A GNSS and inertial navigation combined positioning closed-loop fusion solving method and device

Abstract

The application discloses a GNSS and inertial navigation combined positioning closed-loop fusion solving method and device, relates to the satellite navigation and inertial sensing fusion positioning technical field, and the method comprises the following steps: synchronously collecting satellite navigation observation data and inertial sensing measurement data and preprocessing; solving carrier motion state prediction, generating satellite signal tracking auxiliary prior information and feeding forward to a satellite navigation baseband tracking loop, and outputting effective satellite observation signals; demodulating and extracting pseudo-range observation values and carrier phase observation values, simultaneously extrapolating pseudo-range prediction values by using inertial navigation, and performing coarse error detection and elimination on the pseudo-range observation values; constructing a combined navigation filtering model, estimating system error state quantities by using Kalman filtering; correcting state quantities calculated by using a strapdown inertial navigation system, and reversely inputting the corrected inertial navigation state quantities into the satellite navigation baseband tracking loop, and outputting combined positioning closed-loop fusion solving results. The application can improve signal tracking stability and positioning precision in high dynamic and weak signal scenes.

Description

Technical Field

[0001] This application relates to the field of satellite navigation and inertial sensing fusion positioning technology, and in particular to a GNSS and inertial navigation combined positioning closed-loop fusion solution method and device. Background Technology

[0002] Global Navigation Satellite Systems (GNSS) can provide absolute position and velocity observations without long-term cumulative drift. However, satellite signals are susceptible to building obstruction, electromagnetic interference, and multipath effects. In scenarios such as tunnels, elevated roads, and dense forests, problems such as weak signals, tracking loss, and frequent gross errors are prone to occur. Inertial Navigation Systems (INS) rely on gyroscopes and accelerometers to autonomously calculate the attitude, velocity, and position of the carrier. They have advantages such as high sampling frequency, resistance to external interference, and high short-term positioning accuracy. However, the zero bias and temperature drift of inertial devices can cause calculation errors to accumulate continuously over time, making it impossible to independently support high-precision navigation over long periods.

[0003] Existing GNSS and inertial navigation integration schemes are mostly open-loop fusion architectures, directly feeding the PVT (Position-Velocity-Time) results calculated by GNSS into the Kalman filter-corrected inertial navigation system. The inertial navigation system's calculated state cannot be used to influence the GNSS baseband signal tracking loop. In scenarios with high acceleration / deceleration and low carrier-to-noise ratio, the GNSS baseband's own carrier loop and code loop have large search ranges and poor dynamic adaptability, making it prone to signal loss. This directly causes frequent positioning interruptions in the integrated navigation system, resulting in poor signal tracking stability and low positioning accuracy. Other integration schemes directly fuse pseudorange and pseudorange rate observations, but do not implement feedforward assistance from the inertial state to the baseband tracking layer, remaining a semi-open-loop architecture. This fails to improve the GNSS receiver's anti-interference and anti-dynamic capabilities at the signal tracking level, and also cannot achieve high-precision, highly stable positioning. Summary of the Invention

[0004] The purpose of this application is to provide a GNSS and inertial navigation combined positioning closed-loop fusion solution method and device, which can improve the signal tracking stability and positioning accuracy in high dynamic and weak signal scenarios.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a GNSS and inertial navigation combined positioning closed-loop fusion solution method, including the following steps: Satellite navigation observation data and inertial sensor measurement data are acquired and preprocessed to obtain preprocessed inertial navigation data.

[0006] Based on the preprocessed inertial navigation data, the predicted motion state of the carrier is calculated, satellite signal tracking auxiliary prior information is generated, and the satellite signal tracking auxiliary prior information is fed forward to the satellite navigation baseband tracking loop to assist in the continuous tracking of satellite signals and the output of effective satellite observation signals.

[0007] The effective satellite observation signal is demodulated to extract pseudorange and carrier phase observation values. Simultaneously, the corresponding pseudorange prediction value is estimated by inertial navigation extrapolation. Gross error detection and removal are performed on the pseudorange observation values ​​to obtain pseudorange and carrier phase observation values ​​after removing gross errors.

[0008] A combined navigation filtering model is constructed that includes the state of inertial navigation error and the state of satellite navigation observation error. Based on the combined navigation filtering model, the state variables obtained by strapdown inertial navigation calculation based on the preprocessed inertial navigation data are used as predicted values. The differences between the pseudorange observation value and the carrier phase observation value after removing gross errors and the pseudorange predicted value are used as observations. The system error state variables are estimated online by Kalman filtering.

[0009] The state quantities obtained by the strapdown inertial navigation system are corrected based on the system error state quantities to obtain corrected inertial navigation state quantities. The corrected inertial navigation state quantities are then input back into the satellite navigation baseband tracking loop to iteratively update the satellite signal tracking auxiliary prior information. Finally, the combined positioning closed-loop fusion solution result is output. The combined positioning closed-loop fusion solution result includes the carrier's real-time positioning information, velocity information, and attitude information.

[0010] Secondly, this application provides a GNSS and inertial navigation integrated positioning closed-loop fusion calculation device, including a data acquisition module and a data processing module. The output end of the data acquisition module is connected to the input end of the data processing module. The data acquisition module is used to synchronously acquire satellite navigation observation data and inertial sensing measurement data, and transmit them to the data processing module. The data processing module is used to execute the GNSS and inertial navigation integrated positioning closed-loop fusion calculation method based on the satellite navigation observation data and the inertial sensing measurement data, and finally output the integrated positioning closed-loop fusion calculation result. The integrated positioning closed-loop fusion calculation result includes real-time positioning information, velocity information, and attitude information of the carrier.

[0011] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the GNSS and inertial navigation combined positioning closed-loop fusion solution method.

[0012] Fourthly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the GNSS and inertial navigation combined positioning closed-loop fusion solution method.

