Helicopter dual-axis rotating inertial navigation system off-site self-calibration method

Abstract

The application provides a helicopter double-shaft rotating inertial navigation system off-site self-calibration method, which comprises the following steps: controlling a double-shaft rotating mechanism of the double-shaft rotating inertial navigation system to execute an eight-position rotating scheme and to perform navigation calculation to obtain a first speed error; using a first Kalman filter to estimate the first speed error to obtain a first-stage parameter calibration result; controlling the double-shaft rotating mechanism of the double-shaft rotating inertial navigation system to rotate periodically according to a six-order continuous rotating scheme and to perform navigation calculation and parameter compensation using the first-stage parameter calibration result to obtain a second speed error; using a second Kalman filter to estimate the inner lever arm to obtain a second-stage parameter calibration result; and binding the first-stage parameter calibration result and the second-stage parameter calibration result to the double-shaft rotating inertial navigation system, thereby solving the problem that users need to return the inertial navigation system to the supplier for calibration at regular intervals, greatly improving the maintainability of the product and shortening the maintenance and parking time of the helicopter.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of self-calibration of a dual-axis rotational inertial navigation system, and particularly relates to a method for field self-calibration of a dual-axis rotational inertial navigation system of a helicopter. BACKGROUND

[0002] The inertial navigation system of a helicopter is a key system for measuring key motion parameters of a helicopter, calculating the most basic navigation parameters such as the attitude, speed and position of the helicopter, and is an important prerequisite for the helicopter to perform a flight task and ensure flight safety. The dual-axis rotational inertial navigation system (RINS) is provided with a dual-axis rotating mechanism outside an inertial measurement unit, and the inertial measurement unit rotates according to a set rotating scheme, so that the sensor error can be offset through symmetrical rotating motion to improve the navigation accuracy. The working principle block diagram is shown in Figure 1 .

[0003] According to the use and maintenance requirements of the inertial navigation system in the avionics system of a helicopter, after the component is used for a given flight duration or calendar duration (the earlier one is used as the criterion), the component must be calibrated by the supplier using a special rotating table. However, since the calibration work must be performed through steps such as disassembly of the inertial navigation system, sending to the supplier, calibration, returning to the helicopter, installation, power-on inspection and the like, the entire process takes several weeks to several months, which is inconvenient for the actual use of the helicopter user.

[0004] The parameters that need to be calibrated for the rotational inertial navigation system mainly include the gyro constant drift, the accelerometer constant bias, the gyro scale factor error, the accelerometer scale factor error, the gyro installation error, the accelerometer installation error and the inner lever arm. The field self-calibration of the above parameters has been realized through parameter estimation based on Kalman filtering in a three-axis rotational inertial navigation system. However, in a dual-axis rotational inertial navigation system, limited by the fact that the dual-axis rotating mechanism can only provide rotation in two axial directions, the rotating scheme in the three-axis rotational inertial navigation system cannot be used to fully excite the parameters to be calibrated and make the state vector of the Kalman filter fully observable, so it is necessary to redesign the Kalman filter and the dual-axis rotating scheme.

[0005] In the research on the self-calibration method of the dual-axis rotational inertial navigation system, most of the self-calibration is only performed on the parameters other than the inner lever arm. In the self-calibration method considering all the parameters of the inner lever arm, in order to make the state vector of the Kalman filter fully observable, the carrier needs to be required to perform a maneuver, or the number of parameters to be calibrated of the inner lever arm is simplified, and only the axial components are calibrated.

[0006] The current self-calibration effect of the dual-axis rotational inertial navigation system of a helicopter is poor, and there is still a lot of optimization space. SUMMARY

[0007] The application provides a helicopter double-shaft rotary inertial navigation system outfield self-calibration method, which solves the problem that users need to return the inertial navigation system to the supplier for calibration regularly, greatly improves the maintainability of the product, and shortens the maintenance downtime of the helicopter.

