Dual-antenna gnss-aided attitude and heading measurement method, device and system

Abstract

The application provides a dual-antenna GNSS auxiliary attitude measurement method, which realizes real-time high-precision navigation positioning and orientation through the following steps: constructing a coordinate system projection matrix by using dual-antenna height difference; calculating the heading information delay time of satellite navigation and inertial navigation, and buffering inertial navigation data; finding the inertial navigation heading at the corresponding time in the buffer according to the delay time, and converting the coordinate system through the projection matrix; comparing the converted inertial navigation heading with the satellite navigation heading, and calculating the error; after judging the effectiveness of the error, filtering and iteration are carried out by using a Kalman filter to obtain the optimal estimation of the error term; and the original navigation result is corrected according to the filtering result.

Description

Technical Field

[0001] This invention relates to the field of shipborne navigation technology, specifically to a dual-antenna GNSS-assisted attitude measurement method, equipment, and system. Background Technology

[0002] When shipborne electro-optical systems locate targets, they require an attitude and bearing reference system (AHRS) to provide real-time, high-precision positioning and orientation information. The electro-optical system measures the target's azimuth and pitch angles in the deck coordinate system, uses the AHRS information to convert these into the target's azimuth and distance in the geographic coordinate system, and finally calculates the target's position in the geographic coordinate system by combining this with the AHRS's position information. Therefore, the real-time positioning and orientation accuracy of the AHRS is crucial to the target positioning capabilities of shipborne electro-optical systems.

[0003] In attitude and bearing reference systems, inertial navigation and satellite navigation are the two most commonly used navigation methods. Inertial navigation has advantages such as comprehensive output information, high output frequency, and high short-term accuracy. However, a significant drawback of inertial navigation is that positioning errors accumulate rapidly over time. On the other hand, satellite navigation, such as the Global Navigation Satellite System (GNSS), can provide accurate navigation and positioning information, but its navigation information output frequency is relatively low, and the signal often contains significant noise.

[0004] To combine the advantages of inertial navigation and satellite navigation for real-time, high-precision navigation and positioning, the industry commonly employs a combined navigation approach. However, in practical applications, this combined navigation method also faces a series of technical challenges. Especially for low-cost inertial navigation systems, the limited accuracy of their sensors makes high-precision autonomous orientation difficult. Furthermore, while increasing the baseline length of the dual-antenna satellite navigation for orientation measurements can improve orientation accuracy (e.g., a baseline length greater than 2 meters can achieve a heading accuracy better than 0.1°), the limited antenna positions, particularly in situations with elevation differences, mean that the dual-antenna heading cannot accurately represent the actual heading of the vehicle when it wobbles. In addition, the significant delay in calculating the dual-antenna GNSS heading makes it difficult to meet the stringent requirements of applications such as optoelectronic systems for dynamic, real-time target positioning. Summary of the Invention

[0005] The purpose of this invention is to address the problems existing in the prior art by providing a dual-antenna GNSS-assisted attitude measurement method. By compensating for the height difference between the two antennas and the satellite navigation calculation delay, real-time high-precision navigation, positioning, and orientation are achieved.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A dual-antenna GNSS-assisted attitude measurement method includes:

[0008] The projection matrix from the ship's coordinate system to the coordinate system where the satellite navigation dual antennas are located is constructed using the height difference between the two antennas;

[0009] Calculate the time delay ΔT between satellite navigation heading information and inertial navigation heading information;

[0010] The heading information is obtained using inertial navigation, and the heading information and the corresponding timestamp are cached in an array;

[0011] During the satellite navigation heading update cycle, based on the delay time ΔT, the inertial navigation heading information corresponding to the satellite navigation heading information at the time is found in the cached inertial navigation data.

[0012] The inertial navigation heading information found is transformed from the ship's coordinate system to the satellite navigation dual-antenna coordinate system using the projection matrix.

[0013] The converted inertial navigation heading information is compared with the satellite navigation heading information, and the difference between them is calculated to obtain the errors in position, velocity and heading, which are used as error measurements.

[0014] The validity of the heading error measurement is determined. If it is valid, the next step of Kalman filtering update is performed.

