Non-integrity constraint adaptive measurement method and system considering installation error angle of vehicle-mounted IMU (Inertial Measurement Unit)

Abstract

The invention relates to the technical field of vehicle navigation and positioning, and provides a non-integrity constraint adaptive measurement method and system considering a vehicle-mounted IMU installation error angle, and the method comprises the following steps: carrying out the alignment of the initial attitude of vehicle-mounted integrated navigation; calculating the position and posture of the current vehicle according to the vehicle-mounted integrated navigation, and judging whether the current vehicle moves linearly or not; when the vehicle moves linearly, estimating an IMU installation error angle through a Kalman filter by utilizing the position increment of the vehicle-mounted integrated navigation and the dead reckoning position increment based on a dead reckoning method; according to the estimation precision of the IMU installation error angle, considering the relationship among the vehicle body speed, the angle change and the NHC observation measurement error, and considering the estimation precision of the IMU installation error angle at the same time, adaptively determining NHC measurement noise; and performing NHC measurement updating by using the IMU installation error angle and the NHC measurement noise to complete NHC adaptive measurement.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle navigation and positioning, and more specifically, to a non-integrity constrained adaptive measurement method and system taking into account an installation error angle of an on-board IMU. Background Art

[0002] Nonholonomic Constraint Measurement refers to the measurement of observations based on nonholonomic constraints (NHC) in vehicle navigation and positioning. Nonholonomic constraints refer to the assumption that the vehicle does not skid and is close to the ground during driving. This constraint can be used to introduce NHC observations to suppress the divergence of the combined positioning solution error and ensure the accuracy and reliability of the positioning solution. NHC observations are observations in the vehicle coordinate system, which requires the axis of the IMU to coincide with the vehicle coordinate system as much as possible to fully realize its potential. In actual applications, the installation angle of the IMU relative to the vehicle coordinate system usually has a certain deviation and is difficult to measure accurately. If not properly processed, the effect of NHC in suppressing divergence will be reduced, and even the accuracy of the combined navigation will be adversely affected.

[0003] The existing vehicle-mounted combination NHC algorithm is usually not usable when the vehicle IMU installation angle is unknown; or after measuring the approximate IMU installation angle, the residual error angle of the IMU installation is not calculated, but treated as measurement noise. If there is a large IMU installation angle deviation, the use of the NHC algorithm will affect the combination positioning performance. Moreover, the measurement noise of the NHC algorithm does not fully consider the impact of the accuracy of the IMU installation angle. Summary of the invention

[0004] In order to overcome the defect that the above-mentioned existing NHC algorithm does not consider the installation error angle of the vehicle-mounted IMU, which affects the navigation positioning accuracy, the present invention provides a non-integrity constrained adaptive measurement method and system taking into account the installation error angle of the vehicle-mounted IMU.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A non-integrity constrained adaptive measurement method considering the installation error angle of an on-board IMU comprises the following steps:

[0007] S1. Aligning the initial posture of the vehicle-mounted integrated navigation system;

[0008] S2, according to the vehicle-mounted integrated navigation solution, the current vehicle position and posture are determined to determine whether the current vehicle is moving in a straight line; if so, based on the dead reckoning method, the position increment of the vehicle-mounted integrated navigation and the dead reckoning position increment are used to estimate the IMU installation error angle through the Kalman filter; if not, re-execute S2;

[0009] S3, according to the estimation accuracy of the IMU installation error angle, considering the relationship between the vehicle body speed, angle change and NHC observation error, and considering the estimation accuracy of the IMU installation error angle, adaptively determining the NHC measurement noise;

[0010] S4. Update NHC measurement using the IMU installation error angle and the NHC measurement noise to complete NHC adaptive measurement.

