Uniaxial rotation inertial navigation all zero offset estimation method, equipment and medium

Abstract

The invention relates to the technical field of inertial navigation, in particular to a method and equipment for estimating all zero offsets of single-axis rotation inertial navigation and a medium, the method comprises the following steps: two sets of single-axis rotation inertial navigation systems are mounted on a carrier, a first single-axis rotation inertial navigation system is mounted on a base, and a second single-axis rotation inertial navigation system is mounted on an inclined platform; after inertial navigation startup is stable, the two sets of single-axis rotation inertial navigation systems perform rotation modulation according to different rotation strategies, and angular velocity and specific force information output by the two sets of single-axis rotation inertial navigation systems is collected and demodulated; constructing a measurement equation; constructing a state equation; and according to the measurement equation and the state equation, estimating all zero offsets of the two sets of single-axis rotation inertial navigation systems in real time through a Kalman filtering algorithm. According to the invention, the autonomous navigation performance of single-axis rotation inertial navigation is improved, and the problem that the existing single-axis rotation inertial navigation system is difficult to estimate and compensate the z-axis zero offset of the inertial device is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of inertial navigation, and in particular to a method, device and medium for estimating all zero biases of a single-axis rotating inertial navigation system. Background Art

[0002] Inertial navigation systems (hereinafter referred to as INS) are widely used in the navigation of various carrier platforms due to their strong autonomy, good continuity and high concealment. However, the errors of the gyroscopes and accelerometers (hereinafter referred to as inertial elements) of the inertial measurement unit (IMU) in the INS will cause the autonomous navigation errors of the INS to accumulate over time. In the single-axis rotational inertial navigation system (hereinafter referred to as the single-axis rotational INS), the inertial measurement unit rotates around the z-axis, so that the zero bias error of the inertial element in the horizontal direction can be offset by rotation modulation, thereby improving the accuracy of autonomous navigation. However, the error of the inertial element in the direction of the rotation axis (z-axis) of the single-axis rotational INS cannot be rotationally modulated, and the error of the z-axis inertial element will affect the autonomous navigation performance of the single-axis rotational INS. In order to accurately calibrate the error of the inertial element, the INS needs to be fully calibrated in the laboratory before use. However, the constant zero bias of the gyroscope and accelerometer will change with factors such as the working environment, usage time and startup method.

[0003] The disadvantages of the prior art are as follows:

[0004] (1) Limitation of supporting auxiliary equipment. The existing methods for estimating all zero bias of inertial navigation usually require a high-precision three-axis turntable or the inertial navigation is modulated by dual-axis rotation. Since the high-precision three-axis turntable is large in size and the installation platform needs to be leveled with high precision before use, if a high-precision three-axis turntable is used, the zero bias estimation of inertial navigation can only be achieved in the laboratory and cannot be performed on a carrier, such as when a ship is sailing. Compared with single-axis rotational inertial navigation, dual-axis rotational inertial navigation has higher cost and more complex system design, so single-axis rotational inertial navigation is more widely used. If there is no dual-axis rotational inertial navigation on the ship, the existing technical solution cannot estimate the zero bias in all directions.

[0005] (2) Applicable working environment restrictions. Existing inertial navigation zero bias estimation methods are usually performed under static base conditions. The zero bias of inertial elements will change with factors such as working environment, usage time, inertial navigation startup, fault anomalies, etc., so 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, the zero bias of the inertial navigation in all directions can be estimated under the condition that the carrier is moving.

[0006] In summary, there is an urgent need for a single-axis rotating inertial navigation full zero bias estimation method, device and medium to solve the problems in the prior art. Summary of the invention

[0007] The object of the present invention is to provide a method, device and medium for estimating all zero bias of a single-axis rotating inertial navigation system. The specific technical scheme is as follows:

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

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

[0010] S2: After the inertial navigation system is started and stabilized, the two single-axis rotating inertial navigation systems are rotated and modulated according to different rotation strategies, and the angular velocity and specific force information output by the two single-axis rotating inertial navigation systems are collected and demodulated;

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

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

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

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

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

[0016] Optionally, in S2, the angular velocity and specific force information are demodulated according to the measurement results of the angle measuring mechanism, and the expressions of the demodulated angular velocity and specific force information are as follows:

[0017]

[0018] in, and They represent the xyz axis angular velocity of the gyro output after demodulation of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, and They represent the xyz-axis specific forces output by the demodulated accelerometers of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, respectively, and the superscript T represents the transpose of the matrix; and They represent the xyz axis angular velocity of the gyro output before demodulation of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, and They respectively represent the xyz-axis specific forces output by the accelerometers before demodulation of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system; and Both represent rotation matrices, b 1 and b 2 They represent the carrier coordinate systems corresponding to the installation positions of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, respectively. 1 and S 2 They respectively represent the inertial component coordinate systems of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system.