[0015] A Kalman filter is used to construct a state estimation model and a measurement model. The state estimation is filtered and iterated using error measurements to obtain the optimal estimate of each error term at the current time.

[0016] The original navigation results are corrected based on the output of the Kalman filter.

[0017] Construct the projection matrix from the ship's coordinate system to the coordinate system where the satellite navigation dual antennas are located, including:

[0018] Based on the ship's structural model or actual on-site measurements, determine the vertical distance H and longitudinal distance L between the main antenna and the slave antenna in the ship's coordinate system;

[0019] Based on the vertical distance H and the longitudinal distance L, the pitch angle deviation Δθ between the satellite navigation dual-antenna coordinate system and the ship's coordinate system is calculated using the following formula.

[0020]

[0021] Based on the calculated pitch angle deviation Δθ, the projection matrix from the ship's coordinate system to the satellite navigation dual-antenna coordinate system is constructed as follows.

[0022]

[0023] Calculate the time delay ΔT between satellite navigation heading information and inertial navigation heading information, including:

[0024] Without using dual-antenna heading information assistance, and under dynamic conditions, simultaneously acquire satellite navigation heading information, inertial navigation heading information, and heading angular rate information;

[0025] The heading information obtained from inertial navigation is converted into the coordinate system of the satellite navigation antenna;

[0026] The deviation between the converted inertial navigation heading and the heading measured by the satellite navigation antenna is calculated and denoted as Δyaw;

[0027] Obtain the heading angular velocity from the inertial navigation data, denoted as Δw;

[0028] Using the least squares method, the delay time ΔT of the satellite navigation heading information is calculated based on the heading deviation Δyaw and the inertial navigation heading angular velocity Δw. The calculation formula is as follows.

[0029]

[0030] Based on the delay time ΔT, find the inertial navigation heading information corresponding to the satellite navigation heading information at the time in the cached inertial navigation data, including:

[0031] Traverse the cached inertial navigation data array, compare the timestamp of each data point with the timestamp of the current satellite navigation heading information, and find the data point with the closest time difference ΔT as the matching object.

[0032] Constructing a state estimation model includes:

[0033] Define the system state vector X as follows:

[0034]

[0035] Where δp is the horizontal position error and δv is the horizontal velocity error. For attitude error, δb a For accelerometer zero bias, δb g For zero bias of the gyroscope, δk g This refers to the gyroscope calibration coefficient error;

[0036] The state transition equation is established as follows.

[0037] X k =ψ k，k-1 X k-1 +W k ;

[0038] Among them, X k X is the state vector at the current time k. k-1 It is the state vector of the previous time step k-1, ψk，k-1 W is the state transition matrix. k The system noise matrix;

[0039] Constructing a measurement model includes:

[0040] The measurement quantity Z is defined as follows.

[0041] Z = [δp δv δy];

[0042] Where δp is the horizontal position error, δv is the horizontal velocity error, and δy is the heading angle error;

[0043] The measurement equation is established as follows.

[0044] Z k =H k X k +V k ;

[0045] Among them, Z k It is the measurement of the current time k, X k H is the system state vector at the current time k. k For the measurement matrix, V k This is the measurement noise matrix.

[0046] By filtering and iterating the state estimate using error measurements, the optimal estimate of each error term at the current time is obtained, including:

[0047] Set an initial estimate of the state vector X, and initialize the covariance matrix of the state vector;

[0048] Define the process noise covariance matrix and the measurement noise covariance matrix;

[0049] At the beginning of each filtering cycle, the state transition equation and the state vector estimate of the previous time step are used to predict the prior estimate of the state vector at the current time step. At the same time, the prior estimate of the covariance matrix is ​​updated.

[0050] Substitute the heading error measurement value and use the measurement equation to calculate the measurement residual;

[0051] The Kalman gain is calculated based on the prior estimates of the measurement residuals and covariance matrix, as well as the measurement noise covariance matrix.

[0052] Kalman gain is used to update the posterior estimates of the state vector and the covariance matrix.

[0053] From the updated state vector, extract the estimated values ​​of horizontal position error, horizontal velocity error, and heading angle error.

[0054] The validity of the heading error measurement values ​​is determined, including:

[0055] The true baseline length L1 of the dual-antenna GNSS is measured using measuring equipment. The current baseline length L2 is calculated in real time using the algorithm inside the navigation system. The baseline length deviation is calculated as ΔL = L2 - L1.

