities. Therefore, it is required that the inertial navigation zero bias estimation can also be applied under dynamic base conditions. For example, after the inertial navigation has been working for a period of time, under the condition of carrier motion, it should be possible to estimate the zero bias of the inertial navigation in all directions.

[0006] In summary, there is an urgent need for a method, device, and medium for estimating all zero biases in a single-axis rotating inertial navigation system to solve the problems in the existing technology. Summary of the Invention

[0007] The purpose of this invention is to provide a method, device, and medium for estimating all zero biases in a single-axis rotating inertial navigation system. The specific technical solution is as follows:

[0008] A method for estimating all zero biases in a single-axis rotating inertial navigation system includes the following steps:

[0009] S1: Two single-axis rotating inertial navigation systems are installed on the carrier. The first single-axis rotating inertial navigation system is installed on the base, and the second single-axis rotating inertial navigation system is installed on the inclined platform. The inclined installation relationship between the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system is represented by the installation matrix.

[0010] S2: After the inertial navigation system is powered on and stabilized, the two single-axis rotating inertial navigation systems perform rotation modulation according to different rotation strategies, collect the angular velocity and specific force information output by the two single-axis rotating inertial navigation systems, and demodulate the angular velocity and specific force information;

[0011] S3: Based on the oblique mounting relationship in S1 and the demodulated angular velocity and specific force information in S2, construct the measurement equation;

[0012] S4: Using the constant zero bias of the gyroscope and the constant zero bias of the accelerometer as the state variables of the Kalman filter, construct the state equation;

[0013] S5: Based on the measurement equation and the state equation, the Kalman filter algorithm is used to estimate all zero biases of the two single-axis rotating inertial navigation systems in real time.

[0014] Optionally, in S1, the three-dimensional relative installation angles of the two single-axis rotating inertial navigation systems are [45°, 45°, 0°], and the relative installation relationship of the two single-axis rotating inertial navigation systems is represented by an installation matrix.

[0015] Optionally, in S2, the two single-axis rotating inertial navigation systems perform rotation modulation according to different rotation strategies. The rotation strategy includes rotational angular velocity and stationary time after rotation. The rotational angular velocity ranges from 1 to 50° / s, and the stationary time after rotation is not less than 300s.

[0016] Optionally, in S2, the angular velocity and specific force information are demodulated based on the measurement results from the angle measuring mechanism. The expressions for the demodulated angular velocity and specific force information are as follows:

[0017]

[0018] in, and These represent the x, y, and z axis angular velocities output by the gyroscopes after demodulation of the first and second single-axis rotating inertial navigation systems, respectively. and These represent the xyz axis specific forces output by the accelerometers after demodulation of the first and second single-axis rotating inertial navigation systems, respectively, with the superscript T indicating the transpose of the matrix; and These represent the x, y, and z axis angular velocities output by the gyroscopes before demodulation in the first and second single-axis rotating inertial navigation systems, respectively. and These represent the xyz axis specific forces output by the accelerometers before demodulation in the first and second single-axis rotating inertial navigation systems, respectively. and All represent rotation matrices, b1 and b2 represent the carrier coordinate systems corresponding to the installation positions of the first and second single-axis rotating inertial navigation systems, respectively, and S1 and S2 represent the inertial component coordinate systems of the first and second single-axis rotating inertial navigation systems, respectively.

[0019] Optionally, in S3, the expression for the measurement equation is as follows:

[0020]

[0021] in, and These represent the xyz axis gyroscope zero bias of the first and second single-axis rotating inertial navigation systems, respectively. and The zero bias of the x, y, and z axis accelerometers in the first and second single-axis rotating inertial navigation systems are respectively represented; the measured values ​​of the axis angular velocity are recorded. Shaft proportional measurement value

[0022] The measurement equation can be written in matrix form as follows:

[0023] Z k =H k X k +V k ;

[0024] Where k represents the sampling time, Z k H represents the measurement value at time k. k V represents the measurement transition matrix at time k. k Z represents the measurement noise matrix at time k. k =[Δω k ,Δf k ] T H k Represented as:

[0025]

[0026] Among them, 0 3×3This represents a 3-dimensional zero matrix.

[0027] Optionally, in S4, the state variables of the Kalman filter are: The expression for the state equation is as follows:

[0028] X k =AX k-1 +W k-1 ;

[0029] Among them, X k-1 and X k Let I represent the state variables at times k-1 and k, respectively; A is the state transition matrix, A = I 12×12 , among which, I 12×12 W represents a 12-dimensional identity matrix. k-1 This is the state noise vector at time k-1.

