A dual-antenna heading-based integrated navigation method and system

Abstract

The application provides a kind of combination navigation method and system based on dual-antenna heading, comprising: INS and dual-antenna GNSS system are combined together, and utilize the proposed dual-antenna GNSS / INS combination navigation method, so that the system can output continuous stable high-precision carrier attitude information when GNSS signal is disturbed.In the method, a heading combination update strategy is proposed, and the continuous and stable output of the device heading information is completed by parallel execution of the solving process of dual-antenna orientation and the initialization process of IMU.Meanwhile, the sensor information is detected to judge whether the device is stationary, and if it is stationary, the IMU parameter calibration is carried out.According to whether the sensor is initialized, two different precision positioning and orientation results are obtained.The application combines the advantages of dual-antenna GNSS and INS, and applies the dual-antenna combination strategy, so that continuous and effective positioning and orientation results can be obtained without waiting for a long time, and has good practical value.

Description

Technical Field

[0001] This invention relates to the field of navigation technology, and in particular to a combined navigation method and system based on dual-antenna heading. Background Technology

[0002] In traditional GNSS / INS integrated navigation, the vehicle's attitude is a crucial spatial characteristic of the system. The accuracy of the heading angle directly affects the overall performance of the navigation system and typically requires estimation through static calibration in a stationary state or filtering algorithms during dynamic movement.

[0003] However, existing initialization methods often require lengthy calibration times, especially in dynamic environments. The initialization process can prevent the navigation system from reaching a stable operating state in a timely manner, affecting system continuity and real-time performance. Furthermore, traditional single-antenna GNSS systems have limitations in heading angle estimation, failing to directly provide high-precision heading information, further increasing the complexity of initialization. To address these issues, dual-antenna GNSS systems have been increasingly introduced into integrated navigation. By measuring the baseline vector between the two antennas, dual-antenna GNSS can directly obtain high-precision heading information, providing a more reliable initial heading angle for the integrated navigation system. However, existing dual-antenna GNSS / INS integrated navigation strategies still suffer from long initialization times and slow system response, making it difficult to meet the real-time and stability requirements of high-dynamic, high-precision applications. Summary of the Invention

[0004] This invention provides a combined navigation method and system based on dual-antenna heading, addressing the shortcomings of existing technologies. It utilizes high-precision heading information output from dual-antenna devices to correct the INS divergence error of the combined navigation system. Simultaneously, it implements a parallel processing strategy, simultaneously initializing the IMU and outputting continuous orientation information.

[0005] In a first aspect, the present invention provides a combined navigation method based on dual-antenna heading, comprising:

[0006] Step 1: Obtain the motion status information of the device, collect IMU sensor data for judgment, and initialize the system;

[0007] Step 2: Based on the satellite information from the dual receivers, perform pseudorange and carrier phase differential calculations, and complete the system's positioning and orientation calculations to obtain the dual-antenna calculation results;

[0008] Step 3: Use IMU sensor data to complete the inertial navigation recursive process, construct a Kalman filter system, and obtain the system state prediction value;

[0009] Step 4: Update the system status using the dual-antenna calculation results as the measurement values. If the device is in motion and has not completed the initial calibration in Step 1, it is determined to be a normal accuracy positioning and orientation result. If the device is stationary and has completed the initial calibration in Step 1, it is determined to be a precise accuracy positioning and orientation result.

[0010] According to the present invention, a combined navigation method based on dual-antenna heading is provided, step 1 of which includes:

[0011] Acquire IMU gyroscope data and accelerometer data ,according to To determine if the threshold value is exceeded, the system is considered stationary. Statistical processing is performed on the angular velocity information output by the IMU within a preset time period to determine the system's motion state, and the statistical information is constructed as follows:

[0012]

[0013] in, This represents the constructed statistics, N represents the length of the constructed and stored data, and k represents the current epoch time. This represents the gyro angular velocity vector at the current epoch. This represents the vector of specific force values ​​output by the accelerometer at the current epoch. This represents the gyro angular velocity vector of the previous epoch. This represents the vector of specific force values ​​output by the accelerometer in the previous epoch.