[0056] Over a period of time, the heading data measured by dual-antenna GNSS is continuously recorded, and the standard deviation σyaw of the heading data during this period is calculated using the standard deviation formula.

[0057] Set a baseline length deviation threshold ΔL1 and a dual-antenna heading standard deviation threshold σyaw1. Before each measurement update, check whether the current baseline length deviation value ΔL is less than ΔL1 and whether the dual-antenna heading standard deviation σyaw is less than σyaw1.

[0058] If both conditions are met, the current heading error measurement is considered valid, and the subsequent Kalman filter update step can be performed.

[0059] A dual-antenna GNSS-assisted attitude measurement device includes:

[0060] An inertial navigation system (INS) includes a gyroscope, an accelerometer, a power module, an inertial navigation information (IF) module, a satellite navigation calculation module, and a navigation calculation module. The gyroscope measures the angular velocity of the ship, providing heading and angular rate information. The accelerometer measures the acceleration of the ship, assisting in navigation calculations. The power module provides a stable power supply to the INS. The IF module enables data communication within the INS. The satellite navigation calculation module receives and processes satellite navigation signals, calculating satellite navigation heading information. The navigation calculation module combines the data from the gyroscope and accelerometer with the satellite navigation information to perform navigation calculations, providing initial heading, position, and velocity information.

[0061] A dual-antenna assembly, including a main antenna and a slave antenna, is used to receive satellite navigation signals and calculate satellite navigation heading information using dual-antenna technology.

[0062] A dual-antenna GNSS-assisted attitude measurement system includes:

[0063] The projection matrix calculation module uses the height difference between the two antennas to construct the projection matrix from the ship's coordinate system to the coordinate system where the satellite navigation dual antennas are located;

[0064] The delay time calculation module is used to calculate the delay time ΔT between satellite navigation heading information and inertial navigation heading information;

[0065] The data caching module is used to cache the heading information measured by inertial navigation and the corresponding timestamps into an array;

[0066] The data synchronization module is used to find the inertial navigation heading information corresponding to the satellite navigation heading information in the cached inertial navigation data according to the delay time ΔT during the satellite navigation heading update cycle.

[0067] The coordinate transformation module is used to transform the found inertial navigation heading information from the ship coordinate system to the satellite navigation dual-antenna coordinate system through a projection matrix.

[0068] The error calculation module is used to compare the converted inertial navigation heading information with the satellite navigation heading information and calculate the difference between them, i.e., the heading error measurement value.

[0069] The validity assessment module is used to assess the validity of heading error measurements.

[0070] The Kalman filter module is used to construct a state estimation model and a measurement model when the heading error measurement value is valid. It filters and iterates the state estimation through the error measurement value to obtain the optimal estimate of each error term at the current time.

[0071] A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the above-described method steps.

[0072] Compared with the prior art, the beneficial effects of the present invention are:

[0073] 1. When inertial navigation and satellite navigation are combined, the impact of the height difference between the two antennas on navigation accuracy is taken into account. By constructing a projection matrix, the inertial navigation data is transformed into the dual-antenna coordinate system of satellite navigation, thereby compensating for the heading error caused by the height difference between the antennas and ensuring high heading accuracy.

[0074] 2. By calculating the time delay between satellite navigation heading information and inertial navigation heading information and performing time alignment processing, the impact of satellite navigation heading calculation delay on navigation accuracy is effectively reduced, and precise synchronization of the two types of navigation information in time is achieved.

[0075] 3. Kalman filters are used for heading error estimation and correction, so that the system can maintain high navigation accuracy and stability when facing complex navigation environments and noise interference.

[0076] 4. Through time alignment and real-time processing using a Kalman filter, navigation results can be corrected in real time to meet the requirements of dynamic real-time target positioning. Attached Figure Description

[0077] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0078] Figure 1 This is a flowchart of the dual-antenna GNSS-assisted attitude measurement method in the embodiments of this application;

[0079] Figure 2 This is a schematic diagram of the dual-antenna installation location in an embodiment of this application. Detailed Implementation

[0080] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0081] The following is an explanation of some of the terms used in this application. It should be noted that these explanations are for the convenience of those skilled in the art and are not intended to limit the scope of protection claimed in this application.