[0011] Furthermore, the present invention also proposes a non-integrity constrained adaptive measurement system considering the vehicle-mounted IMU installation error angle, which applies the non-integrity constrained adaptive measurement method proposed in the present invention. The system includes:

[0012] An initial attitude alignment module is used to align the initial attitude of the vehicle-mounted integrated navigation system;

[0013] The vehicle motion judgment module is used to calculate the current vehicle position and posture based on the vehicle-mounted integrated navigation and determine whether the current vehicle is moving in a straight line;

[0014] An IMU installation error angle estimation module is used to estimate the IMU installation error angle through a Kalman filter based on a dead reckoning method using the position increment of the vehicle-mounted integrated navigation and the dead reckoning position increment;

[0015] An NHC measurement noise update module is used to adaptively determine the NHC measurement noise according to the estimation accuracy of the IMU installation error angle, taking into account the relationship between the vehicle body speed, angle change and the NHC observation error, and taking into account the estimation accuracy of the IMU installation error angle;

[0016] The NHC adaptive measurement module is used to perform NHC measurement update by using the IMU installation error angle and the NHC measurement noise to complete NHC adaptive measurement.

[0017] Furthermore, the present invention also proposes a device, including a memory and a processor, wherein the memory stores computer-readable instructions, wherein when the computer-readable instructions are executed by the processor, the processor executes all or part of the steps of the anti-interference vehicle detection method proposed in the present invention.

[0018] Furthermore, the present invention also proposes a storage medium on which computer-readable instructions are stored, wherein the computer-readable instructions, when executed by a processor, implement all or part of the steps of the anti-interference vehicle detection method proposed in the present invention.

[0019] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0020] The present invention can realize the non-integrity constraint measurement of any installation angle of an adaptive vehicle-mounted IMU, and can adaptively calculate the accurate IMU installation angle when the IMU installation angle is unknown or the IMU installation angle has a deviation, thereby improving the navigation positioning accuracy; the present invention adaptively sets a reasonable NHC measurement error according to the calculated IMU installation error angle, and can better ensure the effectiveness of the non-integrity constraint algorithm in the vehicle-mounted condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The flowchart of the method for adaptive measurement of non-holonomic constraints according to one embodiment of the present invention is shown.

[0022] Figure 2 The flowchart of IMU installation error angle calculation according to one embodiment of the present invention is shown.

[0023] Figure 3 FIG. 4 is an architecture diagram of a non-holonomic constrained adaptive measurement system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0025] The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0026] It should be understood that although the terms first, second, third, etc. may be used in the present invention to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0027] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] Example 1

[0029] This embodiment proposes a non-integrity constrained adaptive measurement method considering the installation error angle of the vehicle-mounted IMU, such as Figure 1 FIG. 1 is a flow chart of the non-holonomic constraint adaptive measurement method of this embodiment.

[0030] The non-holonomic constraint adaptive measurement method proposed in this embodiment includes the following steps:

[0031] S1. Aligning the initial posture of the vehicle-mounted integrated navigation system;

[0032] S2, according to the vehicle-mounted integrated navigation solution, the current vehicle position and posture are determined to determine whether the current vehicle is moving in a straight line; if so, based on the dead reckoning (DR) method, the position increment of the vehicle-mounted integrated navigation and the dead reckoning position increment are used to estimate the IMU installation error angle through the Kalman filter; if not, re-execute S2;

[0033] S3, according to the estimation accuracy of the IMU installation error angle, considering the relationship between the vehicle body speed, angle change and NHC observation error, and considering the estimation accuracy of the IMU installation error angle, adaptively determining the NHC measurement noise;

[0034] S4. Update NHC measurement using the IMU installation error angle and the NHC measurement noise to complete NHC adaptive measurement.

[0035] This embodiment proposes a non-integrity constraint algorithm that can adapt to any installation angle of the vehicle-mounted IMU, so that when the IMU installation angle is unknown or the IMU installation angle is biased, the accurate IMU installation angle can be adaptively calculated, thereby improving the navigation positioning accuracy; this embodiment adaptively sets a reasonable NHC measurement error based on the calculated IMU installation error angle, which can better ensure the effectiveness of the non-integrity constraint algorithm in the vehicle-mounted condition.

[0036] Exemplarily, the vehicle-mounted integrated navigation in this embodiment is a GNSS / INS integrated navigation system.