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

[0020]

[0021] in, and They represent the xyz-axis gyro bias of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, and Respectively represent the xyz axis accelerometer zero bias of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system; record the axis angular velocity measurement value Axis ratio measurement value

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

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

[0024] Among them, k represents the sampling time, Z k represents the measured value at time k, H k represents the measurement transfer matrix at time k, V k represents the measurement noise matrix at time k, Z k =[Δω k ,Δf k ] T , H k It is expressed as:

[0025]

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

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

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

[0029] Among them, X k-1 and X k Represent the state quantities at time k-1 and k respectively; A is the state transfer matrix, A=I 12×12 , where I 12×12 Represents the 12-dimensional identity matrix; W k-1 is the state noise vector at time k-1.

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

[0031] The state is predicted one step at a time, and the one-step predicted state is obtained;

[0032] Calculate the one-step prediction root mean square error and 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, obtaining the state estimate at the sampling time;

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

[0036] In addition, the present invention also includes a computer device, including a memory and a processor;

[0037] The memory is used to store a computer program executable on the processor;

[0038] The processor is used to implement the steps of the above-mentioned single-axis rotation inertial navigation full zero bias estimation method when executing the computer program.

[0039] In addition, the present invention also includes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the single-axis rotation inertial navigation full zero bias estimation method as described above are implemented.

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

[0041] The invention discloses a method, a device and a medium for estimating all zero bias of a single-axis rotating inertial navigation. In the method of the invention, different installation modes and rotation strategies are adopted for two sets of single-axis rotating inertial navigation systems. Meanwhile, the method of the invention also designs a Kalman filter specifically for all zero bias estimation of the two sets of single-axis rotating inertial navigation systems. By applying the method of the invention, the autonomous navigation performance of the single-axis rotating inertial navigation can be improved, and the problem that it is difficult for the existing single-axis rotating inertial navigation system to estimate and compensate for the zero bias of the z-axis of the inertial device is solved.

[0042] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 is a flowchart of the steps of a method for estimating all zero biases of a single-axis rotating inertial navigation system in a preferred embodiment of the present invention;

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

[0046] Figure 3 is the SRINS1 gyro bias estimation result;

[0047] Figure 4 is the SRINS2 gyro bias estimation result;

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

[0049] Figure 6 is the SRINS2 accelerometer bias estimation result. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0051] This embodiment is applied 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 (the English abbreviations of the coordinate systems are usually indicated in italics):

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

[0055] ②Carrier coordinate system (b system): With the carrier center of mass as the origin, the z-axis is perpendicular to the carrier plane and points upward, the y-axis coincides with the carrier longitudinal axis and points forward, and the x-axis, y-axis, and z-axis form the right-hand rule and point to the right, also called the right-front-upper coordinate system;

[0056] ③ Inertial unit system (s system): The IMU center of mass is taken as the origin, and xyz points to the upper right front direction of the inertial unit.

[0057] Since the present invention involves 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, corresponding to two inertial group systems s1 and s2.

[0058] ④ Oblique coordinate system (b 2 In this embodiment, SRINS2 is installed obliquely relative to the vector, 2 There is an installation angle relationship between the a system and the b system. SRINS1 is not installed obliquely, so b 1 The a system and the b system coincide.

[0059] The conversion between coordinate systems can be represented by the attitude matrix. For example, if the single-axis rotation inertial navigation system rotates around the z-axis, the s system will have a rotation relationship with the b system, then S 1 The attitude matrix from system A to system B and S 2 The attitude matrix from system A to system B It is expressed as:

[0060]

[0061] Among them, θ 1 ,θ2 Respectively represent the rotation angles of SRINS1 and SRINS2 around the z-axis.

[0062] b 2 There is an installation angle relationship between the system B and the system B. 2 Installation Matrix It is expressed as:

[0063]

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

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

[0066] S1: Install two sets of single-axis rotation inertial navigation systems on a carrier, wherein 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 an inclined platform, and the three-dimensional relative installation angle of the two sets of single-axis rotation inertial navigation systems is not 0.

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

[0068] S2: After the inertial navigation system is started and stabilized, the two single-axis rotating inertial navigation systems are rotated and modulated according to different rotation strategies, which include the rotation angular velocity and the static 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 embodiment, preferably, two sets of single-axis rotation inertial navigation systems perform rotation modulation according to different rotation strategies, and the rotation strategies include rotation angular velocity and stationary time after rotation into position, the rotation angular velocity ranges from 1 to 50° / s, and the stationary time after rotation into position is not less than 300s.