[0013] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a GNSS and inertial navigation integrated positioning closed-loop fusion solution method and apparatus. First, based on preprocessed inertial navigation data, the predicted motion state of the carrier is calculated to generate tracking-aid prior information, which is then fed forward to the satellite navigation baseband tracking loop. This allows the carrier motion prior information output by the inertial sensor to assist the satellite navigation baseband tracking loop in tracking satellite signals, improving the continuity of satellite signal tracking and stably outputting effective satellite observation signals. Second, pseudorange prediction values ​​are obtained through inertial navigation extrapolation. Using these predictions as a benchmark, gross error detection is performed on the demodulated pseudorange observation values, and gross error observations are removed. This filters out abnormal pseudorange observation data, reduces the interference of gross error observations on subsequent filtering solutions, and obtains reliable pseudorange and carrier phase observation values, improving the accuracy and positioning precision of the integrated positioning solution. Furthermore, by constructing a combined navigation filtering model that integrates inertial navigation errors and satellite navigation observation errors, and using the strapdown inertial navigation (SINS) solution state quantity as the predicted value and the observation residual as the observed quantity, Kalman filtering can be used to estimate the system error state quantity of the entire GNSS and inertial navigation integrated positioning system in real time, accurately identifying various system errors existing in the inertial navigation solution. Moreover, the system error state quantity obtained by Kalman filtering is used to correct the strapdown inertial navigation solution state, resulting in a more accurate corrected inertial navigation state quantity. Simultaneously, the corrected inertial navigation state quantity is fed back into the satellite navigation baseband tracking loop to iteratively update the satellite signal tracking auxiliary prior information. This forms a two-way closed-loop iterative optimization mechanism of "inertial assists satellite tracking, satellite observation corrects inertial solution," continuously optimizing the accuracy of the satellite signal tracking auxiliary prior, and further improving signal tracking stability and positioning accuracy. Furthermore, the entire closed-loop fusion link of this application achieves bidirectional complementarity between inertial navigation-assisted satellite signal tracking and satellite observation data inverse correction of inertial navigation calculation errors. Ultimately, it can stably output accurate and reliable real-time positioning information, velocity information, and attitude information of the carrier, realize continuous combined positioning calculation of the carrier, and improve the signal tracking stability and positioning accuracy in high dynamic and weak signal scenarios. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 An application environment diagram of a GNSS and inertial navigation combined positioning closed-loop fusion solution method provided in an embodiment of this application; Figure 2 A flowchart illustrating a GNSS and inertial navigation combined positioning closed-loop fusion solution method provided in an embodiment of this application; Figure 3 This is a schematic diagram illustrating the principle of GNSS and inertial navigation combined positioning closed-loop fusion calculation according to an embodiment of this application; Figure 4 This is a schematic diagram of a satellite navigation baseband tracking loop structure provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] The GNSS and inertial navigation combined positioning closed-loop fusion solution method provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send satellite navigation observation data and inertial sensing measurement data to server 104. After receiving the satellite navigation observation data and inertial sensing measurement data, server 104 preprocesses the satellite navigation observation data and inertial sensing measurement data to obtain preprocessed inertial navigation data. Based on the preprocessed inertial navigation data, it calculates the predicted quantity of the carrier motion state, generates satellite signal tracking auxiliary prior information, and feeds the satellite signal tracking auxiliary prior information forward to the satellite navigation baseband tracking loop to assist in completing continuous satellite signal tracking and outputting effective satellite observation signals. It demodulates the effective satellite observation signals to extract pseudorange observation values ​​and carrier phase observation values. At the same time, it estimates the corresponding pseudorange prediction values ​​using inertial navigation extrapolation, and performs gross error detection and gross error removal on the pseudorange observation values ​​to obtain the gross error-removed pseudorange observation values ​​and carrier phase observation values. The system error state is estimated online using Kalman filtering. The state variables obtained from strapdown inertial navigation (SINS) calculations based on the preprocessed inertial navigation data are used as predicted values. The differences between the pseudorange observations (after removing gross errors) and carrier phase observations and the predicted pseudorange values ​​are used as observations. The system error state variables are then corrected based on these system error state variables to obtain corrected inertial navigation state variables. These corrected inertial navigation state variables are then input back into the satellite navigation baseband tracking loop to iteratively update the satellite signal tracking auxiliary prior information. Finally, the combined positioning closed-loop fusion calculation result is output. The combined positioning closed-loop fusion calculation result includes the carrier's real-time positioning information, velocity information, and attitude information. The server 104 can feed back the obtained combined positioning closed-loop fusion calculation result to the terminal 102. Furthermore, in some embodiments, the GNSS and inertial navigation combined positioning closed-loop fusion solution method can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly perform GNSS and inertial navigation combined positioning closed-loop fusion solution processing on the satellite navigation observation data and inertial sensing measurement data, or the server 104 can obtain the satellite navigation observation data and inertial sensing measurement data from the data storage system and perform GNSS and inertial navigation combined positioning closed-loop fusion solution processing on the satellite navigation observation data and inertial sensing measurement data.

[0019] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, and IoT devices. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0020] In one exemplary embodiment, such as Figure 2 As shown, a GNSS and inertial navigation integrated positioning closed-loop fusion solution method is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the following steps are included: S1: Acquire satellite navigation observation data and inertial sensor measurement data and preprocess them to obtain preprocessed inertial navigation data.

[0021] S2: Based on the preprocessed inertial navigation data, calculate the predicted motion state of the carrier, generate satellite signal tracking auxiliary prior information, and feed the satellite signal tracking auxiliary prior information forward to the satellite navigation baseband tracking loop to assist in completing continuous satellite signal tracking and outputting effective satellite observation signals.

[0022] S3: Demodulate the effective satellite observation signal to extract pseudorange and carrier phase observation values. Simultaneously, estimate the corresponding pseudorange prediction value using inertial navigation extrapolation. Perform gross error detection and removal on the pseudorange observation values ​​to obtain the pseudorange and carrier phase observation values ​​after removing gross errors.

[0023] S4: Construct a combined navigation filtering model that includes the state of inertial navigation error and the state of satellite navigation observation error. Based on the combined navigation filtering model, use the state variables obtained by strapdown inertial navigation calculation based on the preprocessed inertial navigation data as predicted values, and use the differences between the pseudorange observation values ​​and carrier phase observation values ​​after removing gross errors and the pseudorange predicted values ​​as observations. Estimate the system error state variables online using Kalman filtering.

[0024] S5: Based on the system error state quantity, the state quantity obtained by the strapdown inertial navigation system is corrected to obtain the corrected inertial navigation state quantity. The corrected inertial navigation state quantity is then input back into the satellite navigation baseband tracking loop to iteratively update the satellite signal tracking auxiliary prior information. Finally, the combined positioning closed-loop fusion solution result is output. The combined positioning closed-loop fusion solution result includes the carrier's real-time positioning information, velocity information, and attitude information.

[0025] By implementing steps S1 to S5 above, the motion state of the carrier is calculated based on the preprocessed inertial navigation data, satellite signal tracking auxiliary prior information is generated and fed forward to the satellite navigation baseband tracking loop to ensure continuous satellite signal tracking. The pseudorange gross error is eliminated by using the inertial navigation extrapolation pseudorange prediction value. A combined navigation filtering model containing two types of error states is constructed, and Kalman filter error estimation is completed using the strapdown inertial navigation state and observation residuals. Then, the state quantities obtained by the strapdown inertial navigation are corrected using the system error state quantities, and the corrected inertial navigation state quantities are sent back to the satellite navigation baseband tracking loop to iteratively update the tracking prior, forming a two-way closed-loop fusion link. Finally, an accurate and reliable combined positioning closed-loop fusion solution result is output, realizing high-precision and highly stable satellite navigation and inertial navigation combined positioning.

[0026] In this embodiment, the carrier refers to the moving subject carrying satellite navigation receiving equipment (such as a GNSS receiver) and inertial sensing measurement equipment (such as an inertial measurement unit). It is the object described in the positioning, velocity measurement and attitude calculation in the whole calculation process. The real-time positioning information, velocity information and attitude information of the carrier in the final output combined positioning closed-loop fusion calculation result are all motion parameters of the carrier itself. The carrier motion state prediction and carrier coordinate system related calculations are all carried out around the moving subject.