[0030] Optionally, in S5, the Kalman filter algorithm estimates all zero biases of the two single-axis rotating inertial navigation systems in real time as follows:

[0031] One-step state prediction yields one-step predicted state;

[0032] Calculate the root mean square error of the one-step prediction to obtain the one-step prediction error covariance matrix;

[0033] Calculate the filter gain to obtain the Kalman filter gain at the sampling time;

[0034] State estimation yields the state estimate at the sampling time.

[0035] The mean square error is calculated to obtain the estimation error covariance matrix at the sampling time, thus enabling real-time estimation of all zero biases of the two single-axis rotating inertial navigation systems.

[0036] Additionally, the present invention also includes a computer device, comprising a memory and a processor;

[0037] The memory is used to store computer programs that can run on the processor;

[0038] When the processor executes the computer program, it implements the steps of the single-axis rotating inertial navigation system's complete zero-bias estimation method as described above.

[0039] In addition, the present invention also includes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the single-axis rotating inertial navigation system zero-bias estimation method described above.

[0040] The application of the technical solution of the present invention has the following beneficial effects:

[0041] This invention discloses a method, device, and medium for estimating all zero biases in a single-axis rotating inertial navigation system. In this method, two single-axis rotating inertial navigation systems are installed with different methods and rotation strategies. Furthermore, this invention also designs a Kalman filter specifically for estimating all zero biases in the two single-axis rotating inertial navigation systems. By applying this method, the autonomous navigation performance of the single-axis rotating inertial navigation system can be improved, and the problem of difficulty in estimating and compensating for the z-axis zero bias of inertial devices in existing single-axis rotating inertial navigation systems can be solved.

[0042] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart of the steps of the single-axis rotating inertial navigation system zero bias estimation method in a preferred embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of the installation of two sets of single-axis rotating inertial navigation systems in a preferred embodiment of the present invention;

[0046] Figure 3 This is the zero-bias estimation result of the SRINS1 gyroscope;

[0047] Figure 4 This is the zero-bias estimation result of the SRINS2 gyroscope;

[0048] Figure 5 This is the zero bias estimation result of the SRINS1 accelerometer;

[0049] Figure 6 This is the zero-bias estimation result of the SRINS2 accelerometer. Detailed Implementation

[0050] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] This embodiment applies to the field of inertial navigation technology, and the technologies involved include:

[0052] Coordinate system concept:

[0053] The coordinate systems mainly involved in this embodiment include (coordinate system abbreviations are usually indicated in italics):

[0054] ① Inertial coordinate system (i-system): The origin is located at the Earth's center of mass, the z-axis points to the Earth's North Pole, the x-axis points to the vernal equinox, and the y-axis forms a right-handed orthogonal coordinate system with the z-axis and x-axis;

[0055] ② Carrier coordinate system (b system): with the carrier's centroid as the origin, the z-axis is perpendicular to the carrier plane and points upward, the y-axis coincides with the carrier's longitudinal axis and points forward, and the x-axis, together with the y and z axes, forms a right-hand rule pointing to the right. It is also called the right-front-upper coordinate system.

[0056] ③ Inertial module system (S-frame): With the IMU's center of mass as the origin, xyz points in the upper right-front direction of the inertial module.

[0057] Since this invention relates to two sets of single-axis rotating inertial navigation systems, the first single-axis rotating inertial navigation system is denoted as SRINS1 and the second single-axis rotating inertial navigation system is denoted as SRINS2, which correspond to two inertial navigation systems, s1 system and s2 system.

[0058] ④ Inclined Coordinate System (b2 system): In this embodiment, SRINS2 is inclined relative to the carrier, and the b2 system has an installation angle relationship with the b system. SRINS1 is not inclined, so the b1 system coincides with the b system.

[0059] Transformations between coordinate systems can be represented by attitude matrices. For example, if a single-axis rotating inertial navigation system rotates around the z-axis, then the s-frame will have a rotational relationship with the b-frame. Therefore, the attitude matrix from the S1-frame to the b-frame is... attitude matrix from S2 system to b system Represented as:

[0060]

[0061] Where θ1 and θ2 represent the angles of rotation of SRINS1 and SRINS2 around the z-axis, respectively.

