A single antenna anomaly detection and correction method based on doppler velocity measurement

Abstract

The application discloses a single-antenna abnormality detection and correction method based on Doppler velocity measurement, and relates to the technical field of integrated navigation, and has the technical scheme as follows: original observation data of GNSS is acquired, and the original observation data at least includes Doppler velocity measurement information; carrier velocity information at the current moment is solved based on the Doppler velocity measurement information; attitude information of the current carrier is acquired; whether the carrier is in an abnormal motion state is judged based on the carrier velocity information and the attitude information; an NHC model is constructed; according to the judgment result, the constraint strategy of the NHC model is adjusted, the measurement information is constructed based on the adjusted constraint strategy, the integrated navigation filter used for estimating the navigation state of the carrier is observed and updated by using the measurement information, and the navigation error of the carrier is corrected. The application judges the abnormal state by using the Doppler velocity measurement information and adaptively adjusts the NHC constraint strategy, so that the precision of the single-antenna integrated navigation under abnormal working conditions is improved without increasing the hardware cost.

Description

Technical Field

[0001] This invention relates to the field of integrated navigation technology, and more specifically, to a single-antenna anomaly detection and correction method based on Doppler velocity measurement. Background Technology

[0002] In agriculture and construction machinery, single-antenna integrated navigation is the most commonly used orientation / positioning method. The latitude, longitude, altitude, velocity, and three attitude angles (roll, pitch, and heading) output by the integrated navigation equipment are the main inputs to the control algorithm. The working environments in agriculture and construction machinery are harsh, and equipment frequently encounters abnormal scenarios such as sideslip and severe turbulence. In such scenarios, traditional integrated navigation solutions often suffer from significant positioning and orientation deviations due to the susceptibility of a single Global Navigation Satellite System (GNSS) signal to interference and the accelerated accumulation of errors in the Inertial Measurement Unit (IMU), leading to decreased control accuracy and poorer operational performance.

[0003] Chinese patent CN119164389A discloses a multi-sensor fusion positioning method and electronic device. Its key technical feature is the addition of a laser sensor for multi-source fusion, fusing laser point cloud data with GNSS / IMU data to improve attitude stability and accuracy. Chinese patent CN202411315567.8 discloses a multi-sensor fusion-based navigation method for an orchard inspection robot. Its key technical feature is the addition of laser, visual, and odometry information for attitude estimation.

[0004] The above solutions address the issue of decreased accuracy of a single sensor in harsh environments. However, the addition of extra sensors (such as laser sensors, vision sensors, or odometers) or the adoption of dual-antenna schemes significantly increases hardware costs and introduces time synchronization errors between multiple sensors, leading to decreased system complexity and adaptability. Furthermore, some fusion algorithms (such as neural network-based schemes) are computationally intensive and difficult to run in real-time on low-cost microcontrollers.

[0005] Therefore, a new solution is needed to address this problem. Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide a single-antenna anomaly detection and correction method based on Doppler velocity measurement, which utilizes the Doppler velocity information that can be output by GNSS itself to achieve accurate detection and correction of abnormal motion states without the need to add any additional sensors.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a single-antenna anomaly detection and correction method based on Doppler velocities, comprising: Acquire raw GNSS observation data, which includes at least Doppler velocity information; The current velocity information of the carrier is calculated based on the Doppler velocity measurement information; Obtain the current attitude information of the carrier; Based on the carrier speed information and the attitude information, it is determined whether the carrier is in an abnormal motion state; An NHC model is constructed, which is used to provide lateral motion constraints for the vehicle under normal driving conditions; Based on the judgment results, the constraint strategy of the NHC model is adjusted, measurement information is constructed based on the adjusted constraint strategy, and the measurement information is used to update the combined navigation filter used to estimate the navigation state of the vehicle in order to correct the navigation error of the vehicle.

[0008] Preferably, the step of calculating the carrier velocity information at the current moment based on the Doppler velocity measurement information includes: The Doppler residual values ​​for each visible satellite are calculated based on the fundamental Doppler observation equations, which are: , Where Di is the Doppler frequency shift value of the i-th satellite, and is the carrier wavelength. It is the velocity information of the i-th satellite. It is the carrier speed information. It is the receiver clock bias rate of change. Other observation errors; A least-squares problem is constructed based on the Doppler residuals of multiple satellites at the same time, and the carrier velocity information at the current time is obtained by solving the problem. .