[0082] The sequence numbers of each step in this application specification do not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0083] In the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. It should also be understood that although the terms "first," "second," etc., are used in the text to describe various elements in some embodiments of this application, these elements should not be limited by these terms. These terms are merely used to distinguish one element from another. For example, a first table may be named a second table, and similarly, a second table may be named a first table, without departing from the scope of the various described embodiments. Both the first table and the second table are tables, but they are not the same table.

[0084] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0085] Inertial navigation has advantages such as comprehensive output information, high output frequency, and high short-term accuracy; however, positioning errors accumulate rapidly over time. Satellite navigation can provide accurate navigation and positioning information, but its navigation information output frequency is low and it contains significant noise. Combining inertial and satellite navigation can complement each other to achieve real-time, high-precision navigation and positioning.

[0086] However, when inertial navigation and satellite navigation are combined, low-cost inertial navigation systems struggle to achieve high-precision autonomous orientation due to limitations in sensor accuracy. The orientation measurement accuracy of dual-antenna satellite navigation is related to the baseline length of the dual antennas; a baseline length greater than 2m can achieve a heading accuracy better than 0.1°. However, when antenna positions are limited by altitude differences, the dual-antenna heading cannot accurately represent the actual heading of the vehicle during swaying. Furthermore, the calculation delay of the dual-antenna GNSS heading is significant, making it difficult to meet the requirements of dynamic real-time target positioning for optoelectronic systems.

[0087] Regarding the above technical issues, such as Figure 1 As shown in the figure, this application provides a dual-antenna GNSS-assisted attitude measurement method, including the following steps:

[0088] S1. Construct a projection matrix from the ship's coordinate system to the coordinate system where the satellite navigation dual antennas are located using the height difference between the two antennas;

[0089] S2. Calculate the time delay ΔT between the satellite navigation heading information and the inertial navigation heading information;

[0090] S3. Obtain the heading information measured by inertial navigation and cache this information together with the corresponding timestamps into an array;

[0091] S4. During the satellite navigation heading update cycle, based on the delay time ΔT, find the inertial navigation heading information at the time corresponding to the current satellite navigation heading information;

[0092] S5. Using the projection matrix, the found inertial navigation heading information is transformed from the ship's coordinate system to the satellite navigation dual-antenna coordinate system;

[0093] S6. Compare the converted inertial navigation heading information with the satellite navigation heading information, calculate the difference between them, and obtain the errors in position, velocity and heading as error measurement values;

[0094] S7. Determine the validity of the error measurement value. If it is valid, proceed to the next Kalman filter update step.

[0095] S8. Using a Kalman filter, construct a state estimation model and a measurement model. Filter and iterate the state estimation using error measurements to obtain the optimal estimate of each error term at the current time.

[0096] S9. Based on the output of the Kalman filter, correct the original navigation results.

[0097] The method described in this embodiment combines the advantages of inertial navigation and GNSS dual antennas to achieve real-time high-precision navigation and positioning.

[0098] Since inertial navigation data is based on the ship's coordinate system, while satellite navigation data is based on its dual-antenna coordinate system, the impact of the height difference between the two antennas on navigation accuracy is considered when combining inertial and satellite navigation. By constructing a projection matrix, the inertial navigation data is transformed into the dual-antenna coordinate system of satellite navigation, thereby compensating for the heading error caused by the antenna height difference and ensuring high heading accuracy.

[0099] Because satellite navigation heading information updates may be delayed, while inertial navigation provides navigation information in real time, this embodiment first calculates the time delay ΔT between these two types of navigation information. Time alignment is then performed using this delay time, effectively reducing the impact of satellite navigation heading calculation delays on navigation accuracy.

[0100] Using a Kalman filter for heading error estimation and correction ensures high navigation accuracy even in complex navigation environments and noise interference.

[0101] Through time alignment and real-time processing using a Kalman filter, real-time navigation result correction can be achieved, meeting the requirements for dynamic real-time target positioning.

