Combined navigation method and device fused with double-antenna GNSS (Global Navigation Satellite System)

Abstract

The embodiment of the invention provides a combined navigation method and device fused with a double-antenna GNSS, and the method comprises the steps: obtaining various error propagation matrixes related to attitude angles, which are obtained through the collection of a carrier by the double-antenna GNSS, and the carrier comprises a vehicle; calculating and determining a state transition matrix at the current moment at least based on various error propagation matrixes of the attitude angle; performing Kalman filtering time updating on the basis of the state transition matrix and Kalman filtering state data of the carrier at the previous moment to obtain an updating result; determining a pitch angle and a course angle, which are output by the double-antenna GNSS, of a carrier relative to the ground; calculating and determining a Kalman filtering gain based on the updating result, the pitch angle and the course angle; and performing navigation optimal estimation of the attitude angle, the speed and the position based on the Kalman filtering gain.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of antennas, and particularly to a combined navigation method and device integrating dual-antenna GNSS. Background Art

[0002] Combined navigation technology can overcome the limitations of a single navigation system and provide navigation results with higher accuracy and stability. It is widely applied in many fields such as military and civilian. GNSS / INS is a common combination method that circumvents the disadvantages of large cumulative errors of INS and large positioning noise of GNSS (Global Navigation Satellite System), and can provide attitude, velocity, and position information with high accuracy over a long time. When the carrier moves at a low speed, the traditional single-antenna GNSS / INS combined navigation method will exhibit the phenomenon of heading angle drift. To solve the problem of heading angle drift in low-speed scenarios, the method of integrating dual-antenna GNSS navigation has been widely applied in practice.

[0003] Conventional methods for integrating dual-antenna GNSS only fuse the heading angle information provided by dual-antenna GNSS. When the dual-antenna baseline is not coaxial with the carrier, due to the existence of pitch angle errors, the accuracy of the navigation result is reduced; at the same time, the time synchronization error between dual-antenna GNSS and INS is not considered, and the real-time performance deteriorates during rapid course changes. Summary of the Invention

[0004] To solve the above technical problems, the embodiments of the present invention provide a combined navigation method integrating dual-antenna GNSS, including:

[0005] Obtaining various error propagation matrices of the attitude angle collected by dual-antenna GNSS for the carrier, where the carrier includes a vehicle;

[0006] Calculating and determining the state transition matrix at the current moment based on at least the various error propagation matrices of the attitude angle;

[0007] Performing Kalman filter time update based on the state transition matrix and the Kalman filter state data of the carrier at the previous moment to obtain an update result;

[0008] Determining the pitch angle and heading angle of the carrier relative to the ground output by the dual-antenna GNSS;

[0009] Calculating and determining the Kalman filter gain based on the update result and the pitch angle and heading angle;

[0010] Performing optimal estimation of the attitude angle, velocity, and position based on the Kalman filter gain.

[0011] In one embodiment, the obtaining of various error matrices of the attitude angle in dual-antenna GNSS includes:

[0012] Obtain the error propagation matrix of the attitude angle with respect to the attitude angle at the same moment;

[0013] Obtain the error propagation matrix of the attitude angle with respect to the velocity at the same moment;

[0014] Obtain the error propagation matrix of the attitude angle with respect to the position at the same moment;

[0015] Obtain the error propagation matrix of the velocity with respect to the attitude angle at the same moment;

[0016] Obtain the error propagation matrix of the velocity with respect to the velocity at the same moment;

[0017] Obtain the error propagation matrix of the velocity with respect to the position at the same moment;

[0018] Obtain the error propagation matrix of the position with respect to the attitude angle at the same moment;

[0019] Obtain the error propagation matrix of the position with respect to the velocity at the same moment;

[0020] Obtain the error propagation matrix of the position with respect to the position at the same moment.

[0021] In one embodiment, the method further includes:

[0022] Obtain the direction cosine matrix of the carrier for installing the dual-antenna GNSS, which is used to participate in the calculation of the state transition matrix.