[0009] Preferably, obtaining the current carrier's attitude information includes: Acquire measurement data from an IMU, which includes a gyroscope and an accelerometer; The attitude information of the current vehicle is obtained by combining the IMU measurement data with the GNSS signal for navigation measurement processing. The GNSS signal is a signal that meets preset availability conditions.

[0010] Preferably, determining whether the carrier is in an abnormal motion state based on the carrier speed information and the attitude information includes: Based on the attitude information, the calculated carrier velocity information is transformed to the carrier coordinate system to obtain the forward velocity, lateral velocity, and vertical velocity in the carrier coordinate system. The coordinate transformation is expressed as follows: , in, The velocity vector in the carrier coordinate system obtained from Doppler velocity measurement. This is the transformation matrix from the Earth coordinate system to the vehicle coordinate system. To calculate the vehicle's velocity information at the current moment; The lateral velocity in the carrier coordinate system is integrated using a preset sliding window to obtain the cumulative value of the lateral velocity; It is determined whether the cumulative value of the lateral velocity exceeds a preset abnormal threshold within the sliding window, and at the same time, it is determined whether the fluctuation of the Z-axis angular velocity of the IMU is less than a preset fluctuation threshold. If the cumulative value of the lateral velocity exceeds the abnormal threshold and the fluctuation of the Z-axis angular velocity is less than the fluctuation threshold, then the carrier is determined to be in an abnormal motion state.

[0011] Preferably, the preset sliding window is one second, and the abnormal motion state includes a side-sliding state.

[0012] Preferably, the combined navigation filter is a Kalman filter, and its state vector is represented as: , in, This represents the position error in the navigation coordinate system. The velocity error in the navigation coordinate system. This represents the attitude error in the navigation coordinate system. For gyroscope zero bias error, This refers to the zero bias error of the accelerometer.

[0013] Preferably, the step of constructing measurement information based on the adjusted constraint strategy and using the measurement information to update the integrated navigation filter used to estimate the vehicle navigation state includes: The observation innovation vector is obtained by subtracting the calculated transverse error from the INS prediction. The observation equation is expressed as follows: , The lateral error calculation is based on the lateral velocity constraint error of the carrier determined by the NHC model. Denotes the observation information vector at time k. This represents the observation matrix corresponding to the state vector at time k. This represents the state vector at time k. Indicates observational noise; Based on the judgment results, different forms of observation innovation vector representation are used for observation updates. When the carrier is judged to be in normal motion, the observation innovation vector is represented as: , in, It is the y-axis velocity of the carrier coordinate system. It is the z-axis velocity of the carrier coordinate system; When the carrier is determined to be in an abnormal motion state, the observed information vector is represented as: , in, It is the y-axis velocity of the carrier coordinate system calculated based on Doppler velocity measurement. It is the z-axis velocity of the carrier coordinate system based on Doppler velocity measurement.

[0014] Preferably, the preset availability conditions include at least one of the following: The time interval between the GNSS signal and the current IMU signal meets the first preset threshold. The GNSS solution state is a fixed solution; The velocity measurement information in the GNSS buffer queue meets the second set threshold; The position accuracy factor and velocity accuracy factor of the GNSS signal meet the third set threshold; the signal-to-noise ratio of the Doppler velocity measurement value meets the fourth set threshold.

[0015] A single-antenna anomaly detection and correction system based on Doppler velocities includes: The data acquisition module is used to acquire raw GNSS observation data, which includes at least Doppler velocity information. The velocity calculation module is used to calculate the current velocity information of the carrier based on the Doppler velocity measurement information; The attitude acquisition module is used to acquire the attitude information of the current carrier; An anomaly detection module is used to determine whether the carrier is in an abnormal motion state based on the carrier speed information and the attitude information. The model building module is used to build the NHC model, which provides lateral motion constraints for the vehicle under normal driving conditions. The filter update module is used to adjust the constraint strategy of the NHC model according to the judgment result of the anomaly judgment module, construct measurement information based on the adjusted constraint strategy, and use the measurement information to update the combined navigation filter used to estimate the navigation state of the vehicle, so as to correct the navigation error of the vehicle.

[0016] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the described methods for single-antenna anomaly detection and correction based on Doppler velocimetry.