[0027] As an optional implementation, step S1 involves acquiring satellite navigation observation data and inertial sensing measurement data and preprocessing them to obtain preprocessed inertial navigation data. This specifically includes the following steps: S11: Simultaneously acquire satellite navigation observation data and inertial sensor measurement data.

[0028] S12: The satellite navigation observation data is sequentially processed by digital down-conversion, low-pass filtering and sampling rate conversion to obtain pre-processed satellite navigation observation data.

[0029] S13: The inertial sensing measurement data is sequentially processed by zero bias compensation, scale factor correction, temperature compensation and outlier removal to obtain the pre-processed inertial navigation data.

[0030] S14: Perform spatiotemporal registration processing on the pre-processed satellite navigation observation data and the pre-processed inertial navigation data to obtain the pre-processed inertial navigation data and the pre-processed satellite navigation observation data; the pre-processed satellite navigation observation data is used as the radio frequency input of the satellite navigation baseband tracking loop; the pre-processed inertial navigation data is used to calculate the predicted quantity of the carrier motion state, generate satellite signal tracking auxiliary prior information and feed it forward to the satellite navigation baseband tracking loop to assist in completing continuous satellite signal tracking and outputting effective satellite observation signals, and obtain the state quantity through strapdown inertial navigation as the predicted value during Kalman filtering.

[0031] As an optional implementation method, in step S1, the satellite navigation observation data is compatible with multiple global satellite navigation systems and multiple frequency points; during the solution process, satellites with elevation angles higher than a set angle threshold are preferentially selected to participate in the extraction and filtering of pseudorange observations and carrier phase observations, and the combination of satellites participating in the solution is dynamically adjusted according to the satellite geometrical precision factor (GDOP).

[0032] As an optional implementation, in step S2, the predicted motion state of the carrier is calculated based on the preprocessed inertial navigation data, satellite signal tracking auxiliary prior information is generated, and the satellite signal tracking auxiliary prior information is fed forward to the satellite navigation baseband tracking loop to assist in completing continuous satellite signal tracking and outputting effective satellite observation signals. Specifically, this includes the following steps: S21: Based on the preprocessed inertial navigation data, the carrier attitude matrix is ​​updated using the quaternion method, and the real-time velocity and position of the carrier are solved by integration to obtain the predicted motion state of the carrier.

[0033] S22: Based on the predicted motion state of the carrier, combined with the satellite position and velocity obtained from the satellite navigation message analysis, calculate the relative radial velocity between the carrier and each visible navigation satellite to obtain the predicted Doppler frequency value.

[0034] S23: Calculate the code phase prediction value based on the Doppler frequency prediction value and the satellite-to-ground distance.

[0035] S24: The Doppler frequency prediction value and the code phase prediction value are used as the satellite signal tracking auxiliary prior information. The Doppler frequency prediction value is input into the carrier loop corresponding to the satellite navigation baseband tracking loop, and the code phase prediction value is input into the code loop corresponding to the satellite navigation baseband tracking loop. The frequency search range of the carrier loop is narrowed according to the Doppler frequency prediction value. A frequency discriminator is used for carrier frequency tracking. The loop bandwidth of the carrier loop and the code loop is adaptively adjusted according to the dynamic characteristics of the carrier.

[0036] As an optional implementation, the frequency discriminator is an inertial navigation-assisted FFT (Fast Fourier Transform) frequency discriminator. The inertial navigation-assisted FFT frequency discriminator uses partial frequency point FFT transformation to reduce the amount of computation and eliminates the interference of satellite navigation message jumps on the frequency discrimination results through complex square operation, so as to achieve stable tracking of satellite signals with high dynamics and low carrier-to-noise ratio.

[0037] S25: Real-time monitoring of the satellite signal carrier-to-noise ratio (CNR). When the CNR is less than a preset obstruction threshold, the operating mode of the satellite navigation baseband tracking loop is switched to open-loop tracking mode. The self-closed-loop feedback between the carrier loop and the code loop is disconnected. The Doppler frequency prediction value is used to directly control the output of the carrier NCO (numerically controlled oscillator), and the code phase prediction value is used to control the output of the code NCO. The carrier NCO and code NCO are the core execution units of the carrier loop and code loop in the satellite navigation baseband tracking loop. The carrier NCO is used to generate an adjustable local carrier signal to achieve carrier stripping, and the code NCO is used to drive the pseudo-code generator to generate pseudo-random codes to complete signal despreading.

[0038] S26: When the satellite signal carrier-to-noise ratio is greater than the preset recovery threshold for multiple consecutive epochs, the working mode of the satellite navigation baseband tracking loop is switched to closed-loop tracking mode. If the duration of open-loop tracking is greater than the preset time threshold, the satellite search range is compressed according to the Doppler frequency prediction value and the code phase prediction value, triggering the satellite signal fast reacquisition process.

[0039] S27: After stabilizing the satellite radio frequency signal based on the carrier ring and the code ring, perform carrier stripping and pseudo-code despreading processing on the received signal, and output the effective satellite observation signal.

[0040] As an optional implementation, step S3 involves performing gross error detection and removal on the pseudorange observations to obtain the gross error-removed pseudorange observations and carrier phase observations. This specifically includes the following steps: S31: Calculate the difference between the pseudorange prediction value and the pseudorange observation value.

[0041] S32: When the difference is greater than the pseudorange error threshold, the pseudorange observation is determined to be a gross error observation, and the gross error observation is discarded to obtain the pseudorange observation and carrier phase observation after removing the gross error. The pseudorange error threshold is dynamically configured according to the satellite navigation and positioning status; different pseudorange error thresholds correspond to RTK (Real-Time Dynamic Differential Positioning) fixed solutions, RTK floating-point solutions, and GNSS single-point solutions.

[0042] As an optional implementation, in step S4, the state vector of the integrated navigation filtering model is 15-dimensional, including three-dimensional position error, three-dimensional velocity error, three-dimensional attitude misalignment angle, three-dimensional gyroscope zero bias error, and three-dimensional accelerometer zero bias error in the navigation coordinate system.

[0043] When odometer-assisted fusion is used, the state vector of the combined navigation filtering model is expanded from 15 dimensions to 18 dimensions, including three-dimensional position error, three-dimensional velocity error, three-dimensional attitude misalignment angle, three-dimensional gyroscope zero bias error, three-dimensional accelerometer zero bias error, odometer scale factor error, and odometer two-dimensional installation angle error in the navigation coordinate system.

[0044] When odometer-assisted fusion is used, the observation vector of the Kalman filter includes the difference between the position increment calculated by strapdown inertial navigation and the position increment projected by the odometer, as well as the difference between the position increment calculated by strapdown inertial navigation and the position increment of satellite navigation. The system error state quantity is estimated by combining the observation equation of the Kalman filter that fuses satellite navigation, inertial navigation and odometer information.

[0045] As an optional implementation, in step S5, when correcting the state quantities obtained from the strapdown inertial navigation system based on the system error state quantities, the correction frequency is consistent with the output frequency of the satellite navigation observations. The corrected inertial navigation state quantities output the carrier's real-time positioning, velocity, and attitude information according to the original sampling frequency of the inertial sensor, and push satellite signal tracking auxiliary prior information to the satellite navigation baseband tracking loop at a fixed update frequency; when the satellite navigation signal is completely lost, pure inertial dead reckoning is performed based on the corrected inertial navigation state quantities.