[0023] In one embodiment, the calculating and determining the state transition matrix at the current moment based on various error propagation matrices of at least the attitude angle includes:

[0024] Calculate and determine the state transition matrix at the current moment based on each of the error propagation matrices, the carrier direction cosine matrix, the 18th-order identity matrix, and the 3rd-order zero matrix.

[0025] In one embodiment, the performing Kalman filter time update based on the state transition matrix and the Kalman filter state data of the carrier at the previous moment to obtain an update result includes:

[0026] Perform a prior estimation of the Kalman filter state vector based on the state transition matrix and the Kalman filter state vector of the carrier at the previous moment to obtain a first estimation result.

[0027] In one embodiment, the performing Kalman filter time update based on the state transition matrix and the Kalman filter state data of the carrier at the previous moment to obtain an update result further includes:

[0028] Perform a prior estimation of the Kalman filter covariance matrix based on the state transition matrix, the Kalman filter covariance matrix of the vehicle at the previous moment, and the Kalman filter state noise matrix to obtain a second estimation result.

[0029] In one embodiment, the method further includes:

[0030] Calculate the Kalman filter measurement Z based on the pitch angle α and heading angle β of the vehicle relative to the ground output by the dual-antenna GNSS k , and calculate the Kalman filter measurement matrix H based on the Kalman filter measurement k :

[0031]

[0032] where the is the direction cosine matrix between the dual-antenna coordinate system and the navigation coordinate system, the direction cosine matrix between the dual-antenna coordinate system and the vehicle coordinate system, and 03 is a third-order zero matrix.

[0033] In one embodiment, the calculating the Kalman filter measurement Z based on the pitch angle α and heading angle β of the vehicle relative to the ground output by the dual-antenna GNSS k , includes:

[0034] Determine the installation error, vehicle attitude error, and time asynchronization error between the dual-antenna and the vehicle;

[0035] Calculate the Kalman filter measurement Z based on the pitch angle α and heading angle β of the vehicle relative to the ground output by the dual-antenna GNSS, and the installation error, vehicle attitude error, and time asynchronization error between the dual-antenna and the vehicle k .

[0036] In one embodiment, the update result includes a prior estimation of the Kalman filter state vector and a prior estimation of the Kalman filter covariance matrix;

[0037] The calculating and determining the Kalman filter gain based on the update result, the pitch angle, and the heading angle includes:

[0038] Calculate and determine the Kalman filter gain based on the Kalman filter measurement matrix, the prior estimation of the Kalman filter state vector, the prior estimation of the Kalman filter covariance matrix, and the Kalman filter measurement noise matrix.

[0039] Another embodiment of the present invention simultaneously provides a combined navigation device integrating a dual-antenna GNSS, including:

[0040] A first acquisition module, configured to acquire various error propagation matrices of attitude angles obtained by collecting a carrier by a dual-antenna GNSS, where the carrier includes a vehicle;

[0041] A first calculation module, configured to calculate and determine a state transition matrix at the current moment at least according to the various error propagation matrices of the attitude angles;

[0042] An update module, configured to perform Kalman filter time update according to the state transition matrix and the Kalman filter state data of the carrier at the previous moment to obtain an update result;

[0043] A determination module, configured to determine a pitch angle and a heading angle of the carrier relative to the ground output by the dual-antenna GNSS;

[0044] A second calculation module, configured to calculate and determine a Kalman filter gain according to the update result, the pitch angle, and the heading angle;

[0045] An optimal estimation module, configured to perform optimal navigation estimation of attitude angles, speeds, and positions according to the Kalman filter gain.

[0046] Based on the disclosure of the above embodiments, it can be known that the beneficial effects of the embodiments of the present invention include that by using a dual-antenna GNSS, the problem of heading angle divergence of a conventional GNSS / INS integrated navigation in a low-speed scenario is solved. Moreover, by fusing the pitch angle and the heading angle provided by the dual-antenna GNSS, compared with the conventional dual-antenna algorithm that only fuses the heading angle, the solution in this embodiment can also provide a high-precision heading angle in a complex scenario; in addition, this application simultaneously fuses the pitch angle and the heading angle collected by the dual-antenna GNSS, as well as the installation error between the dual-antenna and the carrier, the time asynchronization error between the dual-antenna and the carrier, and the attitude angle of the carrier, so that the accuracy is higher when calculating the Kalman filter observation matrix and the Kalman gain, providing a high-value reference data as a basis for subsequent navigation estimation, and effectively solving the problem of reduced heading angle accuracy of the conventional algorithm during rapid turning.