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

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

[0072]

[0073] in, and They represent the xyz axis angular velocity of the gyro output after demodulation of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, and They represent the xyz-axis specific forces output by the demodulated accelerometers of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, respectively, and the superscript T represents the transpose of the matrix; and They represent the xyz axis angular velocity of the gyro output before demodulation of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, and They respectively represent the xyz-axis specific forces output by the accelerometers before demodulation of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system; and Both represent rotation matrices, b 1 and b 2 They represent the carrier coordinate systems corresponding to the installation positions of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, respectively. 1 and S 2 They respectively represent the inertial component coordinate systems of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system.

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

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

[0076]

[0077] in, and They represent the xyz-axis gyro bias of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, and Respectively represent the xyz-axis accelerometer zero bias of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system. Axis ratio measurement value

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

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

[0080] Among them, k represents the sampling time, Z k represents the measured value at time k, H k represents the measurement transfer matrix at time k, R k represents the measurement noise matrix at time k. Specifically, Z k =[Δω k ,Δf k ] T , H k It is expressed as:

[0081]

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

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

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

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

[0086] Among them, X k-1 and X k Represent the state quantities at time k-1 and k respectively; A is the state transfer matrix, A=I 12×12 , where I 12×12 Represents the 12-dimensional identity matrix; W k-1 is the state noise vector at time k-1.

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

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

[0089] 1) One-step state prediction:

[0090] in represents the one-step prediction state, Represents the state estimate at time k-1;

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

[0092] P k|k-1 =AP k-1 AT +Q k-1 , where P k|k-1 represents the one-step forecast error covariance matrix, P k-1 represents the estimated error covariance matrix at time k-1, represents the state noise variance matrix at time k-1;

[0093] 3) Calculate the filter gain

[0094] Where K k represents the Kalman filter gain at time k, represents 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 ) k|k-1 , where P k Represents the estimated error covariance matrix at time k, realizing real-time estimation of all zero biases of two sets of single-axis rotating inertial navigation.

[0099] In order to verify the feasibility and effectiveness of the method for estimating all zero biases of a single-axis rotating inertial navigation system provided by the present invention, a simulation test was conducted based on the data of a certain navigation test. The simulation test 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 to perform zero bias estimation. The zero bias estimation result is as follows: Figure 3 , Figure 4 , Figure 5 and Figure 6 As shown, Figure 3 is the SRINS1 gyro bias estimation result, Figure 4 is the SRINS2 gyro bias estimation result, Figure 5 is the zero bias estimation result of SRINS1 accelerometer, Figure 6 is the zero bias estimation result of SRINS2 accelerometer.

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

[0104] Table 2 Root mean square error (RMSE) statistics

[0105]

[0106] Based on the above simulation results, it can be seen that the zero bias of the three axes xyz of the SRINS1 and SRINS2 gyroscopes and accelerometers can be accurately estimated, which verifies the effectiveness of the method of the present invention.

[0107] In addition, an embodiment of the present invention further provides a computer device, including a memory and a processor;

[0108] The memory is used to store a computer program executable on the processor;

[0109] The processor is used to implement the steps of the above-mentioned single-axis rotation inertial navigation full zero bias estimation method when executing the computer program.

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

[0111] The computer device may be a computing device such as a mobile phone, a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may include, but is not limited to, a processor and a memory. For example, the computer device may also include an input / output device, a network access device, a bus, etc.

[0112] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

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

[0114] Wherein, if the module / unit integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0115] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned single-axis rotation inertial navigation full zero bias estimation method are implemented.

[0116] The embodiment of the present invention discloses a method for estimating all zero biases of a single-axis rotating inertial navigation system. In the method of the present invention, different installation methods and rotation strategies are adopted for two sets of single-axis rotating inertial navigation systems. Meanwhile, the method of the present invention also specifically designs a Kalman filter for all zero bias estimation of the two sets of single-axis rotating inertial navigation systems. By applying the method of the present invention, the autonomous navigation performance of the single-axis rotating inertial navigation system can be improved, and the problem of poor autonomous navigation performance of the existing single-axis rotating inertial navigation system can be solved.