[0062] The b2 system has an installation angle relationship relative to the b system, and the installation matrix from the b system to the b2 system is shown. Represented as:

[0063]

[0064] In the formula, α x α y These represent the angles at which SRINS2 is obliquely mounted relative to the x-axis and y-axis of the b-system, respectively.

[0065] like Figure 1 As shown in the figure, this embodiment discloses a method for estimating all zero biases in a single-axis rotating inertial navigation system, including the following steps:

[0066] S1: Two single-axis rotating inertial navigation systems are installed on the carrier. The first single-axis rotating inertial navigation system is installed on the base, and the second single-axis rotating inertial navigation system is installed on the inclined platform. The three-dimensional relative installation angle between the two single-axis rotating inertial navigation systems is not 0.

[0067] In this embodiment, preferably, the three-dimensional relative installation angles of the two single-axis rotating inertial navigation systems are [45°, 45°, 0°], and the relative installation relationship of the two single-axis rotating inertial navigation systems is represented by an installation matrix. See the installation diagram of the two single-axis rotating inertial navigation systems. Figure 2 .

[0068] S2: After the inertial navigation system is powered on and stabilized, the two single-axis rotating inertial navigation systems perform rotation modulation according to different rotation strategies. The rotation strategy includes rotational angular velocity and stationary time after rotation. The angular velocity and specific force information output by the two single-axis rotating inertial navigation systems are collected, and the angular velocity and specific force information are demodulated according to the measurement results of the angle measuring mechanism.

[0069] In this preferred embodiment, the two single-axis rotating inertial navigation systems perform rotation modulation according to different rotation strategies. The rotation strategy includes rotational angular velocity and stationary time after rotation. The rotational angular velocity ranges from 1 to 50° / s, and the stationary time after rotation is not less than 300s.

[0070] In a specific implementation case, the first single-axis rotating inertial navigation system has a rotational angular velocity of 15° / s and a stationary time of 300s after rotating to a position, while the second single-axis rotating inertial navigation system has a rotational angular velocity of 10° / s and a stationary time of 300s after rotating to a position.

[0071] Furthermore, the expressions for the demodulated angular velocity and specific force information are as follows:

[0072]

[0073] in, and These represent the x, y, and z axis angular velocities output by the gyroscopes after demodulation of the first and second single-axis rotating inertial navigation systems, respectively. and These represent the xyz axis specific forces output by the accelerometers after demodulation of the first and second single-axis rotating inertial navigation systems, respectively, with the superscript T indicating the transpose of the matrix; and These represent the x, y, and z axis angular velocities output by the gyroscopes before demodulation in the first and second single-axis rotating inertial navigation systems, respectively. and These represent the xyz axis specific forces output by the accelerometers before demodulation in the first and second single-axis rotating inertial navigation systems, respectively. and All represent rotation matrices, b1 and b2 represent the carrier coordinate systems corresponding to the installation positions of the first and second single-axis rotating inertial navigation systems, respectively, and S1 and S2 represent the inertial component coordinate systems of the first and second single-axis rotating inertial navigation systems, respectively.

[0074] S3: Based on the oblique mounting relationship in S1 and the demodulated angular velocity and specific force information in S2, construct the measurement equation.

[0075] Specifically, the expression for the measurement equation is as follows:

[0076]

[0077] in, and These represent the xyz axis gyroscope zero bias of the first and second single-axis rotating inertial navigation systems, respectively. and These represent the zero bias of the x, y, and z axis accelerometers in the first and second single-axis rotating inertial navigation systems, respectively. Record the measured axial angular velocity values. Shaft proportional measurement value

[0078] It should be noted that, in this embodiment, the measurement equation can be written in matrix form, as shown in the following expression:

[0079] Z k =H k X k +R k ;

[0080] Where k represents the sampling time, Z k H represents the measurement value at time k. k R represents the measurement transition matrix at time k. k Z represents the measurement noise matrix at time k. Specifically, Z k =[Δω k ,Δf k ] T H k Represented as:

[0081]

[0082] Among them, 0 3×3This represents a 3-dimensional zero matrix.

[0083] S4: Using the constant zero bias of the gyroscope and the constant zero bias of the accelerometer as the state variables of the Kalman filter, construct the state equation.