[0102] Specifically, in step S1, the projection matrix from the ship's coordinate system to the coordinate system where the satellite navigation dual antennas are located is constructed using the height difference between the two antennas, including:

[0103] S1.1. Based on the ship structure model or actual on-site measurements, determine the vertical distance H and longitudinal distance L between the main antenna and the slave antenna in the ship coordinate system;

[0104] S1.2. Based on the vertical distance H and longitudinal distance L, calculate the pitch angle deviation Δθ between the satellite navigation dual-antenna coordinate system and the ship's coordinate system. The pitch angle deviation reflects the relative tilt of the two coordinate systems in the vertical direction. The formula is as follows:

[0105]

[0106] S1.3. Based on the calculated pitch angle deviation Δθ, construct the projection matrix from the ship's coordinate system to the satellite navigation dual-antenna coordinate system to realize the data transformation between the two coordinate systems, as follows.

[0107]

[0108] Through the above steps, the constructed projection matrix serves as a transformation tool to convert data from the ship's coordinate system to the satellite navigation dual-antenna coordinate system, and compensates for the impact of the height difference between the two antennas.

[0109] Specifically, in step S2, the delay time ΔT between the satellite navigation heading information and the inertial navigation heading information is calculated, including:

[0110] S2.1. Without using dual-antenna heading information assistance, and under dynamic conditions, simultaneously acquire satellite navigation heading information, inertial navigation heading information, and heading angular rate information;

[0111] S2.2. Convert the heading information obtained by inertial navigation to the coordinate system where the satellite navigation antenna is located;

[0112] S2.3 Calculate the deviation between the converted inertial navigation heading and the heading measured by the satellite navigation antenna, denoted as Δyaw;

[0113] S2.4 Obtain the heading angular velocity from the inertial navigation data, denoted as Δw;

[0114] S2.5. Using the least squares method, calculate the delay time ΔT of the satellite navigation heading information based on the heading deviation Δyaw and the inertial navigation heading angular velocity Δw. The calculation formula is as follows.

[0115]

[0116] Least squares is an optimization method used to find the optimal delay time ΔT, which minimizes the deviation between satellite navigation heading information and inertial navigation heading information.

[0117] By following the steps above, the delay time ΔT is calculated, which facilitates the subsequent precise synchronization of satellite navigation and inertial navigation in time.

[0118] Specifically, in step S4, based on the delay time ΔT, the inertial navigation heading information corresponding to the satellite navigation heading information at the time specified in the cached inertial navigation data is found, including:

[0119] Traverse the cached inertial navigation data array, compare the timestamp of each data point with the timestamp of the current satellite navigation heading information, and find the data point with the closest time difference ΔT as the matching object.

[0120] In practical applications, the calculation delay of dual-antenna GNSS heading is relatively large, and there is a time difference between inertial navigation and satellite navigation data. This embodiment effectively eliminates this time difference by calculating the delay time ΔT and performing time synchronization accordingly.

[0121] Specifically, in step S8, the state estimation model is constructed, including:

[0122] Define the system state vector X as follows:

[0123]

[0124] Where δp is the horizontal position error and δv is the horizontal velocity error. For attitude error, δb a For accelerometer zero bias, δb g For zero bias of the gyroscope, δk g This refers to the gyroscope calibration coefficient error;

[0125] The state transition equation is established as follows.

[0126] X k =ψ k，k-1 X k-1 +W k ;

[0127] Among them, X k X is the state vector at the current time k. k-1 It is the state vector of the previous time step k-1, ψ k，k-1 W is the state transition matrix. k This is the system noise matrix.

[0128] Specifically, in step S8, the measurement model is constructed, including:

[0129] The measurement quantity Z is defined as follows.

[0130] Z = [δp δv δy];

[0131] Where δp is the horizontal position error, δv is the horizontal velocity error, and δy is the heading angle error;

[0132] The measurement equation is established as follows.

[0133] Z k =H k X k +V k ;

[0134] Among them, Z k It is the measurement of the current time k, X k H is the system state vector at the current time k. k For the measurement matrix, V k This is the measurement noise matrix.