[0037] The GNSS / INS combined navigation system is a navigation system that integrates the global navigation satellite system (GNSS) and the inertial navigation system (INS). This combination makes full use of the advantages of both systems to provide more accurate and continuous positioning services.

[0038] In an optional embodiment, the initial posture of the vehicle-mounted integrated navigation is aligned in step S1, including the following steps:

[0039] S101, when the vehicle is stationary, perform initial attitude coarse alignment by using a dual-vector attitude determination method, and use an accelerometer and a gyroscope of the IMU to measure the gravity acceleration and the earth's rotation angular velocity respectively;

[0040] S102, when the vehicle is stationary, by judging that the vehicle is stationary, measuring with a speed of zero, and inferring the misalignment angle error from the speed error, to complete static speed precision alignment;

[0041] S103. When the vehicle is in full motion, the azimuth is calculated according to the vehicle's motion direction as a measurement, and dynamic precise alignment is completed to obtain an accurate vehicle-mounted integrated navigation posture.

[0042] In this embodiment, a dual-vector attitude determination method is used under static conditions to perform initial attitude coarse alignment, and then static velocity fine alignment and dynamic fine alignment are performed to obtain an accurate combined navigation attitude.

[0043] Specifically, in the process of performing initial attitude rough alignment by the dual-vector attitude determination method, the vertical direction of the carrier relative to the earth can be determined by measuring the earth's gravity acceleration through an accelerometer; the magnetic north direction of the carrier relative to the earth can be determined by measuring the earth's magnetic field through a magnetometer. The initial attitude of the carrier can be determined by the above two vectors, that is, the azimuth and pitch angle of the carrier relative to the earth coordinate system.

[0044] After the initial posture is roughly aligned, the static velocity of the carrier can be used for fine alignment. Specifically, by measuring the linear velocity and angular velocity of the carrier and combining it with the initial posture information, the posture of the carrier can be further corrected to improve the accuracy of the posture.

[0045] During the dynamic precision alignment process, sensors such as accelerometers, gyroscopes, and magnetometers are used to update and correct the carrier's posture in real time through filtering algorithms such as Kalman filtering to adapt to the carrier's motion changes and complete the initial posture alignment of the navigation.

[0046] In an optional embodiment, in step S2, the position and posture of the current vehicle are calculated according to the vehicle-mounted integrated navigation, and it is determined whether the current vehicle is moving in a straight line, including the following steps:

[0047] S210, storing the historical vehicle position and posture obtained by the vehicle-mounted integrated navigation solution as a sequence of a certain length, and judging whether the vehicle motion state at the current moment is linear motion according to the sequence information; the expression is:

[0048]

[0049] Wherein, dEN is the distance of the plane displacement vector; [dE, dN] is the plane displacement vector between the first position and the last position of the storage sequence window; PThres and YThres represent the thresholds of the displacement and azimuth change within the window, respectively; L is the length of the average value of the direction data, indicating the discreteness of the azimuth in the queue, and 0≤L≤1, the closer the L value is to 1, the smaller the azimuth change is, and the closer the L value is to 0, the larger the azimuth change is; yaw is the average value of the azimuth of the storage window; isLine represents the current vehicle motion state, a value of 1 indicates that the vehicle is in a linear motion state, and a value of 0 indicates that the vehicle is in a non-linear motion state.

[0050] In an optional embodiment, estimating the IMU installation error angle by using a Kalman filter in step S2 includes the following steps:

[0051] S221. Based on the dead reckoning method, the position and attitude solved by the vehicle-mounted integrated navigation and the initial value of the IMU installation error angle filter are considered to determine the position increment and the dead reckoning position increment of the vehicle-mounted integrated navigation; the expression is:

[0052]

[0053] in, represents the dead reckoning position increment in the earth coordinate system e at time k; Indicates that at t k-1 The attitude matrix from the IMU coordinate system b to the earth coordinate system e at the moment; Represents the rotation matrix from the IMU coordinate system b to the vehicle coordinate system v; represents the displacement increment in the vehicle coordinate system v obtained by the vehicle-mounted integrated navigation at time k, and Δs is the corresponding scalar; and Respectively represent t k Time and t k-1 The dead reckoning position at the time;