[0017] Compared with existing technologies, the advantages of the single-antenna anomaly detection and correction method based on Doppler velocimetry disclosed in this invention are: 1. By utilizing the Doppler velocity information that GNSS itself can output, accurate detection and correction of abnormal motion states such as sideslip and severe turbulence are achieved without adding any additional sensors (such as vision, laser, odometer) or dual antennas. This reduces the hardware cost of integrated navigation products and expands the application scope of low-cost single-antenna products. 2. By judging abnormal states based on Doppler velocimetry and adjusting strategies using the NHC model, the entire algorithm process does not involve complex neural networks or other high-computation models. It has a small computational load and can run efficiently on a low-cost microcontroller platform, demonstrating good engineering practicality. 3. By introducing Doppler velocity measurement information to perform independent carrier velocity calculation, and using this as a basis to dynamically adjust the constraint strategy of the NHC model, it is possible to effectively suppress the divergence of attitude and positioning errors of agricultural machinery or engineering vehicles in scenarios such as sideslip and severe bumps, thereby improving the robustness and accuracy of the integrated navigation system under harsh working conditions and ensuring the operation quality of agricultural and engineering machinery. 4. Compared with solutions that rely on specific sensor models or dual-antenna hardware, this invention does not change the original single-antenna hardware architecture, thus it has better adaptability and is easier to upgrade and promote on existing agricultural machinery and engineering vehicle products. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a single-antenna anomaly detection and correction method based on Doppler velocimetry, according to an embodiment of this application. Detailed Implementation

[0020] The technical solution of the present invention will now be clearly and completely described through specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 This application provides a single-antenna anomaly detection and correction method based on Doppler velocities. This method can be applied to agricultural machinery (such as tractors, harvesters, and rice transplanters) or engineering vehicles (such as excavators, bulldozers, and road rollers), and includes: S1. Acquire raw GNSS (Global Navigation Satellite System) observation data, which includes at least Doppler velocity information. Specifically, this involves real-time reception of raw observation data output from a GNSS receiver mounted on agricultural machinery or engineering vehicles. This data includes at least Doppler velocity information for each satellite. Doppler velocity information is a basic output quantity calculated by the GNSS receiver based on changes in the satellite signal carrier frequency, reflecting the relative motion between the carrier and the satellite. As a preferred implementation, an RTK receiver can be used to obtain more accurate raw observation data, providing a reliable data foundation for subsequent anomaly detection.

[0022] S2. The current velocity information of the carrier is calculated based on the Doppler velocity measurement information.

[0023] S3. Obtain the current attitude information of the carrier.

[0024] S4. Based on the carrier's speed and attitude information, determine whether the carrier is in an abnormal motion state.

[0025] S5. Construct the NHC model, which is used to provide lateral motion constraints for the vehicle under normal driving conditions.

[0026] S6. Based on the judgment result, adjust the constraint strategy of the NHC model, construct measurement information based on the adjusted constraint strategy, and use the measurement information to update the combined navigation filter used to estimate the vehicle's navigation state in order to correct the vehicle's navigation error.

[0027] In this embodiment, step S2, calculating the carrier velocity information at the current moment based on Doppler velocity measurement information, includes: First, the Doppler residual values ​​for each visible satellite are calculated based on the fundamental Doppler observation equations. The fundamental Doppler observation equations are as follows: , Where Di is the Doppler frequency shift value of the i-th satellite, and is the carrier wavelength. It is the velocity information of the i-th satellite. It is the carrier speed information. It is the receiver clock bias rate of change. Other observation errors; Then, since multiple satellites can be observed at the same time, a least-squares problem is constructed based on the Doppler residuals of these satellites, and the carrier velocity information at the current moment is obtained by solving the overdetermined equations. This calculation process makes full use of the redundant observation information from multiple satellites, effectively suppressing the influence of single satellite observation noise and obtaining high-precision velocity estimates. The calculated carrier velocity information is a three-dimensional velocity vector of the carrier in the Earth coordinate system, which has the characteristics of good instantaneity and can truly reflect the carrier's motion state at the current moment.

[0028] In this embodiment, step S3, obtaining the current carrier's attitude information, includes: First, measurement data is acquired from the IMU (Inertial Measurement Unit), which includes a gyroscope and an accelerometer. The gyroscope measures the angular velocity of the carrier relative to inertial space, while the accelerometer measures the specific force of the carrier relative to inertial space. The IMU outputs raw measurement data at a fixed sampling frequency, which forms the basis for attitude calculation.

