Strapdown inertial measurement unit alignment precision long-time keeping method based on speed-position information
By establishing the initial alignment error model of the strap-inner inertia group and using the recursive least squares estimation calculation method to correct the mathematical platform's inaccuracy angle, the problem of the alignment accuracy of the strap-inner inertia group diverges over time, and long-term accuracy maintenance is achieved in indoor and special environments.
Patent Information
- Application Number
- CN202510775922.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-11
- Publication Date
- 2025-08-15
AI Technical Summary
After initial alignment of existing strap-inert inertia groups, the error slowly diverges over time, resulting in a decrease in navigation accuracy. The method that relies on satellite navigation systems is limited in autonomy and anti-interference in indoor and special environments.
By analyzing the error source, establishing a short-distance inertial group initial alignment error model, using the speed and position information of the inertial group navigation solution to construct measurements, and using the recursive least squares estimation method to correct the mathematical platform's misalignment angle to achieve long-term maintenance of alignment accuracy.
Without relying on satellite navigation systems, the long-term maintenance of the alignment accuracy of the strap-inerature group is achieved, which improves autonomy and anti-interference, and is suitable for indoor and special environments.
Smart Images
Figure CN120489179A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of strapdown inertial navigation, and in particular to a method for maintaining the alignment accuracy of a strapdown inertial navigation system for a long time based on speed-position information. Background Art
[0002] As a low-cost, miniaturized inertial navigation system, the strapdown inertial navigation system (SINS) boasts a wide range of applications and promising prospects in aerospace, aviation, navigation, and land-based applications. Before entering navigation mode, the SINS must first undergo initial alignment, establishing the orientation and attitude relationship between the carrier coordinate system and the navigation coordinate system—commonly known as attitude measurement and north-finding. After completing initial alignment, the SINS typically enters a stationary tracking navigation state, awaiting instructions for the next mission. This process can take anywhere from tens of seconds to tens of minutes, and in some cases, can even last for dozens of minutes. Inertial navigation systems (INS) inherently suffer from the accumulation of errors over time. After initial alignment, both horizontal attitude and azimuth errors slowly diverge, causing the initial alignment accuracy of the SINS to degrade over time. This decline in initial alignment accuracy inevitably leads to a decrease in subsequent navigation accuracy, necessitating a solution to the long-term maintenance of SINS alignment accuracy.
[0003] Existing methods for maintaining strapdown inertial system (SINS) alignment accuracy typically utilize external navigation information for assistance. For example, these methods utilize high-precision satellite navigation information, particularly dual-antenna satellite positioning and orientation information. By designing relevant error estimation and compensation algorithms, they can continuously correct and compensate for SINS errors, thereby maintaining SINS alignment accuracy over a long period of time. However, this method relies on satellite navigation systems. Because satellite navigation signals are easily interfered with or blocked, their autonomy and anti-interference capabilities are limited, making them impractical for general application in indoor and outdoor environments.
[0004] To this end, this patent proposes a method for maintaining the alignment accuracy of a strapdown inertial system for a long time, which has outstanding advantages such as strong autonomy, good anti-interference ability, and strong environmental adaptability. Summary of the Invention
[0005] To address these issues, the present invention provides a method for maintaining the long-term alignment accuracy of a strapdown inertial navigation system (SINS) based on velocity and position information. First, the method analyzes the error sources that reduce the SINS alignment accuracy and establishes an error model for the initial SINS alignment. Then, based on the fact that the actual velocity is zero and the actual position is known and unchanged after the SINS initial alignment, the method uses the velocity and position information obtained from the SINS navigation solution to construct a measurement, establishing state equations and measurement equations for estimating the alignment error. Finally, a recursive least squares estimation algorithm is used to calculate the misalignment angle of the SINS mathematical platform, which is then used to correct the misalignment angle. This method achieves the long-term maintenance of the SINS alignment accuracy.