[0014] Determine the threshold Determine the state of rest and motion state :

[0015]

[0016] If the system is stationary, collect N sets of sensor data and calculate the zero bias of the IMU gyroscope. :

[0017]

[0018] This represents the gyro angular velocity vector of any set of sensors;

[0019] The gyroscope correction results are obtained:

[0020] .

[0021] According to the combined navigation method based on dual-antenna heading provided by the present invention, step 2 includes:

[0022] Determine pseudorange and carrier phase information in a satellite navigation system:

[0023]

[0024] in Represents pseudorange observations. Represents carrier phase observations, The wavelength represents the frequency of the corresponding satellite positioning system signal. This indicates the geometric distance from the receiver to the GNSS satellite. Indicates receiver, Indicates satellite, These represent the errors that affect the pseudorange and carrier phase respectively during signal propagation. Indicates observation noise;

[0025]

[0026] Select base station location ,right Linearization yields:

[0027]

[0028] in:

[0029]

[0030]

[0031] If the receiver and receiver Simultaneously observing satellite G1 will and The difference between the observation equations is used to obtain the receiver's... and receiver Regarding the single-difference equation for satellite G1:

[0032]

[0033] in Indicates receiver and receiver The baseline vector;

[0034] Similarly, the receiver and receiver By subtracting the observation equations for satellite G2, we obtain the single-difference observation equations for satellite G2.

[0035] By performing inter-satellite subtraction on the single-difference observation equations for satellites G1 and G2, we obtain the double-difference observation equations for pseudorange and carrier phase:

[0036]

[0037]

[0038] Obtain the baseline vector to be determined Double difference ambiguity of carrier phase .

[0039] According to the present invention, a combined navigation method based on dual-antenna heading is provided, if M satellites are received by the receiver and Simultaneously, it was observed that, using G1 as the reference satellite, the double-difference observation equations from satellite G2 to satellite GM were calculated, and the resulting set of observation equations was obtained:

[0040] The baseline vector and double-difference ambiguity are solved using the least squares method to obtain floating-point solutions. The integer ambiguity is fixed using the LAMBDA algorithm to solve for the baseline vector. From the fixed solution, we obtain the heading angle and pitch angle:

[0041]

[0042]

[0043] in, Indicates the heading angle. Indicates the pitch angle.

[0044] According to the combined navigation method based on dual-antenna heading provided by the present invention, step 3 includes:

[0045] Constructing the state vector:

[0046]

[0047] in, This represents the position error, velocity error, attitude error, and zero bias error of the gyroscope and accelerometer in integrated navigation.

[0048] Construct the system state equations:

[0049]

[0050] in, , Let be the state vectors of the system at time k and time k-1, respectively. This represents the one-step transition matrix from time k-1 to time k. The matrix is ​​related to noise. This is system noise;

[0051] Construct the system observation equations:

[0052]

[0053] in, Let k be the observation value at time k. Let be the observation matrix at time k. Pre-state vector, To observe noise;

[0054] The system prediction value obtained by constructing the Kalman filter system is:

[0055]

[0056]

[0057] in, and This represents the current predicted system state and the estimated system state. and This represents the system state prediction covariance and the system state estimation covariance;

[0058] The system noise covariance matrix is ​​represented by the following:

[0059] .

[0060] According to the combined navigation method based on dual-antenna heading provided by the present invention, step 4 includes:

[0061] When updating position measurements, the lever effect is considered to obtain the conversion relationship between antenna position and IMU position:

[0062]

[0063] in This indicates the actual location of the GNSS antenna phase center. Indicates the actual location of the IMU center. The lever arm vector is the vector pointing from the IMU center to the GNSS antenna phase center in the b-frame.

