Heading channel error estimation method and device

Abstract

The present application discloses a heading channel error estimation method and apparatus. The method comprises: determining the values ​​of a state transfer matrix and an observation vector; and, based on the values ​​of the state transfer matrix and the observation vector, performing Kalman filtering on a filter model containing a predetermined state equation and an observation equation to obtain an estimated value of the state vector. The predetermined state equation includes the state transfer matrix, the predetermined observation equation includes the observation vector, the observation vector includes a heading observation error, and the state vector includes a heading gyro bias and a heading gyro scale coefficient error.

Description

Technical Field

[0001] The present application relates to the technical field of integrated navigation, and in particular to a heading channel error estimation method and device. Background Art

[0002] Heading error is one of the primary error sources that must be overcome in ground vehicle navigation. The main sources of heading error include heading gyro bias and heading measurement errors caused by errors in the heading gyro scale factor during heading maneuvers. However, due to the limitations of integrated navigation, the heading gyro bias and scale factor errors are not measurable and difficult to eliminate using traditional integrated navigation methods.

[0003] To reduce the impact of various heading channel errors on inertial navigation accuracy, Kalman filtering is currently used to estimate these errors during the integrated navigation phase. By introducing an online calibration approach, the Kalman filter model decomposes the inertial measurement unit's output error into zero bias, scale factor error, and other components. These errors are then estimated online by introducing a high-precision external information source. One existing solution uses velocity and position observations to estimate nine gyro- and accelerometer-related errors. However, this method places high demands on vehicle maneuverability. Furthermore, due to the principle of low heading channel observability, the estimation of heading channel errors is not effective, especially for heading gyro drift and heading gyro scale factor errors. Consequently, it is also ineffective in improving the observability of the heading channel and cannot meet the accuracy requirements of ground vehicle navigation. Summary of the Invention

[0004] The present application provides a heading channel error estimation method and device, which solves at least one of the above technical problems.

[0005] This application proposes a heading channel error estimation method, including:

[0006] Determine the values ​​of the state transfer matrix and observation vector;

[0007] Based on the state transfer matrix and the value of the observation vector, performing Kalman filtering on a filter model including a predetermined state equation and an observation equation to obtain an estimated value of the state vector;

[0008] The predetermined state equation includes the state transfer matrix, the predetermined observation equation includes the observation vector, the observation vector includes the heading observation error, and the state vector includes the heading gyro zero bias and the heading gyro scale coefficient error.

[0009] The present application provides a device for heading channel error estimation, comprising: a processor and a memory; the memory is used to store a program for performing a heading channel error estimation method, and the processor is used to read and execute the program for performing the heading channel error estimation method, and perform the heading channel error estimation method as described in any of the above embodiments.

[0010] In summary, the heading channel error estimation method of the embodiment of the present application solves the technical problem in the prior art that the observability of the heading channel is low and cannot meet the accuracy requirements of ground vehicle navigation. It can significantly improve the observability of the heading channel to meet the accuracy requirements of ground vehicle navigation.

[0011] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. Other advantages of the present application can be realized and obtained by the solutions described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings are used to provide an understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0013] Figure 1 This is a schematic diagram of an example of the heading angle and track angle of a vehicle in integrated navigation.

[0014] Figure 2 It is a schematic flowchart of the heading channel error estimation method according to an embodiment of the present application.

[0015] Figure 3 3 is a schematic structural diagram of a device for estimating heading channel errors according to an embodiment of the present application.

[0016] Figures 4A to 4D The figure is a simulation result of the heading-related error estimation of the application example of this application.

[0017] Figure 5 This is a simulation comparison diagram of the position error using the heading channel error estimation method of the present application and the pure inertial navigation (6 minutes) using the traditional method. DETAILED DESCRIPTION

[0018] This application describes multiple embodiments, but this description is exemplary rather than restrictive, and it will be apparent to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described herein. Although many possible feature combinations are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0019] This application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features, and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive solution defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other inventive solutions to form another unique inventive solution defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the appended claims and their equivalents, the embodiments are not subject to other limitations. In addition, various modifications and changes may be made within the scope of protection of the appended claims.

