A barometer-assisted urban environment GNSS / INS positioning quality control method and system

Abstract

The application provides a barometer-assisted urban environment GNSS / INS positioning quality control method and system, breaks the strong coupling dependence relationship between GNSS and INS by constructing an INS and barometer combined height reference benchmark, realizes the abnormal identification and suppression of GNSS original observation, ambiguity resolution and final positioning result through a multi-level closed-loop quality control mechanism, maintains the long-term stability of the auxiliary source through sensor error correction, and significantly enhances the state observability in the height direction by precisely embedding the barometer vertical constraint in the loose combination framework. In the GNSS signal severely degraded scene such as urban canyon, underpass, tunnel entrance and the like, the application can still provide continuous and robust lane-level navigation results, and meet the strict application requirements of high reliability and high precision positioning of automatic driving vehicles, intelligent networked terminals, unmanned aerial vehicle inspection platforms and the like.

Description

Technical Field

[0001] This invention belongs to the field of GNSS (Global Navigation Satellite System) and multi-source fusion positioning and navigation technology, specifically relating to a barometer-assisted GNSS / INS positioning quality control method for urban environments. It is applicable to applications requiring high-reliability, high-precision positioning, such as autonomous vehicles, intelligent connected terminals, and unmanned aerial vehicle (UAV) inspection platforms. Background Technology

[0002] In complex urban environments, fusion positioning using Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) has become the mainstream technology, widely applied in fields such as autonomous driving, unmanned inspection, and intelligent surveying. Real-Time Kinematic (RTK) is an algorithm that uses GNSS measurement data to estimate position and velocity; INS uses the device's three-axis angular velocity and acceleration data to calculate the device's position, velocity, and attitude; and the Inertial Measurement Unit (IMU) is mainly used to measure the three-axis angular velocity and acceleration of the carrier device.

[0003] The obstruction effects of dense urban buildings, elevated roads, tunnels, and bridges, as well as electromagnetic interference, multipath propagation, and non-line-of-sight (NLOS) propagation, can easily lead to GNSS signal reception interruptions or distortions. This, in turn, causes a series of positioning accuracy problems such as pseudorange observation deviations, carrier phase cycle slips, and ambiguity fixing errors, severely impacting the positioning reliability of the integrated system. Although INS possesses short-term high-precision autonomous positioning capabilities and can supplement GNSS signals during brief failures, alleviating GNSS positioning deficiencies to some extent and enabling continuous positioning output from the integrated system, existing GNSS / INS fusion schemes have inherent limitations and cannot fundamentally solve the positioning quality control challenges in urban environments. The core issue lies in the strong coupling between INS performance and GNSS measurements; its positioning accuracy is highly dependent on the initial calibration and real-time measurement constraints provided by GNSS. In complex environments such as urban canyons, forest areas, and tunnel entrances / exits, satellite signals are easily affected by obstruction and multipath effects. The altitude information obtained from RTK solutions often contains significant noise or even distortion, resulting in a significant decrease in the vertical accuracy and stability of the integrated navigation system. The inertial devices (gyroscopes, accelerometers) of INS have inherent drift errors, and these errors accumulate over time. Without regular and accurate calibration of GNSS, the positioning deviation can increase sharply in a short period of time.

[0004] In scenarios with severe GNSS signal degradation (such as dense building obstruction) or prolonged interruptions (such as passing through tunnels or underground parking garages), GNSS cannot provide effective measurement constraints for INS. The integrated system will rely entirely on the INS for autonomous positioning. In this situation, accumulated errors diverge rapidly, not only failing to achieve high-precision positioning but also leading to unstable and unreliable positioning results, or even positioning failure. Furthermore, when GNSS observations themselves contain gross errors, cycle slips, or ambiguity fixation errors, the INS, lacking an independent verification mechanism, cannot identify such anomalies. Instead, it may be misled by erroneous GNSS measurement information, further exacerbating the positioning error of the integrated system and making effective positioning quality control difficult. Summary of the Invention

[0005] To address the aforementioned problems, this invention discloses a barometer-assisted GNSS / INS positioning quality control method and system for urban environments.

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

[0007] A barometer-assisted GNSS / INS positioning quality control method for urban environments includes the following steps:

[0008] S1. Acquire barometer measurement data and GNSS observation data, compare the difference between the altitude difference between adjacent GNSS epochs and the barometer altitude change, determine the presence of cycle slips and outliers in the GNSS observation data through thresholds, and perform quality control on the GNSS observation data; GNSS observation data includes time, pseudo-noise random code for each satellite, pseudorange data, carrier phase data, Doppler data, signal-to-noise ratio, position, and velocity, with altitude information included in the position; barometer measurement data includes time, absolute pressure value, and relative pressure change; the barometer altitude change is calculated based on the barometer measurement data.

[0009] S2. Based on RTK calculation, a GNSS floating-point solution is obtained. The consistency of the GNSS floating-point solution is checked using the change in altitude calculated by INS and the altitude measured by barometers. Anomalies in the GNSS floating-point solution are determined by setting a residual threshold test method. If anomalies are found, the filtered and normalized residual is used as the input to the IGG-III model, and the weight of the satellite observation data with the largest residual is reduced. The GNSS floating-point solution calculation and anomaly detection are performed again until the residual threshold test is passed. The GNSS floating-point solution includes time, position, and velocity; the INS calculation result includes position, velocity, and attitude, and the INS calculated altitude is obtained based on the INS calculated position.

[0010] S3. Obtain a fixed GNSS solution by fixing the ambiguity using the GNSS floating-point solution, and calculate the residual between the GNSS fixed solution height, the INS calculated height, and the barometer measured height. Detect anomalies in the GNSS fixed solution based on the residual threshold. If the GNSS fixed solution is abnormal, discard the fixed solution and use the floating-point solution. The barometer measured height is calculated based on the barometer measurement data.

[0011] S4. Construct a GNSS / INS / barometer fusion observation model, using error state extended Kalman filter for solution. The model inputs are the position and velocity calculated by GNSS positioning at the same time, the position and velocity calculated by INS, and the altitude measured by barometer and the altitude change. The model outputs the combined navigation results and sensor errors. The combined navigation results include time, system position, system velocity, and system attitude. The sensor errors include the INS zero bias and the slow-varying altitude bias of the barometer.

[0012] Furthermore, the method also includes the following steps:

[0013] The quality control of barometer altitude measurement is performed using GNSS positioning calculation, which includes a fixed GNSS solution and a floating GNSS solution when no fixed solution is available.