[0047] Other features and advantages of this application will be described in the subsequent description, and part of them will become obvious from the description, or will be understood by implementing this application. The purpose and other advantages of this application can be realized and obtained through the structures specifically pointed out in the written description, claims, and drawings.

[0048] Next, through the drawings and embodiments, the technical solutions of this application will be further described in detail. Description of the Drawings

[0049] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative work, other drawings can also be obtained based on these drawings.

[0050] Figure 1 It is a schematic flowchart of the integrated dual-antenna GNSS integrated navigation method in an embodiment of the present invention.

[0051] Figure 2 It is a schematic application flowchart of the integrated dual-antenna GNSS integrated navigation method in an embodiment of the present invention.

[0052] Figure 3 It is a schematic flowchart of the integrated dual-antenna GNSS integrated navigation method in another embodiment of the present invention.

[0053] Figure 4 It is a structural block diagram of the integrated dual-antenna GNSS integrated navigation device in an embodiment of the present invention. Specific Embodiments

[0054] Next, specific embodiments of the present invention will be described in detail with reference to the drawings, but it is not a limitation of the present invention.

[0055] It should be understood that various modifications can be made to the embodiments disclosed herein. Therefore, the following description should not be regarded as restrictive, but only as an example of the embodiments. Those skilled in the art will think of other modifications within the scope of the present disclosure.

[0056] The drawings included in the specification and forming a part of the specification illustrate the embodiments of the present disclosure, and together with the general description of the present disclosure given above and the detailed description of the embodiments given below, are used to explain the principles of the present disclosure.

[0057] These and other features of the present invention will become apparent from the following description of the preferred forms of the embodiments given by way of non-limiting examples with reference to the drawings.

[0058] It should also be understood that although the present invention has been described with reference to some specific examples, those skilled in the art can surely implement many other equivalent forms of the present invention, which have the features as described in the claims and thus are all within the scope of protection defined thereby.

[0059] When combined with the drawings, the above and other aspects, features, and advantages of the present disclosure will become more apparent in view of the following detailed description.

[0060] Specific embodiments of the present disclosure will be described hereinafter with reference to the accompanying drawings; however, it should be understood that the disclosed embodiments are merely examples of the present disclosure, which can be implemented in various ways. Well-known and / or repetitive functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details disclosed herein are not intended to be limiting, but merely serve as a basis for claims and a representative basis for teaching those skilled in the art to use the present disclosure in substantially any suitable detailed structure in a variety of ways.

[0061] This specification may use the phrase "in one embodiment", "in another embodiment", "in yet another embodiment", or "in other embodiments", which may each refer to one or more of the same or different embodiments according to the present disclosure.

[0062] Next, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0063] An embodiment of the present invention provides a combined navigation method integrating dual-antenna GNSS, including:

[0064] S1: Obtain various error propagation matrices of attitude angles collected by the dual-antenna GNSS for a carrier, where the carrier includes a vehicle;

[0065] S2: Calculate and determine a state transition matrix at the current moment based on at least the various error propagation matrices of the attitude angles;

[0066] S3: Perform Kalman filter time update based on the state transition matrix and the Kalman filter state data of the carrier at the previous moment to obtain an update result;

[0067] S4: Determine the pitch angle and heading angle of the carrier relative to the ground output by the dual-antenna GNSS;

[0068] S5: Calculate and determine a Kalman filter gain based on the update result and the pitch angle and heading angle;

[0069] S6: Perform optimal estimation of attitude angle, speed, and position based on the Kalman filter gain.

