UAV measurement system based on low-altitude meteorological high-precision positioning

Abstract

This invention relates to the field of UAV measurement technology, specifically to a UAV measurement system based on high-precision low-altitude meteorological positioning. The system includes a low-altitude meteorological sensing unit, a positioning fusion calculation unit, a collaborative calibration control unit, and an adaptive measurement unit. In this invention, the low-altitude meteorological sensing unit collects meteorological parameters such as three-dimensional wind speed vectors. The positioning fusion calculation unit uses an atmospheric delay correction model and fuzzy adaptive Kalman filtering to compensate for atmospheric delay errors and adjust the positioning noise matrix to output accurate flight track coordinates. The collaborative calibration control unit establishes the relationship between humidity gradient and positioning altitude error, obtains the optimal compensation coefficient, and converts it into flight control commands. The adaptive measurement unit, based on the flight track and meteorological parameters, controls the multispectral imager to switch bands and adjusts the lidar scanning angle to achieve synchronous inversion of aerosol vertical profiles, thereby improving the positioning accuracy and reliability of measurement data for UAVs under complex meteorological conditions.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) measurement technology, and more specifically, to a UAV measurement system based on high-precision low-altitude meteorological positioning. Background Technology

[0002] Unmanned aerial vehicle (UAV) measurement is an important technology. In the field of UAV measurement technology, the positioning accuracy of UAVs plays a decisive role in the accuracy of measurement results. However, traditional UAV positioning methods have serious defects in low-altitude weather environments.

[0003] The low-altitude meteorological environment is complex and changeable. Meteorological factors such as temperature and humidity gradients and pressure gradients can significantly affect UAV positioning. Atmospheric delays in the ionosphere and troposphere can lead to positioning errors. Traditional positioning systems lack algorithms and models that fully consider low-altitude meteorological factors. When processing positioning data, they cannot effectively incorporate meteorological parameters such as temperature and humidity gradients and pressure gradients into the calculation process. When calculating positioning coordinates, traditional algorithms do not specifically correct for atmospheric delays, making them unable to adapt to complex meteorological changes and thus unable to accurately compensate for these errors. This results in deviations between the actual and expected flight paths of the UAV. Furthermore, noise during the positioning process... Interference is a prominent issue. Conventional positioning algorithms typically rely on pre-set parameters and fixed processing procedures, lacking the ability to monitor and adjust meteorological parameters in real time. They cannot obtain real-time changes in meteorological parameters such as the horizontal pressure gradient field in a timely manner, and therefore cannot dynamically adjust the positioning noise matrix according to these changes. When the horizontal pressure gradient field changes suddenly, traditional algorithms cannot quickly sense and respond, and continue to use the original noise matrix for positioning calculations, thus affecting positioning accuracy and causing deviations in measurement data. To solve this technical problem, we provide a UAV measurement system based on high-precision low-altitude meteorological positioning. Summary of the Invention

[0004] The purpose of this invention is to provide a UAV measurement system based on high-precision low-altitude meteorological positioning to solve the problems mentioned in the background art.

[0005] Traditional UAV positioning methods, lacking algorithms and models that consider low-altitude meteorological factors, struggle to compensate for positioning errors caused by atmospheric delay in low-altitude environments, leading to deviations between the actual and expected flight paths. Therefore, this case study employs a layered atmospheric delay correction model within the positioning fusion computing unit. This model divides the low-altitude meteorological environment into layers based on altitude, calculates and accumulates the ionospheric / tropospheric delay correction for each layer, accurately calculates the impact of atmospheric delay on positioning, and effectively compensates for it, thereby improving UAV positioning accuracy and reducing flight path deviations.

[0006] Because conventional positioning algorithms rely on preset parameters and fixed procedures, they lack the ability to monitor and adjust meteorological parameters in real time. They cannot dynamically adjust the positioning noise matrix according to changes in meteorological parameters such as the horizontal pressure gradient field, which affects positioning accuracy. Therefore, this case introduces a fuzzy adaptive mechanism when using Kalman filtering in the positioning fusion computing unit. Based on the range and rate of change of the horizontal pressure gradient field parameters, the noise covariance matrix of the Kalman filter is dynamically adjusted through a fuzzy rule base. This enables the positioning noise matrix to be adjusted in real time according to changes in meteorological parameters, thereby improving positioning accuracy and ensuring the accuracy of UAV measurement data.