[0020] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps in the specific order described. As will be understood by those skilled in the art, other orders of steps are also possible. Therefore, the specific order of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to performing their steps in the order written, and those skilled in the art can readily understand that these orders can be changed and still remain within the spirit and scope of the embodiments of the present application.

[0021] In order to help those skilled in the art better understand the technical solutions of the present application, the following briefly introduces the technical terms that may be involved in the exemplary embodiments of the present application.

[0022] An inertial measurement unit (IMU), also known as an inertial sensor, is a device in a vehicle that provides all control units with real-time information about the vehicle's motion. It primarily detects and measures the vehicle's acceleration and rotational motion. An IMU typically includes an accelerometer and a gyroscope. The accelerometer detects the acceleration signals of an object along three independent axes in the vehicle's coordinate system, while the gyroscope detects the angular velocity signals of the vehicle relative to the navigation coordinate system.

[0023] The heading angle of a vehicle is the angle between the vehicle's forward direction and the Earth's north. The track angle of a vehicle is usually the angle between the vehicle's trajectory and the Earth's north. Figure 1 This is a schematic diagram of an example of the heading angle and track angle of a vehicle in combined navigation. Figure 1 In the figure, the vehicle is in a forward state, the IMU is located at the center of mass of the vehicle, and the angle between the forward speed of the IMU, that is, the direction of the vehicle, and the North Pole of the earth is the heading angle; the track angle is the angle between the direction of the vehicle and the North Pole of the earth.

[0024] When the satellite navigation receiver is in the positioning state and the horizontal velocity meets the track angle calculation conditions, the track angle is considered valid and the track angle ψ is calculated using the following expression h as follows:

[0025] ψ h =arctan(v e / v n ) (1)

[0026] Here, v e is the eastward velocity, v n is the northbound speed.

[0027] Here, the horizontal velocity meeting the track angle calculation conditions usually means that the horizontal velocity is greater than a preset threshold, wherein the threshold needs to be obtained through multiple debugging based on actual conditions and experience; the effective track angle specifically means that when the speed measurement noise of the satellite navigation receiver is relatively small, the calculated track angle has a high accuracy.

[0028] Attitude transfer matrix It refers to the rotation matrix from the b system (carrier coordinate system) to the n system (geographic coordinate system).

[0029] Figure 2 is a schematic flow chart of a heading channel error estimation method according to an embodiment of the present application. The heading channel error estimation method according to an embodiment of the present application includes:

[0030] Step S1: Determine the values ​​of the state transfer matrix and the observation vector;

[0031] Step S2: Based on the values ​​of the state transfer matrix and the observation vector, a Kalman filter is performed on the filter model including the predetermined state equation and the observation equation to obtain an estimated value of the state vector.

[0032] The predetermined state equation includes a state transfer matrix, the predetermined observation equation includes an observation vector, the observation vector includes a heading observation error, and the state vector includes a heading gyro zero bias and a heading gyro scale coefficient error.

[0033] Here, the filtering model including the predetermined state equation and observation equation is constructed based on the following assumptions:

[0034] The front and rear antennas of the satellite navigation receiver are basically aligned with the forward direction of the vehicle body;

[0035] During driving, the installation relationship between the front and rear antennas and the vehicle body remains unchanged; and

[0036] The installation relationship between the inertial measurement unit and the vehicle body remains unchanged.

[0037] In an exemplary embodiment, the state vector further includes a heading error and a heading installation angle error.

[0038] In some existing technical solutions, the heading error included in the state vector does not take into account the heading error caused by the heading installation angle error, resulting in a less precise and low-accuracy heading error estimate. The embodiments of the present application consider the heading installation angle error separately and add it to the state vector, significantly improving the accuracy of the heading error estimate obtained by filtering, thereby greatly improving the observability of the heading channel and meeting the accuracy requirements of ground vehicle navigation.

[0039] In an exemplary embodiment, the preset state equation represents the relationship between the state vector at the current moment and the state vector at the next moment, wherein the state vector at the next moment is related to the product of the state vector at the current moment and the state transfer matrix at the current moment, and the system random noise vector at the current moment; the preset observation equation represents the relationship between the observation vector at the current moment and the state vector at the current moment, wherein the observation vector at the current moment is related to the product of the state vector at the current moment and the observation matrix at the current moment, and the observation noise matrix at the current moment.