[0014] Furthermore, in S1, the difference between the altitude difference between adjacent GNSS epochs and the change in barometer altitude are compared, and a threshold determination is made using the following formula:

[0015] ;

[0016] in, This represents the height difference between adjacent GNSS epochs at time k. This represents the change in altitude measured by the barometer at time k. Indicates the threshold;

[0017] The height increment between adjacent epochs is defined as:

[0018] ;

[0019] in, , These represent the GNSS altitudes at time k and time k-1, respectively. , These represent the barometer's measured height at time k and time k-1, respectively.

[0020] Furthermore, in S1, the step of detecting cycle slips and outliers in GNSS observation data by using threshold determination, and performing quality control on the GNSS observation data,

[0021] The quality control of GNSS observation data is specifically carried out as follows: if cycle slips occur, they are marked and the ambiguity is reset; if outliers exist, the original residuals of each satellite are solved by least squares and sorted from largest to smallest, and the carrier phase data of the satellite with the largest residuals are weighted according to the IGG-III model.

[0022] The method for determining cycle slips and outliers is as follows: If the difference between the altitude difference between adjacent GNSS epochs and the change in barometer altitude exceeds a threshold, the original residuals of each satellite are solved using least squares and sorted from largest to smallest. The carrier phase data of the satellite with the largest residual is weighted according to the IGG-III model. The difference between the altitude difference between adjacent GNSS epochs and the change in barometer altitude is calculated again. If it still exceeds the threshold, it indicates that the carrier phase data of that satellite has experienced a cycle slip. If it does not exceed the threshold, it indicates that there is an outlier.

[0023] Furthermore, in S2, the formula for calculating the residual in the GNSS floating-point solution height consistency detection is as follows:

[0024]

[0025] in, This represents the residual of the floating-point solution at time k. This represents the GNSS floating-point solution height at time k. This indicates the INS-estimated altitude at time k. This indicates the height measured by the barometer at time k. This is the initial height offset for the barometer.

[0026] Furthermore, in S3, the residuals between the GNSS fixed solution altitude, the INS estimated altitude, and the barometer measured altitude are calculated, as follows:

[0027] ;

[0028] in, This represents the residual of the fixed solution at time k. This represents the fixed GNSS solution altitude at time k. This indicates the INS-estimated altitude at time k. This indicates the height measured by the barometer at time k. This is the initial height offset for the barometer.

[0029] Furthermore, in S3, when calculating the GNSS fixed solution, if the ambiguities of at least four satellites are successfully fixed, the corresponding position and velocity are calculated using the fixed ambiguity values ​​of these four satellites based on the least squares method as the GNSS fixed solution.

[0030] Furthermore, the quality control of barometer altitude measurement using GNSS positioning calculation specifically includes:

[0031] The barometer altitude measurement model is as follows:

[0032] ;

[0033] in, This indicates the height measured by the barometer at time k. For the actual height, This indicates that the barometer's altitude measurement is slowly biased. This indicates zero-mean measurement noise;

[0034] When the GNSS positioning solution is reliable, the slowly varying barometer altitude offset is updated using an exponential smoothing method, specifically in a time-recursive form as follows:

[0035] ;

[0036] in, This indicates that the barometer height at time k is slowly biased after the update. This indicates that the barometer height at time k-1 is slowly biased. To update the coefficients, balancing convergence speed and smoothness. .

[0037] Furthermore, in S4, the construction of the GNSS / INS / barometer fusion observation model employs error Kalman filtering for solution. The model inputs are the time, position, and velocity calculated from GNSS positioning, the time, position, and velocity derived from INS calculations, and the time, altitude, and altitude change measured by the barometer. The model outputs are the integrated navigation results and sensor errors. The integrated navigation results include time, system position, system velocity, and system attitude. The sensor errors include the INS zero bias and the slowly varying altitude bias parameters of the barometer, as detailed below:

[0038] Define the state vector output by the GNSS / INS / barometer combined system at time t:

[0039]

[0040] in, Let be the state vector at time t. For system location, The system velocity is indicated by the superscript 'n', which represents the navigation coordinate system. For the system attitude, and These are the zero bias of the INS accelerometer and the zero bias of the gyroscope, respectively. The barometer height is slowly deflected.

[0041] During the solution process, the state error vector at time t is represented in the form of state error:

[0042]

[0043] in, Let be the error of the state vector at time t. For system position error, For system speed error, For system attitude error, and These are the zero bias errors of the INS accelerometer and the gyroscope, respectively. The barometer altitude slowly varying offset error; T represents the transpose of the matrix;

[0044] right The differential equation of the continuous-time system after differentiation is:

[0045]

[0046] In the formula, Let be the system noise vector. For the system matrix, This is the system noise distribution matrix;

[0047] right The continuous-time state error differential equation obtained by differentiating each component is as follows:

[0048] ;

[0049] in, Indicates an antisymmetric matrix; This represents the direction cosine matrix from the system equipment coordinate system b to the navigation coordinate system n; This represents the specific force output by the accelerometer in the b-series. This represents the projection of the gyroscope angle increment of the navigation coordinate system relative to the inertial INS coordinate system onto the navigation coordinate system n. Represents a three-dimensional identity matrix;

[0050] ;

[0051] ;

[0052] ;

[0053] ;

[0054] ;

[0055] in, , and The relevant time for a first-order Gaussian Markov process; and These are the random noises of the INS accelerometer and the gyroscope, respectively. , and The driving white noise for a first-order Gaussian Markov process; , , These are the velocity components in the direction of the ground, eastward, and northward, respectively. , These are the radii of the meridian circle and the radii of the east-west circle, respectively. This is the Earth's rotational angular velocity; h is the heading angle of the system equipment, and h is the elevation of the combined equipment system;

[0056] In the error state Kalman filter design of the GNSS / INS / barometer integrated navigation system, the difference between the position and velocity calculated by GNSS positioning and the position and velocity calculated by INS, as well as the elevation changes of GNSS and barometer, are used as constraints to construct an observation vector. This vector serves as the model input to update the Kalman filter state. The fusion observation model of the GNSS / INS / barometer integrated navigation system in the n-system is shown below:

[0057] ;

[0058] in: and These represent the position and velocity calculated by INS, respectively. and These represent the position and velocity calculated using GNSS positioning, respectively. and These represent the changes in elevation measured by barometer and the changes in elevation measured by GNSS, respectively. Represents the unit vector of the celestial direction in the ENU coordinate system; The lever arm vector between INS and GNSS;

[0059]

[0060] in, It is the projection vector of the Earth's rotational angular velocity in the n-frame. It is the angular velocity vector of the gyroscope in the b-frame;

[0061] After filtering and solving, the optimal estimate of the integrated navigation at time t is obtained. It is expressed as follows:

[0062] .