[0054] S222, constructing a system state equation based on a Kalman filter; the system state equation includes an IMU installation angle error, an attitude error, and a position increment error; the expression is:

[0055]

[0056]

[0057] in, represents the position error in the earth coordinate system e at time k; δΔψ and δΔθ are the heading and pitch IMU installation angle errors respectively; δκ is the scale factor error of the distance measurement; Represents the attitude matrix from the IMU coordinate system b to the earth coordinate system e including the error, Represents the attitude matrix from the IMU coordinate system b to the vehicle coordinate system v containing the error, Represents the displacement increment including error; φ=[φ p ,φ r ,φ h ] represents the attitude error in pitch, roll and heading directions;

[0058] S223, establish the observation equation of the Kalman filter; its expression is:

[0059]

[0060] in represents the vehicle integrated navigation position coordinates in the earth coordinate system e at time k; ε k represents the measurement noise at time k; the observation equation is expressed in matrix form:

[0061] Z k =H k X k +σ k

[0062] H k =[I 3×3 0 3×3 0 3×3 ]

[0063] Where Z k is δz k Matrix form of a vector; H k is the measurement coefficient matrix; is the parameter to be estimated; σ k is the measurement noise ε k The matrix form of I 3×3 represents the three-dimensional identity matrix, 0 3×3 represents a three-dimensional zero matrix;

[0064] S224, according to the measurement coefficient matrix H k , taking the coordinate standard deviation of the vehicle-mounted integrated navigation as the measurement error, performing Kalman filter measurement update, and then performing feedback compensation on the state parameters in the system state equation according to the updated Kalman filter result, and recording the estimated attitude angle error during each filter update; after the filter converges, taking the average of the estimated attitude angle error for a certain period of time as the IMU installation error angle.

[0065] For example, Figure 2 As shown, this is the IMU installation error angle estimation flow chart of this embodiment.

[0066] In this embodiment, according to the trajectory similarity theory, the dead reckoning method is used to estimate the IMU installation error angle in the Kalman filter. Among them, the DR method is a navigation positioning method, which estimates the position at the next moment by the known current position, moving speed and direction, and can be used to effectively compensate for the IMU installation angle error, thereby improving the accuracy and reliability of the navigation system. Further using the position increment and DR position increment of the vehicle-mounted combined navigation, the IMU installation error angle is estimated through the Kalman filter, and the accurate IMU installation error angle can be adaptively calculated.

[0067] For the system state equation in step S222, it first models the parameters such as the IMU installation angle error, attitude error, and displacement increment error derived by DR:

[0068] δx=[δr x δr y δr z δΔψδκδΔθφ p φ r φ h ]

[0069] Among them, δr x ,δr y ,δr z is the position error in the ECEF coordinate system (Earth-Centered, Earth-Fixed), in meters; δΔψ and δΔθ are the heading and pitch IMU installation angle errors, respectively; δκ is the scale factor error of the distance measurement; φ p ,φ r ,φ h is the attitude error of IMU.

[0070] Since the DR recursive equation is affected by factors such as position error, attitude error, installation angle residual error, and distance measurement error proportional factor, we have:

[0071]

[0072] in,

[0073]

[0074] In the formula, and Indicates t k Time and t k-1 The e-coordinates containing errors at all times; Indicates t k-1 The attitude matrix from the b system to the e system containing the error at all times; Represents the attitude matrix from b system to e system including the error; Indicates the displacement increment including error; δr e =[δr x ,δr y ,δr z ] represents the position error; φ=[φ p ,φ r ,φ h ] represents the attitude error; Indicates the IMU installation angle error, is the roll IMU installation angle error, which does not participate in the state equation; r e Indicates the real position coordinates; C b e Represents the true attitude matrix from b system to e system; Represents the true attitude matrix from b system to e system.

[0075] The system state equation is obtained by sorting:

[0076]

[0077] in,

[0078]

[0079] Its matrix form is expressed as:

[0080]

[0081] Among them, w δΔψ 、w δΔθ are the driving noise in the heading and pitch directions of the installation angle model respectively; w δκ is the driving noise of the distance measurement scale factor; and are the driving noises in the pitch, roll and heading directions of the attitude angle model respectively; I 33 Represents the identity matrix of dimension 3*3.