[0046] like Figure 3 As shown, the inertial navigation system outputs the inertial navigation position increment through its internal navigation algorithm and outputs attitude information to the odometer. The odometer converts the distance increment into the odometer position increment based on the attitude information. The GNSS receiver outputs the GNSS position increment. The inertial navigation position increment is subtracted from the odometer position increment and the GNSS position increment to obtain two sets of residuals, which are then input into the Kalman filter. The Kalman filter outputs the final position information after Kalman filtering and generates two feedback correction signals, which are sent back to the inertial navigation system and the odometer, respectively, to achieve multi-sensor error closed-loop correction.

[0047] In an exemplary embodiment, to illustrate the technical solution provided by the embodiments of this application in detail, a GNSS and inertial navigation combined positioning closed-loop fusion solution method is provided, specifically including the following steps: Step 1: Synchronously acquire satellite navigation observation data and inertial sensor measurement data.

[0048] In this embodiment, the satellite navigation observation data is the raw GNSS intermediate frequency observation data output by the GNSS receiver, and the inertial sensing measurement data is the raw triaxial angular velocity and triaxial acceleration measurement data output by the inertial measurement unit (IMU).

[0049] In this embodiment, preprocessing of satellite navigation observation data and inertial sensing measurement data yields preprocessed inertial navigation data. The preprocessing includes: digital down-conversion, low-pass filtering, and sampling rate conversion of the satellite navigation observation data; zero-bias compensation, scale factor correction, temperature compensation, and outlier removal of the inertial sensing measurement data; and time synchronization of the satellite navigation observation data and inertial sensing measurement data, as well as spatial alignment between the carrier coordinate system and the navigation coordinate system.

[0050] This embodiment uses the 1PPS pulse output by the GNSS receiver as the global time reference. The internal sampling clock of the inertial measurement unit is strictly phase-locked with the 1PPS signal to ensure that the timestamp error between the two types of data is less than 1 microsecond. During the acquisition process, the GNSS receiver continuously outputs raw GNSS intermediate frequency observation data, and the inertial measurement unit synchronously outputs raw digital quantities of triaxial angular velocity and triaxial acceleration. All data are stored in a circular buffer with high-precision timestamps. The high-frequency signal and the intermediate frequency signal are mixed to zero intermediate frequency through a digital down-conversion module. Out-of-band noise and interference signals are filtered out through a multi-stage low-pass filter, and the sampling rate is converted by decimation interpolation to match the processing rate of the satellite navigation baseband tracking loop. The preprocessed inertial navigation data is stored in frames according to satellite channels. Each frame contains the intermediate frequency sampling sequence and time information of the corresponding satellite. Initial zero-bias compensation is performed using the factory-calibrated static zero-bias parameters, and real-time temperature compensation is performed by combining the measurement values ​​of the built-in temperature sensor with the factory-calibrated temperature zero-bias fitting curve. Scale factor correction, based on a laboratory-calibrated nonlinear scale factor matrix, converts the digital output of the inertial measurement unit (IMU) into physical quantities. Outlier removal employs a sliding window 3σ criterion, statistically analyzing measurements within the window to identify outliers exceeding three standard deviations and replacing them with linear interpolated values ​​from the previous time step. Time alignment is achieved through timestamp matching, interpolating satellite navigation observation data and inertial sensor measurement data at different sampling rates to a unified time node. Spatial alignment consists of two stages: coarse alignment and fine alignment. Coarse alignment uses the gravity vector and Earth's rotation angular velocity vector statically measured by the IMU to calculate the initial attitude matrices of the carrier coordinate system and the navigation coordinate system. Fine alignment uses high-precision position and velocity information provided by the GNSS receiver to construct a Kalman filter model to estimate the initial attitude error, achieving precise alignment of the two coordinate systems with an accuracy better than 0.1°.

[0051] Step 2: Based on the preprocessed inertial navigation data, calculate the predicted motion state of the carrier, generate satellite signal tracking auxiliary prior information, and feed the satellite signal tracking auxiliary prior information forward to the satellite navigation baseband tracking loop to assist in completing continuous satellite signal tracking and outputting effective satellite observation signals.

[0052] In this embodiment, the carrier motion state prediction (including the carrier's real-time attitude, velocity, and position predictions) is calculated based on the preprocessed inertial navigation data, and satellite signal tracking auxiliary prior information (including Doppler frequency predictions and code phase predictions for each visible navigation satellite) is calculated accordingly. This satellite signal tracking auxiliary prior information is fed back to the satellite navigation baseband tracking loop to assist the carrier loop and code loop in tracking the satellite signal and obtaining effective satellite observation signals.

[0053] In this embodiment, the auxiliary carrier loop and code loop for satellite signal tracking specifically include: narrowing the frequency search range of the carrier loop based on the Doppler frequency prediction value; using an inertial navigation-assisted FFT discriminator to achieve carrier frequency tracking in dynamic weak signal environments, and adaptively adjusting the loop bandwidth of the carrier loop and code loop according to the dynamic characteristics of the carrier; when the satellite signal carrier-to-noise ratio is detected to be less than a preset obstruction threshold (the conventional engineering range of the preset obstruction threshold is 18~22dB-Hz, with a typical configuration value of 20dB-Hz), switching to open-loop tracking mode, where the corresponding carrier NCO output and code NCO output are directly controlled by the Doppler frequency prediction value and the code phase prediction value). The inertial navigation-assisted FFT discriminator uses partial frequency point FFT transformation to reduce computational complexity and eliminates the influence of navigation message jumps on the discrimination results through complex square operations, achieving stable tracking of a 20dB-Hz weak signal under 100g acceleration / deceleration dynamic conditions.

[0054] In this embodiment, under open-loop tracking mode, the satellite signal carrier-to-noise ratio (CNR) is monitored in real time. When the CNR exceeds a preset recovery threshold for three or more consecutive epochs (the conventional engineering range for the preset recovery threshold is 22-27 dB-Hz, with a typical configuration of 24-25 dB-Hz), the system automatically switches back to closed-loop tracking mode. If the duration of a single open-loop tracking session is less than a preset time threshold (e.g., 20 seconds), a rapid reacquisition process is triggered after 20 seconds. This rapid reacquisition process refers to the satellite signal re-acquisition process initiated when the satellite signal has been operating in open-loop tracking mode for an extended period and the open-loop duration exceeds the preset time threshold of 20 seconds. This process relies on the Doppler frequency prediction and code phase prediction values ​​obtained from inertial navigation calculations to narrow the global search range of the carrier loop and code loop, replacing the large-scale blind search without assistance, and quickly re-locking the satellite carrier and pseudo-code signal. After reacquisition, the tracking mode is switched based on the satellite signal CNR to shorten the satellite search time and prevent complete loss of satellite lock and positioning interruption.