[0063] This invention provides a barometer-assisted GNSS / INS positioning quality control system for urban environments, which includes the following modules:

[0064] The cycle slip and outlier detection module acquires barometer measurement data and GNSS observation data, compares the difference between adjacent GNSS epoch altitudes and the barometer altitude change, determines the presence of cycle slips and outliers in the GNSS observation data using thresholds, and performs quality control on the GNSS observation data. The GNSS observation data includes time, pseudo-noise random code for each satellite, pseudorange data, carrier phase data, Doppler data, signal-to-noise ratio, position, and velocity; the position includes altitude information. The barometer measurement data includes time, absolute pressure value, and relative pressure change; the barometer altitude change is calculated based on the barometer measurement data.

[0065] The high consistency detection module obtains the GNSS floating-point solution based on RTK calculation. It constructs a consistency detection method for the GNSS floating-point solution using the change in altitude calculated by INS and the altitude measured by barometer. It judges the anomalies of the GNSS floating-point solution by setting a residual threshold test method. If anomalies are found, the filtered and normalized residuals are used as the input of the IGG-III model, and the weight of the satellite observation data with the largest residual is reduced. The GNSS floating-point solution calculation and anomaly detection are performed again until the residual threshold detection is passed. The GNSS floating-point solution includes time, position, and velocity; the INS calculation result includes position, velocity, and attitude, and the INS calculated altitude is obtained based on the INS calculated position.

[0066] The ambiguity fixation detection module uses the GNSS floating-point solution to fix ambiguity and obtain a GNSS fixed solution. It then calculates the residual between the GNSS fixed solution height, the INS calculated height, and the barometer measured height. Based on the residual threshold, it detects anomalies in the GNSS fixed solution. If the GNSS fixed solution is abnormal, it discards the fixed solution and uses the floating-point solution. The barometer measured height is calculated based on the barometer measurement data.

[0067] The fusion positioning module constructs a GNSS / INS / barometer fusion observation model, employing an error-state extended Kalman filter for solution. The model inputs include the position and velocity calculated by GNSS positioning at the same time, the position and velocity calculated by INS, and the altitude measured by barometer and its altitude change. The model outputs integrated navigation results and sensor errors. The integrated navigation results include time, system position, system velocity, and system attitude. The sensor errors include the INS zero bias and the slow-varying altitude bias of the barometer.

[0068] Compared with the prior art, the present invention has the following advantages:

[0069] This invention addresses the technical shortcomings of existing GNSS / INS integrated positioning systems in complex urban environments, such as insufficient observability in the altitude direction, rapid error accumulation, strong coupling dependence on GNSS measurements, and lack of effective quality control mechanisms. It proposes a multi-dimensional collaborative quality control method based on independent elevation observations from barometers.

[0070] This invention introduces a barometer-assisted GNSS / INS positioning quality control method for urban environments. This method employs a barometer elevation measurement system decoupled from GNSS measurements, overcoming the performance dependency limitations of GNSS / INS fusion. By utilizing the stable and continuous elevation changes of the barometer, it enables reliable judgment of GNSS outliers, cycle slips, and ambiguity fixation, providing independent elevation constraints for INS and suppressing error accumulation. This effectively improves the stability and reliability of positioning in complex urban environments when GNSS degradation or interruption occurs, optimizing positioning quality control and thus adapting to the precise positioning needs of various scenarios.

[0071] This invention uses a barometer as a vertical constraint source that is completely decoupled from the physical principles of GNSS and is embedded in the loosely integrated GNSS / INS navigation architecture. It constructs a closed-loop quality control system that covers observation anomaly detection, cycle slip identification, ambiguity fixation verification, barometer correction, and three-source fusion positioning. Without increasing the hardware complexity of the system, it significantly improves the vertical positioning stability and overall navigation reliability.

[0072] This invention provides a loosely integrated GNSS / INS / barometer system. This system employs a barometer elevation measurement system as a low-cost, highly stable independent measurement module. Its measurement principle is completely independent of GNSS measurements, effectively breaking the coupling limitations between GNSS and INS and providing independent quality control constraints for the integrated system. The barometer senses changes in atmospheric pressure and can continuously output the elevation change over adjacent time periods, unaffected by urban obstruction, electromagnetic interference, or other factors, maintaining stable operation even in GNSS signal failure scenarios. Based on the independent elevation information provided by the barometer, multi-dimensional positioning quality control can be achieved: height consistency detection verifies the rationality of the GNSS / INS fusion results, quickly detects outliers and cycle slips in GNSS observations, accurately judges the reliability of ambiguity fixation results, and simultaneously provides independent elevation constraints for the INS, suppressing its error accumulation and significantly improving the positioning stability and reliability of the integrated system in GNSS degradation or interruption scenarios.

[0073] This invention introduces barometer measurement data, breaking the strong coupling dependency between GNSS and INS. Through multi-level quality control, it achieves anomaly identification and suppression in raw GNSS observations, ambiguity resolution, and final positioning results. Sensor error correction maintains the long-term stability of the auxiliary source. Precisely embedding barometer vertical constraints within a loosely coupled framework significantly enhances the observability of the altitude direction. Even in scenarios with severely degraded GNSS signals, such as urban canyons, under overpasses, and tunnel entrances / exits, this invention can still provide continuous and robust lane-level navigation results, meeting the stringent application requirements for high-reliability and high-precision positioning in autonomous vehicles, intelligent connected terminals, and drone inspection platforms. Attached Figure Description

[0074] Figure 1 This is a flowchart of the method of the present invention;

[0075] Figure 2 This is a schematic diagram of the installation of the device of the present invention;

[0076] Figure 3 This is a navigation trajectory diagram for a dynamic open scene in Test Example 1;

[0077] Figure 4 This is a navigation trajectory map for the main urban area scenario in Example 2;

[0078] Figure 5 For test example two, the route passes through a tunnel, where (a) is the positioning trajectory diagram of the system of the present invention; (b) is the single GNSS positioning trajectory;

[0079] Figure 6 This is a navigation trajectory map for the test example 3, which crosses a city scene;