[0064] Considering the disturbance error, we get:

[0065]

[0066] Expanding, we get:

[0067]

[0068] Since the Kalman filter is an error Kalman filter, the calculated position observations of the system are:

[0069]

[0070] The observation matrix obtained by performing only position updates is:

[0071]

[0072] When updating the heading angle measurement, the system-derived heading angle expression in the n-frame is obtained as follows:

[0073]

[0074] The heading angle information obtained by dual-antenna GNSS calculation is represented as follows:

[0075]

[0076] The heading observation value of the calculation system is then:

[0077]

[0078] The observation matrix can then be represented as:

[0079]

[0080] in:

[0081]

[0082] Perform measurement updates:

[0083]

[0084]

[0085]

[0086] in, Here is the gain matrix. To measure the noise covariance matrix, This is the optimal estimate of the state at time k. This is the optimal estimate of the state covariance at time k. The value is the measurement at time k.

[0087] Secondly, the present invention also provides a combined navigation system based on dual-antenna heading, comprising:

[0088] The first processing module is used to acquire the motion status information of the device, collect IMU sensor data information for judgment, and initialize the system;

[0089] The second acquisition module is used to perform pseudorange and carrier phase difference calculations based on the satellite information from the dual receivers, and to perform positioning and orientation calculations for the system to obtain the dual-antenna calculation results.

[0090] The third acquisition module is used to complete the inertial navigation recursion process using IMU sensor data information, construct a Kalman filter system, and obtain the system state prediction value.

[0091] The fourth acquisition module is used to update the system status using the dual-antenna calculation results as measurement values. If the device is in motion and has not completed the initial calibration, it is determined to be a normal accuracy positioning and orientation result. If the device is stationary and has completed the initial calibration, it is determined to be a precise accuracy positioning and orientation result.

[0092] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the combined navigation method based on dual-antenna heading as described above.

[0093] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the combined navigation method based on dual-antenna heading as described above.

[0094] This invention provides a dual-antenna heading-based integrated navigation method and system. By combining an INS (Instrumentation System) with a dual-antenna GNSS system, and utilizing the proposed dual-antenna GNSS / INS integrated navigation method, the system's positioning and orientation accuracy is improved. This method proposes a heading integration update strategy, which achieves continuous and stable output of equipment heading information by executing the dual-antenna orientation calculation process and the IMU initialization process in parallel. Simultaneously, sensor information is detected to determine whether the equipment is stationary; if stationary, IMU parameter calibration is performed. Two different levels of positioning and orientation accuracy are obtained depending on whether the sensors are initialized. This method combines the advantages of both dual-antenna GNSS and INS, and applies a dual-antenna integration strategy, obtaining continuous and effective positioning and orientation results without a long waiting time, thus possessing significant practical value. Attached Figure Description

[0095] To more clearly illustrate the technical solutions in this 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 some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0096] Figure 1 This is a flowchart illustrating the combined navigation method based on dual-antenna heading provided by the present invention;

[0097] Figure 2 This is a flowchart of the update process for the dual-antenna GNSS / INS integrated navigation system provided by the present invention;

[0098] Figure 3 This is a schematic diagram of the dual-antenna directional principle provided by the present invention;

[0099] Figure 4 This is a schematic diagram of the structure of the integrated navigation system based on dual-antenna heading provided by the present invention;

[0100] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0101] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0102] To obtain more accurate and stable carrier attitude information, this invention proposes a combined navigation strategy based on dual-antenna GNSS / INS. The strategy utilizes the high-precision heading information output by the dual-antenna device to correct the INS divergence error of the combined navigation system. At the same time, a parallel processing strategy is implemented to simultaneously initialize the IMU and output continuous orientation information.