[0055] This application employs a quaternion method for attitude update. The quaternion change is obtained by integrating the three-axis angular velocity output from the inertial measurement unit (IMU) to update the carrier attitude matrix. The specific force measured by the accelerometer is converted to the Northeast-Eastern Sky navigation coordinate system via the attitude matrix. After subtracting gravitational acceleration and Coriolis acceleration, velocity is obtained through a first integration, and position through a second integration. The calculation frequency is consistent with the original sampling frequency of the IMU to ensure state continuity under high dynamic conditions. Based on the carrier velocity and position calculated by the IMU, combined with the satellite position and velocity calculated from the navigation message, the relative radial velocity between the satellite and the carrier is calculated and then converted into a carrier Doppler frequency prediction value. Based on the predicted relative distance between the carrier and the satellite, the code phase prediction value is obtained by dividing by the pseudocode chip length. The update frequency of the prediction values ​​is synchronized with the IMU calculation frequency to ensure that the tracking loop obtains continuous prior information.

[0056] In this embodiment, the satellite navigation baseband tracking loop assistance process is as follows: the frequency search range of the carrier loop is reduced from the traditional ±10kHz to within ±100Hz, significantly shortening the acquisition time and reducing the probability of false acquisition; based on the carrier acceleration and jerk calculated by the inertial navigation system, the loop bandwidth of the carrier loop and code loop is adjusted in real time, increasing the bandwidth under high dynamic conditions to improve tracking stability, and decreasing the bandwidth under low dynamic conditions to reduce noise; partial frequency point FFT transformation is used to replace full frequency point search, reducing computational complexity, and at the same time, the influence of navigation message jumps on the frequency discrimination results is eliminated through complex square operations, achieving stable tracking of weak signals of 20dB-Hz under 100g acceleration and deceleration dynamic conditions; when the satellite signal carrier-to-noise ratio is less than the preset obstruction threshold, it automatically switches to open-loop tracking mode, and the carrier NCO output and code NCO output are directly controlled by the inertial navigation prediction value to avoid tracking loop lock-up. During open-loop tracking mode, the carrier-to-noise ratio is monitored in real time. When the carrier-to-noise ratio is higher than the preset recovery threshold for more than 3 consecutive epochs, the system smoothly switches back to closed-loop tracking mode. If the open-loop tracking duration exceeds 20 seconds, a fast reacquisition process is triggered, which uses the inertial navigation prediction value to compress the search range to a very small area to achieve millisecond-level signal reacquisition.

[0057] Step 3: Demodulate the effective satellite observation signal to extract pseudorange and carrier phase observation values. Simultaneously, use inertial navigation extrapolation to estimate the corresponding pseudorange prediction values. Perform gross error detection and removal on the pseudorange observation values ​​to obtain the pseudorange and carrier phase observation values ​​after removing gross errors.

[0058] In this embodiment, the effective satellite observation signal is despread and demodulated to extract pseudorange and carrier phase observations. Simultaneously, pseudorange predictions are obtained using inertial navigation extrapolation. Gross errors are detected in the pseudorange observations, and those identified as having gross errors are removed, resulting in pseudorange and carrier phase observations after removing gross errors. Specifically, a pseudo-random code synchronized with the satellite signal is generated locally, and despreading is performed using the received intermediate frequency signal. The carrier is then removed through carrier demodulation to obtain the navigation message data. Simultaneously, pseudorange observations are extracted from the code loop tracking results, and carrier phase observations are extracted from the carrier loop tracking results. The output frequency of the observations is consistent with the original observation frequency of the GNSS receiver.

[0059] In this embodiment, the pseudorange prediction value for the current epoch is extrapolated using the inertial navigation state corrected in the previous epoch. Since the cumulative error of the inertial navigation is extremely small over a short period, the accuracy of the extrapolated pseudorange is much higher than that of the GNSS single-point pseudorange; therefore, it is used as the benchmark for gross error detection. The difference between the GNSS-extracted pseudorange and the inertial navigation extrapolated pseudorange is calculated. When the difference exceeds the pseudorange error threshold corresponding to the current positioning state, the pseudorange observation is determined to be a gross error and discarded. The pseudorange error threshold is dynamically set according to the GNSS positioning state: 0.2m in RTK fixed solution state, 1m in RTK floating-point solution state, and 3m in GNSS single-point solution state.

[0060] In this embodiment, for the observations that pass the gross error detection, quality weighting is performed based on the satellite elevation angle and carrier-to-noise ratio. The observations with higher elevation angles and better carrier-to-noise ratios are assigned greater weights, thereby reducing the adverse effects of low-elevation-angle and weak-signal observations on the filtering solution.

[0061] Step 4: Construct a combined navigation filter model that includes the state of inertial navigation error and the state of satellite navigation observation error. Based on the combined navigation filter model, use the state variables obtained by strapdown inertial navigation calculation of preprocessed inertial navigation data as predicted values. Use the differences between the pseudorange observation value and the carrier phase observation value after removing gross errors and the pseudorange prediction value as observations. Estimate the system error state variables online through Kalman filtering.

[0062] In this embodiment, the error state quantity is used as the filtering state to avoid the problems of large linearization error and poor numerical stability caused by directly using the absolute state quantity. The filtering process is divided into a time update stage and a measurement update stage. In the time update stage, the current error state and covariance matrix are predicted according to the inertial navigation error model. This inertial navigation error model is a commonly used error propagation mathematical model for strapdown inertial navigation and is used to predict the current error state and covariance matrix. In the measurement update stage, the pseudorange observations and carrier phase observations after removing gross errors are used to correct the error state and covariance matrix.

[0063] In this embodiment, the state vector of the integrated navigation filtering model is 15-dimensional, including three-dimensional position error, three-dimensional velocity error, three-dimensional attitude misalignment angle, three-dimensional gyroscope zero bias error, and three-dimensional accelerometer zero bias error in the northeast-northeast coordinate system. When odometer assistance is connected, the state vector is extended to 18-dimensional, adding odometer scale factor error and installation angle error in the heading and pitch directions, so as to realize online estimation and compensation of odometer system error.

[0064] In this embodiment, the position increment is used as the observation measure to avoid the influence of systematic errors present in absolute position observations. The Kalman filter observation equation that fuses satellite navigation, inertial navigation, and odometer information is as follows: ; in, For the first The observation vector for each filtering cycle; For inertial navigation in the first The position increment in the northeast celestial coordinate system calculated within each filtering cycle; For the odometer at the 1st The position increment measured and projected onto the northeast celestial coordinate system within each filtering cycle; For GNSS receivers in the first The position increment in the northeast celestial coordinate system calculated within each filtering cycle; For the first The observation matrix for each filtering period; For the first The system state vector for each filtering cycle includes inertial navigation position error, velocity error, misalignment angle and odometer installation angle error, and scale factor error; For the first The observed noise vector for each filtering period follows a Gaussian distribution with a mean of zero.

[0065] In this embodiment, the fusion logic of satellite navigation, inertial navigation and odometer information includes: when the satellite navigation signal is normal, the observations of both satellite navigation and odometer are used for measurement updates; when the satellite navigation signal is partially lost, only the observations of available satellite navigation and odometer are used for fusion; when the satellite navigation signal is completely lost, only the observations of odometer are used for measurement updates, further suppressing the accumulation of inertial navigation errors.