[0135] Specifically, in step S8, the state estimate is filtered and iterated using the error measurement values ​​to obtain the optimal estimate of each error term at the current time, including:

[0136] S8.1 Set the initial estimate of the state vector and initialize the covariance matrix of the state vector;

[0137] S8.2, Set the process noise covariance matrix and the measurement noise covariance matrix;

[0138] S8.3 At the beginning of each filtering period (consistent with the satellite navigation update period), the state transition equation and the state vector estimate of the previous time step are used to predict the prior estimate of the state vector at the current time step; at the same time, the prior estimate of the covariance matrix is ​​updated based on the state transition matrix and the posterior estimate of the covariance matrix at the previous time step.

[0139] S8.4 When new error measurements (position, velocity, and heading) are received, the measurement residuals are calculated using the measurement equations.

[0140] S8.5 Calculate the Kalman gain based on the prior estimates of the measurement residuals and covariance matrix, as well as the measurement noise covariance matrix;

[0141] S8.6 Use Kalman gain to update the posterior estimates of the state vector and the covariance matrix;

[0142] S8.7 Extract the estimated values ​​of horizontal position error, horizontal velocity error, and heading angle error from the updated state vector.

[0143] In step S9, the original navigation results are corrected based on the output of the Kalman filter.

[0144] For example, the estimated values ​​of horizontal position error and velocity error are added to the original position and velocity data respectively to obtain more accurate position and velocity information; the estimated value of heading angle error is added to the original heading angle to obtain more accurate heading information.

[0145] Specifically, in step S7, the validity of the heading error measurement value is determined, including:

[0146] S7.1. Use measuring equipment to measure the true baseline length L1 of the dual-antenna GNSS, and calculate the current baseline length L2 in real time through the algorithm inside the navigation system. The baseline length deviation value is calculated as ΔL=L2-L1.

[0147] S7.2. Record the heading data measured by dual-antenna GNSS continuously over a period of time (a few minutes or tens of seconds), and calculate the standard deviation σyaw of the heading data during this period using the standard deviation formula.

[0148] S7.3 Set the baseline length deviation threshold ΔL1 and the dual-antenna heading standard deviation threshold σyaw1. Before each measurement update, check whether the current baseline length deviation value ΔL is less than ΔL1 and whether the dual-antenna heading standard deviation σyaw is less than σyaw1.

[0149] S7.4 If both conditions are met, the current heading error measurement is considered valid, and the subsequent Kalman filter update step can be performed.

[0150] S7.5 If any condition is not met, the current heading error measurement is considered invalid, and the use of this measurement for Kalman filter updates should be suspended.

[0151] like Figure 2 As shown, a second aspect of this application provides a dual-antenna GNSS-assisted attitude measurement device, comprising:

[0152] An inertial navigation system (INS) includes a gyroscope, an accelerometer, a power module, an inertial navigation information (IF) module, a satellite navigation calculation module, and a navigation calculation module. The gyroscope measures the angular velocity of the ship, providing heading and angular rate information. The accelerometer measures the acceleration of the ship, assisting in navigation calculations. The power module provides a stable power supply to the INS. The IF module enables data communication within the INS. The satellite navigation calculation module receives and processes satellite navigation signals, calculating satellite navigation heading information. The navigation calculation module combines data from the INS and the satellite navigation calculation module to perform fusion calculations, providing initial heading, position, and velocity information.

[0153] A dual-antenna assembly, including a main antenna and a slave antenna, is used to receive satellite navigation signals and calculate satellite navigation heading information using dual-antenna technology.

[0154] A third aspect of this application provides a dual-antenna GNSS-assisted attitude measurement system, comprising:

[0155] The projection matrix calculation module is used to construct the projection matrix from the ship's coordinate system to the satellite navigation dual-antenna coordinate system;

[0156] The delay time calculation module is used to calculate the delay time ΔT between satellite navigation heading information and inertial navigation heading information;

[0157] The data caching module is used to cache the heading information measured by inertial navigation and the corresponding timestamps into an array;

[0158] The data synchronization module is used to find the inertial navigation heading information corresponding to the satellite navigation heading information in the cached inertial navigation data according to the delay time ΔT during the satellite navigation heading update cycle.

[0159] The coordinate transformation module is used to transform the found inertial navigation heading information from the ship coordinate system to the satellite navigation dual-antenna coordinate system through a projection matrix.

[0160] The error calculation module is used to compare the converted inertial navigation heading information with the satellite navigation heading information and calculate the difference between them, i.e., the heading error measurement value.