[0008] The application provides a helicopter double-shaft rotary inertial navigation system outfield self-calibration method, which includes the following steps:

[0009] Step 1, power-on inspection is performed on the double-shaft rotary inertial navigation system, and alignment is performed when the power-on inspection result is normal;

[0010] Step 2, a double-shaft rotating mechanism of the double-shaft rotary inertial navigation system is controlled to execute an eight-position rotating scheme, navigation solving is performed, a first speed error is obtained, the first speed error is taken as observation Z of a first Kalman filter, the first Kalman filter is used to estimate gyro constant drift, accelerometer constant bias, gyro scale factor error, accelerometer scale factor error, gyro installation error and accelerometer installation error, and a first-stage parameter calibration result is obtained;

[0011] Step 3, the double-shaft rotating mechanism of the double-shaft rotary inertial navigation system is periodically rotated according to a six-order continuous rotating scheme, navigation solving is performed, and parameter compensation is performed by using the first-stage parameter calibration result, a second speed error is obtained, the second speed error is taken as observation Z of a second Kalman filter, the second Kalman filter is used to estimate an inner rod arm, and a second-stage parameter calibration result is obtained;

[0012] Step 4, the first-stage parameter calibration result and the second-stage parameter calibration result are bound to the double-shaft rotary inertial navigation system.

[0013] Optionally, the eight-position rotating scheme includes the following steps:

[0014] S11, an initial position is set; when the initial position is set, the x s axis points to the east, the y s axis points to the north, and the z s axis points to the sky;

[0015] S12, the y s axis is counterclockwise rotated by 270 degrees;

[0016] S13, the y s axis is clockwise rotated by 180 degrees;

[0017] S14, the z s axis is counterclockwise rotated by 270 degrees;

[0018] S15, the z s axis is clockwise rotated by 180 degrees;

[0019] S16, rotate around y s axis clockwise by 180°;

[0020] S17, first rotate around z s axis clockwise by 45°; then rotate around y s axis clockwise by 45°;

[0021] S18, rotate around y s axis clockwise by 180°;

[0022] x s axis, y s axis, z s axis, y s axis and z s axis coincide; Ox s y s z s The origin O of the coordinate system is the rotation center of the double-axis indexing mechanism.

[0023] Optionally, the six-order continuous indexing scheme comprises:

[0024] S21, rotate around y s axis and z s axis simultaneously perform uniform angular motion with a first preset angular acceleration rate for a preset time length;

[0025] S22, rotate around y s axis perform uniform angular motion with a first angular velocity; simultaneously, rotate around z s axis perform uniform angular motion with a first preset angular acceleration rate for a preset time length;

[0026] S23, rotate around y s axis perform uniform angular motion with a first preset angular acceleration rate for a preset time length;

[0027] S24, rotate around z s axis perform uniform angular motion with a first preset angular acceleration rate for a preset time length;

[0028] S25, rotate around y s axis perform uniform angular motion with a first preset angular acceleration rate; simultaneously, rotate around z s axis perform uniform angular motion with a first angular velocity for a preset time length;

[0029] S26, rotate around y s axis and z s axis simultaneously perform uniform angular motion with a first preset angular acceleration rate for a preset time length.

[0030] Optionally, the first preset angular acceleration rate

[0031] The first angular velocity is determined according to the first preset angular acceleration rate and a preset duration.

[0032] Optionally, the first Kalman filter comprises a first state equation and a first observation equation.

[0033] The first state equation is

[0034] wherein X is a first state vector,

[0035] φ n , δV n are respectively a first attitude error and a first velocity error.

[0036] The first attitude error and the first velocity error equations of the inertial navigation system are:

[0037]

[0038] wherein,

[0039] n, s, i, e in the superscript and / or subscript respectively represent a navigation coordinate system, a rotating coordinate system, an earth-centered inertial coordinate system, and an earth coordinate system.

[0040] C represents a direction cosine matrix from the rotating coordinate system to the navigation coordinate system.

[0041] is an earth rotation angular velocity, is an angular velocity caused by the movement of the carrier.

[0042] f represents a specific force output of the accelerometer.

[0043] δω s is a gyro error, and an error model thereof is:

[0044]

[0045] is a gyro constant drift.

[0046] S g = [s gx s gy s gz ] T is a gyro scale factor error.

[0047] is an angular velocity of the rotating coordinate system relative to the inertial coordinate system.

[0048] M g = [a xz a xy a yz a yx a zy a zx ] T is the gyro installation error;

[0049] v g is the gyro angular rate white noise;

[0050] δf s is the accelerometer error, whose error model is:

[0051]

[0052] is the accelerometer constant bias;

[0053] S a = [s ax s ay s az ] T is the accelerometer scale factor error;

[0054] M a = [β xz β xy β yz β yx β zy β zx ] T is the accelerometer installation error.