[0080] Figure 7 For test example three, crossing a river tunnel, (a) is the positioning trajectory diagram of the system of the present invention; (b) is the single GNSS positioning trajectory;

[0081] Figure 2 In the middle, 1-GNSS signal receiving antenna; 2-hardware integrated terminal. Detailed Implementation

[0082] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0083] Example 1:

[0084] like Figure 1As shown, this invention discloses a barometer-assisted GNSS / INS positioning quality control method for urban environments. This method is used in real-time positioning and navigation systems and can also be used for quality control of historical data. The method of this invention includes the following steps:

[0085] S1. Acquire barometer measurement data and GNSS observation data, compare the difference between the altitude difference between adjacent GNSS epochs and the barometer altitude change, determine the presence of cycle slips and outliers in the GNSS observation data using thresholds, and perform quality control on the GNSS observation data; GNSS observation data includes time, pseudo-noise random code for each satellite, pseudorange data, carrier phase data, Doppler data, signal-to-noise ratio, position, and velocity; barometer measurement data includes time, absolute pressure value, and relative pressure change; the barometer uses the absolute pressure value and relative pressure change to convert barometric altitude to barometer measured altitude and relative altitude change. In this invention, position and velocity in the data are both three-dimensional vectors, and position includes altitude information.

[0086] S2. Based on RTK calculation, a GNSS floating-point solution is obtained. The consistency of the GNSS floating-point solution is checked using the change in altitude calculated by INS and the altitude measured by barometers. Anomalies in the GNSS floating-point solution are determined by setting a residual threshold test method. If anomalies are found, the filtered and normalized residual is used as the input to the IGG-III model, and the weight of the satellite observation data with the largest residual is reduced. GNSS floating-point solution calculation and anomaly detection are performed again until the residual threshold test is passed. The GNSS floating-point solution includes time, position, and velocity; the INS calculation result includes position, velocity, and attitude; the RTK solution process utilizes pseudorange, carrier phase, and Doppler data, as well as signal-to-noise ratio processing, mainly resolving the integer ambiguity of the carrier phase, including the GNSS floating-point solution; the INS calculation result includes position, velocity, and attitude, and the INS calculated altitude is obtained based on the INS calculated position.

[0087] S3. Obtain a fixed GNSS solution by fixing ambiguity using the GNSS floating-point solution, and calculate the residual between the GNSS fixed solution height, the height calculated by INS, and the height measured by the barometer. Detect anomalies in the GNSS fixed solution based on the residual threshold. If the GNSS fixed solution is abnormal, discard it and use the floating-point solution. The height measured by the barometer is calculated based on the barometer measurement data.

[0088] S4. Construct a GNSS / INS / barometer fusion observation model, using error state extended Kalman filter for solution. The model inputs are the position and velocity calculated by GNSS positioning at the same time, the position and velocity calculated by INS, and the altitude and altitude change measured by barometer. The model outputs are the integrated navigation results and sensor errors. The integrated navigation results include time, system position, system velocity, and system attitude. The sensor errors include the INS zero bias and the slow-varying altitude bias of the barometer.

[0089] In the above steps, the threshold in S1, the residual threshold in S3, and the residual threshold in S3 are generally limited to below 1m.

[0090] Example 2:

[0091] A further design feature of this embodiment is that the barometer-assisted GNSS / INS positioning quality control method for urban environments of the present invention also includes the following steps:

[0092] The quality control of barometer altitude measurement is performed using GNSS positioning calculation, which includes a fixed solution for GNSS calculation and a floating-point solution for when there is no fixed solution.

[0093] The barometer altitude measurement model is represented as follows:

[0094] ;

[0095] in, This indicates the height measured by the barometer at time k. For the actual height, This indicates that the barometer's altitude measurement is slowly biased. Zero-mean measurement noise;

[0096] When calculating the reliability of GNSS positioning, the slowly varying altitude offset of the barometer is updated using an exponential smoothing method. Considering the timeliness of the algorithm, a time-recursive update method is used, as shown below:

[0097] ;

[0098] in, This indicates that the barometer height at time k is slowly biased after the update. This indicates that the barometer height at time k-1 is slowly biased. To update the coefficients, balancing convergence speed and smoothness. .

[0099] The updated altitude offset is corrected in the barometer measurements to maintain good short-term continuity of barometer altitude, allowing it to be used as an independent altitude constraint in the aforementioned GNSS data quality control process. If the GNSS positioning solution fails the check or is in a fixed error state, the offset update is frozen, with the initial value of the barometer slow-varying offset being 0.

[0100] To ensure the reliability of multi-source quality control, this example assesses the quality of the barometer observation data itself. When an anomaly is detected in a barometer observation, its weight should be temporarily reduced or the observation removed to avoid misleading GNSS data quality assessments. Abnormal data caused by factors such as airflow disturbances and sudden changes in environmental pressure can be identified by detecting the barometer altitude change rate and sliding window variance. Considering that barometer altitude error mainly manifests as a slow-varying altitude bias caused by meteorological changes and sensor temperature drift, the barometer altitude bias parameter is estimated and updated when the GNSS positioning solution is reliable; when the GNSS positioning solution is unreliable, the bias parameter remains unchanged to avoid adverse effects of abnormal GNSS observations on the barometer correction results. This method effectively compensates for long-term barometer altitude drift and maintains the stability of its short-term altitude changes, thereby improving the barometer's auxiliary capabilities in GNSS cycle slip detection, altitude consistency detection, and ambiguity fixation reliability assessment.

[0101] Example 3:

[0102] This embodiment further incorporates a barometer-assisted urban environment GNSS / INS positioning quality control method. In step S1, the difference between the altitude difference between adjacent GNSS epochs and the change in barometer altitude is compared, and a threshold is determined. The following formula can be used to determine if the current epoch GNSS observation contains a suspected cycle slip or anomaly. This method does not rely on dual-frequency observations or satellite geometry and maintains good robustness even with insufficient satellite numbers or poor signal quality.

[0103] ;

[0104] in, This represents the height difference between adjacent GNSS epochs at time k. This represents the change in altitude measured by the barometer at time k. This indicates a threshold, which is generally limited to below 1m.

[0105] Barometer altitude changes exhibit good temporal continuity, while the vertical motion of a real vehicle typically shows a smooth change over a short period. Therefore, cycle slips and sudden anomalies can be detected by comparing GNSS altitude changes with barometer-measured altitude changes. The altitude increment between adjacent epochs is defined as:

[0106] ;

[0107] in, , These represent the GNSS altitudes at time k and time k-1, respectively. , These represent the barometer's measured altitude at time k and time k-1, respectively.