[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:
[0007] A method for maintaining the alignment accuracy of a strapdown inertial system for a long time based on speed-position information comprises the following steps:
[0008] S1: Analyze the error sources that cause the reduction of the strapdown inertial system alignment accuracy and establish the strapdown inertial system initial alignment error model;
[0009] S2: establishing a state equation for estimating the alignment error based on the initial alignment error model of the strapdown inertial system; constructing a measurement using the velocity and position information obtained by the inertial system navigation solution, and establishing a measurement equation for estimating the alignment error;
[0010] S3: Using a recursive least squares estimation algorithm, recursively solving the state equation and the measurement equation to obtain an estimated value of the mathematical platform misalignment angle, and using the estimated value to perform real-time correction on the mathematical platform misalignment angle of the strapdown inertial system.
[0011] Furthermore, the strapdown inertial system initial alignment error model in step S1 includes an inertial device error, a strapdown inertial system mathematical platform attitude error, a strapdown inertial system velocity error, and a strapdown inertial system position error.
[0012] Furthermore, the inertial device error includes a gyroscope error and an accelerometer error;
[0013] The model of the gyroscope error is
[0014] ε k =ε bk +w gk (k=x,y,z)
[0015] where ε k is the error of the gyroscope, ε bk is the successive start-up drift of the gyroscope, w gk is the fast-changing drift of the gyroscope;
[0016] The accelerometer error is modeled as
[0017]
[0018] in is the error of the accelerometer, is the random constant error of the accelerometer, w ak is the fast-changing error of the accelerometer.
[0019] Furthermore, the strapdown inertial system mathematical platform attitude error includes a deviation angle φ between the coordinate system n and the P system;
[0020] The n system is the navigation coordinate system required to be simulated by the strapdown inertial system mathematical platform, and the P system is the coordinate system actually established by the navigation computer. Let the projection of the deviation angle φ of the P system relative to the n system on the n system be φ n =[φ E φ N φ U ] T , the φ n Satisfies the following model
[0021]
[0022]
[0023] Where L, λ, and h are the latitude, longitude, and altitude of the strapdown inertial unit, respectively; v E 、v N 、v U are the eastward, northward and celestial velocities of the strapdown inertial unit; ω ie is the angular velocity of the Earth's rotation; R M 、R N are respectively the main curvature radius of the local meridian and the main curvature radius of the meridian perpendicular to the meridian plane; φ E 、φ N 、φ U are the attitude angle errors of the strapdown inertial system mathematical platform along the east, north and celestial directions, also known as the east, north and celestial misalignment angles; δv E ,δv N ,δv U are the easting, northing and celestial velocity errors of the strapdown inertial system; δL, δλ and δh are the latitude, longitude and altitude errors of the strapdown inertial system; ε bx , ε by , ε bz is the random constant drift of the gyroscope on the x, y, and z axes of the carrier; w gx 、w gy 、w gz is the white noise of the gyroscope on the x, y, and z axes; Tij (i,j=1,2,3) is the strapdown inertial system attitude matrix The element in the i-th row and j-th column of .
[0024] Furthermore, the strapdown inertial system speed error includes the actual output speed of the strapdown inertial system. The deviation from the true velocity V is denoted as δV. Let the projection of the velocity deviation δV on the n-system be δV n =[δv E ,δv N ,δv U ] T , the δV n Satisfies the following model
[0025]
[0026]
[0027] Among them, f E 、f N 、f U is the projection component of the accelerometer output in the east, north and sky directions, is the random constant error of the accelerometer on the x, y, and z axes of the carrier, w ax 、w ay 、w az is the white noise of the accelerometer on the x, y, and z axes of the carrier.