[0161] The validity assessment module is used to assess the validity of heading error measurements.

[0162] The Kalman filter module is used to construct a state estimation model and a measurement model when the heading error measurement value is valid. By inputting the heading error measurement value and filtering iteratively, the optimal estimate of the heading attitude at the current moment is obtained, and the corrected navigation result is output.

[0163] A fourth aspect of this application provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the above-described method steps.

[0164] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A dual-antenna GNSS-assisted attitude measurement method, characterized in that, include: The projection matrix from the ship's coordinate system to the satellite navigation dual-antenna coordinate system is constructed using the height difference between the two antennas; Calculate the time delay ΔT between satellite navigation heading information and inertial navigation heading information; The heading information is obtained using inertial navigation, and the heading information and the corresponding timestamp are cached in an array; During the satellite navigation heading update cycle, based on the delay time ΔT, the inertial navigation heading information corresponding to the satellite navigation heading information at the time is found in the cached inertial navigation data. The inertial navigation heading information found is transformed from the ship's coordinate system to the satellite navigation dual-antenna coordinate system using the projection matrix. The converted inertial navigation heading information is compared with the satellite navigation heading information, and the difference between them is calculated to obtain the errors in position, velocity and heading, which are used as error measurements. The validity of the heading error measurement is determined. If it is valid, the next step of Kalman filtering update is performed. A Kalman filter is used to construct a state estimation model and a measurement model. The state estimation is filtered and iterated using error measurements to obtain the optimal estimate of each error term at the current time. The original navigation results are corrected based on the output of the Kalman filter.

2. The dual-antenna GNSS-assisted attitude measurement method according to claim 1, characterized in that, Construct the projection matrix from the ship's coordinate system to the coordinate system where the satellite navigation dual antennas are located, including: Based on the ship's structural model or actual on-site measurements, determine the vertical distance H and longitudinal distance L between the main antenna and the slave antenna in the ship's coordinate system; Based on the vertical distance H and the longitudinal distance L, the pitch angle deviation Δθ between the satellite navigation dual-antenna coordinate system and the ship's coordinate system is calculated using the following formula. ； Based on the calculated pitch angle deviation Δθ, the projection matrix from the ship's coordinate system to the satellite navigation dual-antenna coordinate system is constructed as follows. 。 3. The dual-antenna GNSS-assisted attitude measurement method according to claim 1, characterized in that, Calculate the time delay ΔT between satellite navigation heading information and inertial navigation heading information, including: Without using dual-antenna heading information assistance, and under dynamic conditions, simultaneously acquire satellite navigation heading information, inertial navigation heading information, and heading angular rate information; The heading information obtained from inertial navigation is converted into the coordinate system of the satellite navigation antenna; The deviation between the converted inertial navigation heading and the heading measured by the satellite navigation antenna is calculated and denoted as Δyaw; Obtain the heading angular velocity from the inertial navigation data, denoted as Δw; Using the least squares method, the delay time ∆T of the satellite navigation heading information is calculated based on the heading deviation Δyaw and the inertial navigation heading angular velocity Δw. The calculation formula is as follows. 。 4. The dual-antenna GNSS-assisted attitude measurement method according to claim 1, characterized in that, Based on the delay time ΔT, find the inertial navigation heading information corresponding to the satellite navigation heading information at the time in the cached inertial navigation data, including: Traverse the cached inertial navigation data array, compare the timestamp of each data point with the timestamp of the current satellite navigation heading information, and find the data point with the closest time difference ΔT as the matching object.

5. The dual-antenna GNSS-assisted attitude measurement method according to claim 1, characterized in that, Constructing a state estimation model includes: Define the system state vector X as follows: ； in, For horizontal position error, For horizontal velocity error, For attitude error, To achieve zero bias in the accelerometer, To achieve zero bias in the gyroscope, This refers to the gyroscope calibration coefficient error; The state transition equation is established as follows. ； Among them, X k X is the state vector at the current time k. k-1 It is the state vector of the previous time step k-1. Let W be the state transition matrix. k The system noise matrix; Constructing a measurement model includes: The measurement quantity Z is defined as follows. ； in, For horizontal position error, For horizontal velocity error, Heading angle error; The measurement equation is established as follows. ； Among them, Z k It is the measurement of the current time k, X k H is the system state vector at the current time k. k For the measurement matrix, V k This is the measurement noise matrix.