[0108] S1 uses threshold judgment to detect cycle slips and outliers in GNSS observation data and performs quality control on the GNSS observation data, specifically as follows:

[0109] Quality control is performed on GNSS observation data: if cycle slips occur, they are marked and ambiguities are reset; if outliers exist, the original residuals of each satellite are solved by least squares and sorted from largest to smallest, and the weight of the carrier phase data of the satellite with the largest residual is reduced according to the IGG-III model.

[0110] The method for determining cycle slips and outliers is as follows: if the threshold is exceeded, the original residuals of each satellite are solved by least squares and sorted from largest to smallest. The weight of the carrier phase data of the satellite with the largest residual is reduced according to the IGG-III model. The difference between the altitude difference of adjacent GNSS epochs and the change in barometer altitude is calculated again. If the difference still exceeds the threshold, it indicates that the carrier phase data of that satellite has a cycle slip. If the difference does not exceed the threshold, it indicates that there is an outlier.

[0111] In S2, the elevation error convergence is relatively slow during the GNSS initialization phase. However, the INS exhibits good continuity and stability over a short period, and the barometer is highly sensitive to altitude changes and unaffected by the satellite signal environment. Therefore, the consistency between the INS-calculated altitude and the barometer's relative altitude change can be used to check the GNSS altitude. The GNSS floating-point solution altitude consistency check process is as follows:

[0112]

[0113] in, This represents the residual of the floating-point solution at time k. This represents the GNSS floating-point solution height at time k. This indicates the INS-estimated altitude at time k. This indicates the height measured by the barometer at time k. This is the initial height offset for the barometer.

[0114] Example 4:

[0115] This embodiment further incorporates the following design: In the barometer-assisted urban environment GNSS / INS positioning quality control method of the present invention, S3 involves calculating the residual between the GNSS fixed solution altitude, the INS calculated altitude, and the barometer measured altitude, as follows:

[0116] ;

[0117] in, This represents the residual of the fixed solution at time k. This represents the fixed GNSS solution altitude at time k. This indicates the INS-estimated altitude at time k. This indicates the height measured by the barometer at time k. This is the initial altitude offset for the barometer. In high-precision positioning, the reliability of ambiguity fixation directly affects the accuracy and stability of the positioning results. To address the increased risk of ambiguity misfixation in complex environments, the fixed solution can be independently verified using altitude information provided by the INS and barometer.

[0118] The fixed solution is calculated as follows: if the ambiguities of at least four satellites are successfully fixed, the corresponding position and velocity are calculated using the fixed ambiguity values ​​of these four satellites based on the least squares method, which serves as the GNSS fixed solution.

[0119] Example 5:

[0120] A further design of this embodiment is that, in the barometer-assisted urban environment GNSS / INS positioning quality control method of the present invention, in S5, a GNSS / INS / barometer fusion observation model is constructed, and error Kalman filtering is used for solution. The model input is the time, position and velocity calculated by GNSS positioning, the time, position and velocity calculated by INS, and the time, altitude and altitude change measured by barometer. The model output is the combined navigation result and sensor error. The combined navigation result includes time, system position, system velocity and system attitude. The sensor error includes the INS zero bias and the barometer altitude slow-varying bias parameter.

[0121] This invention employs a loosely integrated GNSS / INS / barometer system. Position and velocity obtained independently from GNSS, position and velocity derived from INS, and altitude and altitude change measured by the barometer are input into a fusion observation model to calculate and output the combined navigation results and sensor errors. Within this loosely integrated framework, each navigation subsystem can independently complete navigation calculations. The integrated system uses RTK position and velocity observations to correct the system state and feeds back the corrected navigation information to the INS for recursive calculations at the next time step. This structure has advantages such as simple implementation, low computational load, and strong engineering feasibility. Furthermore, when one subsystem fails, the remaining systems can still maintain basic navigation output capabilities, thereby improving the system's reliability and robustness, as detailed below:

[0122] Define the state vector output by the GNSS / INS / barometer combined system at time t:

[0123]

[0124] in, Let be the state vector at time t. For system output location, The system output speed is indicated by the superscript 'n', which represents the navigation coordinate system. Output attitude to the system. and The zero bias of the INS accelerometer and gyroscope are respectively. The barometer height is slowly deflected.

[0125] During the solution process, the state error vector at time t is represented in the form of state error:

[0126]

[0127] in, Let be the error of the state vector at time t. For system position error, For system speed error, For system attitude error, and The zero bias errors of the INS accelerometer and gyroscope are respectively. The barometer altitude slowly varying offset error; T represents the transpose of the matrix;

[0128] Barometers can reflect changes in the relative altitude of a carrier by varying ambient air pressure. They are characterized by low noise and high short-term stability. They can provide effective vertical observation constraints for integrated navigation systems without significantly increasing system complexity, thereby compensating for the insufficient altitude accuracy of RTK and the altitude drift problem of INS.

[0129] To obtain the system state equations, we first need to solve... The derivative with respect to time yields the following state differential equation for the continuous-time system:

[0130]

[0131] In the formula, Let be the system noise vector. For the system matrix, The system noise distribution matrix; for Vector differentiation requires taking the derivative of each component of the vector with respect to time t. The specific details of the continuous-time state error differential equation are as follows:

[0132] ;

[0133] in, Indicates an antisymmetric matrix; This represents the direction cosine matrix from the system equipment coordinate system b to the navigation coordinate system n; This represents the specific force output by the accelerometer in the b-series. This represents the projection of the gyroscope angle increment of the navigation coordinate system relative to the inertial INS coordinate system onto the navigation coordinate system n. Represents a three-dimensional identity matrix;

[0134] ;

[0135] ;

[0136] ;

[0137] ;

[0138] ;

[0139] , and The relevant time for a first-order Gaussian Markov process; and These are the random noises of the INS accelerometer and gyroscope, respectively. , and The driving white noise for a first-order Gaussian Markov process; , , These are the velocity components in the direction of the ground, eastward, and northward, respectively. , These are the radii of the meridian circle and the east-west circle, respectively. This is the Earth's rotational angular velocity; h is the heading angle of the system equipment, and h is the elevation of the combined equipment system;