[0066] In this embodiment, the integrated navigation filtering model refers to the core computational model that achieves complementary fusion of satellite navigation and inertial navigation errors. This integrated navigation filtering model incorporates two error dimensions: inertial navigation error state and satellite navigation observation error state. The state variables obtained by strapdown inertial navigation calculation based on preprocessed inertial navigation data are used as the filtering prediction input. The difference between the pseudorange observation value after removing gross errors and the carrier phase observation value and the inertial navigation extrapolated pseudorange prediction value are used as the filtering observation. The Kalman filter is used to realize the online estimation of all system error state variables. The obtained system error state variables are used to correct the strapdown inertial navigation calculation results. By integrating absolute observations from satellite navigation, high-frequency autonomous observations from inertial navigation (INS), and odometer observations from optional odometry into a single framework, this approach balances the long-term drift-free accuracy of satellite navigation, the short-term high-frequency output characteristics of INS, and the anti-interference capability of odometry. Filtering is used to estimate the position, velocity, and attitude errors of the INS, as well as the zero-bias errors of the gyroscope and accelerometer, to correct the INS calculation results and fundamentally suppress the inherent defect of INS error accumulation over time. Dual-observation fusion is performed when satellite navigation is normal; when satellite navigation is partially lost, fusion with available satellites and odometry is retained; when satellite navigation is completely lost, only odometry observations constrain INS drift; and when there are no external observations, it degenerates into pure INS recursion, ensuring continuous and uninterrupted positioning. Using error quantities rather than absolute quantities as states reduces linearization errors caused by nonlinearity, improving the numerical stability and estimation accuracy of filtering.

[0067] In this embodiment, the working process of the integrated navigation filtering model includes: In each inertial navigation sampling period, the integrated navigation filtering model, based on the inertial navigation error propagation law, recursively predicts the prior value of the error state and the corresponding covariance matrix at the current moment from the error state estimate and covariance matrix of the previous moment. This stage requires no external observation input, relying solely on the error evolution characteristics of the inertial navigation system itself to complete the prediction, and is performed synchronously with the inertial navigation solution. When the satellite navigation observation or odometer observation reaches the filtering period node, this stage is executed: The qualified satellite navigation observation after gross error removal and quality weighting, and the odometer mileage observation are input; the differences between the inertial navigation solution position increment and the satellite navigation position increment and odometer position increment are calculated respectively, forming the observation residual; the observation residual is mapped to the error state space through the observation matrix, and the Kalman gain is calculated based on the statistical characteristics of the observation noise; the prior prediction value of the error state is corrected using the Kalman gain to obtain the optimal posterior estimate of the error state at the current moment, and the covariance matrix is ​​updated simultaneously.

[0068] like Figure 4As shown, the antenna receives satellite radio frequency signals and sends them to the radio frequency front end. After passing through a low-noise amplifier and a bandpass filter, the signals are mixed with the local oscillator signal and converted into digital signals by an ADC, which are then input to the satellite navigation baseband tracking loop. The digital signals are sequentially processed by carrier stripping, pseudo-code stripping, and integral zeroing, and then sent to the carrier loop discriminator and code loop discriminator, respectively. After passing through the carrier loop filter and code loop filter, control signals are output to adjust the carrier NCO and code NCO, respectively. The code NCO drives the code generator to generate local pseudo-code. The satellite navigation baseband tracking loop outputs observations and navigation messages to the PVT calculation module for PVT calculation, completing the position, velocity, and time calculations. At the same time, the satellite navigation baseband tracking loop is equipped with an external input path, which can access the carrier motion prior information output by the inertial navigation system and feed it forward to the code NCO and carrier NCO to realize inertial navigation prior-assisted satellite signal tracking.

[0069] Step 5: Correct the state variables obtained from the strapdown inertial navigation system based on the system error state variables to obtain the corrected inertial navigation state variables. Then, input the corrected inertial navigation state variables in reverse into the satellite navigation baseband tracking loop to iteratively update the satellite signal tracking auxiliary prior information, forming a two-way closed-loop fusion link of inertial-assisted satellite tracking and satellite observation correction inertial calculation.

[0070] In this embodiment, the estimated system error state variables are used to correct the state variables of the attitude, velocity and position results obtained by the strapdown inertial navigation system, so as to obtain the corrected inertial navigation state variables. At the same time, the corrected inertial navigation state variables are used as the auxiliary input of the satellite navigation baseband tracking loop in the next epoch, thereby forming a closed-loop fusion solution of "inertial-assisted satellite tracking and satellite observation to correct inertial solution".

[0071] In this embodiment, during the correction process, position and velocity are corrected additively by subtracting the position and velocity errors estimated by the filter directly from the position and velocity calculated by the inertial navigation system; attitude is corrected multiplicatively by constructing a correction matrix using the attitude misalignment angle estimated by the filter and multiplying it with the original attitude matrix to obtain the corrected attitude matrix; and the gyroscope and accelerometer bias are corrected by feedback by updating the bias error estimated by the filter to the compensation parameters of the integrated navigation filter model in real time, thereby suppressing the drift error of the inertial devices from the source.

[0072] In this embodiment, the correction frequency of the inertial navigation state variables is consistent with the output frequency of the satellite navigation observations, ensuring that error correction is performed every time a satellite navigation observation arrives. The corrected inertial navigation position, velocity, and attitude information are output at the original inertial navigation sampling frequency, while the Doppler frequency prediction and code phase prediction values ​​are updated to the satellite navigation baseband tracking loop at a frequency of 1Hz, balancing computational load and auxiliary accuracy. When the satellite navigation signal is completely lost and there is no odometer assistance, the system automatically switches to pure inertial dead reckoning mode. At this time, the Kalman filter stops measurement updates and only performs time updates, continuously compensating for the drift of inertial devices using the previously estimated zero bias errors of the gyroscopes and accelerometers, ensuring navigation accuracy for a short period of time, with a reckoning error not exceeding 0.1 nautical miles per hour.

[0073] The embodiments of this application can improve the tracking capability of satellite navigation in weak signal and high dynamic environments through inertial navigation assistance, enabling satellite navigation to output more stable and reliable observation values; the satellite navigation observation values ​​then correct the cumulative error of the inertial navigation in real time, ensuring the long-term accuracy of the inertial navigation state. The two promote each other and form a virtuous cycle.

[0074] Step 6: Output the combined positioning closed-loop fusion solution result after closed-loop fusion solution.

[0075] In this embodiment, the combined positioning closed-loop fusion solution includes real-time positioning information, velocity information, and attitude information of the carrier. These information can be categorized into core navigation information and auxiliary information. Core navigation information includes the carrier's longitude, latitude, altitude, eastward velocity, northward velocity, azimuth velocity, pitch angle, and roll angle. Auxiliary information includes positioning status indicators, satellite status information, and error estimation information. Positioning status indicators include GNSS single-point solutions, RTK floating-point solutions, RTK fixed solutions, combined navigation solutions, and pure inertial navigation solutions. Satellite status information includes the number of visible navigation satellites, the carrier-to-noise ratio of each satellite, and the satellite geometrical accuracy factor. Error estimation information includes estimated position errors, estimated velocity errors, and estimated attitude errors.