[0103] Figure 1 This is a flowchart illustrating the integrated navigation method based on dual-antenna heading provided in an embodiment of the present invention, as shown below. Figure 1 As shown, it includes:

[0104] Step 1: Obtain the motion status information of the device, collect data information from the inertial measurement unit (IMU) sensor for judgment, and initialize the system;

[0105] Step 2: Based on the satellite information from the dual receivers, perform pseudorange and carrier phase differential calculations, and complete the system's positioning and orientation calculations to obtain the dual-antenna calculation results;

[0106] Step 3: Use IMU sensor data to complete the inertial navigation recursive process, construct a Kalman filter system, and obtain the system state prediction value;

[0107] Step 4: Update the system status using the dual-antenna calculation results as the measurement values. If the device is in motion and has not completed the initial calibration in Step 1, it is determined to be a normal accuracy positioning and orientation result. If the device is stationary and has completed the initial calibration in Step 1, it is determined to be a precise accuracy positioning and orientation result.

[0108] This invention optimizes the INS initialization process by fully utilizing heading information provided by dual-antenna GNSS, and simultaneously performs combined filtering and initialization calibration in parallel when GNSS signals are available, significantly improving the system's initialization efficiency and real-time performance. Experimental results show that this strategy effectively shortens the initialization time and enhances the system's continuity and stability while maintaining navigation accuracy, providing a new solution for high-precision integrated navigation.

[0109] In one embodiment, such as Figure 2 The flowchart shown illustrates the update process for a dual-antenna GNSS / INS integrated navigation system. First, after power-on, the system simultaneously acquires data from both the IMU sensor and the receiver. Statistical values ​​are then used to determine the system's motion state, and the next step is determined based on the system's state. If the system is in motion, sensor alignment is not performed; instead, the data is fused directly with the dual-antenna orientation results, resulting in a less accurate positioning result. If the system is stationary, sensor alignment is completed, and the data is fused with the dual-antenna information to obtain a more accurate positioning and orientation result.

[0110] Specifically, step 1 includes:

[0111] Read IMU gyroscope data and accelerometer data By judgment Whether the threshold value is exceeded determines whether the system is stationary. Here, the angular velocity information output by the IMU over a period of time is statistically analyzed and processed to determine the system's motion state. The constructed statistical information is as follows:

[0112] (1)

[0113] in, Here, N represents the constructed statistics, N represents the length of the constructed and stored data, and k represents the time of the current epoch. This represents the gyroscope's angular velocity vector. This represents the vector of specific force values ​​output by the accelerometer. This represents the gyro angular velocity vector of the previous epoch. This represents the specific force vector output by the accelerometer at the previous epoch. This statistic tracks the changes in angular velocity and accelerometer readings across multiple adjacent epochs. A larger statistic indicates greater changes in the vehicle's motion at that epoch, and vice versa. Therefore, the vehicle's motion state can be calculated based on the results of this statistic. A threshold can be applied to this statistic.

[0114] (2)

[0115] If the system is stationary, collect N sets of sensor data and calculate the zero bias of the IMU gyroscope:

[0116] (3)

[0117] By calculating the zero bias of the IMU gyroscope, more accurate angular velocity information can be obtained, improving the accuracy of the INS system's calculations and avoiding error accumulation. The subsequent correction result for the gyroscope output should then be:

[0118] (4)

[0119] Step 2 mainly involves calculating the heading information from the dual antennas. The main method is to calculate the heading information based on the pseudorange and carrier phase information of multiple satellites received by the two receivers in the dual antenna system, as detailed below:

[0120] Dual-antenna heading information calculation:

[0121] like Figure 3 The diagram illustrates the composition of a dual-antenna system and the meaning of its key parameters. This system consists of a receiver... Receiver It consists of satellites 1 to M, and the baseline vector can be clearly seen from the diagram. The physical meaning of the pseudorange and carrier phase double-difference observations is explained. The double-difference observation information of pseudorange and carrier phase is calculated and expressed as an expression related to the baseline vector through linearization. A system of equations is formed by constructing double-difference observation expressions for multiple satellites. Subsequently, the floating-point solutions of the baseline vector and carrier phase integer values ​​are obtained using the least squares algorithm, and the fixed solutions are obtained using the LAMBDA method. Finally, the corresponding heading angle information is calculated based on the baseline vector value.