[0140] In the error state Kalman filter design of the GNSS / INS / barometer integrated navigation system, the difference between the position and velocity calculated by GNSS positioning and the position and velocity calculated by INS, as well as the altitude change obtained by recursion from the previous epoch using the combination of GNSS and barometer, are used as constraints to construct an observation vector. This vector serves as the input of the observation data and is used to update the Kalman filter state. The fusion observation model of the GNSS / INS / barometer integrated navigation system in the n-frame is shown below:

[0141] ;

[0142] in: and These represent the position and velocity calculated by INS, respectively. and These represent the position and velocity calculated using GNSS positioning, respectively. and These represent the changes in elevation measured by barometer and the changes in elevation measured by GNSS, respectively. Represents the unit vector of the celestial direction in the ENU coordinate system; The lever arm vector between INS and GNSS;

[0143]

[0144] in, It is the projection vector of the Earth's rotational angular velocity in the n-frame. It is the angular velocity vector of the gyroscope in the b-frame;

[0145] After filtering and solving, the optimal estimate of the integrated navigation at time t is obtained. It is expressed as follows:

[0146] .

[0147] Example 6:

[0148] This embodiment provides a barometer-assisted GNSS / INS positioning quality control system for urban environments, which includes the following modules:

[0149] The cycle slip and outlier detection module acquires barometer measurement data and GNSS observation data, compares the difference between adjacent GNSS epoch altitudes and the barometer altitude change, determines the presence of cycle slips and outliers in the GNSS observation data using thresholds, and performs quality control on the GNSS observation data. The GNSS observation data includes time, pseudo-noise random code for each satellite, pseudorange data, carrier phase data, Doppler data, signal-to-noise ratio, position, and velocity; the position includes altitude information. The barometer measurement data includes time, absolute pressure value, and relative pressure change; the barometer altitude change is calculated based on the barometer measurement data.

[0150] The high consistency detection module obtains GNSS floating-point solutions based on RTK calculations. It constructs a consistency detection mechanism using the changes in altitude estimated by INS and measured by barometers. Anomalies in the GNSS floating-point solutions are identified by setting a residual threshold test method. If anomalies are found, the filtered and normalized residuals are used as input to the IGG-III model, and the weight of the satellite observation data with the largest residual is reduced. The GNSS floating-point solution calculation and anomaly detection are performed again until the residual threshold test is passed. The GNSS floating-point solution includes time, position, and velocity; the INS calculation results include position, velocity, and attitude, and the INS calculated altitude is obtained based on the INS calculated position.

[0151] The ambiguity fixation detection module uses the GNSS floating-point solution to fix ambiguity and obtain the GNSS fixed solution. It then calculates the residual between the GNSS fixed solution height, the INS calculated height, and the barometer measured height. Based on the residual threshold, it detects anomalies in the GNSS fixed solution. If the GNSS fixed solution is abnormal, it is discarded and the floating-point solution is used. The barometer measured height is calculated based on the barometer measurement data.

[0152] The fusion positioning module constructs a GNSS / INS / barometer fusion observation model, which is solved using an error state extended Kalman filter. The model inputs include the position and velocity calculated by GNSS positioning at the same time, the position and velocity calculated by INS, and the altitude measured by barometer and the altitude change. The model outputs the combined navigation results and sensor errors. The combined navigation results include time, system position, system velocity, and system attitude. The sensor errors include the INS zero bias and the slow-varying altitude bias of the barometer.

[0153] Test example:

[0154] The equipment used in the testing process, such as Figure 2 As shown, the device is fixed to the carrier. The black box on the left is the hardware integrated terminal 2 of this invention, and the white disk is the GNSS signal receiving antenna 1 (common in the GNSS field). The device also includes an inertial measurement unit (IMU) and a barometer (barometer elevation measurement system), with the IMU used to receive INS data. The barometer-assisted urban environment GNSS / INS positioning quality control method of this invention is loaded into the hardware integrated terminal 2 as software. Connecting the device to power and the data serial port allows data to be stored locally. If no data is stored, the device supports broadcasting the calculation results to the application platform via the network.

[0155] To fully evaluate the performance of the device of this invention, three test examples will be used: open scene, complex scene, and GNSS denial scene. The specific quantitative statistics will be the device fixation rate within the test section, the root mean square error (RMSE) of the device positioning result deviation, and the deviations at the 50th, 68th, and 95th quantiles. Specific test examples are as follows:

[0156] Test Example 1:

[0157] To effectively evaluate performance in open scenarios, the most open test segment with minimal interference was selected. For example... Figure 3 As shown, the test was conducted on a section of road in the Qinling Mountains of Xi'an, Shaanxi Province. The environment was open with no obstacles or buildings obstructing the view, although large trucks caused some interference at certain times.

[0158] The test in this example took place from 6:30 AM to 8:00 AM UTC on April 10, 2026. Statistics show that the device's fixation rate reached 99.87% during this test segment. Table 1 shows the quantification results of the device's deviation. The RMSE in the Northeast-Heaven (ENU) direction of the device is less than [number] centimeters, and the 95th percentile deviation is approximately 1 centimeter, indicating the superior performance of the integrated navigation device in open environments.

[0159] Table 1. Quantification results of positioning deviation in this invention

[0160] E 0.0062 0.0038 0.0058 0.0123 N 0.0069 0.0045 0.0067 0.0137 U 0.0072 0.0045 0.0068 0.0141

[0161] Test Example 2:

[0162] Figure 4 The navigation trajectory for this example is located in the main urban area of ​​Xi'an, with a distance of approximately 74.835 km. The measurement period is from 1:22 AM to 3:56 AM UTC on April 11, 2026, and the measurement segment mainly covers the main urban area of ​​Xi'an.

[0163] Statistics show that the fixed rate of the device of this invention is 96.42%. The deep coupling of signals in the device of this invention effectively ensures the tracking performance of GNSS signals and guarantees a high fixed rate, which proves that the hardware design of the device of this invention is sound and the algorithm is robust and reliable.

[0164] Table 2 shows the quantitative results of the segment positioning deviation in this example. The RMSE in the ENU direction is at the centimeter level, and the 95th percentile deviation is about 3 centimeters, proving that the device of this invention can still guarantee positioning performance on major urban roads.