[0040] In an exemplary embodiment, the preset state equation is:

[0041] X k+1 =F k X k +w k (2)

[0042] Among them, F kis the state transfer matrix at time k, w k is the system random noise vector at time k, X k is the state vector at time k, X k+1 is the state vector at time k+1.

[0043] In an exemplary embodiment, the preset observation equation is:

[0044] Z k =H k X k +v k (3)

[0045] Among them, Z k is the observation vector at time k, H k is the observation matrix at time k, v k is the observation noise matrix at time k;

[0046]

[0047] v k =[v vel v pos v ψ ]v vel is the satellite navigation velocity measurement noise, v pos is the position measurement noise, v ψ The noise is measured for heading.

[0048] In an exemplary embodiment, the value of the heading observation error is determined by the values ​​of the track angle and the heading angle of the inertial navigation.

[0049] In an exemplary embodiment, the expression of the observation vector is as follows:

[0050]

[0051] in, is the velocity error of inertial navigation, is the position error of the inertial navigation, is the heading observation error of the inertial navigation.

[0052] The value of the observation vector in step S1 is determined by determining The value of The value of can be calculated by the following expressions (6)-(8).

[0053] In an exemplary embodiment, the heading observation error Calculated by expression (6):

[0054]

[0055] Among them, ψ h is the track angle, ψ ins is the heading angle of inertial navigation.

[0056] In an exemplary embodiment, It can be expressed as:

[0057]

[0058] in, is the north velocity of inertial navigation, is the north velocity of the satellite navigation receiver; is the celestial velocity of inertial navigation, is the celestial velocity of the satellite navigation receiver; is the eastward speed of inertial navigation, is the eastward speed of the satellite navigation receiver.

[0059] In an exemplary embodiment, It can be expressed as:

[0060]

[0061] in, is the north position of inertial navigation, is the north position of the satellite navigation receiver; is the celestial position of inertial navigation, is the north position of the satellite navigation receiver; is the east position of inertial navigation, is the easting position of the satellite navigation receiver.

[0062] In an exemplary embodiment, the state transition matrix is:

[0063]

[0064] in,

[0065]

[0066] V N is the north velocity, V U is the celestial velocity, V E is the eastward speed, L is the latitude, H is the height, λ is the longitude, R N and R M are the radius of the meridian circle and the radii of the meridian circle, ω ie is the angular rate of the Earth's rotation.

[0067]

[0068]

[0069] Here, They are the north, celestial and east accelerations in the navigation coordinate system respectively.

[0070]

[0071] Among them, C ij is the attitude transfer matrix The element at the i-th row and j-th column of .

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] The other elements of the F matrix are 0.

[0079] F 13 The expression is as follows:

[0080]

[0081] Among them, C 12 、C 22 、C 32 They are the attitude transfer matrices The value of the corresponding subscript position in ω y The angular rate measurement output of the heading gyro, in rad / s.

[0082] The determination of the state transfer matrix in step S1 can be achieved by obtaining corresponding numerical values ​​and substituting the obtained numerical values ​​into the expression of the state transfer matrix.

[0083] Attitude transfer matrix The expression is as follows:

[0084]

[0085] Where γ, θ, ψ are attitude angles.

[0086] In an exemplary embodiment, obtaining an estimated value of the state vector includes:

[0087] Obtaining estimated values ​​of the heading gyro zero bias and the heading gyro scale coefficient error;

[0088] After obtaining the estimated values ​​of the heading gyro zero bias and the heading gyro scale coefficient error, the method further includes:

[0089] The heading gyro zero bias and the heading gyro scale coefficient error are corrected by using the estimated values ​​of the heading gyro zero bias and the heading gyro scale coefficient error.

[0090] In an exemplary embodiment, obtaining an estimated value of the state vector includes:

[0091] Obtaining an estimated value of the heading gyro zero bias, the heading gyro scale coefficient error, and the heading error;

[0092] After obtaining the heading gyro zero bias, the heading gyro scale coefficient error, and the estimated value of the heading error, the method further includes:

[0093] The heading gyro zero bias, the heading gyro scale coefficient error and the heading error are corrected using the estimated values ​​of the heading gyro zero bias, the heading gyro scale coefficient error and the heading error.