[0055] v a is the accelerometer specific force white noise;

[0056] F is the first system matrix, which can be obtained according to the attitude and velocity error equations:

[0057]

[0058] diag() represents converting a 3x1 vector into a 3x3 matrix whose vector elements are on the diagonal;

[0059] fun() represents converting a 3x1 vector into the following 3x6 matrix:

[0060]

[0061] W is the white noise of the gyro and the accelerometer;

[0062]

[0063] The first observation equation is:

[0064] Z = HX + v;

[0065] wherein, Z is a first observation vector, Z = δV n ;

[0066] H is a first observation matrix, H = [0 3×3 Ι 3×3 0 3×24 ], v is observation noise.

[0067] Optionally, the second Kalman filter comprises a second state equation and a second observation equation; the second state equation is:

[0068]

[0069] wherein,

[0070] the second state vector

[0071] are respectively a second attitude error and a second speed error;

[0072]

[0073] According to the attitude and speed error equation, a system matrix F2 can be obtained:

[0074]

[0075] The second observation equation is:

[0076] Z2 = H2X2 + v

[0077] wherein, Z2 is an observation vector,

[0078] H2 is an observation matrix, H2 = [0 3×3 Ι 3×3 0 3×9 ];

[0079] r R = [(r X ) T (r Y ) T (r Z ) T ] T ;

[0080] r R is an inner arm, r X , r Y , r Z respectively represent three direction error vectors between the sensitive points of the three accelerometers and the rotation center point of the double-axis rotation mechanism, rX = [r Xx r Xy r Xz ] T , r Y = [r Yx r Yy r Yz ] T , r Z = [r Zx r Zy r Zz ] T ;

[0081] δf = M vR r R ;

[0082]

[0083] ω is and ω is = [ω x ω y ω z ] T ;

[0084] U X , U Y and U Z represent the unit vectors corresponding to each accelerometer sensitive axis.

[0085] Optionally, before step 4, the method further comprises:

[0086] determining that the difference between the first-stage parameter calibration result and the corresponding historical parameter calibration result is within a preset range;

[0087] Optionally, before step 3, the method further comprises:

[0088] determining that the difference between the first-stage parameter calibration result and the corresponding historical parameter calibration result is within a preset range;

[0089] Correspondingly, before step 4, the method further comprises:

[0090] determining that the difference between the second-stage parameter calibration result and the corresponding historical parameter calibration result is within a preset range.

[0091] The beneficial technical effects of the present application are:

[0092] The application provides a self-calibration method for a helicopter double-shaft rotary inertial navigation system, according to the characteristics that inner bar arms are only excited when the rotating mechanism rotates, the parameters to be calibrated are divided into two categories, and the calibration is completed in two stages. The self-calibration method can realize the self-calibration of 33 parameters including 9 inner bar arm parameters of the helicopter double-shaft rotary inertial navigation system, solves the problem that the user needs to return the inertial navigation system to the supplier for calibration regularly, greatly improves the maintainability of the product, and shortens the maintenance and parking time of the helicopter. BRIEF DESCRIPTION OF DRAWINGS

[0093] Figure 1 It is a working principle diagram of the double-shaft RINS;

[0094] Figure 2 It is a self-calibration algorithm flowchart of the double-shaft rotary inertial navigation system;

[0095] Figure 3 It is an eight-position rotating scheme diagram of the first stage of the double-shaft RINS self-calibration algorithm;

[0096] Figure 4 It is an inner bar arm diagram;

[0097] Figure 5 It is a six-order rotating scheme diagram of the second stage of the double-shaft RINS self-calibration algorithm. DETAILED DESCRIPTION

[0098] The specific content of the technical scheme provided by the application will be introduced below in combination with the drawings.