[0165] Table 2. Quantification results of positioning deviation in this invention

[0166] E 0.035 0.005 0.008 0.030 N 0.018 0.006 0.009 0.029 U 0.022 0.008 0.012 0.041

[0167] like Figure 5 As shown in Figure (a), the red trajectory represents the device of this invention, and the green trajectory in Figure (b) represents a single GNSS device. When plotted as points, it can be seen that there is no single GNSS device trajectory during tunneling. The results from the device of this invention still maintain lane-level positioning results, which are smooth and continuous, proving that the INS / barometric pressure calculation method of this invention is robust and effective.

[0168] Test Example 3:

[0169] Figure 6 This is the navigation trajectory map for this example, which crosses urban scenarios. The distance is approximately 48.388 km, and the measurement period is from 4:27 AM to 6:08 AM UTC on April 12, 2026. It mainly crosses the Xi'an urban area.

[0170] According to statistics, the fixed rate of the device of this invention is 96.69%. Table 3 shows the quantitative results of the measurement segment deviation in this example. The RMSE in the ENU direction is about 1 cm, and the 95th percentile deviation is about 3 cm, proving that the positioning of the device of this invention remains robust and reliable when traversing urban scenes.

[0171] Table 3. Quantification results of positioning deviation in this invention

[0172] E 0.011 0.005 0.008 0.030 N 0.012 0.005 0.007 0.021 U 0.017 0.008 0.013 0.033

[0173] like Figure 7As shown in Figure (a), the red trajectory represents the device of this invention, and the green trajectory represents a single GNSS device. When plotted as points, it can be seen that there is no trajectory of a single GNSS device during the crossing of the river tunnel; the combined device results still maintain lane-level positioning results, and are smooth and continuous, proving the robust positioning reliability of the device of this invention.

[0174] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A barometer-assisted GNSS / INS positioning quality control method for urban environments, characterized in that, Includes the following steps: S1. Acquire barometer measurement data and GNSS observation data, compare the difference between the altitude difference between adjacent GNSS epochs and the change in barometer altitude, determine the existence of cycle slips and outliers in the GNSS observation data through thresholds, and perform quality control on the GNSS observation data. GNSS observation data includes time, pseudo-noise random code for each satellite, pseudorange data, carrier phase data, Doppler data, signal-to-noise ratio, position, and velocity; barometer measurement data includes time, absolute pressure value, and relative pressure change. The change in altitude measured by the barometer is calculated based on the barometer measurement data. S2. Based on RTK calculation, the GNSS floating-point solution is obtained. The consistency of the GNSS floating-point solution is constructed by using the change in altitude estimated by INS and the altitude measured by barometer. The abnormal situation of GNSS floating-point solution is determined by setting the residual threshold test method. If there is an anomaly, the filtered and normalized residual is used as the input of IGG-III model, and the weight of satellite observation data with the largest residual is reduced. The GNSS floating-point solution and anomaly detection are performed again until the residual threshold is passed; the GNSS floating-point solution includes time, position, and velocity; the INS calculation result includes position, velocity, and attitude, and the INS calculation altitude is obtained based on the INS calculation position; S3. Use the GNSS floating-point solution to fix ambiguity to obtain the GNSS fixed solution, and calculate the residual between the GNSS fixed solution height, the INS calculated height, and the barometer measured height. Detect abnormalities in the GNSS fixed solution based on the residual threshold. If the GNSS fixed solution is abnormal, discard the fixed solution and use the floating-point solution. The altitude measured by the barometer is calculated based on the barometer measurement data. S4. Construct a GNSS / INS / barometer fusion observation model, using error state extended Kalman filter for solution. The model inputs are the position and velocity calculated by GNSS positioning at the same time, the position and velocity calculated by INS, and the height and height change measured by barometer. The model outputs the combined navigation results and sensor errors. The combined navigation results include time, system position, system velocity, and system attitude. The sensor errors include the zero bias of INS and the slow-varying height bias of barometer. Also includes: The quality control of barometer altitude measurement is performed using GNSS positioning calculation, which includes a fixed GNSS solution and a floating GNSS solution when there is no fixed solution. The quality control of barometer altitude measurement using GNSS positioning calculation is specifically as follows: The barometer altitude measurement model is as follows: ； in, This indicates the height measured by the barometer at time k. For the actual height, This indicates that the barometer's altitude measurement is slowly biased. This indicates zero-mean measurement noise; When the GNSS positioning solution is reliable, the slowly varying barometer altitude offset is updated using an exponential smoothing method, specifically in a time-recursive form as follows: ； in, This indicates that the barometer height at time k is slowly biased after the update. This indicates that the barometer height at time k-1 is slowly biased. To update the coefficients, .

2. The barometer-assisted urban environment GNSS / INS positioning quality control method according to claim 1, characterized in that, In S1, the difference between the altitude difference between adjacent GNSS epochs and the change in barometer altitude are compared, and a threshold is determined using the following formula: ； in, This represents the height difference between adjacent GNSS epochs at time k. This represents the change in altitude measured by the barometer at time k. Indicates the threshold; The height increment between adjacent epochs is defined as: ； in, , These represent the GNSS altitudes at time k and time k-1, respectively. , These represent the barometer's measured height at time k and time k-1, respectively.

3. The barometer-assisted urban environmental GNSS / INS positioning quality control method according to claim 2, characterized in that, In S1, the step of detecting cycle slips and outliers in GNSS observation data through threshold determination and performing quality control on the GNSS observation data is described. The quality control of GNSS observation data is specifically carried out as follows: if cycle slips occur, they are marked and the ambiguity is reset; if outliers exist, the original residuals of each satellite are solved by least squares and sorted from largest to smallest, and the carrier phase data of the satellite with the largest residuals are weighted according to the IGG-III model. The method for determining cycle slips and outliers is as follows: if the difference between the altitude difference between adjacent GNSS epochs and the change in barometer altitude exceeds the threshold, the original residuals of each satellite are solved by least squares and sorted from largest to smallest. The weight of the carrier phase data of the satellite with the largest residual is reduced according to the IGG-III model. The difference between the altitude difference between adjacent GNSS epochs and the change in barometer altitude is recalculated. If it still exceeds the threshold, it indicates that the carrier phase data of the satellite has experienced a cycle slip. If it does not exceed the threshold, it indicates that there is an anomaly.

4. The barometer-assisted urban environmental GNSS / INS positioning quality control method according to claim 3, characterized in that, In S2, the formula for calculating the residual in the GNSS floating-point solution height consistency detection is as follows: ； in, This represents the residual of the floating-point solution at time k. This represents the GNSS floating-point solution height at time k. This indicates the INS-estimated altitude at time k. This indicates the height measured by the barometer at time k. This is the initial height offset for the barometer.