[0070] The method in this embodiment can be applied to the navigation scenario during the automatic driving process of a vehicle. The specific type of the vehicle is not fixed and can be any electric vehicle, fuel vehicle, solar vehicle, etc., or it can also be other carriers similar to a vehicle and traveling on the ground. In this embodiment, the carrier is taken as an example of a vehicle for illustration, but it is not limited to a vehicle.

[0071] The method described in this embodiment is used to achieve accurate and reliable output of navigation information (attitude angle, speed, and position) by fusing the pitch angle and heading angle information provided by a dual-antenna GNSS under low-speed conditions of the carrier. That is, by using a dual-antenna GNSS, the problem of heading angle divergence in a conventional GNSS / INS integrated navigation in a low-speed scenario is solved. Moreover, by fusing the pitch angle and heading angle provided by the dual-antenna GNSS, the solution in this embodiment can also provide a high-precision heading angle in a complex scenario compared with a conventional dual-antenna algorithm that only fuses the heading angle. In addition, the solution proposed in this embodiment simultaneously fuses the pitch angle and heading angle collected by the dual-antenna GNSS, as well as the installation error between the dual-antenna and the carrier, the time asynchronization error between the dual-antenna and the carrier, and the carrier attitude angle, making the accuracy higher when calculating the Kalman filter observation matrix and Kalman gain, providing high-value reference data as a basis for subsequent navigation estimation, and effectively solving the problem of reduced heading angle accuracy in a conventional algorithm during a rapid turn.

[0072] Specifically, when the vehicle starts and runs, first initialize the integrated navigation parameters of the dual-antenna GNSS, including the Kalman filter state vector X0, the Kalman filter covariance matrix P0, the Kalman filter state noise matrix Q, and the Kalman filter observation noise matrix R.

[0073]

[0074] P0 = I 18

[0075] Q = 0.1 * I 18

[0076] R = 0.01 * I3;

[0077] Where, T represents the transpose matrix, and I 18 represents an 18th-order identity matrix, and I3 represents a 3rd-order identity matrix.

[0078] The Kalman filter state vector, abbreviated as the state quantity, includes at least one or more of the following elements:

[0079] The attitude angle error, speed error, position error, accelerometer zero bias error, gyroscope angular velocity error, installation error between the dual-antenna GNSS and the carrier, and time asynchronization error between the dual-antenna GNSS and the carrier of the carrier.

[0080] Furthermore, obtaining various error matrices regarding the attitude angle in the dual-antenna GNSS includes:

[0081] Obtaining the error propagation matrix Maa of the attitude angle with respect to the attitude angle at the same moment k ;

[0082] Obtain the error propagation matrix Mav of the attitude angle with respect to the velocity at the same moment k ;

[0083] Obtain the error propagation matrix Map of the attitude angle with respect to the position at the same moment k ;

[0084] Obtain the error propagation matrix Mva of the velocity with respect to the attitude angle at the same moment k ;

[0085] Obtain the error propagation matrix Mvv of the velocity with respect to the velocity at the same moment k ;

[0086] Obtain the error propagation matrix Mvp of the velocity with respect to the position at the same moment k ;

[0087] Obtain the error propagation matrix Mpa of the position with respect to the attitude angle at the same moment k ;

[0088] Obtain the error propagation matrix Mpv of the position with respect to the velocity at the same moment k ;

[0089] Obtain the error propagation matrix Mpp of the position with respect to the position at the same moment k .

[0090] The method further includes:

[0091] S9: Obtain the direction cosine matrix of the carrier for installing the dual-antenna GNSS for participating in the calculation of the state transition matrix.

[0092] The above-mentioned k represents the kth moment.

[0093] Calculating and determining the state transition matrix at the current moment based on at least the various error propagation matrices of the attitude angle includes:

[0094] S10: Calculate and determine the state transition matrix at the current moment based on each of the error propagation matrices, the carrier direction cosine matrix, the 18th-order identity matrix, and the 3rd-order zero matrix (O3).