[0028] Furthermore, the strapdown inertial system position error includes the latitude, longitude and altitude errors δL, δλ, δh of the strapdown inertial system, and the δL, δλ, δh satisfy the following model:
[0029]
[0030] Furthermore, in step S2, the state equation for the alignment error estimation is:
[0031]
[0032] Among them, F is the system state matrix, G is the system noise driving matrix, W is the system white noise sequence, W=[w gx ,w gy ,w gz ,w ax ,w ay ,w az ] T , X is the state vector of the alignment error estimate;
[0033] The measurement equation for the alignment error estimation is:
[0034] Z=HX+V
[0035] Where V is the measured white noise sequence, are the eastward, northward and skyward velocity noises of the strapdown inertial system, w δL 、w δλ 、w δh are the latitude, longitude and altitude noise of the strapdown inertial unit, H is the measurement matrix, and the measurement Z is
[0036]
[0037] Among them, v ES 、v NS 、v US L is the east, north and sky speed output by the strapdown inertial navigation system solution; S ,λ S 、h S are the latitude, longitude, and altitude output by the strapdown inertial navigation system solution; L0, λ0, and h0 are the precise latitude, longitude, and altitude of the location being aimed at, all of which are known in advance.
[0038] Furthermore, in step S3, the recursive solution result based on the state equation and the measurement equation is the state estimation at the kth moment described Calculated by the following recursive formula
[0039]
[0040] Where, the subscript k represents the kth moment, K k is the filter gain matrix, H k is the k-th moment measurement matrix, Z k is the measured value at the kth moment;
[0041]
[0042] P k =(IK k H k )P k-1
[0043] in, is the estimated value of the estimated state vector X at the kth moment, P k is the state estimate at the kth moment The mean square error matrix, V k The k-th moment is the measurement white noise sequence, and the mean square error matrix of the noise is R k , I is the identity matrix.
[0044] The beneficial effects of the present invention are:
[0045] The present invention analyzes the error sources that cause the strapdown inertial navigation system (SINS) alignment accuracy to decrease, establishes an error model for the initial SINS alignment, and a state equation and a measurement equation for estimating the alignment error from the perspective of a mathematical model, adopts a recursive least squares estimation algorithm to recursively solve and obtain an estimated result of the SINS mathematical platform misalignment angle, and uses the estimated result to correct the SINS mathematical platform misalignment angle. This not only solves the problem of maintaining the SINS alignment accuracy for a long time, but also does not require high-precision satellite navigation information for establishment and solution of the mathematical model, has strong autonomy, anti-interference performance, and environmental adaptability, and is generally applicable in indoor environments and special outdoor environments. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] Figure 1 The output data of the gyroscope installed along the x, y, and z axes of the carrier;
[0047] Figure 2 is the output data of the accelerometer installed along the x, y, and z axes of the carrier;
[0048] Figure 3 Maintain the error curve for the strapdown inertial system alignment accuracy. DETAILED DESCRIPTION
[0049] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.
[0050] A method for maintaining the alignment accuracy of a strapdown inertial system for a long time based on velocity-position information comprises the following steps:
[0051] S1. Establish the initial alignment error model of the strapdown inertial system.
[0052] Strapdown inertial system (SINS) errors typically come from inertial device errors, initial condition errors, and navigation algorithm errors. Inertial device errors are the most significant and cannot be eliminated. Initial condition errors can be effectively reduced through high-precision initial alignment and precise geodetic measurements. Navigation algorithm errors can be significantly reduced by designing high-precision navigation algorithms. Therefore, the primary source of SINS initial alignment errors is inertial device errors. According to the mechanics of inertial navigation, a SINS is a dead-reckoning navigation system. Therefore, inertial device errors inevitably lead to mathematical platform attitude errors, velocity errors, and position errors, and these errors are interrelated and mutually influential. Therefore, the error sources that reduce SINS alignment accuracy come from the various errors mentioned above, including inertial device errors, mathematical platform attitude errors, velocity errors, and position errors.
[0053] Considering that the purpose of the initial alignment of the strapdown inertial system is to estimate and correct the mathematical platform attitude error (i.e., the mathematical platform misalignment angle) of the strapdown inertial system, and that the various errors of the strapdown inertial system are interrelated and influence each other, it is necessary to analyze and model the above errors of the strapdown inertial system, including the mathematical platform misalignment angle, respectively, that is, to establish the error model of the initial alignment of the strapdown inertial system. Specifically, it includes: inertial device error model, and the mathematical platform attitude error model, velocity error model, and position error model of the strapdown inertial system. The specific operation includes the following steps:
[0054] S11: Establish an inertial device error model.