6. The dual-antenna GNSS-assisted attitude measurement method according to claim 5, characterized in that, By filtering and iterating the state estimate using error measurements, the optimal estimate of each error term at the current time is obtained, including: Set an initial estimate of the state vector X, and initialize the covariance matrix of the state vector; Define the process noise covariance matrix and the measurement noise covariance matrix; At the beginning of each filtering cycle, the state transition equation and the state vector estimate of the previous time step are used to predict the prior estimate of the state vector at the current time step. At the same time, the prior estimate of the covariance matrix is ​​updated. Substitute the heading error measurement value and use the measurement equation to calculate the measurement residual; The Kalman gain is calculated based on the prior estimates of the measurement residuals and covariance matrix, as well as the measurement noise covariance matrix. Kalman gain is used to update the posterior estimates of the state vector and the covariance matrix. From the updated state vector, extract the estimated values ​​of horizontal position error, horizontal velocity error, and heading angle error.

7. The dual-antenna GNSS-assisted attitude measurement method according to claim 1, characterized in that, The validity of the heading error measurement values ​​is determined, including: The true baseline length L1 of the dual-antenna GNSS is measured using measuring equipment. The current baseline length L2 is calculated in real time using the algorithm inside the navigation system. The baseline length deviation is calculated as ΔL = L2 - L1. Over a period of time, the heading data measured by dual-antenna GNSS is continuously recorded, and the standard deviation σyaw of the heading data during this period is calculated using the standard deviation formula. Set a baseline length deviation threshold ΔL1 and a dual-antenna heading standard deviation threshold σyaw1. Before each measurement update, check whether the current baseline length deviation value ΔL is less than ΔL1 and whether the dual-antenna heading standard deviation σyaw is less than σyaw1. If both conditions are met, the current heading error measurement is considered valid, and the subsequent Kalman filter update step can be performed.

8. A measuring device for the dual-antenna GNSS-assisted attitude measurement method as described in any one of claims 1-7, characterized in that, include: An inertial navigation component, comprising a gyroscope, an accelerometer, a power module, an IF module, a satellite navigation calculation module, and a navigation calculation module; The gyroscope measures the angular velocity of the hull, providing heading and angular rate information; the accelerometer measures the acceleration of the hull, assisting in navigation calculations; the power module provides a stable power supply to the inertial components; the IF module enables data communication for the inertial components; the satellite navigation calculation module receives and processes satellite navigation signals, calculating satellite navigation heading information; the navigation calculation module combines the data from the gyroscope and accelerometer with the satellite navigation information to perform navigation calculations, providing initial heading, position, and velocity information. A dual-antenna assembly, including a main antenna and a slave antenna, is used to receive satellite navigation signals and calculate satellite navigation heading information using dual-antenna technology.

9. A measurement system for the dual-antenna GNSS-assisted attitude measurement method as described in any one of claims 1-7, characterized in that, include: The projection matrix calculation module is used to construct the projection matrix from the ship's coordinate system to the satellite navigation dual-antenna coordinate system; The delay time calculation module is used to calculate the delay time ΔT between satellite navigation heading information and inertial navigation heading information; The data caching module is used to cache the heading information measured by inertial navigation and the corresponding timestamps into an array; The data synchronization module is used to find the inertial navigation heading information corresponding to the satellite navigation heading information in the cached inertial navigation data according to the delay time ΔT during the satellite navigation heading update cycle. The coordinate transformation module is used to transform the found inertial navigation heading information from the ship coordinate system to the satellite navigation dual-antenna coordinate system through a projection matrix. The error calculation module is used to compare the converted inertial navigation heading information with the satellite navigation heading information and calculate the difference between them, i.e., the heading error measurement value. The validity assessment module is used to assess the validity of heading error measurements. The Kalman filter module is used to construct a state estimation model and a measurement model when the heading error measurement value is valid. By inputting the heading error measurement value and filtering iteratively, the optimal estimate of the heading attitude at the current moment is obtained, and the corrected navigation result is output.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the method steps of any one of claims 1 to 7.

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