[0122] In satellite navigation systems, the main observables used for position calculation are pseudorange information and carrier phase information, whose specific expressions are as follows:

[0123] (5)

[0124] (6)

[0125] in Represents pseudorange observations. Represents carrier phase observations, The wavelength represents the frequency of the corresponding satellite positioning system signal. This indicates the geometric distance from the receiver to the GNSS satellite. Indicates receiver, Indicates a satellite. This refers to various errors that affect pseudorange and carrier phase during signal propagation, such as ionospheric and tropospheric delays, and multipath effects errors. This indicates observation noise.

[0126] in, It can be represented as:

[0127] (7)

[0128] Select base station location ,right Linearization yields:

[0129] (8)

[0130] in:

[0131] (9)

[0132] (10)

[0133] It can be observed that if the receiver and receiver By simultaneously observing satellite G1, the difference between the observation equations of the two satellites can be calculated to obtain the receiver's result. and receiver Regarding the single-difference equation for satellite G1:

[0134] (11) (12)

[0135] This eliminates the influence of satellite clock bias on the observation equations. Simultaneously, it reduces the impact of ionospheric and tropospheric errors. Indicates receiver and receiver The baseline vector.

[0136] Similarly, the receiver can be and receiver The observation equations for G2 are subtracted to obtain the single-difference observation equations for G2. Finally, the single-difference observation equations for both satellites G1 and G2 are subtracted again to further eliminate receiver clock errors and reduce ionospheric and tropospheric errors, resulting in the double-difference observation equations for pseudorange and carrier phase.

[0137] (13)

[0138] (14)

[0139] It can be seen that only the baseline vector remains to be determined in the formula. Double difference ambiguity of carrier phase .

[0140] If at this time there are M satellites being received by the receiver and Simultaneously, it was observed that, using G1 as the reference satellite, the double-difference observation equations from satellite G2 to satellite GM were calculated, and a set of observation equations could be derived. At this point, the floating-point solutions for the baseline vector and double-difference ambiguities could be obtained using the least squares method. Further, by using the LAMBDA algorithm to fix the integer ambiguities, the baseline vector could be solved. The fixed solution. Therefore, the heading angle and pitch angle can be further calculated using the following formulas:

[0141] (15)

[0142] (16)

[0143] in, Indicates the heading angle. Indicates the pitch angle.

[0144] Step 3: Establish a suitable Kalman filter model based on the existing parameters, and combine it with the results of mechanical arrangement of IMU sensor data to perform the Kalman filter prediction process.

[0145] Kalman filtering:

[0146] In GNSS / INS integrated navigation, the main information we focus on includes position, velocity, attitude, and sensor parameters. Therefore, this invention selects values ​​related to these information as system state variables. To avoid nonlinearity issues, this invention uses the error values ​​of the aforementioned information as the system state variables.

[0147] (17)

[0148] in, This represents the position error, velocity error, attitude error, and zero bias error of the gyroscope and accelerometer in integrated navigation.

[0149] Then construct the system state equations:

[0150] (18)

[0151] In the formula, , Let be the state vectors of the system at time k and time k-1, respectively. This represents the one-step transition matrix from time k-1 to time k. The matrix is ​​related to noise. This represents system noise. Finally, the system observation equations are constructed:

[0152] (19)

[0153] In the formula, Let k be the observation value at time k. Let be the observation matrix at time k. Pre-state vector, To observe noise.

[0154] The system prediction value obtained through the Karl filter system constructed above is:

[0155] (20)

[0156] (twenty one)

[0157] in, and This represents the current predicted and estimated system state values. and This represents the system state prediction covariance and the system state estimation covariance. Let represent the system noise covariance matrix, and satisfy:

[0158] (twenty two)

[0159] Step 4 determines the next combination method based on the system's motion state, as follows:

[0160] If the system is in motion and sensor calibration is not yet complete, the data is directly combined with the dual-antenna information to obtain a result with moderate orientation and positioning accuracy. If the system is stationary, sensor calibration is performed, and then the data is fused with the dual-antenna heading information to obtain a high-precision positioning and orientation result. This Kalman filter update process mainly involves measurement updates based on position information and measurement updates based on dual-antenna heading information.