[0084] Specifically, the state variables of the Kalman filter are: The expression for the state equation is as follows:

[0085] X k =AX k-1 +W k-1 ;

[0086] Among them, X k-1 and X k Let I represent the state variables at times k-1 and k, respectively; A is the state transition matrix, A = I 12×12 , among which, I 12×12 W represents a 12-dimensional identity matrix. k-1 This is the state noise vector at time k-1.

[0087] S5: Based on the measurement equation and the state equation, the Kalman filter algorithm is used to estimate all zero biases of the two single-axis rotating inertial navigation systems in real time.

[0088] Specifically, the process of the Kalman filter algorithm estimating all zero biases of the two single-axis rotating inertial navigation systems in real time is as follows:

[0089] 1) One-step state prediction:

[0090] in Indicates the state predicted in one step. This represents the state estimate at time k-1;

[0091] 2) Calculate the root mean square error of the one-step prediction:

[0092] P k|k-1 =AP k-1 A T +Q k-1 , where P k|k-1 Let P represent the one-step prediction error covariance matrix. k-1 This represents the estimation error covariance matrix at time k-1. This represents the state noise variance matrix at time k-1;

[0093] 3) Calculate the filter gain

[0094] Where K k This represents the Kalman filter gain at time k. Let k represent the measurement noise variance matrix at time k;

[0095] 4) State estimation:

[0096] in Represents the state estimate at time k;

[0097] 5) Calculate the estimated mean square error:

[0098] P k =(IK k H k )P k|k-1 , where P k This represents the estimation error covariance matrix at time k, enabling real-time estimation of all zero biases in two sets of single-axis rotating inertial navigation systems.

[0099] To verify the feasibility and effectiveness of the method for estimating all zero biases of a single-axis rotating inertial navigation system provided by this invention, a simulation experiment was conducted based on data from a certain sea trial. The simulation experiment lasted for 4 hours, and the simulation conditions were set as shown in Table 1.

[0100] Table 1 Simulation conditions

[0101]

[0102] The method proposed in this embodiment is used for zero-bias estimation, and the result of the zero-bias estimation is as follows: Figure 3 , Figure 4 , Figure 5 and Figure 6 As shown, where, Figure 3 The results are the zero-bias estimation results for the SRINS1 gyroscope. Figure 4 The results are the zero-bias estimation results for the SRINS2 gyroscope. Figure 5 The zero bias estimation results for the SRINS1 accelerometer. Figure 6 This is the zero bias estimation result for the SRINS2 accelerometer.

[0103] Furthermore, the root mean square error (RMSE) statistics for the zero-bias estimation are shown in Table 2 below.

[0104] Table 2. Root Mean Square Error (RMSE) Statistics

[0105]

[0106] Based on the simulation results above, it can be seen that the zero bias of the three axes of SRINS1 and SRINS2 gyroscopes and accelerometers (x, y, z) can be accurately estimated, verifying the effectiveness of the method of the present invention.

[0107] In addition, embodiments of the present invention also provide a computer device, including a memory and a processor;

[0108] The memory is used to store computer programs that can run on the processor;

[0109] When the processor executes the computer program, it implements the steps of the single-axis rotating inertial navigation system's complete zero-bias estimation method as described above.

[0110] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device.

[0111] The computer device may be a mobile phone, desktop computer, laptop, handheld computer, or cloud server, etc. The computer device may include, but is not limited to, a processor and memory. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0112] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the computer device, connecting various parts of the computer device via various interfaces and lines.

[0113] The memory can be used to store the computer program and / or modules. The processor implements the computer program by running or executing the computer program and / or modules stored in the memory, and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0114] Wherein, if the modules / units integrated into the computer device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0115] In addition, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for estimating all zero biases of a single-axis rotating inertial navigation system.

[0116] This invention discloses a method for estimating all zero biases in a single-axis rotating inertial navigation system. In this method, two single-axis rotating inertial navigation systems are installed with different methods and rotation strategies. Furthermore, this invention also designs a Kalman filter specifically for estimating all zero biases in the two single-axis rotating inertial navigation systems. By applying this method, the autonomous navigation performance of the single-axis rotating inertial navigation system can be improved, solving the problem of poor autonomous navigation performance in existing single-axis rotating inertial navigation systems.