[0082] Further, in an optional embodiment, S224 includes the following steps:

[0083] Let the velocity V of the vehicle coordinate system v be v and the velocity V of the IMU coordinate system b b The origin is the same, and the velocity of the vehicle coordinate system v is V v =[0norm(V b )0] T , then the speed V b To speed V v The rotation matrix is ​​expressed as:

[0084] R=I 33 +ωsin(θ)+ω 2 (1-cosθ)

[0085] in,

[0086]

[0087] Among them, (·)× represents the antisymmetric matrix, θ is the rotation angle, and ω is the antisymmetric matrix of the rotation axis unit vector; by converting the rotation matrix R into Euler angles, the roll angle is taken as the roll component of the IMU installation angle error The final IMU installation error angle α is expressed as:

[0088]

[0089] Wherein, T is a preset filtering period, and the average value of the IMU installation angle error estimation value after T / 2 time is taken as the IMU installation error angle; and when the vehicle motion state changes to non-linear motion before the filtering period is full, the filter is reset and re-estimated when the motion state changes to linear motion again.

[0090] In an optional embodiment, the adaptive determination of the NHC measurement noise in step S3 includes the following steps:

[0091] Taking the attitude angle error estimated in the Kalman filter as the estimation accuracy of the IMU installation error angle, the first NHC measurement noise dv1 caused by the IMU installation error angle estimation is calculated; its expression is:

[0092] dv1=R att V v

[0093] Among them, R att V is the rotation matrix converted from the attitude angle error estimated in the Kalman filter; v is the velocity in the vehicle coordinate system v;

[0094] Considering the relationship between vehicle speed, angle change and NHC observation error, the second NHC measurement noise dv2 is calculated; its expression is:

[0095]

[0096] Among them, dyaw v V represents the change in heading angle in the vehicle coordinate system v; u v is the speed V v The elevation component of

[0097] Considering the control conditions of vehicle motion, the adaptive NHC measurement noise is obtained as:

[0098]

[0099] Among them, R r and R u are the lateral and vertical measurement variances of NHC respectively; dv1 r and dv1 u are the right and vertical components of the first NHC measurement noise dv1; dv2 r and dv2 u are the right and vertical components of the second NHC measurement noise dv2 respectively; k max1 and k min1 are the upper and lower threshold parameters of dv1, which are used to control the NHC measurement noise caused by the installation error angle estimation; k max2 and k min2 They are the upper and lower threshold parameters of dv2, which are used to control the NHC measurement noise corresponding to the changes in vehicle speed and angle.

[0100] In this embodiment, according to the estimation accuracy of the IMU installation error angle after convergence, the relationship between the vehicle speed, angle change and the NHC observation error is considered, and the estimation accuracy of the IMU installation error angle is considered at the same time, and a certain control is applied to adaptively determine the NHC measurement noise.

[0101] Further, in an optional embodiment, in step S4, using the IMU installation error angle and the NHC measurement noise to perform NHC measurement update includes the following steps:

[0102] Based on the IMU installation error angle α and the adaptive NHC measurement noise after the IMU installation angle estimation is completed and in the case of straight line operation, the measurement coefficient matrix is ​​updated according to the non-completeness constraint and the Kalman filter measurement update is performed, and the state parameters of the vehicle-mounted integrated navigation are feedback compensated according to the filtering results.

[0103] Among them, according to the assumption of NHC, there is no side slip during the operation of the carrier and it is close to the ground, and the observation equation is:

[0104]

[0105] Then, the measurement coefficient matrix is ​​updated according to the non-completeness constraint and the Kalman filter measurement update is performed, which means that the measurement coefficient matrix is ​​obtained according to the observation equation and the Kalman filter measurement update is performed. Finally, the state parameters of the integrated navigation are feedback compensated according to the filtering results, that is, the NHC adaptive measurement considering the installation error angle of the vehicle-mounted IMU is completed.