[0055] Inertial device error is the primary source of error in a strapdown inertial system (SINS), having the most direct impact on its initial alignment. After factory calibration and periodic calibration compensation, the remaining inertial device error primarily involves random drift error, which cannot be determined through calibration. Therefore, it is necessary to analyze and model its variation characteristics.
[0056] The random drift error of a gyroscope usually consists of two components: start-up drift and rapid drift. The start-up drift of the gyroscope depends on factors such as the environmental conditions at the time of startup and the randomness of electrical parameters. Once the startup is completed, it remains at a random fixed value. Therefore, the start-up drift of the gyroscope ε bm It can be described by a random constant, namely
[0057]
[0058] Wherein, m=x,y,z represents the gyroscope installed along the x,y,z axis of the carrier.
[0059] The rapid drift of the gyroscope is manifested as a chaotic high-frequency jump based on the successive start-up drift, and the dependence of the drift values at two adjacent time points is very weak or almost non-existent. gm It can be described as a white noise process, which satisfies E[w gm (t)w gm (τ)]=q gm δ(t-τ), where m=x,y,z,q gm is the variance intensity of the gyroscope white noise.
[0060] Therefore, the error model of the gyroscope can be described as
[0061] ε m =ε bm +w gm ( m=x,y,z) (2)
[0062] Similar to the error model of the gyroscope, the random drift error of the accelerometer also contains two components: random constant error and fast-changing error. Therefore, the error model of the accelerometer can be described as
[0063]
[0064] in, is the error of the accelerometer, m = x, y, z represents the accelerometer installed along the x, y, z axis of the carrier, is the random constant error of the accelerometer, which satisfies
[0065]
[0066] And w am is the fast-changing error of the accelerometer, also known as white noise, which satisfies E[w am (t)w am (τ)]=q am δ(t-τ), where q am is the variance intensity of the accelerometer white noise.
[0067] S12. Establish a strapdown inertial system mathematical platform attitude error model.
[0068] Assume that the navigation coordinate system that the strapdown inertial group mathematical platform requires to simulate is the n system, which is the so-called "ideal platform coordinate system". Here, the northeastern sky geographic coordinate system is selected as the navigation coordinate system; the coordinate system actually established by the navigation computer is recorded as the P system. Due to the influence of error sources such as inertial device errors and initial condition errors, there is a deviation angle φ between the P system and the n system required to be simulated, which is the mathematical platform attitude error, also known as the mathematical platform misalignment angle. Let the projection of the deviation angle φ of the P system relative to the n system on the n system be φ n =[φ E φ N φ U ] T , which satisfies the following model
[0069]
[0070]
[0071] Where L, λ, and h are the latitude, longitude, and altitude of the strapdown inertial unit, respectively; v E 、v N 、v U are the eastward, northward and celestial velocities of the strapdown inertial unit; ω ie is the angular velocity of the Earth's rotation; R M 、R N are respectively the main curvature radius of the local meridian and the main curvature radius of the meridian perpendicular to the meridian plane; φ E、φ N 、φ U are the attitude angle errors of the strapdown inertial system mathematical platform along the east, north and celestial directions, also known as the east, north and celestial misalignment angles; δv E ,δv N ,δv U are the easting, northing and celestial velocity errors of the strapdown inertial system; δL, δλ and δh are the latitude, longitude and altitude errors of the strapdown inertial system; ε bx , ε by , ε bz is the random constant drift of the gyroscope on the x, y, and z axes of the carrier; w gx 、w gy 、w gz is the white noise of the gyroscope on the x, y, and z axes; T ij (i,j=1,2,3) is the strapdown inertial system attitude matrix The element in the i-th row and j-th column of .
[0072] S13. Establish a strapdown inertial system velocity error model.