[0007] To achieve the above objectives, a UAV measurement system based on low-altitude meteorological high-precision positioning is provided, comprising the following cooperating units:

[0008] The low-altitude meteorological sensing unit collects three-dimensional wind speed vectors, temperature stratification, absolute humidity, and horizontal pressure gradient fields through a sensor array.

[0009] The positioning fusion calculation unit receives the three-dimensional wind speed vector and temperature stratification parameters, and substitutes the temperature and humidity gradient parameters into the refractive index calculation formula through the atmospheric delay correction model to calculate the ionosphere / troposphere joint correction amount. Based on the horizontal pressure gradient field parameters, it uses Kalman filtering to adjust the positioning noise matrix and outputs the track coordinates.

[0010] The collaborative calibration control unit establishes a linear relationship between the humidity gradient and the positioning altitude error based on the track coordinates and absolute humidity parameters, and constructs a cost function that includes the heading angle deviation and humidity gradient observations. The optimal compensation coefficient is obtained through the QR decomposition method, and then the optimal compensation coefficient is converted into flight control commands.

[0011] The adaptive measurement unit controls the multispectral imager to automatically switch bands based on the track coordinates and temperature stratification parameters, and adjusts the lidar scanning angle based on the horizontal air pressure gradient field parameters to achieve synchronous inversion of the aerosol vertical profile.

[0012] As a further improvement to this technical solution, the atmospheric delay correction model in the positioning fusion calculation unit adopts a layered modeling approach:

[0013] The low-altitude meteorological environment is divided into multiple layers according to altitude. The temperature and humidity gradient parameters in each layer are considered to be uniformly distributed. For each layer, the temperature and humidity gradient parameters are substituted into the refractive index calculation formula to calculate the ionospheric / tropospheric delay correction amount for each layer. Finally, the correction amounts of each layer are summed to obtain the total ionospheric / tropospheric joint correction amount.

[0014] As a further improvement to this technical solution, a fuzzy adaptive mechanism is introduced in the positioning fusion calculation unit when adjusting the positioning noise matrix using Kalman filtering:

[0015] Based on the range and rate of change of the horizontal pressure gradient field parameters, the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter are dynamically adjusted by a fuzzy rule base. The fuzzy rule base divides the horizontal pressure gradient field into different fuzzy levels according to the magnitude and trend of the horizontal pressure gradient field, and sets corresponding adjustment coefficients for each level.

[0016] As a further improvement to this technical solution, the positioning fusion calculation unit employs a particle filter-based trajectory smoothing algorithm when outputting trajectory coordinates:

[0017] A set of particles is generated based on the preliminary track coordinates obtained by Kalman filtering. Each particle represents a possible track state. The weight of each particle is then calculated based on the horizontal pressure gradient field and the joint correction of the ionosphere / troposphere. Particles whose weights exceed the preset weight threshold are retained through a resampling process. Finally, the retained particles are weighted and averaged to obtain the smoothed track coordinates.

[0018] As a further improvement to this technical solution, the least squares method is used for parameter estimation in the collaborative calibration control unit when establishing the linear relationship between humidity gradient and positioning height error:

[0019] Multiple sets of flight path coordinates and absolute humidity parameters under different flight conditions are collected. The humidity gradient and positioning altitude error are calculated. Then, the linear equation between the humidity gradient and positioning altitude error is fitted by the least squares method to obtain the optimal parameter estimates.

[0020] As a further improvement to this technical solution, the cost function constructed in the collaborative calibration control unit adopts the form of weighted least squares:

[0021] Cost function ;in This represents the actual heading angle deviation. This is the estimated value of the heading angle deviation. These are humidity gradient observations. This is an estimate of the humidity gradient. These are weighting coefficients, set based on historical data. This represents the number of samples.