[0095] The Kalman state transition matrix F constructed at the kth moment k is:

[0096]

[0097] In one embodiment, as Figure 2 shown, performing Kalman filter time update based on the state transition matrix and the Kalman filter state data of the carrier at the previous moment to obtain an update result, including:

[0098] S11: Based on the state transition matrix and the Kalman filter state vector of the carrier at the previous moment, perform a prior estimation of the Kalman filter state vector to obtain a first estimation result.

[0099] The performing of the Kalman filter time update based on the state transition matrix and the Kalman filter state data of the carrier at the previous moment to obtain an update result further includes:

[0100] S12: Based on the state transition matrix, the Kalman filter covariance matrix of the carrier at the previous moment, and the Kalman filter state noise matrix, perform a prior estimation of the Kalman filter covariance matrix to obtain a second estimation result.

[0101] For example, first determine the Kalman filter state data X of the carrier at the previous moment k-1 and the Kalman filter covariance matrix P of the carrier at the previous moment k-1 , and at the same time calculate and determine the transpose matrix of the Kalman state transition matrix After that, the first estimation result can be calculated by combining the following formula and the second estimation

[0102]

[0103] result

[0104] In another embodiment, the method further includes:

[0105] S13: Calculate the Kalman filter observation quantity Z based on the pitch angle α and the heading angle β of the carrier relative to the ground output by the dual-antenna GNSS k , and calculate the Kalman filter observation matrix H based on the Kalman filter observation quantity k :

[0106]

[0107] Among them, the is the direction cosine matrix of the dual-antenna coordinate system and the navigation coordinate system, and the is the direction cosine matrix of the dual-antenna coordinate system and the carrier coordinate system, and 03 is a third-order zero matrix.

[0108] Among them, as Figure 3 shown, the calculating of the Kalman filter observation quantity Z based on the pitch angle α and the heading angle β of the carrier relative to the ground output by the dual-antenna GNSS k includes:

[0109] S14: Determine the installation error between the dual antennas and the carrier, the carrier attitude error, and the time asynchronization error between the dual antennas and the carrier.

[0110] S15: Based on the pitch angle α and heading angle β of the carrier relative to the ground output by the dual-antenna GNSS, as well as the installation error, the carrier attitude error, and the time asynchronization error between the dual antennas and the carrier, calculate the Kalman filter observation quantity Z. k 。

[0111] In this embodiment, actually, through the Kalman filter observation matrix H k correlate the Kalman filter observation quantity with the attitude error in the Kalman state quantity, the installation error between the dual-antenna GNSS and the carrier, and the time asynchronization error between the dual-antenna GNSS and the carrier, and then participate in the optimal estimation of the subsequent navigation information, so as to improve the estimation accuracy, that is, improve the navigation accuracy in any speed scenario, especially in the low-speed scenario.

[0112] As described above, the update result in this embodiment includes the prior estimate of the Kalman filter state vector and the prior estimate of the Kalman filter covariance matrix. Based on this, the calculation of the Kalman filter gain based on the update result and the pitch angle and heading angle includes:

[0113] S16: Based on the Kalman filter observation matrix, the prior estimate of the Kalman filter state vector, the prior estimate of the Kalman filter covariance matrix, and the Kalman filter observation noise matrix, calculate and determine the Kalman filter gain.

[0114] In this embodiment, the Kalman gain K is specifically calculated in combination with the following formula k :

[0115]

[0116] The is the transpose matrix of H k .

[0117] When all the parameter information for estimating and calculating the navigation information is obtained, the system then performs estimation and calculation based on the obtained parameter information to determine the optimal estimation parameters of the navigation information at time k, including the optimal estimation of the attitude angle, speed, and position of the carrier. Then repeat the above process to implement the iterative calculation of the Kalman filter state, and perform estimation and calculation based on the results of the iterative calculation to obtain the optimal estimation of the state quantity at time k + 1, that is, based on the information that has been calculated, the optimal estimation of the navigation information at time k + 1 can be performed. That is, it is necessary to combine the optimal estimation value of the previous moment and the Kalman filter state vector to perform the optimal estimation of the navigation information at the next moment. The optimal estimation also includes the optimal estimation of the attitude angle, speed, and position of the carrier. For example:

[0118] Att k+1 = Att k + X k (1:3).