[0161] When updating the position measurement, considering the lever arm effect, the relationship between the antenna position and the IMU position is transformed as follows:

[0162] (twenty three)

[0163] in This indicates the actual location of the GNSS antenna phase center. Indicates the actual location of the IMU center. This represents the boom vector, which is the vector pointing from the IMU center to the GNSS antenna phase center in the b-frame. The boom vector is typically calibrated using measuring equipment after equipment installation for boom compensation during actual calculations. It is generally considered that its measurement error is small and can be ignored.

[0164] Considering the disturbance error, we can obtain:

[0165] (twenty four)

[0166] Expanding on this, we get:

[0167] (25)

[0168] Since the Kalman filter is an error Kalman filter, the calculated position observations of the system are:

[0169] (26)

[0170] Therefore, the observation matrix calculated when only position updates are performed is:

[0171] (27)

[0172] When updating the heading angle measurement, the system-derived heading angle expression in the n-frame is obtained as follows:

[0173] (28)

[0174] The heading angle information obtained by dual-antenna GNSS calculation can be expressed as:

[0175] (29)

[0176] The heading observation value of the calculation system is then:

[0177] (30)

[0178] The observation matrix can then be represented as:

[0179] (31)

[0180] in:

[0181] (32)

[0182] Finally, the measurement is updated:

[0183] (33)

[0184] (34)

[0185] (35)

[0186] In the formula, Here is the gain matrix. To measure the noise covariance matrix, This is the optimal estimate of the state at time k. This is the optimal estimate of the state covariance at time k. The value is the measurement at time k.

[0187] The dual-antenna heading-based integrated navigation system provided by the present invention is described below. The dual-antenna heading-based integrated navigation system described below can be referred to in correspondence with the dual-antenna heading-based integrated navigation method described above.

[0188] Figure 4 This is a schematic diagram of the structure of the integrated navigation system based on dual-antenna heading provided in an embodiment of the present invention, as shown below. Figure 4 As shown, it includes: a first processing module 41, a second acquisition module 42, a third acquisition module 43, and a fourth acquisition module 44, wherein:

[0189] The first processing module 41 is used to acquire the motion state information of the device, collect IMU sensor data information for judgment, and initialize the system; the second acquisition module 42 is used to complete the pseudorange and carrier phase difference calculation based on the satellite information of the dual receivers, and complete the positioning and orientation calculation of the system to obtain the dual-antenna calculation result; the third acquisition module 43 is used to complete the inertial navigation recursive process using IMU sensor data information, construct a Kalman filter system, and obtain the system state prediction value; the fourth acquisition module 44 is used to update the system state with the dual-antenna calculation result as the measurement value. If the device is in motion and has not completed the initial calibration, it is determined to be a normal accuracy positioning and orientation result. If the device is stationary and has completed the initial calibration, it is determined to be a precise accuracy positioning and orientation result.

[0190] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call logic instructions in the memory 530 to execute a combined navigation method based on dual-antenna heading. The method includes: Step 1, acquiring the motion state information of the device, collecting IMU sensor data information for judgment, and initializing the system; Step 2, based on the satellite information from the dual receivers, completing pseudorange and carrier phase differential calculations, and completing the system's positioning and orientation calculation to obtain the dual-antenna calculation result; Step 3, using the IMU sensor data information to complete the inertial navigation recursion process, constructing a Kalman filter system, and obtaining the system state prediction value; Step 4, updating the system state with the dual-antenna calculation result as the measurement value. If the device is in motion in Step 1 and has not completed initial calibration, it is determined to be a normal accuracy positioning and orientation result; if the device is stationary in Step 1 and has completed initial calibration, it is determined to be a precise accuracy positioning and orientation result.