[0094] In an exemplary embodiment, correcting the heading gyro bias and the heading gyro scale coefficient error includes:

[0095] The heading gyro bias is corrected using expression (12) to obtain the heading angular rate after correcting the heading gyro bias:

[0096]

[0097] Among them, X ωy is the estimated value of the heading gyro bias, is the heading angular rate output by the heading gyro, The heading angular rate after correcting the heading gyro zero bias;

[0098] The heading gyro scale coefficient error is corrected using expression (13) to obtain the heading angular rate after correcting the heading gyro zero bias and the heading gyro scale coefficient error:

[0099]

[0100] Among them, X kgyy is the estimated value of the heading gyro scale coefficient error, The heading angular rate is obtained after correcting the heading gyro zero bias and the heading gyro scale coefficient error.

[0101] In an exemplary embodiment, correcting the heading error includes:

[0102] The heading error is corrected using expression (14) to obtain the attitude transfer matrix after the heading error is corrected:

[0103]

[0104] Among them, X ψ is the estimated value of the heading error, C(X ψ ) is the estimated value of heading error X ψ The error correction matrix, is the attitude transfer matrix before correcting the heading error, is the attitude transfer matrix after correcting the heading error.

[0105] By introducing heading observations into filtering, the embodiments of this application significantly improve the observability of heading error and heading gyro bias. These can be considered strongly observable, converging to near-accurate values ​​within 30 filter calculations for correction. However, accurate estimation of the heading gyro scale coefficient error and installation angle error requires certain maneuvering conditions (typically turns or acceleration / deceleration). Therefore, the standard deviation of the heading gyro scale coefficient error estimate within a near-window can be used as a correction criterion.

[0106] In an exemplary embodiment, when expression (15) holds true, the condition for correcting the heading gyro scale coefficient error is satisfied:

[0107] std(X kgyy )<5e-4 (15)

[0108] Among them, X kgyy is the estimated value of the heading gyro scale coefficient error, and std() represents the standard deviation.

[0109] In an exemplary embodiment, the state vector includes north velocity error, celestial velocity error, east velocity error, latitude error, altitude error, longitude error, north misalignment angle, heading error, east misalignment angle, X-axis gyro bias of the carrier system, Y-axis gyro bias of the carrier system, Z-axis gyro bias of the carrier system, X-axis accelerometer bias of the carrier system, Y-axis accelerometer bias of the carrier system, Z-axis accelerometer bias of the carrier system, time delay, heading gyro scale coefficient error, heading installation angle error, and pitch installation angle error.

[0110] In an exemplary embodiment, the state vector X kIt can be a 19-dimensional matrix, which includes the north sky east velocity error (unit: m / s), latitude error (unit: rad), altitude error (unit: m), longitude error (unit: rad), north sky east misalignment angle (unit: rad), carrier system XYZ direction gyro drift (unit: rad / s), carrier system XYZ direction accelerometer zero bias (unit: m / s2), time delay (unit: s), heading gyro scale coefficient (unitless), heading installation angle error (unit: rad), pitch installation angle error (unit: rad), that is, X k It can be expressed by expression (16):

[0111]

[0112] In the above expression (16), V N is the north velocity error, V U is the celestial velocity error, V E is the eastward velocity error, L is the latitude error, H is the height error, λ is the longitude error, is the north misalignment angle, is the heading error, is the east misalignment angle, ε bx is the X-axis gyro bias of the carrier system, ε by is the Y-axis gyro bias of the carrier system, ε bz is the Z-axis gyro bias of the carrier system, is the X-axis accelerometer bias of the load system, is the Y-axis accelerometer bias of the load system, is the Z-axis accelerometer bias of the load system, T d is the time delay, k gyy is the heading gyro scale coefficient error, θ y is the heading installation angle error, θ p is the pitch installation angle error.

[0113] In summary, the heading channel error estimation method proposed in the embodiment of the present application introduces the track angle as the observation quantity and the heading gyro scale coefficient error as the state quantity, thereby improving the observability of the heading channel and further improving the accuracy of the heading gyro zero bias and scale coefficient error correction.