[0029] Then, the IMU measurement data and GNSS signals are combined for navigation measurement processing to obtain the current attitude information of the vehicle. Specifically, the angular velocity and acceleration measurements from the IMU are integrated to obtain predicted values ​​of the vehicle's position, velocity, and attitude. At the same time, the position and velocity observations provided by GNSS are used as measurement information to correct the predicted values. Through this fusion processing, complete attitude information such as the vehicle's roll angle, pitch angle, and yaw angle can be estimated.

[0030] The GNSS signal is a signal that meets preset availability conditions. Preset availability conditions include at least one of the following: The time interval between the GNSS signal and the current IMU signal meets a first set threshold. This threshold is used to ensure that the GNSS observations and IMU measurements are strictly aligned in time, avoiding additional combined navigation errors caused by time asynchrony. The GNSS solution status is a fixed solution. A fixed solution is the highest level of solution status in RTK technology to achieve centimeter-level positioning accuracy. It indicates that the current GNSS observations have extremely high accuracy and reliability. This condition requires that the positioning status flag bit output by the GNSS receiver be a fixed solution, so as to ensure that the GNSS position and velocity information used for attitude calculation has the best quality. The speed measurement information in the GNSS buffer queue meets the second set threshold. This threshold is used to judge the continuity and stability of the GNSS speed measurement information and eliminate abnormal speed values ​​caused by signal interruption or abnormal jumps. The second set threshold can be set according to the maximum possible acceleration of the vehicle and the GNSS data update rate. For example, it can be set to ensure that the speed change between adjacent epochs does not exceed a certain limit in order to filter out unreasonable large speed jumps. The position accuracy factor and velocity accuracy factor of the GNSS signal meet the third set threshold. The position accuracy factor and velocity accuracy factor are indicators that measure the impact of satellite geometry on positioning and velocity measurement accuracy. The smaller the value, the better the satellite distribution and the higher the solution accuracy. The third set threshold can be set according to the accuracy requirements of the actual operation scenario. The signal-to-noise ratio (SNR) of the Doppler velocity measurement meets the fourth set threshold. The SNR is an important indicator for measuring the quality of satellite signals. The higher the SNR, the stronger the signal and the lower the observation noise. The fourth set threshold can be set according to the performance of the GNSS receiver and the actual operating environment.

[0031] In this embodiment, step S4, determining whether the carrier is in an abnormal motion state based on the carrier velocity information and attitude information, includes: First, based on the attitude information, the calculated carrier velocity information is transformed into the carrier coordinate system to obtain the forward velocity, lateral velocity, and vertical velocity in the carrier coordinate system. The coordinate transformation is expressed as follows: , in, The velocity vector in the carrier coordinate system obtained from Doppler velocity measurement. This is the transformation matrix from the Earth coordinate system to the vehicle coordinate system. To calculate the vehicle's velocity information at the current moment; Then, the lateral velocity in the carrier coordinate system is integrated using a preset sliding window to obtain the cumulative value of the lateral velocity. The preset sliding window is one second. Finally, it is determined whether the cumulative value of the horizontal velocity exceeds the preset abnormal threshold within the sliding window, and at the same time, it is determined whether the fluctuation of the Z-axis angular velocity of the IMU is less than the preset fluctuation threshold. If the cumulative lateral velocity exceeds the abnormal threshold and the Z-axis angular velocity fluctuation is less than the fluctuation threshold, the carrier is determined to be in an abnormal motion state, which includes sideslip.

[0032] In this embodiment, the combined navigation filter is a Kalman filter, and its state vector is represented as follows: , in, This represents the position error in the navigation coordinate system. The velocity error in the navigation coordinate system. This represents the attitude error in the navigation coordinate system. For gyroscope zero bias error, This refers to the zero bias error of the accelerometer.

[0033] In this embodiment, constructing measurement information based on the adjusted constraint strategy and using the measurement information to update the combined navigation filter used to estimate the vehicle navigation state includes: The observation innovation vector is obtained by subtracting the calculated lateral error from the INS (Inertial Navigation System) prediction. The INS prediction refers to the calculated position, velocity, and attitude obtained from the integrated IMU data. The observation equation is expressed as follows: , Among them, the lateral error calculation is based on the lateral velocity constraint error of the carrier determined by the NHC model. Denotes the observation information vector at time k. This represents the observation matrix corresponding to the state vector at time k. This represents the state vector at time k. Indicates observational noise; Based on the judgment results, different forms of observation innovation vector representation are used for observation updates. When the carrier is judged to be in normal motion, the observation innovation vector is represented as: , in, It is the y-axis velocity of the carrier coordinate system. It is the z-axis velocity of the carrier coordinate system; When the carrier is determined to be in an abnormal motion state, the observed information vector is represented as: , in, It is the y-axis velocity of the carrier coordinate system calculated based on Doppler velocity measurement. It is the z-axis velocity of the carrier coordinate system based on Doppler velocity measurement.