[0022] As a further improvement to this technical solution, in the collaborative calibration control unit, when obtaining the optimal compensation coefficients through the QR decomposition method, the cost function is expressed in matrix form. ,in The coefficient matrix, Let be the vector of compensation coefficients to be determined. For the observation vector, This is the weight matrix. For the transpose operation, on the coefficient matrix Perform QR decomposition to obtain ,in It is an orthogonal matrix. It is an upper triangular matrix, and then it is solved... Obtain the optimal compensation coefficient vector .

[0023] As a further improvement to this technical solution, the collaborative calibration control unit employs a proportional-integral-derivative controller when converting the optimal compensation coefficient into flight control commands:

[0024] The heading angle and altitude adjustment are calculated based on the optimal compensation coefficient. The heading angle and altitude adjustment are used as inputs to the proportional-integral-derivative (PID) controller. The corresponding control quantities are calculated through the proportional, integral, and derivative components of the PID controller. The control quantities are then converted into flight control commands and sent to the UAV.

[0025] As a further improvement to this technical solution, the adaptive measurement unit employs a spectral matching-based method when controlling the multispectral imager to automatically switch bands based on track coordinates and temperature stratification parameters.

[0026] Establish a spectral library of typical ground features under different temperature stratifications, determine the current measurement area of ​​the UAV based on the flight track coordinates, obtain the temperature stratification parameters of the area, and then match the spectral data collected by the multispectral imager with the spectra in the spectral library, selecting the band with the highest matching degree as the current measurement band.

[0027] As a further improvement to this technical solution, the adaptive measurement unit employs a fuzzy control algorithm when adjusting the lidar scanning angle based on the horizontal air pressure gradient field parameters.

[0028] The horizontal pressure gradient field parameters are divided into different fuzzy levels. Based on the different fuzzy levels, the scanning angle adjustment of the lidar is determined by the fuzzy rule library. The fuzzy rule library is designed according to the influence of the horizontal pressure gradient field on the vertical profile distribution of aerosols.

[0029] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0030] In a UAV measurement system based on low-altitude meteorological high-precision positioning, the positioning fusion computing unit effectively compensates for positioning errors caused by atmospheric delay through a hierarchical atmospheric delay correction model and fuzzy adaptive Kalman filtering adjustment. It dynamically adjusts the positioning noise matrix, greatly improving the positioning accuracy of the UAV and making the flight path more accurate. The collaborative calibration control unit establishes a linear relationship between humidity gradient and positioning altitude error, constructs a cost function and obtains the optimal compensation coefficient, and converts it into flight control commands. This allows for real-time calibration of positioning deviations, ensuring flight stability. The adaptive measurement unit controls the multispectral imager to switch bands and adjust the lidar scanning angle based on flight path coordinates, temperature stratification, and horizontal air pressure gradient field parameters, achieving synchronous inversion of aerosol vertical profiles and improving the targeting and accuracy of measurements. Attached Figure Description

[0031] Figure 1 This is a flowchart illustrating the overall workflow of the present invention.

[0032] The meanings of the labels in the diagram are as follows:

[0033] 1. Low-altitude meteorological sensing unit; 2. Positioning fusion computing unit; 3. Collaborative calibration control unit; 4. Adaptive measurement unit. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] This invention provides a UAV measurement system based on high-precision low-altitude meteorological positioning. Please refer to [link / reference]. Figure 1 As shown, it includes the following collaborative units:

[0036] The low-altitude meteorological sensing unit 1 collects three-dimensional wind speed vectors, temperature stratification, absolute humidity, and horizontal pressure gradient field through a sensor array.

[0037] The positioning fusion calculation unit 2 receives the three-dimensional wind speed vector and temperature stratification parameters, and substitutes the temperature and humidity gradient parameters into the refractive index calculation formula through the atmospheric delay correction model to calculate the ionospheric / tropospheric joint correction amount. Based on the horizontal pressure gradient field parameters, it uses Kalman filtering to adjust the positioning noise matrix and outputs the track coordinates.