[0114] Figure 3 Schematic diagram of the structure of the device for estimating heading channel error according to an embodiment of the present application. Figure 3 As shown in the schematic diagram, the apparatus for estimating heading channel error of this embodiment includes a memory 100 and a processor 200. In particular:

[0115] The memory 100 is used to store a program for performing a heading channel error estimation method;

[0116] The processor 200 is configured to read and execute a program for performing a heading channel error estimation method, and perform the following operations:

[0117] Determine the values ​​of the state transfer matrix and observation vector;

[0118] Based on the values ​​of the state transfer matrix and the observation vector, a Kalman filter is performed on a filter model including a predetermined state equation and an observation equation to obtain an estimated value of the state vector;

[0119] The predetermined state equation includes a state transfer matrix, the predetermined observation equation includes an observation vector, the observation vector includes a heading observation error, and the state vector includes a heading gyro zero bias and a heading gyro scale coefficient error.

[0120] In an exemplary embodiment, the state vector further includes a heading error and a heading installation angle error.

[0121] In some existing technical solutions, the heading error included in the state vector does not take into account the heading error caused by the heading installation angle error, resulting in a less precise and low-accuracy heading error estimate. The embodiments of the present application consider the heading installation angle error separately and add it to the state vector, significantly improving the accuracy of the heading error estimate obtained by filtering, thereby greatly improving the observability of the heading channel and meeting the accuracy requirements of ground vehicle navigation.

[0122] In an exemplary embodiment, the preset state equation represents the relationship between the state vector at the current moment and the state vector at the next moment, wherein the state vector at the next moment is related to the product of the state vector at the current moment and the state transfer matrix at the current moment, and the system random noise vector at the current moment; the preset observation equation represents the relationship between the observation vector at the current moment and the state vector at the current moment, wherein the observation vector at the current moment is related to the product of the state vector at the current moment and the observation matrix at the current moment, and the observation noise matrix at the current moment.

[0123] In an exemplary embodiment, the value of the heading observation error is determined by the values ​​of the track angle and the heading angle of the inertial navigation.

[0124] In an exemplary embodiment, the state transition matrix is:

[0125]

[0126] Among them, F1 to F 13 For the specific meaning of , please refer to the specific description in the embodiment of the heading channel error estimation method of the present application.

[0127] In an exemplary embodiment, obtaining an estimate of the state vector includes:

[0128] Obtain the estimated values ​​of the heading gyro zero bias and heading gyro scale coefficient error;

[0129] After obtaining the estimated values ​​of the heading gyro zero bias and the heading gyro scale coefficient error, the processor 200 reads and executes a program for performing a heading channel error estimation method, and further performs the following operations:

[0130] The heading gyro zero bias and the heading gyro scale coefficient error are corrected using the estimated values ​​of the heading gyro zero bias and the heading gyro scale coefficient error.

[0131] In an exemplary embodiment, obtaining an estimate of the state vector includes:

[0132] Obtain estimated values ​​of heading gyro zero bias, heading gyro scale coefficient error and heading error;

[0133] After obtaining the estimated values ​​of the heading gyro zero bias, the heading gyro scale coefficient error, and the heading error, the processor 200 reads and executes a program for performing a heading channel error estimation method, and further performs the following operations:

[0134] The heading gyro zero bias, the heading gyro scale coefficient error and the heading error are corrected using the estimated values ​​of the heading gyro zero bias, the heading gyro scale coefficient error and the heading error.

[0135] In an exemplary embodiment, correcting the heading gyro bias and the heading gyro scale coefficient error includes:

[0136] The heading gyro bias is corrected using expression (12) to obtain the heading angular rate after correcting the heading gyro bias:

[0137]

[0138] Among them, X ωy is the estimated value of the heading gyro bias, is the heading angular rate output by the heading gyro, The heading angular rate after correcting the heading gyro zero bias;

[0139] The heading gyro scale coefficient error is corrected using expression (13) to obtain the heading angular rate after correcting the heading gyro zero bias and the heading gyro scale coefficient error:

[0140]

[0141] Among them, X kgyy is the estimated value of the heading gyro scale coefficient error, The heading angular rate is obtained after correcting the heading gyro zero bias and the heading gyro scale coefficient error.