[0034] Through the above adaptive adjustment mechanism, error divergence is suppressed by the complete constraints of the NHC model under normal conditions, and Doppler velocity-assisted constraint strategy is switched to under abnormal conditions to avoid introducing measurement errors by using incorrect lateral velocity constraints, and finally to effectively correct the navigation error of the vehicle.

[0035] This invention also discloses a single-antenna anomaly detection and correction system based on Doppler velocities, comprising: The data acquisition module is used to acquire raw GNSS observation data, which includes at least Doppler velocity information. The velocity calculation module is used to calculate the current velocity information of the carrier based on Doppler velocity measurement information; The attitude acquisition module is used to acquire the attitude information of the current carrier; The anomaly detection module is used to determine whether the carrier is in an abnormal motion state based on the carrier's speed and attitude information. The model building module is used to build the NHC model, which provides lateral motion constraints for the vehicle under normal driving conditions. The filter update module is used to adjust the constraint strategy of the NHC model based on the judgment result of the anomaly judgment module, construct measurement information based on the adjusted constraint strategy, and use the measurement information to update the combined navigation filter used to estimate the vehicle navigation state in order to correct the navigation error of the vehicle.

[0036] The present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any single-antenna anomaly detection and correction method based on Doppler velocimetry.

[0037] In summary, this invention utilizes Doppler velocity information output by GNSS itself to accurately detect and correct abnormal motion states such as sideslip and severe turbulence without adding any additional sensors (such as vision, laser, or odometers) or dual antennas. This reduces the hardware cost of integrated navigation products and expands the application scope of low-cost single-antenna products. Through abnormal state judgment based on Doppler velocity and strategy adjustment of the NHC model, the entire algorithm does not involve complex neural networks or other computationally intensive models, resulting in low computational load and efficient operation on low-cost microcontroller platforms, demonstrating good engineering practicality. By introducing Doppler velocity information for independent carrier velocity calculation and dynamically adjusting the constraint strategy of the NHC model based on this, the divergence of attitude and positioning errors in agricultural machinery or engineering vehicles under scenarios such as sideslip and severe turbulence can be effectively suppressed, improving the robustness and accuracy of the integrated navigation system under harsh conditions, thereby ensuring the operational quality of agricultural and engineering machinery. Compared to solutions that rely on specific sensor models or dual-antenna hardware, this invention does not change the original single-antenna hardware architecture, thus offering better adaptability and making it easier to upgrade and promote in existing agricultural machinery and engineering vehicle products.

[0038] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A single-antenna anomaly detection and correction method based on Doppler velocities, characterized in that, include: Acquire raw GNSS observation data, which includes at least Doppler velocity information; The current velocity information of the carrier is calculated based on the Doppler velocity measurement information; Obtain the current attitude information of the carrier; Based on the carrier speed information and the attitude information, it is determined whether the carrier is in an abnormal motion state; An NHC model is constructed, which is used to provide lateral motion constraints for the vehicle under normal driving conditions; Based on the judgment results, the constraint strategy of the NHC model is adjusted, measurement information is constructed based on the adjusted constraint strategy, and the measurement information is used to update the combined navigation filter used to estimate the navigation state of the vehicle in order to correct the navigation error of the vehicle.

2. The single-antenna anomaly detection and correction method based on Doppler velocities according to claim 1, characterized in that, The carrier velocity information calculated based on the Doppler velocity measurement information at the current moment includes: The Doppler residual values ​​for each visible satellite are calculated based on the fundamental Doppler observation equations, which are: ， Where Di is the Doppler frequency shift value of the i-th satellite, and is the carrier wavelength. It is the velocity information of the i-th satellite. It is the carrier speed information. It is the receiver clock bias rate of change. Other observation errors; A least-squares problem is constructed based on the Doppler residuals of multiple satellites at the same time, and the carrier velocity information at the current time is obtained by solving the problem. .

3. The single-antenna anomaly detection and correction method based on Doppler velocities according to claim 2, characterized in that, The acquisition of the current carrier's attitude information includes: Acquire measurement data from an IMU, which includes a gyroscope and an accelerometer; The attitude information of the current vehicle is obtained by combining the IMU measurement data with the GNSS signal for navigation measurement processing. The GNSS signal is a signal that meets preset availability conditions.