[0106] Example 2

[0107] This embodiment proposes a non-holonomic constraint adaptive measurement system that takes into account the vehicle-mounted IMU installation error angle, and applies the non-holonomic constraint adaptive measurement method proposed in Embodiment 1. Figure 3 , which is a structural diagram of the non-holonomic constraint adaptive measurement system of this embodiment.

[0108] The non-holonomic constraint adaptive measurement system proposed in this embodiment includes:

[0109] An initial attitude alignment module is used to align the initial attitude of the vehicle-mounted integrated navigation system;

[0110] The vehicle motion judgment module is used to calculate the current vehicle position and posture based on the vehicle-mounted integrated navigation and determine whether the current vehicle is moving in a straight line;

[0111] An IMU installation error angle estimation module is used to estimate the IMU installation error angle through a Kalman filter based on a dead reckoning method using the position increment of the vehicle-mounted integrated navigation and the dead reckoning position increment;

[0112] An NHC measurement noise update module is used to adaptively determine the NHC measurement noise according to the estimation accuracy of the IMU installation error angle, taking into account the relationship between the vehicle body speed, angle change and the NHC observation error, and taking into account the estimation accuracy of the IMU installation error angle;

[0113] The NHC adaptive measurement module is used to perform NHC measurement update by using the IMU installation error angle and the NHC measurement noise to complete NHC adaptive measurement.

[0114] It can be understood that the system of this embodiment corresponds to the method of the above-mentioned embodiment 1, and the options in the above-mentioned embodiment 1 are also applicable to this embodiment, so they will not be described repeatedly here.

[0115] Example 3

[0116] This embodiment proposes a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes all or part of the steps of the non-integrity constraint adaptive measurement method proposed in Example 1.

[0117] Example 4

[0118] This embodiment provides a storage medium on which computer-readable instructions are stored, wherein the computer-readable instructions, when executed by a processor, implement all or part of the steps of the non-holonomic constraint adaptive measurement method provided in Embodiment 1.

[0119] Exemplarily, the storage medium includes, but is not limited to, a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0120] Exemplarily, the instructions, programs, code sets or instruction sets may be implemented using conventional programming languages.

[0121] Exemplarily, the processor includes but is not limited to a smart phone, a personal computer, a server, a network device, etc., and is used to execute all or part of the steps of the adaptive measurement method against non-integrity constraints described in Example 1.

[0122] The terms in the drawings are only used for illustrative purposes and are not to be construed as limiting the present invention;

[0123] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. The non-integrity constrained adaptive measurement method considering the installation error angle of the vehicle-mounted IMU is characterized by: The following steps are involved: S1. Aligning the initial posture of the vehicle-mounted integrated navigation system; S2, according to the vehicle-mounted integrated navigation solution, the current vehicle position and posture are determined to determine whether the current vehicle is moving in a straight line; if so, based on the dead reckoning method, the position increment of the vehicle-mounted integrated navigation and the dead reckoning position increment are used to estimate the IMU installation error angle through the Kalman filter; if not, re-execute S2; S3, according to the estimation accuracy of the IMU installation error angle, considering the relationship between the vehicle body speed, angle change and NHC observation error, and considering the estimation accuracy of the IMU installation error angle, adaptively determining the NHC measurement noise; S4. Update NHC measurement using the IMU installation error angle and the NHC measurement noise to complete NHC adaptive measurement.

2. The non-holonomic constraint adaptive measurement method according to claim 1, characterized in that: The initial posture of the vehicle-mounted integrated navigation system is aligned, comprising the following steps: S101, when the vehicle is stationary, perform initial attitude coarse alignment by using a dual-vector attitude determination method, and use an accelerometer and a gyroscope of the IMU to measure the gravity acceleration and the earth's rotation angular velocity respectively; S102, when the vehicle is stationary, by judging that the vehicle is stationary, measuring with a speed of zero, and inferring the misalignment angle error from the speed error, to complete static speed precision alignment; S103. When the vehicle is in full motion, the azimuth is calculated according to the vehicle's motion direction as a measurement, and dynamic precise alignment is completed to obtain an accurate vehicle-mounted integrated navigation posture.