[0142] In an exemplary embodiment, correcting the heading error includes:

[0143] The heading error is corrected using expression (14) to obtain the attitude transfer matrix after the heading error is corrected:

[0144]

[0145] Among them, X ψ is the estimated value of the heading error, C(X ψ ) is the estimated value of heading error X ψ The error correction matrix, is the attitude transfer matrix before correcting the heading error, is the attitude transfer matrix after correcting the heading error.

[0146] In an exemplary embodiment, when expression (15) holds true, the condition for correcting the heading gyro scale coefficient error is satisfied:

[0147] std(X kgyy )<5e-4 (15)

[0148] Among them, X kgyy is the estimated value of the heading gyro scale coefficient error, and std() represents the standard deviation.

[0149] In an exemplary embodiment, the state vector includes north velocity error, celestial velocity error, east velocity error, latitude error, altitude error, longitude error, north misalignment angle, heading error, east misalignment angle, X-axis gyro bias of the carrier system, Y-axis gyro bias of the carrier system, Z-axis gyro bias of the carrier system, X-axis accelerometer bias of the carrier system, Y-axis accelerometer bias of the carrier system, Z-axis accelerometer bias of the carrier system, time delay, heading gyro scale coefficient error, heading installation angle error, and pitch installation angle error.

[0150] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0151] The following is an application example according to an embodiment of the present application. In this application example, a simulation is performed to compare the heading error estimation results of the present application's heading channel error estimation method with those of a traditional velocity position observation method, as well as the position error of the inertial navigation after error correction. Figures 4A to 4DThe figure is a simulation result of the heading-related error estimation of the application example of this application. Figure 5 This is a simulation comparison diagram of the position error of pure inertial navigation for 6 minutes after error correction for the application example of this application.

[0152] In this application example, the simulated vehicle maneuvers are as follows:

[0153] 1) Stand still for 300 seconds;

[0154] 2) Accelerate to 10 m / s within 5 s;

[0155] 3) Change heading by 90° at a maximum angular rate of 6° / s

[0156] 4) Maintain a constant speed for 300 seconds;

[0157] 5) Turn 90° at a maximum angular rate of 6° / s

[0158] 6) Maintain a constant speed.

[0159] The total length of the simulation data is about 900s. During the simulation, the initial heading error is 3°, the gyro scale coefficient error is 2%, the heading gyro drift is 50° / h, and the deviation between the track angle and the inertial navigation heading angle (heading installation angle error) is 2°.

[0160] Simulation is performed according to the above conditions to obtain the simulation results of the heading channel error estimation method according to the present application, namely, the heading error estimation value, the heading gyro bias error estimation value, the heading installation angle error estimation value, and the heading gyro scale coefficient error estimation value. Simulation is also performed according to the above conditions to obtain the simulation results of the traditional speed and position observation method, namely, the heading error estimation value and the heading gyro bias error estimation value. Since the state vector in the filter model corresponding to the traditional speed and position observation method does not include the heading installation angle error and the heading gyro scale coefficient error, the simulation performed according to the traditional speed and position observation method cannot estimate the heading installation angle error and the heading gyro scale coefficient error. In other words, the simulation results according to the traditional speed and position observation method do not include the heading installation angle error estimation value and the heading gyro scale coefficient error estimation value.

[0161] Figure 4A The following is a simulation comparison diagram of the heading error estimation value using the heading channel error estimation method of the present application and the heading error estimation value using the traditional method. Figure 4A It can be seen that due to the low observability, the traditional method is almost unable to estimate the heading error in the stationary phase. Due to the improved observability of the heading error under the vehicle acceleration condition, the heading error estimate after acceleration is basically close to the true value, but with large fluctuations (about 1°). Moreover, the heading error caused by the heading gyro scale coefficient during the two turns (300 seconds and 600 seconds) was never estimated.

[0162] Figure 4B This is a simulation comparison diagram of the heading channel error estimation method of the present application and the heading gyro bias error estimation value using the traditional method. Figure 4B It can be seen that the traditional method has a poor effect on the estimation of heading gyro bias, with an overshoot of about 20° / h and a steady-state oscillation amplitude of about 10° / h.