[0073] Due to the existence of various errors, the actual output speed of the strapdown inertial system is There is a deviation from the true velocity V, which is recorded as δV. Let the projection of the velocity deviation δV on the n system be δV n =[δv E ,δv N ,δv U ] T , which satisfies the following model
[0074]
[0075]
[0076] Among them, f E 、f N 、f U is the projection component of the accelerometer output in the east, north and sky directions, is the random constant error of the accelerometer on the x, y, and z axes of the carrier; w ax 、w ay 、w az is the white noise of the accelerometer on the x, y, and z axes of the carrier.
[0077] S14. Establish a strapdown inertial system position error model.
[0078] The latitude, longitude and altitude errors of the strapdown inertial system δL, δλ, δh satisfy the following model
[0079]
[0080] Further, S2: establish the state equation and measurement equation for alignment error estimation.
[0081] Based on the error model of the initial alignment of the strapdown inertial system (SINS), various errors of the initial alignment of the SINS are selected as the system state for alignment error estimation, and a state equation for alignment error estimation can be established. The speed / position measurement is constructed using information such as the speed, position, and position of the alignment point output by the SINS navigation solution, and a measurement equation for alignment error estimation is established. The alignment error estimation algorithm is designed using recursive least squares estimation, and estimation results of various errors such as the misalignment angle of the SINS mathematical platform are obtained through recursive calculation. The estimation results are used to perform real-time correction of the misalignment angle of the SINS mathematical platform.
[0082] Therefore, according to the above analysis, in the design of the alignment error estimation algorithm, various errors of the initial alignment of the strapdown inertial system are selected as the system state of the alignment error estimation, that is, the misalignment angles φ of the strapdown inertial system mathematical platform in the east, north and celestial directions E 、φ N 、φ U , eastward, northward, and celestial velocity errors δv E ,δv N ,δv U , latitude, longitude, altitude errors δL, δλ, δh, gyroscope drift on x, y, z axis starting up ε bx , ε by , ε bz , random constant error of the accelerometer on the x, y, and z axes Thus, the state vector X of the alignment error estimate is
[0083]
[0084] According to the initial alignment error model of the strapdown inertial system established in S1 and combined with the expression of the system state vector X, the system state equation for alignment error estimation can be written as
[0085]
[0086] Among them, F is the system state matrix; G is the system noise driving matrix; W is the system white noise sequence, W = [w gx ,w gy ,w gz ,w ax ,w ay ,w az ] T .
[0087] Here, the system state matrix F is a 15×15 matrix, in which the non-zero elements are
[0088] F(1,10)=-T 11 ,F(1,11)=-T 12 ,F(1,12)=-T 13 ;
[0089] F(2,1)=-F(1,2),
[0090] F(2,7)=-ω ie sinL, F(2,10)=-T 21 ,F(2,11)=-T 22 ,F(2,12)=-T 23 ;
[0091] F(3,1)=-F(1,3),F(3,2)=-F(2,3), F(3,10)=-T 31 ,F(3,11)=-T 32 ,F(3,12)=-T 33 ;
[0092] F(4,13)=T 11 ,F(4,14)=T 12 ,F(4,15)=T 13 ;
[0093] F(5,1)=-F(4,2), F(5,13)=T 21 ,F(5,14)=T 22 ,F(5,15)=T 23 ;
[0094] F(6,1)=-F(4,3), F(6,7)=-2ω ie v E sinL, F(6,13)=T 31 ,F(6,14)=T 32 ,F(6,15)=T 33 ;
[0095] F(9,6)=1。
[0096] The system noise driving matrix G is a 15×6 matrix, specifically in the form of
[0097]
[0098] Under the condition of stationary base, since the actual speed of the strapdown inertial system is zero, it is obvious that the speed solved by the strapdown inertial system navigation is the speed error. On the other hand, the position information of the location where the strapdown inertial system is initially aligned is accurately known, and the position of the strapdown inertial system does not change during the period of maintaining alignment accuracy, so the position error solved by the strapdown inertial system navigation can also be known. Therefore, the speed output by the strapdown inertial system navigation solution is used as one of the speed / position measurements, and the difference between the position output by the strapdown inertial system navigation solution and the position of the alignment location is used as the second speed / position measurement, that is, the measurement Z is
[0099]
[0100] Among them, v ES 、v NS 、v US L is the east, north and sky speed output by the strapdown inertial navigation system solution; S ,λ S 、h S are the latitude, longitude, and altitude output by the strapdown inertial navigation system solution; L0, λ0, and h0 are the precise latitude, longitude, and altitude of the location being aimed at, all of which are known in advance.