[0117] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0118] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A single-axis rotation inertial navigation all-bias estimation method, characterized in that, The method comprises the following steps: S1: two sets of single-axis rotation inertial navigation systems are installed on a carrier, a first single-axis rotation inertial navigation system is installed on a base, and a second single-axis rotation inertial navigation system is installed on a platform, and a mounting matrix is used to represent the oblique installation relationship between the first single-axis rotation inertial navigation system and the second single-axis rotation inertial navigation system; S2: after the inertial navigation system is started and stabilized, the two sets of single-axis rotation inertial navigation systems are rotated according to different rotation strategies, angular velocity and specific force information output by the two sets of single-axis rotation inertial navigation systems are collected, and the angular velocity and specific force information are demodulated; S3: a measurement equation is constructed according to the oblique installation relationship in S1 and the demodulated angular velocity and specific force information in S2; S4: the constant zero bias of the gyroscope and the constant zero bias of the accelerometer are taken as state variables of a Kalman filter, and a state equation is constructed; S5: the measurement equation and the state equation are used to estimate all zero biases of the two sets of single-axis rotation inertial navigation systems in real time through a Kalman filtering algorithm. In S2, the angular velocity and specific force information are demodulated according to the measurement results of the angle measuring mechanism, and the demodulated angular velocity and specific force information are expressed as follows: ； ； ； ； wherein, and and represent the x-y-z axis angular velocity of the first and second single-axis rotation inertial navigation system after demodulation of the gyro output, and and represent the x-y-z axis specific force of the first and second single-axis rotation inertial navigation system after demodulation of the accelerometer output, the superscript T represents the transpose of the matrix; and and represent the x-y-z axis angular velocity of the first and second single-axis rotation inertial navigation system before demodulation of the gyro output, and and represent the x-y-z axis specific force of the first and second single-axis rotation inertial navigation system before demodulation of the accelerometer output; and both represent a rotation matrix, and and represent the carrier coordinate system corresponding to the installation position of the first and second single-axis rotation inertial navigation system, and and represent the inertial component coordinate system of the first and second single-axis rotation inertial navigation system; In S3, the measurement equation is expressed as follows: ； ； wherein, and respectively represent the x-y-z axis gyroscopic zero bias of the first and second single-axis rotation inertial navigation systems, and respectively represent the x-y-z axis accelerometer zero bias of the first and second single-axis rotation inertial navigation systems; let the angular velocity difference be , and the specific force difference be ; The measurement equation is written in a matrix form, and the expression is as follows: ； wherein, denotes a sampling instant, denotes a measurement value at an instant, denotes a measurement transition matrix at an instant, denotes a measurement noise matrix at an instant, , is denoted as: ； wherein represents a zero matrix in 3 dimensions.

2. The single-axis rotation inertial navigation system with no bias estimation method according to claim 1, wherein, In S1, the mounting matrix of the two sets of single-axis rotation inertial navigation systems is [45°, 45°, 0°].

3. The single-axis rotation inertial navigation system with no bias estimation method according to claim 2, characterized in that, In S2, the two sets of single-axis rotation inertial navigation systems are rotated according to different rotation strategies, the rotation strategies include a rotation angular velocity and a static time after the rotation is completed, the rotation angular velocity ranges from 1 to 50° / s, and the static time after the rotation is completed is not less than 300 s.

4. The single-axis rotation inertial navigation system with no bias estimation method according to claim 3, characterized in that, In S4, the state quantity of the Kalman filter is The expression of the state equation is as follows: ； in, and They represent and State quantity at any given moment; Here is the state transition matrix. ,in, Represents a 12-dimensional identity matrix; for The state noise vector at time t.

5. The single-axis rotation inertial navigation system with no bias estimation method according to claim 4, characterized in that, In S5, the process that the Kalman filtering algorithm estimates all zero biases of the two sets of single-axis rotation inertial navigation systems in real time is as follows: Step 1: one-step state prediction is performed to obtain one-step predicted state; Step 2: one-step predicted root mean square error is calculated to obtain one-step predicted error covariance matrix; Step 3: filtering gain is calculated to obtain Kalman filtering gain at a sampling time; Step 4: state estimation is performed to obtain state estimation at the sampling time; Step 5: estimation mean square error is calculated to obtain estimation error covariance matrix at the sampling time, and all zero biases of the two sets of single-axis rotation inertial navigation systems are estimated in real time.

6. A computer device, comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the single-axis rotation inertial navigation all zero bias estimation method according to any one of claims 1 to 5. The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the single-axis rotation inertial navigation all zero bias estimation method according to any one of claims 1 to 5. ​ 7. A computer-readable storage medium, characterized in that, ​

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