[0163] It can be seen that the heading error estimation value and the heading gyro bias error estimation value obtained by the traditional method are less accurate. Using these estimation results for error correction will inevitably affect the accuracy of inertial navigation.

[0164] In contrast, Figure 4A and 4B It can be seen that the method of the present application greatly improves the observability of the heading error. Within 30 seconds after the start of filtering, the heading error estimate reflects the trend of the heading error angle continuously increasing under the influence of the heading gyro zero bias, and well estimates the heading error change caused by the heading gyro scale coefficient error during the two turns. The heading gyro zero bias estimate is basically consistent with the theoretical value, and the estimation time is less than 50 seconds.

[0165] Compared with the traditional method, the method of the present application can also estimate the heading installation angle error and the heading gyro scale coefficient error. Figure 4C This is a simulation diagram of the heading installation angle error estimation value using the heading channel error estimation method of the present application. Figure 4D This is a simulation diagram of the heading gyro scale coefficient error estimation value using the heading channel error estimation method of this application. Figure 4C and 4D It can be seen that after the turning maneuver, the estimated value of the heading installation angle error and the estimated value of the heading gyro scale coefficient error quickly converge to the true value, and the efficiency and accuracy of the error estimation are high.

[0166] Through the above analysis of the simulation results of the heading-related error estimation value, it can be seen that the heading channel error estimation method according to the present application has a significant effect on the heading-related error estimation, improves the accuracy and efficiency of the heading-related error, and significantly improves the observability of the heading channel and the accuracy of the combined navigation.

[0167] Figure 5 This is a simulation comparison of the position error of the pure inertial navigation (6 minutes) using the heading channel error estimation method of this application and the traditional method. After estimating the heading-related error using the heading channel error estimation method of this application, the above-mentioned heading-related error estimation value is corrected using the error correction of the aforementioned embodiment of this application, and a 6-minute pure inertial navigation simulation is performed. The simulation results are: Figure 5The position error curve marked as "this method" in the figure is as follows. After estimating the heading-related error using the traditional method and correcting the heading-related error estimate using the error correction of the traditional method, a 6-minute pure inertial navigation simulation is also performed. The simulation results are as follows: Figure 5 The position error curve marked as “traditional method” in the figure. Figure 5 The “reference” curve in the figure is the position error reference curve. Figure 5 It can be seen intuitively that by using the method of this application to correct the heading error, heading gyro zero bias, and heading gyro scale coefficient error, the pure inertial positioning error is significantly improved compared with the traditional method. In order to have a further understanding of the improvement effect, the following Figure 5 The curve is used to quantify the specific value of the improvement. The longitude and latitude of the method of the present application are respectively subtracted from the reference value to obtain the positioning error a in longitude and latitude units, and then the positioning error a in longitude and latitude units is converted into the positioning error b in length units (meters). It can be obtained that the positioning error b in length units (meters) using the method of the present application is 30m in the north and 28m in the east. The positioning error b' in length units (meters) using the traditional method is calculated in the same way to be 70m in the north and 230m in the east. Here, when converting the positioning error a in longitude and latitude units into the positioning error b in length units (meters), the following approximate relationship can also be used: b=a / 57.3*6378137. It can be seen that compared with the traditional method, the positioning error of the method of the present application is reduced from 70m in the north and 230m in the east to 30m in the north and 28m in the east, which is a reduction of 57% and 88% respectively, and the effect is very significant.

[0168] Through the analysis of the simulation results after the heading-related error estimation value is corrected, it can be seen that the heading channel error estimation method according to the present application further improves the heading channel observability and the accuracy of the integrated navigation after the heading-related error estimation value is corrected.

[0169] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media generally embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0170] The above are only optional embodiments of the present application. Of course, the present application may have many other embodiments. Without departing from the spirit and essence of the present application, technical personnel familiar with the art can make various corresponding changes and modifications based on the present application, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present application.