3. The non-holonomic constraint adaptive measurement method according to claim 1, characterized in that: The method of calculating the position and posture of the current vehicle according to the vehicle-mounted integrated navigation and determining whether the current vehicle is moving in a straight line comprises the following steps: The historical vehicle position and posture obtained by vehicle-mounted integrated navigation are stored as a sequence of a certain length, and the current vehicle motion state is determined based on the sequence information. The expression is: Wherein, dEN is the distance of the plane displacement vector; [dE, dN] is the plane displacement vector between the first position and the last position of the storage sequence window; PThres and YThres represent the thresholds of the displacement and azimuth change within the window, respectively; L is the length of the average value of the direction data, indicating the discreteness of the azimuth in the queue, and 0≤L≤1, the closer the L value is to 1, the smaller the azimuth change is, and the closer the L value is to 0, the larger the azimuth change is; yaw is the average value of the azimuth of the storage window; isLine represents the current vehicle motion state, a value of 1 indicates that the vehicle is in a linear motion state, and a value of 0 indicates that the vehicle is in a non-linear motion state.

4. The non-holonomic constraint adaptive measurement method according to claim 1, characterized in that: The method of estimating the IMU installation error angle by using a Kalman filter comprises the following steps: S221. Based on the dead reckoning method, the position and attitude solved by the vehicle-mounted integrated navigation and the initial value of the IMU installation error angle filter are considered to determine the position increment and the dead reckoning position increment of the vehicle-mounted integrated navigation; the expression is: in, represents the dead reckoning position increment in the earth coordinate system e at time k; Indicates that at t k-1 The attitude matrix from the IMU coordinate system b to the earth coordinate system e at the moment; Represents the rotation matrix from the IMU coordinate system b to the vehicle coordinate system v; represents the displacement increment in the vehicle coordinate system v obtained by the vehicle-mounted integrated navigation at time k, and Δs is the corresponding scalar; and Respectively represent t k Time and t k-1 The dead reckoning position at the time; S222, constructing a system state equation based on a Kalman filter; the system state equation includes an IMU installation angle error, an attitude error, and a position increment error; the expression is: in, represents the position error in the earth coordinate system e at time k; δΔψ and δΔθ are the heading and pitch IMU installation angle errors respectively; δκ is the scale factor error of the distance measurement; Represents the attitude matrix from the IMU coordinate system b to the earth coordinate system e including the error, Represents the attitude matrix from the IMU coordinate system b to the vehicle coordinate system v containing the error, Represents the displacement increment including error; φ=[φ p ,φ r ,φ h ] represents the attitude error in pitch, roll and heading directions; S223, establish the observation equation of the Kalman filter; its expression is: in represents the vehicle integrated navigation position coordinates in the earth coordinate system e at time k; ε k represents the measurement noise at time k; the observation equation is expressed in matrix form: Z k =H k X k +σ k H k =[I 3×3 0 3×3 0 3×3 ] Where Z k is δz k Matrix form of a vector; H k is the measurement coefficient matrix; is the parameter to be estimated; σ k is the measurement noise ε k The matrix form of I 3×3 represents the three-dimensional identity matrix, 0 3×3 represents a three-dimensional zero matrix; S224, according to the measurement coefficient matrix H k , taking the coordinate standard deviation of the vehicle-mounted integrated navigation as the measurement error, performing Kalman filter measurement update, and then performing feedback compensation on the state parameters in the system state equation according to the updated Kalman filter result, and recording the estimated attitude angle error during each filter update; after the filter converges, taking the average of the estimated attitude angle error for a certain period of time as the IMU installation error angle.