[0119] The said X k (1:3) represents the first to third columns of the X k matrix.

[0120] As Figure 4 shown, another embodiment of the present invention also provides a combined navigation device 100 integrating dual-antenna GNSS, including:

[0121] A first acquisition module, configured to acquire various error propagation matrices of the attitude angle collected by the dual-antenna GNSS for the carrier, where the carrier includes a vehicle;

[0122] A first calculation module, configured to calculate and determine a state transition matrix at the current moment at least according to the various error propagation matrices of the attitude angle;

[0123] An update module, configured to perform Kalman filter time update according to the state transition matrix and the Kalman filter state data of the carrier at the previous moment to obtain an update result;

[0124] A determination module, configured to determine the pitch angle and heading angle of the carrier relative to the ground output by the dual-antenna GNSS;

[0125] A second calculation module, configured to calculate and determine a Kalman filter gain according to the update result and the pitch angle and heading angle;

[0126] An optimal estimation module, configured to perform optimal navigation estimation of the attitude angle, speed, and position according to the Kalman filter gain.

[0127] In one embodiment, the acquisition of various error matrices of the attitude angle in the dual-antenna GNSS includes:

[0128] Acquiring an error propagation matrix of the attitude angle with respect to the attitude angle at the same moment;

[0129] Acquiring an error propagation matrix of the attitude angle with respect to the speed at the same moment;

[0130] Acquiring an error propagation matrix of the attitude angle with respect to the position at the same moment;

[0131] Acquiring an error propagation matrix of the speed with respect to the attitude angle at the same moment;

[0132] Acquiring an error propagation matrix of the speed with respect to the speed at the same moment;

[0133] Acquiring an error propagation matrix of the speed with respect to the position at the same moment;

[0134] Obtain the error propagation matrix of the attitude angle with respect to the position at the same moment;

[0135] Obtain the error propagation matrix of the velocity with respect to the position at the same moment;

[0136] Obtain the error propagation matrix of the position with respect to the position at the same moment.

[0137] In one embodiment, the device further includes:

[0138] A second obtaining module, configured to obtain the direction cosine matrix of the carrier for installing the dual-antenna GNSS, which is used to participate in the calculation of the state transition matrix.

[0139] In one embodiment, calculating and determining the state transition matrix at the current moment based on at least the various error propagation matrices of the attitude angle includes:

[0140] Calculate and determine the state transition matrix at the current moment based on each of the error propagation matrices, the carrier direction cosine matrix, the 18th-order identity matrix, and the 3rd-order zero matrix.

[0141] In one embodiment, performing Kalman filter time update based on the state transition matrix and the Kalman filter state data of the carrier at the previous moment to obtain an update result, including:

[0142] Perform a prior estimation of the Kalman filter state vector based on the state transition matrix and the Kalman filter state vector of the carrier at the previous moment to obtain a first estimation result.

[0143] In one embodiment, performing Kalman filter time update based on the state transition matrix and the Kalman filter state data of the carrier at the previous moment to obtain an update result further includes:

[0144] Perform a prior estimation of the Kalman filter covariance matrix based on the state transition matrix, the Kalman filter covariance matrix of the carrier at the previous moment, and the Kalman filter state noise matrix to obtain a second estimation result.

[0145] In one embodiment, the device further includes:

[0146] A third calculation module, configured to calculate the Kalman filter observation quantity Z based on the pitch angle α and the heading angle β of the carrier relative to the ground output by the dual-antenna GNSS k , and calculate the Kalman filter observation matrix H based on the Kalman filter observation quantity k :

[0147]

[0148] Wherein, The is the direction cosine matrix between the dual-antenna coordinate system and the navigation coordinate system, and the direction cosine matrix between the dual-antenna coordinate system and the carrier coordinate system, and 03 is a third-order zero matrix.