[0101] Obviously, according to the previous analysis, formula (17) can be transformed into
[0102]
[0103] At this time, combined with the state vector X estimated by the alignment error, Equation (18) can be written as
[0104] Z=HX+V (19)
[0105] Where V is the measurement white noise sequence, here are the eastward, northward and skyward velocity noises of the strapdown inertial system, w δL 、w δλ 、w δh are the latitude, longitude and altitude noise of the strapdown inertial unit respectively; H is the measurement matrix, which is in the form of
[0106]
[0107] Therefore, Equations (15) and (19) are the state equation and measurement equation for alignment error estimation.
[0108] Further, S3: establishing an alignment error estimation algorithm based on recursive least squares estimation.
[0109] To effectively estimate the system state vector X described by the state equation and measurement equation in step S2, an optimal estimation method is required. Recursive least squares estimation, as an optimal estimation method, offers high accuracy, a simple algorithm, and high computational efficiency. It also requires less storage space than traditional least squares estimation, which eliminates the need for comprehensive processing of all measurement data. Therefore, this recursive least squares estimation algorithm is used here to estimate the strapdown inertial system alignment error.
[0110] Assume that the discrete state equation is described as
[0111] X k =Φ k,k-1 X k-1 +W k-1 (twenty one)
[0112] Where, subscript k represents the kth moment, X k is the value of the estimated state vector X at the kth moment, Φ k,k-1 is the one-step transfer matrix from the k-1th moment to the kth moment, which can be calculated based on the system state matrix F in the continuous state equation (15); W k is the system white noise sequence at the kth moment.
[0113] The discrete measurement equation is described as
[0114] Z k =H k X k +V k (twenty two)
[0115] Where Z k is the measured value at the kth moment, H k is the measurement matrix at the kth moment; V k The k-th moment is the measurement white noise sequence, and the mean square error matrix of the noise is R k .
[0116] According to the principle of recursive least squares estimation, the estimated value of the state vector It can be calculated by the following recursive least squares estimation algorithm
[0117]
[0118] P k =(IK k H k )P k-1 (25)
[0119] Where, P k is the state estimate at the kth moment The mean square error matrix, I is the identity matrix, K k is the filter gain matrix.
[0120] Therefore, according to equations (21) to (25), the recursive least squares estimation of the strapdown inertial system alignment error vector can be realized, that is, the state estimate at the kth moment can be obtained by calculation: At this time, the estimated value is used to make real-time corrections to the mathematical platform misalignment angle of the strapdown inertial system, which can effectively ensure that the alignment accuracy of the strapdown inertial system is maintained for a long time.
[0121] Simulation experiment
[0122] The proposed method for maintaining the alignment accuracy of a strapdown inertial system (SINS) for a long period of time based on velocity and position information was simulated and verified. First, the output data of the gyroscope and accelerometer in the SINS were generated through simulation. The method was then verified by fully considering the inertial device errors and initial condition errors.
[0123] The gyroscope start-up drift in the strapdown inertial system is set to 0.01° / h, and its random walk is The random constant error of the accelerometer is 10 -4 g, whose random walk is The output frequency of the gyroscope and accelerometer is 200Hz. The alignment location is 34°14.763′N, 108°54.579′E, and 380m above sea level. The initial heading angle is 30°, the pitch angle is 0°, and the roll angle is 0°. The strapdown inertial unit is in a static state and generates output data of the gyroscope and accelerometer for 3600s. Figures 1-2 shown.
[0124] The azimuth error at the end of the initial strapdown inertial group alignment is set to 3', the horizontal attitude error is set to 1'; the initial velocity error is 0.01m / s; and the initial error is 10m. A simulation is conducted to verify the long-term maintenance method of the strapdown inertial group alignment accuracy based on velocity / position information. The test results are as follows: Figure 3 shown.