[0099] Please refer to Figures 1-5 The application provides a double-shaft RINS self-calibration method, which realizes the self-calibration of a total of 33 parameters including 9 inner bar arm parameters under the static base of a helicopter. The idea of the method is that according to the characteristics that inner bar arms are only excited when the rotating mechanism rotates, the parameters to be calibrated are divided into two categories, and the calibration is completed in two stages. The first category includes 24 parameters such as constant drift, constant bias of the accelerometer, scale factor error of the gyroscope, scale factor error of the accelerometer, installation error of the gyroscope, and installation error of the accelerometer. The second category includes 9 inner bar arm parameters. The double-shaft rotary inertial navigation system self-calibration algorithm of the application designs Kalman filter 2 and a six-order continuous rotating scheme for the calibration of the 9 inner bar arm parameters, and proves that the 9 inner bar arm parameters are fully excited and the state vector of Kalman filter 2 is completely observable through the PWCS observability analysis method.

[0100] The application provides a self-calibration method, which mainly includes the following technical steps:

[0101] Step 1, preparation. The dual-axis rotating inertial navigation system is powered on and checked to be normal, and initial alignment is performed.

[0102] Step 2, after the initial alignment is completed, the self-calibration algorithm of the dual-axis rotating inertial navigation system is executed (see Figure 2 ).

[0103] Step 3, the technical personnel check the parameter results output by the self-calibration algorithm of the dual-axis inertial navigation system, and bind them to the dual-axis rotating inertial navigation system. Note: if the difference between the parameter results of this calibration and the parameter results of previous calibrations exceeds a given value, the system still needs to be returned to the supplier for calibration using a special turntable.

[0104] Step 2 includes two stages:

[0105] Stage 1: Perform the first type of parameter calibration. The dual-axis rotating inertial navigation system starts navigation solution, the dual-axis positioning mechanism remains stationary at position 1 of the eight-position positioning scheme for 5 minutes, and Kalman filter 1 starts state estimation. After 5 minutes, the rotation of the switching position is executed, Kalman filter 1 skips the observation update, and after the dual-axis positioning mechanism arrives at the next position after the rotation ends, Kalman filter 1 resumes the observation update. In this way, the eight-position positioning scheme is executed, and the first stage ends. The first type of parameter calibration result is obtained from the state vector.

[0106] Stage 2: Perform the second type of parameter calibration. The dual-axis rotating inertial navigation system uses the calibration results of the first type of parameters in the navigation solution process for parameter compensation, the dual-axis positioning mechanism performs periodic rotation according to the six-order continuous positioning scheme, and Kalman filter 2 starts state estimation. After executing the six-order continuous positioning scheme for 20 times, the second stage ends, and the calibration value of the second type of parameters is obtained from the state vector.

[0107] Specifically, step 2 can include two stages.

[0108] Stage 1: The dual-axis rotating inertial navigation system starts navigation solution, the dual-axis positioning mechanism executes the eight-position positioning scheme, and Kalman filter 1 estimates the gyro constant drift, accelerometer constant bias, gyro scale factor error, accelerometer scale factor error, gyro installation error, and accelerometer installation error using the navigation solution speed error as the observation, to obtain the parameter calibration result.

[0109] Stage 2: The dual-axis rotating inertial navigation system uses the parameter calibration results of the first stage in the navigation solution process for parameter compensation, the dual-axis positioning mechanism performs periodic rotation according to the six-order continuous positioning scheme, and Kalman filter 2 estimates the inner rod arm using the navigation solution speed error as the observation, to obtain the parameter calibration result.

[0110] Exemplarily, the eight-position indexing scheme performed in the first stage means rotating the Oxy s y s z s coordinate system through the biaxial indexing mechanism to be in eight different positions. Among them, Oxy s y s z s coordinate system represents a rotating coordinate system, the origin O of the coordinate system is the rotation center of the biaxial indexing mechanism, the x s axis, the y s axis and the z s axis are defined as the ideal sensitive axis directions of the x-direction gyroscope, the y-direction gyroscope and the z-direction gyroscope, and the outer rotation axis and the inner rotation axis of the biaxial indexing mechanism are respectively coincident with the y s axis and the z s axis.

[0111] The definition of the eight-position indexing scheme is specifically shown in Table 1 (see the schematic diagram in Figure 3 ) :

[0112] Table 1 Definition of the eight-position indexing scheme

[0113]

[0114] Exemplarily, in the first stage, the Kalman filter 1 constructed is composed of a state equation and an observation equation.