[0149] In one embodiment, the Kalman filter observation quantity Z is calculated based on the pitch angle α and the heading angle β of the carrier relative to the ground output by the dual-antenna GNSS k , including:

[0150] Determine the installation error between the dual-antenna and the carrier, the carrier attitude error, and the time asynchronization error between the dual-antenna and the carrier;

[0151] Based on the pitch angle α and the heading angle β of the carrier relative to the ground output by the dual-antenna GNSS, and the installation error, the carrier attitude error, and the time asynchronization error between the dual-antenna and the carrier, the Kalman filter observation quantity Z is calculated k .

[0152] In one embodiment, the update result includes the prior estimate of the Kalman filter state vector and the prior estimate of the Kalman filter covariance matrix;

[0153] The determination of the Kalman filter gain based on the update result and the pitch angle and the heading angle includes:

[0154] Based on the Kalman filter observation matrix, the prior estimate of the Kalman filter state vector, the prior estimate of the Kalman filter covariance matrix, and the Kalman filter observation noise matrix, the Kalman filter gain is calculated and determined.

[0155] Furthermore, another embodiment of the present invention also provides an electronic device, including: one or more processors;

[0156] A memory configured to store one or more programs;

[0157] When the one or more programs are executed by the one or more processors, the one or more processors implement the integrated navigation method of the dual-antenna GNSS as described in any one of the above embodiments.

[0158] Furthermore, an embodiment of the present invention also provides a storage medium, on which a computer program is stored, and when the program is executed by a controller, the integrated navigation method of the dual-antenna GNSS as described above is implemented. It should be understood that each of the solutions in this embodiment has the corresponding technical effects in the above method embodiment, and will not be elaborated here.

[0159] Furthermore, an embodiment of the present invention also provides a computer program product, which is tangibly stored on a computer-readable medium and includes computer-readable instructions. When the computer-executable instructions are executed, at least one controller is caused to execute a combined navigation method of fusing dual-antenna GNSS as in the embodiments described above.

[0160] It should be noted that the computer storage medium of the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable medium can, for example but is not limited to, be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access storage medium (RAM), a read-only storage medium (ROM), an erasable programmable read-only storage medium (EPROM or flash memory), an optical fiber, a portable compact disk read-only storage medium (CD-ROM), an optical storage medium, a magnetic storage medium, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program configured to be used by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, antenna, optical cable, RF, etc., or any suitable combination of the above.

[0161] In addition, those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0162] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a system for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or one or more of the blocks.

[0163] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufacture including an instruction system that implements the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or one or more of the blocks.

[0164] Those of ordinary skill in the art should understand that the discussion of any of the above embodiments is exemplary only and is not intended to imply that the scope of the present application is limited to these examples; under the concept of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of one or more of the embodiments in the present application as described above, and they are not provided in detail for the sake of brevity.

Claims

1. A combined navigation method integrating dual-antenna GNSS, characterized in that, Including: Obtaining various error propagation matrices of attitude angles collected by a dual-antenna GNSS for a carrier, where the carrier includes a vehicle; Calculating and determining a state transition matrix at the current moment based at least on the various error propagation matrices of the attitude angles; Performing Kalman filter time update based on the state transition matrix and the Kalman filter state data of the carrier at the previous moment to obtain an update result; Determining the pitch angle and heading angle of the carrier relative to the ground output by the dual-antenna GNSS; Calculating and determining a Kalman filter gain based on the update result, the pitch angle, and the heading angle; Performing optimal navigation estimation of attitude angles, speed, and position based on the Kalman filter gain.

2. The integrated navigation method of the fusion dual-antenna GNSS according to claim 1, wherein The obtaining of various error matrices of attitude angles in the dual-antenna GNSS includes: Obtaining an error propagation matrix of attitude angles to attitude angles at the same moment; Obtaining an error propagation matrix of attitude angles to speed at the same moment; Obtaining an error propagation matrix of attitude angles to position at the same moment; Obtaining an error propagation matrix of speed to attitude angles at the same moment; Obtaining an error propagation matrix of speed to speed at the same moment; Obtaining an error propagation matrix of speed to position at the same moment; Obtaining an error propagation matrix of position to attitude angles at the same moment; Obtaining an error propagation matrix of position to speed at the same moment; Obtaining an error propagation matrix of position to position at the same moment.