[0171] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media generally embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A heading channel error estimation method, comprising: Determine the values ​​of the state transfer matrix and observation vector; Based on the state transfer matrix and the value of the observation vector, performing Kalman filtering on a filter model including a predetermined state equation and an observation equation to obtain an estimated value of the state vector; In which, the predetermined state equation includes the state transfer matrix, the predetermined observation equation includes the observation vector, the observation vector includes the heading observation error, the value of the heading observation error is determined by the values ​​of the track angle and heading angle of inertial navigation, and the state vector includes the heading gyro zero bias and the heading gyro scale coefficient error.

2. The method according to claim 1, characterized in that The state vector also includes a heading error and a heading installation angle error.

3. The method according to claim 1, characterized in that The predetermined state equation represents a relationship between a state vector at a current moment and a state vector at a subsequent moment, wherein the state vector at the subsequent moment is related to a product of the state vector at the current moment and a state transfer matrix at the current moment, and a system random noise vector at the current moment; The predetermined observation equation represents the relationship between the observation vector at the current moment and the state vector at the current moment, wherein the observation vector at the current moment is related to the product of the state vector at the current moment and the observation matrix at the current moment, and the observation noise matrix at the current moment.

4. The method according to claim 1, wherein The state transfer matrix is: in, F5＝0 Among them, V N is the north velocity, V U is the celestial velocity, V E is the eastward speed, L is the latitude, H is the height, λ is the longitude, R N and R M are the radius of the meridian circle and the radii of the meridian circle, ω ie is the Earth's rotation angular rate, where are the north, sky and east accelerations in the navigation coordinate system, respectively. ij is the attitude transfer matrix The element in row i and column j of C 12 、C 22 、C 32 They are the attitude transfer matrices The value of the corresponding subscript position in ω y The angular rate measurement output by the heading gyro, in rad / s.

5. The method according to claim 1, wherein The step of obtaining an estimated value of the state vector includes: Obtaining estimated values ​​of the heading gyro zero bias and the heading gyro scale coefficient error; After obtaining the estimated values ​​of the heading gyro zero bias and the heading gyro scale coefficient error, the method further includes: The heading gyro zero bias and the heading gyro scale coefficient error are corrected by using the estimated values ​​of the heading gyro zero bias and the heading gyro scale coefficient error.

6. The method according to claim 2, characterized in that The step of obtaining an estimated value of the state vector includes: Obtaining an estimated value of the heading gyro zero bias, the heading gyro scale coefficient error, and the heading error; After obtaining the heading gyro zero bias, the heading gyro scale coefficient error, and the estimated value of the heading error, the method further includes: The heading gyro zero bias, the heading gyro scale coefficient error and the heading error are corrected using the estimated values ​​of the heading gyro zero bias, the heading gyro scale coefficient error and the heading error.

7. The method according to claim 5 or 6, characterized in that Correcting the heading gyro zero bias and the heading gyro scale coefficient error includes: The heading gyro bias is corrected using the following expression to obtain the heading angular rate after correcting the heading gyro bias: Among them, X ωy is the estimated value of the heading gyro bias, is the heading angular rate output by the heading gyro, The heading angular rate after correcting the heading gyro zero bias; The heading gyro scale coefficient error is corrected using the following expression to obtain the heading angular rate after correcting the heading gyro zero bias and the heading gyro scale coefficient error: Among them, X kgyy is the estimated value of the heading gyro scale coefficient error, The heading angular rate is obtained after correcting the heading gyro zero bias and the heading gyro scale coefficient error.

8. The method according to claim 6, characterized in that Correcting the heading error includes: The heading error is corrected using the following expression to obtain the attitude transfer matrix after the heading error is corrected: Among them, X ψ is the estimated value of the heading error, C(X ψ ) is the estimated value X of the heading error ψ The error correction matrix, To correct the attitude transfer matrix before the heading error, is the attitude transfer matrix after correcting the heading error.

9. The method according to claim 7, characterized in that When the following expression is established, the condition for correcting the heading gyro scale coefficient error is met: std(X kgyy )<5e-4 Among them, X kgyy is the estimated value of the heading gyro scale coefficient error, and std() represents the standard deviation.

10. A device for estimating a heading channel error, comprising: processor and memory; Its characteristics are: The memory is used to store a program for performing a heading channel error estimation method, and the processor is used to read and execute the program for performing a heading channel error estimation method to perform the heading channel error estimation method according to any one of claims 1 to 9.

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