[0191] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0192] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the combined navigation method based on dual-antenna heading provided by the above methods. The method includes: Step 1, acquiring the motion state information of the device, collecting IMU sensor data information for judgment, and initializing the system; Step 2, based on the satellite information of the dual receivers, completing the pseudorange and carrier phase difference calculation, and completing the system positioning and orientation solution to obtain the dual-antenna solution result; Step 3, using the IMU sensor data information to complete the inertial navigation recursion process, constructing a Kalman filter system, and obtaining the system state prediction value; Step 4, using the dual-antenna solution result as the measurement value to update the system state. If the device is in motion in Step 1 and has not completed the initial calibration, it is determined to be a normal accuracy positioning and orientation result. If the device is stationary in Step 1 and has completed the initial calibration, it is determined to be a precise accuracy positioning and orientation result.

[0193] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the combined navigation method based on dual-antenna heading provided by the above methods. The method includes: Step 1, acquiring the motion state information of the device, collecting IMU sensor data information for judgment, and initializing the system; Step 2, based on the satellite information of the dual receivers, completing the pseudorange and carrier phase difference calculation, and completing the system positioning and orientation solution to obtain the dual-antenna solution result; Step 3, using the IMU sensor data information to complete the inertial navigation recursive process, constructing a Kalman filter system, and obtaining the system state prediction value; Step 4, using the dual-antenna solution result as the measurement value to update the system state. If the device is in motion in Step 1 and has not completed the initial calibration, it is determined to be a normal accuracy positioning and orientation result; if the device is stationary in Step 1 and has completed the initial calibration, it is determined to be a precise accuracy positioning and orientation result.

[0194] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0195] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0196] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A combined navigation method based on dual-antenna heading, characterized in that, include: Step 1: Obtain the motion status information of the device, collect data information from the inertial measurement unit (IMU) sensor for judgment, and initialize the system; Step 2: Based on the satellite information from the dual receivers, perform pseudorange and carrier phase differential calculations, and complete the system's positioning and orientation calculations to obtain the dual-antenna calculation results; Step 3: Use IMU sensor data to complete the inertial navigation recursive process, construct a Kalman filter system, and obtain the system state prediction value; Step 4: Update the system status using the dual-antenna calculation results as measurement values. If the device was in motion in Step 1 and initial calibration was not completed, the result is determined to be a normal accuracy positioning and orientation result. If the device was stationary in Step 1 and initial calibration was completed, the result is determined to be a precise accuracy positioning and orientation result. Specifically, this includes: When updating position measurements, the lever effect is considered to obtain the conversion relationship between antenna position and IMU position: in This indicates the actual location of the GNSS antenna phase center. Indicates the actual location of the IMU center. The lever arm vector is the vector pointing from the IMU center to the GNSS antenna phase center in the b-frame. Considering the disturbance error, we get: Expanding, we get: Since the Kalman filter is an error Kalman filter, the calculated position observations of the system are: The observation matrix obtained by performing only position updates is: When updating the heading angle measurement, the system-derived heading angle expression in the n-frame is obtained as follows: The heading angle information obtained by dual-antenna GNSS calculation is represented as follows: The heading observation value of the calculation system is then: The observation matrix is ​​then expressed as: in: Perform measurement updates: in, Here is the gain matrix. To measure the noise covariance matrix, This is the optimal estimate of the state at time k. Let k be the optimal estimate of the state covariance at time k. The measurement value at time k is given.