4. The single-antenna anomaly detection and correction method based on Doppler velocities according to claim 3, characterized in that, The method of determining whether the carrier is in an abnormal motion state based on the carrier speed information and the attitude information includes: Based on the attitude information, the calculated carrier velocity information is transformed to the carrier coordinate system to obtain the forward velocity, lateral velocity, and vertical velocity in the carrier coordinate system. The coordinate transformation is expressed as follows: ， in, The velocity vector in the carrier coordinate system obtained from Doppler velocity measurement. This is the transformation matrix from the Earth coordinate system to the vehicle coordinate system. To calculate the vehicle's velocity information at the current moment; The lateral velocity in the carrier coordinate system is integrated using a preset sliding window to obtain the cumulative value of the lateral velocity; It is determined whether the cumulative value of the lateral velocity exceeds a preset abnormal threshold within the sliding window, and at the same time, it is determined whether the fluctuation of the Z-axis angular velocity of the IMU is less than a preset fluctuation threshold. If the cumulative value of the lateral velocity exceeds the abnormal threshold and the fluctuation of the Z-axis angular velocity is less than the fluctuation threshold, then the carrier is determined to be in an abnormal motion state.

5. The single-antenna anomaly detection and correction method based on Doppler velocities according to claim 4, characterized in that, The preset sliding window is one second, and the abnormal motion state includes the side-sliding state.

6. The single-antenna anomaly detection and correction method based on Doppler velocities according to claim 3, characterized in that, The combined navigation filter is a Kalman filter, and its state vector is represented as follows: ， in, Position error in the navigation coordinate system The velocity error in the navigation coordinate system. This represents the attitude error in the navigation coordinate system. For gyroscope zero bias error, This refers to the zero bias error of the accelerometer.

7. The single-antenna anomaly detection and correction method based on Doppler velocities according to claim 6, characterized in that, The step of constructing measurement information based on the adjusted constraint strategy and using the measurement information to update the integrated navigation filter used to estimate the vehicle navigation state includes: The observation innovation vector is obtained by subtracting the calculated transverse error from the INS prediction. The observation equation is expressed as follows: ， The lateral error calculation is based on the lateral velocity constraint error of the carrier determined by the NHC model. Denotes the observation information vector at time k. This represents the observation matrix corresponding to the state vector at time k. This represents the state vector at time k. Indicates observational noise; Based on the judgment results, different forms of observation innovation vector representation are used for observation updates. When the carrier is judged to be in normal motion, the observation innovation vector is represented as: ， in, It is the y-axis velocity of the carrier coordinate system. It is the z-axis velocity of the carrier coordinate system; When the carrier is determined to be in an abnormal motion state, the observed information vector is represented as: ， in, It is the y-axis velocity of the carrier coordinate system calculated based on Doppler velocity measurement. It is the z-axis velocity of the carrier coordinate system based on Doppler velocity measurement.

8. The single-antenna anomaly detection and correction method based on Doppler velocities according to claim 3, characterized in that, The preset availability conditions include at least one of the following: The time interval between the GNSS signal and the current IMU signal meets the first preset threshold. The GNSS solution state is a fixed solution; The velocity measurement information in the GNSS buffer queue meets the second set threshold; The position accuracy factor and velocity accuracy factor of the GNSS signal meet the third set threshold; the signal-to-noise ratio of the Doppler velocity measurement value meets the fourth set threshold.

9. A single-antenna anomaly detection and correction system based on Doppler velocities, characterized in that, include: The data acquisition module is used to acquire raw GNSS observation data, which includes at least Doppler velocity information. The velocity calculation module is used to calculate the current velocity information of the carrier based on the Doppler velocity measurement information; The attitude acquisition module is used to acquire the attitude information of the current carrier; An anomaly detection module is used to determine whether the carrier is in an abnormal motion state based on the carrier speed information and the attitude information. The model building module is used to build the NHC model, which provides lateral motion constraints for the vehicle under normal driving conditions. The filter update module is used to adjust the constraint strategy of the NHC model according to the judgment result of the anomaly judgment module, construct measurement information based on the adjusted constraint strategy, and use the measurement information to update the combined navigation filter used to estimate the navigation state of the vehicle, so as to correct the navigation error of the vehicle.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the single-antenna anomaly detection and correction method based on Doppler velocimetry as described in any one of claims 1 to 8.

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