5. The barometer-assisted urban environmental GNSS / INS positioning quality control method according to claim 4, characterized in that, In S3, the residuals between the GNSS fixed solution altitude, the INS calculated altitude, and the barometer measured altitude are calculated as follows: ； in, This represents the residual of the fixed solution at time k. This represents the fixed GNSS solution altitude at time k. This indicates the INS-estimated altitude at time k. This indicates the height measured by the barometer at time k. This is the initial height offset for the barometer.

6. The barometer-assisted urban environment GNSS / INS positioning quality control method according to claim 5, characterized in that, In S3, when calculating the GNSS fixed solution, if the ambiguities of at least four satellites are successfully fixed, the corresponding positions and velocities are calculated using the fixed ambiguity values ​​of these four satellites based on the least squares method, which serves as the GNSS fixed solution.

7. The barometer-assisted urban environmental GNSS / INS positioning quality control method according to claim 6, characterized in that, In S4, the GNSS / INS / barometer fusion observation model is constructed using error Kalman filtering. The model inputs include time, position, and velocity calculated from GNSS positioning, time, position, and velocity derived from INS, and time, altitude, and altitude change measured by the barometer. The model outputs integrated navigation results and sensor errors. The integrated navigation results include time, system position, system velocity, and system attitude. The sensor errors include the INS zero bias and the barometer altitude slow-varying bias parameters, as detailed below: Define the state vector output by the GNSS / INS / barometer combined system at time t: ； in, Let be the state vector at time t. For system location, The system velocity is indicated by the superscript 'n', which represents the navigation coordinate system. For the system attitude, and These are the zero bias of the INS accelerometer and the zero bias of the gyroscope, respectively. The barometer height is slowly deflected. During the solution process, the state error vector at time t is represented in the form of state error: ； in, Let be the error of the state vector at time t. For system position error, For system speed error, For system attitude error, and These are the zero bias errors of the INS accelerometer and the gyroscope, respectively. The barometer altitude slowly varying offset error; T represents the transpose of the matrix; right The differential equation of the continuous-time system after differentiation is: ； In the formula, Let be the system noise vector. For the system matrix, This is the system noise distribution matrix; right The continuous-time state error differential equation obtained by differentiating each component is as follows: ； in, Indicates an antisymmetric matrix; This represents the direction cosine matrix from the system equipment coordinate system b to the navigation coordinate system n; This represents the specific force output by the accelerometer in the b-series. This represents the projection of the gyroscope angle increment of the navigation coordinate system relative to the inertial INS coordinate system onto the navigation coordinate system n. Represents a three-dimensional identity matrix; ； ； ； ； ； in, , and The relevant time for a first-order Gaussian Markov process; and These are the random noises of the INS accelerometer and the gyroscope, respectively. , and The driving white noise for a first-order Gaussian Markov process; , , These are the velocity components in the direction of the ground, eastward, and northward, respectively. , These are the radii of the meridian circle and the radii of the east-west circle, respectively. This is the Earth's rotational angular velocity; h is the heading angle of the system equipment, and h is the elevation of the combined equipment system; In the error state Kalman filter design of the GNSS / INS / barometer integrated navigation system, the difference between the position and velocity calculated by GNSS positioning and the position and velocity calculated by INS, as well as the elevation changes of GNSS and barometer, are used as constraints to construct an observation vector. This vector serves as the model input to update the Kalman filter state. The fusion observation model of the GNSS / INS / barometer integrated navigation system in the n-system is shown below: ； in: and These represent the position and velocity calculated by INS, respectively. and These represent the position and velocity calculated using GNSS positioning, respectively. and These represent the changes in elevation measured by barometer and the changes in elevation measured by GNSS, respectively. Represents the unit vector of the celestial direction in the ENU coordinate system; The lever arm vector between INS and GNSS; ； in, It is the projection vector of the Earth's rotational angular velocity in the n-frame. It is the angular velocity vector of the gyroscope in the b-frame; After filtering and solving, the optimal estimate of the integrated navigation at time t is obtained. It is expressed as follows: 。 8. A barometer-assisted GNSS / INS positioning quality control system for urban environments, characterized in that, This system, based on the barometer-assisted urban environment GNSS / INS positioning quality control method described in any one of claims 1-7, includes the following modules: The cycle slip and outlier detection module acquires barometer measurement data and GNSS observation data, compares the difference between the altitude difference between adjacent GNSS epochs and the change in barometer altitude, determines the presence of cycle slips and outliers in the GNSS observation data through thresholds, and performs quality control on the GNSS observation data. GNSS observation data includes time, pseudo-noise random code for each satellite, pseudorange data, carrier phase data, Doppler data, signal-to-noise ratio, position, and velocity, with altitude information included in the position data; barometer measurement data includes time, absolute pressure value, and relative pressure change; the barometer-measured altitude change is calculated based on the barometer measurement data. The high consistency detection module obtains the GNSS floating-point solution based on RTK calculation. It constructs a consistency detection of the GNSS floating-point solution by using the change in altitude estimated by INS and the altitude measured by barometer. It judges the abnormality of the GNSS floating-point solution by setting a residual threshold test method. If there is an anomaly, the filtered and normalized residual is used as the input of the IGG-III model, and the weight of the satellite observation data with the largest residual is reduced. The GNSS floating-point solution and anomaly detection are performed again until the residual threshold is passed; the GNSS floating-point solution includes time, position, and velocity; the INS calculation result includes position, velocity, and attitude, and the INS calculation altitude is obtained based on the INS calculation position; The ambiguity fixation detection module uses the GNSS floating-point solution to fix ambiguity and obtain a GNSS fixed solution. It also calculates the residual between the GNSS fixed solution height, the INS calculated height, and the barometer measured height. Based on the residual threshold, it detects abnormalities in the GNSS fixed solution. If the GNSS fixed solution is abnormal, it discards the fixed solution and uses the floating-point solution. The altitude measured by the barometer is calculated based on the barometer measurement data. The fusion positioning module constructs a GNSS / INS / barometer fusion observation model, employing an error-state extended Kalman filter for solution. The model inputs include the position and velocity calculated by GNSS positioning at the same time, the position and velocity calculated by INS, and the altitude measured by barometer and its altitude change. The model outputs integrated navigation results and sensor errors. The integrated navigation results include time, system position, system velocity, and system attitude. The sensor errors include the INS zero bias and the slow-varying altitude bias of the barometer.

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