2. The combined navigation method based on dual-antenna heading according to claim 1, characterized in that, Step 1 includes: Acquire IMU gyroscope data and accelerometer data ,according to To determine if the threshold value is exceeded, the system is considered stationary. Statistical processing is performed on the angular velocity information output by the IMU within a preset time period to determine the system's motion state, and the statistical information is constructed as follows: in, This represents the constructed statistics, N represents the length of the constructed and stored data, and k represents the current epoch time. This represents the gyro angular velocity vector at the current epoch. This represents the vector of specific force values ​​output by the accelerometer at the current epoch. This represents the gyro angular velocity vector of the previous epoch. This represents the vector of specific force values ​​output by the accelerometer in the previous epoch. Determine the threshold Determine the state of rest and motion state : If the system is stationary, collect N sets of sensor data and calculate the zero bias of the IMU gyroscope. : This represents the gyro angular velocity vector of any set of sensors; The gyroscope correction results are obtained: 。 3. The combined navigation method based on dual-antenna heading according to claim 1, characterized in that, Step 2 includes: Determine pseudorange and carrier phase information in a satellite navigation system: in Represents pseudorange observations. Represents carrier phase observations, The wavelength represents the frequency of the corresponding satellite positioning system signal. This indicates the geometric distance from the receiver to the GNSS satellite. Indicates receiver, Indicates satellite, These represent the errors that affect the pseudorange and carrier phase respectively during signal propagation. Indicates observation noise; Select base station location ,right Linearization yields: in: If the receiver and receiver Simultaneously observing satellite G1 will and The difference between the observation equations is used to obtain the receiver's... and receiver Regarding the single-difference equation for satellite G1: in Indicates receiver and receiver The baseline vector; Similarly, the receiver and receiver By subtracting the observation equations for satellite G2, we obtain the single-difference observation equations for satellite G2. By performing inter-satellite subtraction on the single-difference observation equations for satellites G1 and G2, we obtain the double-difference observation equations for pseudorange and carrier phase: Obtain the baseline vector to be determined Double difference ambiguity of carrier phase .

4. The combined navigation method based on dual-antenna heading according to claim 3, characterized in that, If M satellites are received by the receiver and Simultaneously, it was observed that, using G1 as the reference satellite, the double-difference observation equations from satellite G2 to satellite GM were calculated, and the resulting set of observation equations was obtained: The baseline vector and double-difference ambiguity are solved using the least squares method to obtain floating-point solutions. The integer ambiguity is fixed using the LAMBDA algorithm to solve for the baseline vector. With a fixed solution, we obtain the heading angle and pitch angle: in, Indicates the heading angle. Indicates the pitch angle.

5. The combined navigation method based on dual-antenna heading according to claim 1, characterized in that, Step 3 includes: Constructing the state vector: in, This represents the position error, velocity error, attitude error, and zero bias error of the gyroscope and accelerometer in the integrated navigation system. Construct the system state equations: in, , Let be the state vectors of the system at time k and time k-1, respectively. This represents the one-step transition matrix from time k-1 to time k. The matrix is ​​related to noise. This is system noise; Construct the system observation equations: in, Let k be the observation value at time k. Let be the observation matrix at time k. Pre-state vector, To observe noise; The system prediction value obtained by constructing the Kalman filter system is: in, and This represents the current predicted system state and the estimated system state. and This represents the system state prediction covariance and the system state estimation covariance; The system noise covariance matrix is ​​represented by the following: 。 6. A combined navigation system based on dual-antenna heading, based on the combined navigation method based on dual-antenna heading according to any one of claims 1 to 5, characterized in that, include: The first processing module is used to acquire the motion status information of the device, collect IMU sensor data information for judgment, and initialize the system; The second acquisition module is used to perform pseudorange and carrier phase difference calculations based on the satellite information from the dual receivers, and to perform positioning and orientation calculations for the system to obtain the dual-antenna calculation results. The third acquisition module is used to complete the inertial navigation recursion process using IMU sensor data information, construct a Kalman filter system, and obtain the system state prediction value. The fourth acquisition module is used to update the system status using the dual-antenna calculation results as measurement values. If the device is in motion and has not completed the initial calibration, it is determined to be a normal accuracy positioning and orientation result. If the device is stationary and has completed the initial calibration, it is determined to be a precise accuracy positioning and orientation result.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the combined navigation method based on dual-antenna heading as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the combined navigation method based on dual-antenna heading as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the combined navigation method based on dual-antenna heading as described in any one of claims 1 to 5.

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