[0115] The state equation is:

[0116]

[0117] wherein,

[0118] the state vector

[0119] φ n and δV n are respectively the attitude error and the velocity error;

[0120] The attitude and velocity error equations of the inertial navigation system are:

[0121]

[0122] In the formula,

[0123] The superscripts n, s, i and e respectively represent the navigation coordinate system, the rotating coordinate system, the geocentric inertial coordinate system and the earth coordinate system;

[0124] Cn s represents the direction cosine matrix of the rotating coordinate system to the navigation coordinate system;

[0125] is the earth rotation angular velocity, angular velocity due to carrier motion, angular velocity of the rotating coordinate frame with respect to the inertial coordinate frame;

[0126] f represents the specific force output of the accelerometer;

[0127] δω s is the gyro error, whose error model is:

[0128]

[0129] is the gyro constant bias;

[0130] S g = [s gx s gy s gz ] T is the gyro scale factor error;

[0131] Μ g = [α xz α xy α yz α yx α zy α zx ] T is the gyro misalignment error;

[0132] v g is the gyro angular rate white noise;

[0133] δf s is the accelerometer error, whose error model is:

[0134]

[0135] is the accelerometer constant bias;

[0136] S a = [s ax s ay s az ] T is the accelerometer scale factor error;

[0137] Μ a = [β xz β xy β yz β yx β zy β zx ] T is the accelerometer misalignment error.

[0138] v aThe accelerometer specific force white noise.

[0139] F is the system matrix, which can be obtained according to the attitude and velocity error equations:

[0140]

[0141] diag() represents converting a 3x1 vector into a 3x3 matrix with the vector elements on the diagonal;

[0142] fun() represents converting a 3x1 vector into a 3x6 matrix as follows:

[0143]

[0144] W is the white noise of the gyro and the accelerometer.

[0145]

[0146] The observation equation is:

[0147] Z = HX + v

[0148] where Z is the observation vector, and the velocity error is selected as the observation vector, i.e., Z = δV n .

[0149] H is the observation matrix, and H = [0 3×3 I 3×3 0 3×24 ], and v is the observation noise.

[0150] Exemplarily, the two-axis rotation inertial navigation system rotates according to a preset eight-position rotation scheme, and remains stationary for 5 minutes at each position, and the Kalman filter 1 performs state estimation during the stationary period. During the rotation of the two-axis rotation mechanism to the next position, the Kalman filter 1 skips the observation update, and suspends the state estimation of the calibration parameters. When the two-axis rotation mechanism performs the eight-position rotation scheme, the first stage ends, and the estimated values of the gyro constant drift, the accelerometer constant bias, the gyro scale factor error, the accelerometer scale factor error, the gyro installation error, and the accelerometer installation error in the state vector are read as the parameter calibration results.

[0151] Exemplarily, the inner arm calibrated in the second stage refers to three vectors from the rotation center of the two-axis rotation mechanism to the sensitive centers of the three accelerometers, and corresponds to nine parameters (see Figure 4 ). The error vectors between the sensitive points of the three accelerometers A j (j = X, Y, Z) and the rotation center O point are represented as r X = [r Xx r Xy r Xz ] T, r Y = [r Yx r Yy r Yz ] T , r Z = [r Zx r Zy r Zz ] T , U X , U Y and U Z denote the unit vectors corresponding to the sensitive axes of the accelerometers.

[0152] When the dual-axis RINS has angular motion relative to the inertial space, the output f j of the accelerometers A j (j = X, Y, Z) is:

[0153]

[0154] where,

[0155] f O denotes the specific force at point O, denotes the projection of f O along the sensitive axis U j ;

[0156] ω is and denote the angular velocity and angular acceleration of the rotating coordinate system relative to the inertial coordinate system, and have ω is = [ω x ω y ω z ] T ;

[0157]

[0158] (j = X, Y, Z) is the accelerometer specific force output error caused by the coupling of the inner rod arm and the angular motion. Its vector form is:

[0159] δf = M vR r R

[0160] where,

[0161]

[0162] r R = [(r X ) T (r Y ) T (r Z )T ] T

[0163] For example, in the second stage, the constructed Kalman filter 2 is composed of state equation and observation equation.