[0076] In this embodiment, the format and frequency of the output combined positioning closed-loop fusion solution are as follows: It supports both the NMEA0183 standard text format and a custom binary format, which can be flexibly selected according to application requirements. The basic output frequency of the navigation information is 1Hz, and it can support high-frequency output consistent with the sampling frequency of the inertial measurement unit to meet the needs of different scenarios.

[0077] In this embodiment of the application, the satellite selection strategy is as follows: satellite navigation observation data supports multi-system and multi-frequency signal processing of GPSL1 / L2, Beidou B1i / B2i, and GalileoE1 / E5b; during the calculation, satellites with an elevation angle higher than 20° are preferentially selected to participate in the observation value extraction and filtering calculation, and the combination of satellites participating in the calculation is dynamically adjusted according to the satellite geometric accuracy factor.

[0078] In this embodiment, the combined positioning closed-loop fusion calculation result can be used to provide continuous and highly reliable carrier position, speed, attitude and positioning information to the carrier's upper-level control system, scheduling platform and monitoring terminal; and dynamically execute strategy switching according to positioning mode flag, satellite status and error estimation to support carrier autonomous motion control, status monitoring, trajectory recording, safety alarm and other services.

[0079] This application's embodiments feed back the predicted Doppler frequency and code phase values ​​calculated by the inertial navigation system (INS) to the satellite navigation baseband tracking loop, improving satellite navigation tracking capabilities at the signal level. High-precision satellite navigation observations are used to estimate and correct the INS' attitude, velocity, and position errors, as well as device bias, in real time. The corrected INS state variables continue to serve as tracking auxiliary inputs for the next epoch. When the satellite signal carrier-to-noise ratio (CNR) is less than a preset obstruction threshold, the open-loop tracking mode is automatically switched, with the INS prediction value directly controlling the NCO output. After signal recovery, the loop can smoothly switch back to closed-loop tracking. After an open-loop timeout, millisecond-level rapid reacquisition is achieved based on INS prediction, ensuring positioning continuity. Using the high-precision pseudorange extrapolated from the INS in a short time as a benchmark, differentiated gross error removal thresholds are set for three positioning states: RTK fixed solution, RTK floating-point solution, and GNSS single-point solution. This accurately identifies and removes abnormal observations caused by multipath interference. Simultaneously, qualified observations are quality-weighted according to satellite elevation angle and CNR, reducing the adverse effects of low-elevation angle and weak signal observations on the filtering results.

[0080] Based on the same inventive concept, this application also provides a GNSS and inertial navigation integrated positioning closed-loop fusion calculation device for implementing the GNSS and inertial navigation integrated positioning closed-loop fusion calculation method described above. The solution provided by this device is similar to the implementation scheme described in the above method. Therefore, the specific limitations in the GNSS and inertial navigation integrated positioning closed-loop fusion calculation device embodiments provided below can be found in the limitations of the GNSS and inertial navigation integrated positioning closed-loop fusion calculation method described above, and will not be repeated here.

[0081] In an exemplary embodiment, a GNSS and inertial navigation integrated positioning closed-loop fusion calculation device is provided, including a data acquisition module and a data processing module. The output end of the data acquisition module is connected to the input end of the data processing module. The data acquisition module is used to synchronously acquire satellite navigation observation data and inertial sensing measurement data, and transmit them to the data processing module. The data processing module is used to execute the GNSS and inertial navigation integrated positioning closed-loop fusion calculation method based on the satellite navigation observation data and the inertial sensing measurement data, and finally output the integrated positioning closed-loop fusion calculation result. The integrated positioning closed-loop fusion calculation result includes real-time positioning information, velocity information, and attitude information of the carrier.

[0082] This application embodiment simultaneously acquires satellite navigation observation data and inertial sensor measurement data, and performs preprocessing. Based on the attitude, velocity, and position calculated by the inertial navigation system, it predicts the satellite's Doppler frequency and code phase, and feeds this data back to the satellite navigation baseband tracking loop to assist carrier loop and code loop tracking. It then despreads and demodulates to extract satellite navigation observation values; constructs a combined navigation filter model; uses the position increment as the observation quantity; and estimates the system error state quantity using Kalman filtering. The system error state quantity is used to correct the inertial navigation state, and the corrected inertial navigation state quantity is used as an auxiliary input to the satellite navigation baseband tracking loop. By constructing a bidirectional closed-loop fusion architecture, it significantly improves signal tracking stability and positioning accuracy in high-dynamic and weak-signal scenarios, supports odometry extension fusion, and is suitable for continuous high-precision positioning in multiple scenarios such as vehicle-mounted and surveying applications.

[0083] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 5 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores satellite navigation observation data and inertial sensing measurement data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a GNSS and inertial navigation integrated positioning closed-loop fusion solution method.

[0084] Figure 5The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0085] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0086] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0087] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations and be authorized by the owner of the corresponding device.

[0088] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0089] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments provided in this application may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0090] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0091] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A GNSS and inertial navigation combined positioning closed-loop fusion solution method, characterized in that, include: Acquire satellite navigation observation data and inertial sensor measurement data and preprocess them to obtain preprocessed inertial navigation data; Based on the preprocessed inertial navigation data, the predicted motion state of the carrier is calculated, satellite signal tracking auxiliary prior information is generated, and the satellite signal tracking auxiliary prior information is fed forward to the satellite navigation baseband tracking loop to assist in completing continuous satellite signal tracking and outputting effective satellite observation signals. The effective satellite observation signal is demodulated to extract pseudorange and carrier phase observation values. At the same time, the corresponding pseudorange prediction value is estimated by inertial navigation extrapolation. Gross error detection and gross error removal are performed on the pseudorange observation values ​​to obtain the pseudorange and carrier phase observation values ​​after removing gross errors. A combined navigation filtering model is constructed that includes the state of inertial navigation error and the state of satellite navigation observation error. Based on the combined navigation filtering model, the state variables obtained by strapdown inertial navigation calculation based on the preprocessed inertial navigation data are used as predicted values. The differences between the pseudorange observation value and the carrier phase observation value after removing gross errors and the pseudorange predicted value are used as observations. The system error state variables are estimated online by Kalman filtering. The state quantities obtained by the strapdown inertial navigation system are corrected based on the system error state quantities to obtain corrected inertial navigation state quantities. The corrected inertial navigation state quantities are then input back into the satellite navigation baseband tracking loop to iteratively update the satellite signal tracking auxiliary prior information. Finally, the combined positioning closed-loop fusion solution result is output. The combined positioning closed-loop fusion solution results include the carrier's real-time positioning information, velocity information, and attitude information.