[0125] The strapdown inertial system alignment accuracy retention error curve shows that during the first 500 seconds of the simulation, the azimuth alignment error (i.e., heading angle error) converges slowly over time. After approximately the 500th second of the simulation, the azimuth alignment error no longer fluctuates significantly, and the alignment accuracy stabilizes. Ultimately, the azimuth alignment accuracy remains within 1.08' for up to 3600 seconds.
[0126] It can also be seen that during the alignment accuracy maintenance process, the pitch angle error and roll angle error have basically the same changing trends, both of which converge rapidly within the first few tens of seconds of the simulation test. After the convergence process, the pitch angle error and roll angle error remain stable, and the final horizontal alignment accuracy is maintained within 0.48', and the maintenance time is also 3600s.
[0127] It can be seen that when the base is stationary, the long-term maintenance method of alignment accuracy based on velocity / position information can effectively solve the problem of alignment error accumulating and diverging over time after the initial alignment of the strapdown inertial system, which is of great significance for improving the actual performance of the strapdown inertial system.
[0128] Therefore, to overcome the problem of alignment errors accumulating and diverging over time after the initial alignment of the strapdown inertial system (SINS), this patent proposes a method for maintaining alignment accuracy over a long period of time based on velocity / position information. This method first analyzes the error sources that cause the SINS alignment accuracy to decrease and establishes an error model for the initial SINS alignment. It then constructs measurements using the velocity and position information obtained from the SINS navigation solution, establishing state equations and measurement equations for estimating the alignment error. Finally, a recursive least squares estimation algorithm is used to calculate estimates of various errors, including the SINS mathematical platform misalignment angle. These estimates are used to correct the SINS mathematical platform misalignment angle, achieving long-term maintenance of the SINS alignment accuracy. This method boasts high accuracy, strong independence and anti-interference capabilities, good environmental adaptability, and ease of engineering implementation. It can maintain alignment accuracy over a long period of time using only the SINS itself, and has broad application prospects in both civilian and military fields.
[0129] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.
Claims
1. A method for maintaining the alignment accuracy of a strapdown inertial system (SINS) for a long period of time based on velocity-position information, characterized by: The following steps are included: S1: Analyze the error sources that cause the reduction of the strapdown inertial system alignment accuracy and establish the strapdown inertial system initial alignment error model; S2: establishing a state equation for estimating the alignment error based on the initial alignment error model of the strapdown inertial system; constructing a measurement using the velocity and position information obtained by the inertial system navigation solution, and establishing a measurement equation for estimating the alignment error; S3: Using a recursive least squares estimation algorithm, recursively solving the state equation and the measurement equation to obtain an estimated value of the mathematical platform misalignment angle, and using the estimated value to perform real-time correction on the mathematical platform misalignment angle of the strapdown inertial system.
2. The method for maintaining the alignment accuracy of a strapdown inertial system based on velocity-position information according to claim 1, characterized in that: The strapdown inertial system initial alignment error model in step S1 includes an inertial device error, a strapdown inertial system mathematical platform attitude error, a strapdown inertial system velocity error, and a strapdown inertial system position error.
3. The method for maintaining the alignment accuracy of a strapdown inertial system based on velocity-position information according to claim 2, characterized in that: The inertial device error includes a gyroscope error and an accelerometer error; The model of the gyroscope error is ε m =ε bm +w gm (m=x,y,z) where ε m is the error of the gyroscope, ε bm is the successive start-up drift of the gyroscope, w gm is the fast-changing drift of the gyroscope; The accelerometer error is modeled as in is the error of the accelerometer, is the random constant error of the accelerometer, w am is the fast-changing error of the accelerometer.