[0164] The state equation is:

[0165]

[0166] Wherein,

[0167] The state vector

[0168] The attitude error and the velocity error, respectively;

[0169] Since the solution process of the dual-axis rotation RINS has been compensated by the first stage parameter calibration result when calibrating in the second stage, the attitude error and the velocity error equations become:

[0170]

[0171] According to the attitude and velocity error equations, the system matrix F2 can be obtained:

[0172]

[0173] The observation equation is:

[0174] Z2=H2X2+v

[0175] Wherein, Z2 is the observation vector, and the velocity error is selected as the observation vector, that is,

[0176] H2 is the observation matrix, then H2=[0 3×3 I 3×3 0 3×9 ].

[0177] For example, in the second stage, the definition of the six-order continuous indexing scheme executed by the dual-axis indexing mechanism is shown in Table 2. (See the schematic diagram in Figure 5 )

[0178] Table 2 Definition of six-order continuous indexing scheme

[0179]

[0180]

[0181] For example, the observability of the inner lever arm under the six-order continuous indexing scheme is proved as follows

[0182] The PWCS (Piece-Wise Constant System) observability analysis method uses the striping observability matrix (SOM) to analyze the observability of the system.

[0183] Since the PWCS observability analysis is independent of the system noise and the observation noise, the state equation and the observation equation of the Kalman filter 2 can be discretized and simplified as follows:

[0184]

[0185] In the formula, F j and H j are the state transition matrix and the observation matrix of the system in the jth time interval, respectively, and r is the number of selected time intervals.

[0186] Then, the observability matrix of the system is as follows:

[0187]

[0188] The striping observability matrix SOM is as follows:

[0189]

[0190] When Rank(Q SOM (r)) = n, the system is completely observable,

[0191] When Rank(Q SOM (r)) < n, the system is not completely observable.

[0192] The observability of the Kalman filter 2 under the six-order rotation scheme is analyzed, the sampling interval is selected as 0.5 s, Q SOM (r) is gradually increased with time, and reaches the full rank in the seventh time interval, i.e., Rank(Q SOM (7)) = 15. It is shown that the six-order rotation scheme designed in the application can ensure that all state quantities of the Kalman filter 2 are observable, and thus the Kalman filter 2 can be used for estimation to realize the calibration of the nine inner rod arm parameters.

[0193] For example, the dual-axis rotating inertial navigation system is continuously rotated according to the above-described six-order rotation scheme, and the constructed Kalman filter 2 is used for state estimation. The six-order rotation scheme is repeatedly executed for 20 times, a total of 6 min, the second phase is ended, and the estimated values of the inner rod arm in the state vector are read as the parameter calibration results.

Claims

1. A method for field self-calibration of a helicopter dual-axis rotary inertial navigation system, characterized in that, include: Step 1: Perform a power-on check on the dual-axis rotary inertial navigation system. If the power-on check result is normal, proceed with alignment. Step 2: Control the dual-axis rotation mechanism of the dual-axis rotational inertial navigation system to execute the eight-position rotation scheme and perform navigation calculations to obtain the first velocity error. Use the first velocity error as the observation Z of the first Kalman filter. Use the first Kalman filter to estimate the gyroscope constant drift, accelerometer constant bias, gyroscope scale factor error, accelerometer scale factor error, gyroscope installation error, and accelerometer installation error to obtain the first stage parameter calibration results. Step 3: The dual-axis rotation mechanism of the axis rotation inertial navigation system rotates periodically according to the six-sequence continuous rotation scheme, and performs navigation calculations and parameter compensation using the first-stage parameter calibration results to obtain the second velocity error. The second velocity error is used as the observation Z of the second Kalman filter. The second Kalman filter is used to estimate the inner arm to obtain the second-stage parameter calibration results. Step 4: Bind the first-stage parameter calibration results and the second-stage parameter calibration results to the dual-axis rotating inertial navigation system; Eight-position transposition schemes include: S11, Set to the initial orientation; In the initial orientation... The axis points east. The axis points north. The axis points to the sky; S12, around The axis rotates counterclockwise ; S13, around The axis rotates clockwise ; S14, around The axis rotates counterclockwise ; S15, around The axis rotates clockwise ; S16, around The axis rotates clockwise ; S17, First around The axis rotates clockwise ; then go around The axis rotates clockwise ; S18, around The axis rotates 180° clockwise. axis, axis, The axes are respectively To the spinning top, To the spinning top and Towards the ideal sensitive axis of the gyroscope, the outer and inner rotation axes of the dual-axis indexing mechanism are respectively aligned with... shaft and Axis coincidence; Origin of coordinate system It serves as the rotation center of the dual-axis indexing mechanism; The six-order successive transposition scheme includes: S21, around shaft and The axis simultaneously undergoes uniformly accelerated angular motion with a first preset angular acceleration rate, and continues for a preset duration; S22, around The axis undergoes uniform angular motion with a first angular velocity; simultaneously, it revolves around... The axis undergoes uniformly decelerated angular motion with a first preset angular acceleration rate, and continues for a preset duration; S23, around The axis undergoes uniformly decelerated angular motion with a first preset angular acceleration rate, and continues for a preset duration; S24, around The axis undergoes uniformly accelerated angular motion with a first preset angular acceleration rate, and continues for a preset duration; S25, around The axis undergoes uniformly accelerated angular motion with a first preset angular acceleration rate; simultaneously, it revolves around... The axis performs uniform angular motion at the first angular velocity for a preset duration; S26, around shaft and The shaft simultaneously undergoes uniformly decelerated angular motion with a first preset angular acceleration rate, and continues for a preset duration.