3. The integrated navigation method of the fusion dual-antenna GNSS according to claim 2, wherein The method further includes: Obtaining a direction cosine matrix of the carrier for installing the dual-antenna GNSS for participating in the calculation of the state transition matrix.

4. The integrated navigation method of the fusion dual-antenna GNSS according to claim 3, wherein The calculating and determining the state transition matrix at the current moment based at least on the various error propagation matrices of the attitude angles includes: Calculating and determining the state transition matrix at the current moment based on each of the error propagation matrices, the carrier direction cosine matrix, an 18th-order identity matrix, and a 3rd-order zero matrix.

5. The integrated navigation method of the integrated dual-antenna GNSS according to claim 1, wherein The performing Kalman filter time update based on the state transition matrix and the Kalman filter state data of the carrier at the previous moment to obtain an update result includes: Performing a prior estimation of the Kalman filter state vector based on the state transition matrix and the Kalman filter state vector of the carrier at the previous moment to obtain a first estimation result.

6. The integrated navigation method of the fusion dual-antenna GNSS according to claim 5, wherein The performing Kalman filter time update based on the state transition matrix and the Kalman filter state data of the carrier at the previous moment to obtain an update result further includes: Performing a prior estimation of the Kalman filter covariance matrix based on the state transition matrix, the Kalman filter covariance matrix of the carrier at the previous moment, and the Kalman filter state noise matrix to obtain a second estimation result.

7. The integrated navigation method of the fusion dual-antenna GNSS according to claim 1, wherein The method further includes: Calculate the Kalman filter observation quantity Z based on the pitch angle α and heading angle β of the carrier relative to the ground output by the dual-antenna GNSS k and calculate the Kalman filter observation matrix H based on the Kalman filter observation quantity k : Among them, the is the direction cosine matrix between the dual-antenna coordinate system and the navigation coordinate system, and the is the direction cosine matrix between the dual-antenna coordinate system and the carrier coordinate system, and 03 is a third-order zero matrix.

8. The integrated navigation method of the fusion dual-antenna GNSS according to claim 7, characterized in that Calculating the Kalman filter observation quantity Z based on the pitch angle α and heading angle β of the carrier relative to the ground output by the dual-antenna GNSS k , including: Determining the installation error between the dual-antenna GNSS and the carrier where it is located, the carrier attitude error, and the time asynchronization error between the dual-antenna GNSS and the carrier; Based on the pitch angle α and heading angle β of the carrier relative to the ground output by the dual-antenna GNSS, as well as the installation error, carrier attitude error, and time asynchronization error between the dual-antenna and the carrier, the Kalman filter observation quantity Z is calculated. k .

9. The integrated navigation method of the fusion dual-antenna GNSS according to claim 7, wherein The update result includes a prior estimation of the Kalman filter state vector and a prior estimation of the Kalman filter covariance matrix; The calculating and determining the Kalman filter gain based on the update result, the pitch angle, and the heading angle includes: Calculating and determining the Kalman filter gain based on the Kalman filter observation matrix, the prior estimation of the Kalman filter state vector, the prior estimation of the Kalman filter covariance matrix, and the Kalman filter observation noise matrix.

10. A combined navigation device integrating dual-antenna GNSS, characterized in that, Including: A first acquisition module, configured to acquire various error propagation matrices of attitude angles obtained by collecting a carrier by a dual-antenna GNSS; A first calculation module, configured to calculate and determine a state transition matrix at the current moment at least according to the various error propagation matrices of the attitude angles; An update module, configured to perform Kalman filter time update according to the state transition matrix and the Kalman filter state data of the carrier at the previous moment to obtain an update result; A determination module, configured to determine a pitch angle and a heading angle of the carrier relative to the ground output by the dual-antenna GNSS; A second calculation module, configured to calculate and determine a Kalman filter gain according to the update result and the pitch angle and the heading angle; An optimal estimation module, configured to perform optimal navigation estimation of attitude angles, speeds, and positions according to the Kalman filter gain.

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