5. The non-holonomic constraint adaptive measurement method according to claim 4, characterized in that: In the S224, The following steps are involved: Let the velocity V of the vehicle coordinate system v be v and the velocity V of the IMU coordinate system b b The origin is the same, and the velocity of the vehicle coordinate system v is V v =[0norm(V b )0] T , then the speed V b To speed V v The rotation matrix R is expressed as: R=I 33 +ωsin(θ)+ω 2 (1-cosθ) in, Among them, (·)× represents the antisymmetric matrix, θ is the rotation angle, and ω is the antisymmetric matrix of the rotation axis unit vector; By converting the rotation matrix R into Euler angles, the roll angle is taken as the roll component of the IMU installation angle error The final IMU installation error angle α is expressed as: Wherein, T is a preset filtering period, and the average value of the IMU installation angle error estimation value after T / 2 time is taken as the IMU installation error angle; and when the vehicle motion state changes to non-linear motion before the filtering period is full, the filter is reset and re-estimated when the motion state changes to linear motion again.

6. The non-holonomic constraint adaptive measurement method according to claim 1, characterized in that: The adaptive determination of NHC measurement noise comprises the following steps: Taking the attitude angle error estimated in the Kalman filter as the estimation accuracy of the IMU installation error angle, the first NHC measurement noise dv1 caused by the IMU installation error angle estimation is calculated; its expression is: dv1=R att V v Among them, R att V is the rotation matrix converted from the attitude angle error estimated in the Kalman filter; v is the velocity in the vehicle coordinate system v; Considering the relationship between vehicle speed, angle change and NHC observation error, the second NHC measurement noise dv2 is calculated; its expression is: Among them, dyaw v Indicates the change in heading angle in the vehicle coordinate system v; is the speed V v The elevation component of Considering the control conditions of vehicle motion, the adaptive NHC measurement noise is obtained as: Among them, R r and R u are the lateral and vertical measurement variances of NHC respectively; dv1 r and dv1 u are the right and vertical components of the first NHC measurement noise dv1; dv2 r and dv2 u are the right and vertical components of the second NHC measurement noise dv2 respectively; k max1 and k min1 are the upper and lower threshold parameters of dv1, which are used to control the NHC measurement noise caused by the installation error angle estimation; k max2 and k min2 They are the upper and lower threshold parameters of dv2, which are used to control the NHC measurement noise corresponding to the changes in vehicle speed and angle.

7. The non-holonomic constraint adaptive measurement method according to any one of claims 1 to 6, characterized in that: The method of updating the NHC measurement by using the IMU installation error angle and the NHC measurement noise comprises the following steps: Based on the IMU installation error angle α and the adaptive NHC measurement noise after the IMU installation angle estimation is completed and in the case of straight line operation, the measurement coefficient matrix is ​​updated according to the non-completeness constraint and the Kalman filter measurement update is performed, and the state parameters of the vehicle-mounted integrated navigation are feedback compensated according to the filtering results.

8. A non-holonomic constraint adaptive measurement system considering the installation error angle of a vehicle-mounted IMU, applying the non-holonomic constraint adaptive measurement method according to any one of claims 1 to 7, characterized in that: include: An initial attitude alignment module is used to align the initial attitude of the vehicle-mounted integrated navigation system; The vehicle motion judgment module is used to calculate the current vehicle position and posture based on the vehicle-mounted integrated navigation and determine whether the current vehicle is moving in a straight line; An IMU installation error angle estimation module is used to estimate the IMU installation error angle through a Kalman filter based on a dead reckoning method using the position increment of the vehicle-mounted integrated navigation and the dead reckoning position increment; An NHC measurement noise update module is used to adaptively determine the NHC measurement noise according to the estimation accuracy of the IMU installation error angle, taking into account the relationship between the vehicle body speed, angle change and the NHC observation error, and taking into account the estimation accuracy of the IMU installation error angle; The NHC adaptive measurement module is used to perform NHC measurement update by using the IMU installation error angle and the NHC measurement noise to complete NHC adaptive measurement.

9. A device comprising a memory and a processor, wherein the memory stores computer-readable instructions, characterized in that: When the computer-readable instructions are executed by the processor, the processor is enabled to execute all or part of the steps of the non-holonomic constraint adaptive measurement method according to any one of claims 1 to 7.

10. A storage medium having computer-readable instructions stored thereon, characterized in that: When the computer-readable instructions are executed by a processor, all or part of the steps of the non-holonomic constraint adaptive measurement method according to any one of claims 1 to 7 are implemented.

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