2. The GNSS and inertial navigation combined positioning closed-loop fusion solution method according to claim 1, characterized in that, Acquire satellite navigation observation data and inertial sensor measurement data and preprocess them to obtain preprocessed inertial navigation data, specifically including: Simultaneously acquire satellite navigation observation data and inertial sensor measurement data; The satellite navigation observation data is sequentially subjected to digital down-conversion, low-pass filtering, and sampling rate conversion to obtain pre-processed satellite navigation observation data. The inertial sensing measurement data is sequentially processed with zero bias compensation, scale factor correction, temperature compensation, and outlier removal to obtain pre-processed inertial navigation data. The pre-processed satellite navigation observation data and the pre-processed inertial navigation data are spatiotemporally registered to obtain the pre-processed inertial navigation data and the pre-processed satellite navigation observation data. The pre-processed satellite navigation observation data is used as the radio frequency input of the satellite navigation baseband tracking loop. The pre-processed inertial navigation data is used to calculate the predicted motion state of the vehicle, generate satellite signal tracking auxiliary prior information and feed it forward to the satellite navigation baseband tracking loop to assist in the continuous tracking of satellite signals and output effective satellite observation signals. The state variables are also calculated by strapdown inertial navigation and used as the predicted values ​​during Kalman filtering.

3. The GNSS and inertial navigation combined positioning closed-loop fusion solution method according to claim 1, characterized in that, Based on the preprocessed inertial navigation data, the predicted motion state of the carrier is calculated, satellite signal tracking auxiliary prior information is generated, and this satellite signal tracking auxiliary prior information is fed forward to the satellite navigation baseband tracking loop to assist in completing continuous satellite signal tracking and outputting effective satellite observation signals. Specifically, this includes: Based on the preprocessed inertial navigation data, the quaternion method is used to update the carrier attitude matrix, and the real-time velocity and position of the carrier are solved by integration to obtain the predicted carrier motion state. Based on the predicted motion state of the carrier, combined with the satellite position and velocity obtained from the satellite navigation message analysis, the relative radial velocity between the carrier and each visible navigation satellite is calculated to obtain the predicted Doppler frequency value. Based on the Doppler frequency prediction value and the satellite-to-ground distance, the code phase prediction value is calculated. The Doppler frequency prediction value and the code phase prediction value are used as the satellite signal tracking auxiliary prior information. The Doppler frequency prediction value is input into the carrier loop corresponding to the satellite navigation baseband tracking loop, and the code phase prediction value is input into the code loop corresponding to the satellite navigation baseband tracking loop. The frequency search range of the carrier loop is narrowed according to the Doppler frequency prediction value. A frequency discriminator is used for carrier frequency tracking. The loop bandwidth of the carrier loop and the code loop is adaptively adjusted according to the dynamic characteristics of the carrier. The satellite signal carrier-to-noise ratio is monitored in real time. When the satellite signal carrier-to-noise ratio is less than a preset blocking threshold, the working mode of the satellite navigation baseband tracking loop is switched to open-loop tracking mode, the self-closed-loop feedback of the carrier loop and the code loop is disconnected, and the carrier NCO output is directly controlled by the Doppler frequency prediction value and the code NCO output is controlled by the code phase prediction value. When the satellite signal carrier-to-noise ratio is greater than the preset recovery threshold for multiple consecutive epochs, the working mode of the satellite navigation baseband tracking loop is switched to closed-loop tracking mode. If the duration of open-loop tracking is greater than the preset time threshold, the satellite search range is compressed according to the Doppler frequency prediction value and the code phase prediction value, triggering the satellite signal fast reacquisition process. After the satellite radio frequency signal is stably tracked based on the carrier loop and the code loop, the received signal is subjected to carrier stripping and pseudo-code despreading processing to output the effective satellite observation signal.

4. The GNSS and inertial navigation combined positioning closed-loop fusion solution method according to claim 3, characterized in that, The frequency discriminator is an inertial navigation-assisted FFT frequency discriminator. The inertial navigation-assisted FFT frequency discriminator uses partial frequency point FFT transformation to reduce the amount of computation and eliminates the interference of satellite navigation message jumps on the frequency discrimination results through complex number square operation.

5. The GNSS and inertial navigation combined positioning closed-loop fusion solution method according to claim 1, characterized in that, Gross error detection and removal are performed on the pseudorange observations to obtain the pseudorange observations and carrier phase observations after removing gross errors, specifically including: Calculate the difference between the pseudorange prediction and the pseudorange observation; When the difference is greater than the pseudorange error threshold, the pseudorange observation is determined to be a gross error observation, and the gross error observation is removed to obtain the pseudorange observation and carrier phase observation after removing the gross error.

6. The GNSS and inertial navigation combined positioning closed-loop fusion solution method according to claim 1, characterized in that, The state vector of the integrated navigation filtering model is 15-dimensional, including three-dimensional position error, three-dimensional velocity error, three-dimensional attitude misalignment angle, three-dimensional gyroscope zero bias error and three-dimensional accelerometer zero bias error in the navigation coordinate system; When odometer-assisted fusion is used, the state vector of the combined navigation filtering model is expanded from 15 dimensions to 18 dimensions, including three-dimensional position error, three-dimensional velocity error, three-dimensional attitude misalignment angle, three-dimensional gyroscope zero bias error, three-dimensional accelerometer zero bias error, odometer scale factor error and odometer two-dimensional installation angle error in the navigation coordinate system. When odometer-assisted fusion is used, the observation vector of the Kalman filter includes the difference between the position increment calculated by strapdown inertial navigation and the position increment projected by the odometer, as well as the difference between the position increment calculated by strapdown inertial navigation and the position increment of satellite navigation. The system error state quantity is estimated by combining the observation equation of the Kalman filter that fuses satellite navigation, inertial navigation and odometer information.

7. The GNSS and inertial navigation combined positioning closed-loop fusion solution method according to claim 6, characterized in that, The Kalman filter observation equation that integrates satellite navigation, inertial navigation, and odometry information is as follows: ； in, For the first The observation vector for each filtering cycle; For inertial navigation in the first The position increment in the northeast celestial coordinate system calculated within each filtering cycle; For the odometer at the 1st The position increment measured and projected onto the northeast celestial coordinate system within each filtering cycle; For GNSS receivers in the first The position increment in the northeast celestial coordinate system calculated within each filtering cycle; For the first The observation matrix for each filtering period; For the first The system state vector for each filtering cycle; For the first The observation noise vector for each filtering cycle.

8. A GNSS and inertial navigation combined positioning closed-loop fusion calculation device, characterized in that, The system includes a data acquisition module and a data processing module. The output of the data acquisition module is connected to the input of the data processing module. The data acquisition module is used to simultaneously acquire satellite navigation observation data and inertial sensing measurement data, and transmit them to the data processing module. The data processing module is used to execute the GNSS and inertial navigation combined positioning closed-loop fusion solution method according to any one of claims 1-7 based on the satellite navigation observation data and the inertial sensing measurement data, and finally output the combined positioning closed-loop fusion solution result. The combined positioning closed-loop fusion solution result includes the carrier's real-time positioning information, velocity information, and attitude information.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the GNSS and inertial navigation combined positioning closed-loop fusion solution method according to any one of claims 1-7.

10. A 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 GNSS and inertial navigation combined positioning closed-loop fusion solution method as described in any one of claims 1-7.

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