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

[0118] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for estimating all zero bias of a single-axis rotating inertial navigation system, characterized in that: The steps include: S1: Two sets of single-axis rotation inertial navigation systems are installed on the carrier, the first single-axis rotation inertial navigation system is installed on the base, and the second single-axis rotation inertial navigation system is installed on the oblique platform, and the oblique installation relationship between the first single-axis rotation inertial navigation system and the second single-axis rotation inertial navigation system is represented by the installation matrix; S2: After the inertial navigation system is started and stabilized, the two single-axis rotating inertial navigation systems are rotated and modulated according to different rotation strategies, and the angular velocity and specific force information output by the two single-axis rotating inertial navigation systems are collected and demodulated; S3: construct the measurement equation based on the oblique mounting relationship in S1 and the demodulated angular velocity and specific force information in S2; S4: Taking the gyroscope constant zero bias and the accelerometer constant zero bias as the state variables of the Kalman filter, constructing the state equation; S5: Based on the measurement equation and state equation, the Kalman filter algorithm is used to estimate the total zero bias of the two single-axis rotating inertial navigation systems in real time.

2. The method for estimating all zero biases of a single-axis rotating inertial navigation according to claim 1, characterized in that: In S1, the installation matrix of the two single-axis rotating inertial navigation systems is [45°, 45°, 0°].

3. The method for estimating all zero biases of a single-axis rotating inertial navigation according to claim 2, characterized in that: In S2, two sets of single-axis rotating inertial navigation systems perform rotation modulation according to different rotation strategies, wherein the rotation strategies include rotation angular velocity and stationary time after rotation into position, the rotation angular velocity ranges from 1 to 50° / s, and the stationary time after rotation into position is not less than 300s.

4. The method for estimating all zero biases of a single-axis rotating inertial navigation according to claim 3, characterized in that: In S2, the angular velocity and specific force information are demodulated according to the measurement results of the angle measuring mechanism. The expressions of the demodulated angular velocity and specific force information are as follows: in, and They represent the xyz axis angular velocity of the gyro output after demodulation of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, and They represent the xyz-axis specific forces output by the demodulated accelerometers of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, respectively, and the superscript T represents the transpose of the matrix; and They represent the xyz axis angular velocity of the gyro output before demodulation of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, and Respectively represent the xyz-axis specific forces output by the accelerometers before demodulation of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system; and Both represent rotation matrices, b1 and b2 represent the carrier coordinate systems corresponding to the installation positions of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, respectively, and S1 and S2 represent the inertial component coordinate systems of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, respectively.

5. The method for estimating all zero biases of a single-axis rotating inertial navigation according to claim 4, characterized in that: In S3, the expression of the measurement equation is as follows: in, and They represent the xyz-axis gyro bias of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system, and Respectively represent the xyz axis accelerometer zero bias of the first single-axis rotating inertial navigation system and the second single-axis rotating inertial navigation system; record the axis angular velocity measurement value Axis ratio measurement value The measurement equation is written in matrix form as follows: Z k =H k X k +V k ; Among them, k represents the sampling time, Z k represents the measured value at time k, H k represents the measurement transfer matrix at time k, V k represents the measurement noise matrix at time k, Z k =[Δω k ,Δf k ] T , H k It is expressed as: Among them, 0 3×3 Represents a 3-dimensional zero matrix.

6. The method for estimating all zero biases of a single-axis rotating inertial navigation according to claim 5, characterized in that: In S4, the state of the Kalman filter is The expression of the state equation is as follows: X k =AX k-1 +W k-1 ; Among them, X k-1 and X k Represent the state quantities at time k-1 and k respectively; A is the state transfer matrix, A=I 12×12 , where I 12×12 Represents the 12-dimensional identity matrix; W k-1 is the state noise vector at time k-1.

7. The method for estimating all zero biases of a single-axis rotating inertial navigation according to claim 6, characterized in that: In S5, the process of the Kalman filter algorithm estimating the total zero bias of the two single-axis rotating inertial navigation systems in real time is as follows: The state is predicted one step at a time, and the one-step predicted state is obtained; Calculate the one-step prediction root mean square error and obtain the one-step prediction error covariance matrix; Calculate the filter gain to obtain the Kalman filter gain at the sampling time; State estimation, obtaining the state estimate at the sampling time; The estimated mean square error is calculated to obtain the estimated error covariance matrix at the sampling time, and all zero biases of the two single-axis rotating inertial navigation systems can be estimated in real time.

8. A computer device, characterized in that: including memory and processor; The memory is used to store a computer program executable on the processor; The processor is used to implement the steps of the single-axis rotating inertial navigation full zero bias estimation method according to any one of claims 1 to 7 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the single-axis rotation inertial navigation full zero bias estimation method according to any one of claims 1 to 7 are implemented.

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