2. The field self-calibration method for a helicopter dual-axis rotary inertial navigation system according to claim 1, characterized in that, First preset angular acceleration rate ; The first angular velocity is determined based on the first preset angular acceleration rate and the preset duration.

3. The field self-calibration method for a helicopter dual-axis rotary inertial navigation system according to claim 1, characterized in that, The first Kalman filter includes a first state equation and a first observation equation; The first state equation is: ; Where X is the first state vector. ; These are the first attitude error and the first velocity error, respectively. The equations for the first attitude error and the first velocity error of the inertial navigation system are as follows: ； In the formula, The superscript and / or subscript indicate the navigation coordinate system, the rotating coordinate system, the geocentric inertial coordinate system, and the Earth coordinate system, respectively. This represents the direction cosine matrix from the rotated coordinate system to the navigation coordinate system; , This is the Earth's rotational angular velocity. The angular velocity caused by the motion of the carrier; f This indicates the specific force output of the accelerometer; The error model for the gyroscope is as follows: This represents the constant drift of the gyroscope. This refers to the gyroscope scaling factor error; The angular velocity of the rotating coordinate system relative to the inertial coordinate system; This is due to gyroscope installation error; This is white noise representing the gyroscope's angular rate. For accelerometer error, its error model is: This is the accelerometer constant bias value; This refers to the accelerometer scale factor error. This is due to accelerometer installation error; For accelerometer specific force white noise; The first system matrix can be obtained from the attitude and velocity error equations: diag() means to Vector to vector elements on the diagonal matrix; fun() means to... The vector is converted to the following: matrix; ； White noise for gyroscopes and accelerometers; The first observation equation is: ； in, The first observation vector, ; This is the first observation matrix. , To observe noise.

4. The field self-calibration method for a helicopter dual-axis rotary inertial navigation system according to claim 3, characterized in that, The second Kalman filter includes a second state equation and a second observation equation; The second state equation is: ； in, Second state vector X2 ; These are the second attitude error and the second velocity error, respectively. ； The system matrix can be obtained from the attitude and velocity error equations. F 2: The second observation equation is: in, Z 2 is the observation vector. ; H 2 represents the observation matrix. ; ； r R For the inner arm, r X r Y r Z These represent the error vectors in three directions between the sensitive points of the three accelerometers and the rotation center point of the dual-axis indexing mechanism. , , ; ； ； ； and These represent the angular velocity and angular acceleration of the rotating coordinate system relative to the inertial coordinate system. ; , and This represents the unit vector corresponding to the sensitive axis of each accelerometer.

5. The field self-calibration method for a helicopter dual-axis rotary inertial navigation system according to claim 1, characterized in that, Prior to step 4, the method further includes: The difference between the first-stage parameter calibration results, the second-stage parameter calibration results, and the historical parameter calibration results is determined to be within a preset range.

6. The field self-calibration method for a helicopter dual-axis rotary inertial navigation system according to claim 1, characterized in that, Prior to step 3, the method further includes: Ensure that the difference between the parameter calibration results in the first stage and the corresponding historical parameter calibration results is within a preset range; Correspondingly, prior to step 4, the method further includes: The difference between the second-stage parameter calibration results and the corresponding historical parameter calibration results is determined to be within a preset range.

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