4. The method for maintaining the alignment accuracy of a strapdown inertial system for a long time based on velocity-position information according to claim 3, characterized in that: The strapdown inertial system mathematical platform attitude error includes the deviation angle φ between the coordinate system n and the P system; The n system is the navigation coordinate system required to be simulated by the strapdown inertial system mathematical platform, and the P system is the coordinate system actually established by the navigation computer. Let the projection of the deviation angle φ of the P system relative to the n system on the n system be φ n =[φ E φ N φ U ] T , the φ n Satisfies the following model Where L, λ, and h are the latitude, longitude, and altitude of the strapdown inertial unit, respectively; v E 、v N 、v U are the eastward, northward and celestial velocities of the strapdown inertial unit; ω ie is the angular velocity of the Earth's rotation; R M 、R N are respectively the main curvature radius of the local meridian and the main curvature radius of the meridian perpendicular to the meridian plane; φ E 、φ N 、φ U are the attitude angle errors of the strapdown inertial system mathematical platform along the east, north and celestial directions, also known as the east, north and celestial misalignment angles; δv E ,δv N ,δv U are the easting, northing and celestial velocity errors of the strapdown inertial system; δL, δλ and δh are the latitude, longitude and altitude errors of the strapdown inertial system; ε bx , ε by , ε bz is the random constant drift of the gyroscope on the x, y, and z axes of the carrier; w gx 、w gy 、w gz is the white noise of the gyroscope on the x, y, and z axes; T ij (i,j=1,2,3) is the strapdown inertial system attitude matrix The element in the i-th row and j-th column of .
5. The method for maintaining the alignment accuracy of a strapdown inertial system based on velocity-position information according to claim 4, characterized in that: The strapdown inertial system speed error includes the speed actually output by the strapdown inertial system The deviation from the true velocity V is denoted as δV. Let the projection of the velocity deviation δV on the n-system be δV n =[δv E ,δv N ,δv U ] T , the δV n Satisfies the following model Among them, f E 、f N 、f U is the projection component of the accelerometer output in the east, north and sky directions, is the random constant error of the accelerometer on the x, y, and z axes of the carrier, w ax 、w ay 、w az is the white noise of the accelerometer on the x, y, and z axes of the carrier.
6. The method for maintaining the alignment accuracy of a strapdown inertial system for a long time based on velocity-position information according to claim 5, characterized in that: The strapdown inertial system position error includes the latitude, longitude and altitude errors δL, δλ, δh of the strapdown inertial system, and the δL, δλ, δh satisfy the following model 7. The method for maintaining the alignment accuracy of a strapdown inertial system for a long time based on velocity-position information according to claim 6, characterized in that: In step S2, the state equation for the alignment error estimation is: Among them, F is the system state matrix, G is the system noise driving matrix, W is the system white noise sequence, W=[w gx ,w gy ,w gz ,w ax ,w ay ,w az ] T , X is the state vector of the alignment error estimate; The measurement equation for the alignment error estimation is: Z=HX+V Where V is the measured white noise sequence, are the eastward, northward and skyward velocity noises of the strapdown inertial system, w δL 、w δλ 、w δh are the latitude, longitude and altitude noise of the strapdown inertial unit, H is the measurement matrix, and the measurement Z is Among them, v ES 、v NS 、v US L is the east, north and sky speed output by the strapdown inertial navigation system solution; S ,λ S 、h S are the latitude, longitude, and altitude output by the strapdown inertial navigation system solution; L0, λ0, and h0 are the precise latitude, longitude, and altitude of the location being aimed at, all of which are known in advance.
8. The method for maintaining the alignment accuracy of a strapdown inertial system for a long time based on velocity-position information according to claim 7, characterized in that: In step S3, the recursive solution result based on the state equation and the measurement equation is the state estimation at the kth moment described Calculated by the following recursive formula Where, the subscript k represents the kth moment, K k is the filter gain matrix, H k is the k-th moment measurement matrix, Z k is the measured value at the kth moment; P k =(I-K k H k )P k-1 in, is the estimated value of the estimated state vector X at the kth moment, P k is the state estimate at the kth moment The mean square error matrix, V k The k-th moment is the measurement white noise sequence, and the mean square error matrix of the noise is R k , I is the identity matrix.