[0038] In the positioning fusion computing unit 2, the atmospheric delay correction model adopts a hierarchical modeling approach:

[0039] The low-altitude meteorological environment is divided into multiple layers according to altitude. The temperature and humidity gradient parameters within each layer are considered to be uniformly distributed, simplifying subsequent calculations and improving their accuracy and efficiency. For each layer, temperature and humidity gradient parameters measured by multiple sensors are collected, and the average value of these parameters is calculated as the representative value of the temperature and humidity gradient parameters for that layer. The temperature and humidity gradient parameters are then substituted into the refractive index calculation formula. ;in For refractive index, For air pressure, For temperature, For water vapor pressure, Using an empirical constant, the refractive index of the layer is calculated. Then, based on the propagation path length and refractive index of the signal within the layer, the ionospheric / tropospheric delay correction for each layer is calculated. Finally, the corrections for each layer are summed to obtain the total ionospheric / tropospheric joint correction. In actual positioning calculations, using this total correction can significantly improve the accuracy and reliability of positioning and reduce positioning errors caused by atmospheric delay.

[0040] In the positioning fusion calculation unit 2, a fuzzy adaptive mechanism is introduced when adjusting the positioning noise matrix using Kalman filtering:

[0041] Air pressure data is collected in real time by the sensor array in the low-altitude meteorological sensing unit 1. The air pressure difference between adjacent locations is calculated to obtain the horizontal air pressure gradient field parameters. Simultaneously, the horizontal air pressure gradient field parameters at different times are recorded, and their rate of change is calculated (i.e., the difference between the horizontal air pressure gradient field parameters at adjacent times divided by the time interval). Based on the range and rate of change of the horizontal air pressure gradient field parameters, the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter are dynamically adjusted using a fuzzy rule base. The fuzzy rule base classifies the horizontal air pressure gradient field into different fuzzy levels according to its magnitude and trend, and sets corresponding adjustment coefficients for each level. For example, the magnitude of the horizontal air pressure gradient field can be divided into three levels: "small," "medium," and "large," and the trend of change can be divided into three levels: "slow," "medium," and "fast." Corresponding adjustment coefficients are set for each combination of fuzzy levels. For example, when the magnitude of the horizontal air pressure gradient field is "small" and the trend of change is "slow," the process noise covariance matrix is ​​set to... and measurement noise covariance matrix The adjustment coefficient is set to 0.8. When the horizontal pressure gradient field size is "large" and the changing trend is "rapid," the adjustment coefficient is set to 1.2. This allows for rapid and accurate adjustment of the noise matrix based on different horizontal pressure gradient field conditions, improving the Kalman filter's adaptability to different meteorological environments. The current horizontal pressure gradient field size and rate of change are mapped to corresponding fuzzy levels, obtaining the membership degree of each fuzzy level. For example, if the current horizontal pressure gradient field size is 5 hPa / km, based on the threshold for fuzzy level division, the membership degree of the "small" level is 0.2, the "medium" level is 0.8, and the "large" level is 0. Based on the membership degrees obtained through fuzzification, corresponding rules are searched in the fuzzy rule base, and reasoning is performed based on the rule weights to obtain the final adjustment coefficient. For example, the process noise covariance matrix is ​​calculated using a weighted average method. The adjustment factor is 0.9. Under different meteorological conditions, adjusting the noise matrix through the fuzzy adaptive mechanism can significantly improve the positioning accuracy and reduce the positioning error.

[0042] In the positioning fusion calculation unit 2, a track smoothing algorithm based on particle filtering is used when outputting track coordinates:

[0043] While Kalman filtering can provide initial trajectory coordinates, the real-world environment presents uncertainties, and a single coordinate cannot fully reflect the trajectory state. Therefore, a set of particles is generated based on the initial trajectory coordinates obtained from the Kalman filter, with each particle representing a possible trajectory state. Assuming the initial trajectory coordinates obtained from the Kalman filter are... Centered on this coordinate, randomizes the data within a certain range according to a Gaussian distribution. Particles Each particle Representing a possible trajectory state, describing the trajectory state through particle swarm optimization can more comprehensively consider the uncertainties that may exist in the trajectory, providing a foundation for subsequent more accurate trajectory estimation. Then, the weight of each particle is calculated based on the horizontal pressure gradient field and the joint correction amount of the ionosphere / troposphere. Combined with horizontal pressure gradient field information Combined correction with ionosphere / troposphere Construct a likelihood function ,in Calculate the weight of each particle based on the position information acquired by the sensor. Initial weights Then, the weights of all particles are normalized, enabling the particle filtering algorithm to adaptively adjust the confidence level for different trajectory states based on environmental factors, thus improving the accuracy of trajectory estimation. Furthermore, a resampling process is used to retain particles whose weights exceed a preset weight threshold. Finally, a weighted average is performed on the retained particles to obtain the smoothed trajectory coordinates, with a preset weight threshold set. Iterate through all particles and filter out the weights. The selected particles are copied and added to the particle set, restoring the particle set to its original size. This avoids the problem of inaccurate trajectory estimation caused by particle degradation, improving the stability and reliability of the algorithm, especially for the preserved particle set. Calculate its in The weighted average of the directions, the smoothed track coordinates are as follows: In practical applications, it can significantly reduce track fluctuations, improve track continuity and accuracy, and provide more reliable positioning information for subsequent UAV control and measurement.

[0044] The collaborative calibration control unit 3 establishes a linear relationship between the humidity gradient and the positioning altitude error based on the track coordinates and absolute humidity parameters, and constructs a cost function that includes the heading angle deviation and humidity gradient observation values. The optimal compensation coefficient is obtained through the QR decomposition method, and then the optimal compensation coefficient is converted into flight control commands.

[0045] In the collaborative calibration control unit 3, the least squares method was used for parameter estimation when establishing the linear relationship between the humidity gradient and the positioning height error.

[0046] During different flight states of the UAV, track coordinates and absolute humidity parameters are continuously recorded. Multiple sets of track coordinates and absolute humidity parameters under different flight states are collected. Track coordinates can be obtained from the output of positioning fusion computing unit 2, and absolute humidity parameters are measured by the humidity sensor in low-altitude meteorological sensing unit 1. The recorded data should include timestamps for accurate matching and analysis later. For the collected absolute humidity parameters, two adjacent measurement points are selected, the humidity difference between them is calculated, and divided by the vertical distance between the two points to obtain the humidity gradient. The altitude value in the track coordinates is subtracted from the known true altitude value to obtain the positioning altitude error, which provides accurate data for subsequent least squares fitting and helps to obtain a more accurate linear equation. Then, the linear equation between the humidity gradient and the positioning altitude error is fitted by least squares to obtain the optimal parameter estimate. During subsequent UAV flight, the positioning altitude error can be predicted using this equation based on the real-time measured humidity gradient, and corresponding calibration can be performed. In practical applications, this can effectively reduce the positioning altitude error caused by the influence of humidity gradient and improve the overall performance of the UAV measurement system.

[0047] In the collaborative calibration control unit 3, the constructed cost function adopts the form of weighted least squares:

[0048] To comprehensively account for the errors in heading angle deviation and humidity gradient observations, a cost function needs to be constructed. Based on system requirements and error analysis, the cost function is determined. ;in This represents the actual heading angle deviation. This is the estimated value of the heading angle deviation. These are humidity gradient observations. This is an estimate of the humidity gradient. These are weighting coefficients, set based on historical data. For sample size, the actual heading angle deviation The humidity gradient observation value can be obtained by comparing the gyroscope reading with the system's current heading angle. The estimated heading angle deviation is obtained by measuring and calculating the humidity sensor in the low-altitude meteorological sensing unit 1. and humidity gradient estimates The prediction can be made based on the previously established linear relationship model between humidity gradient and positioning altitude error. Weighting coefficients are determined according to factors such as data source, measurement accuracy, and impact on system performance, and the collected actual data is then used. , and estimated data , The determined weight coefficients are substituted into the cost function for calculation. The cost function is optimized using an optimization algorithm. The parameter values ​​are continuously updated based on the derivative information of the cost function with respect to the parameters until the cost function reaches the minimum value or meets the preset convergence condition. After parameter adjustment, the heading angle deviation and humidity gradient estimation error of the system are effectively reduced, improving the overall performance of the UAV measurement system.

[0049] To facilitate the solution using the QR decomposition method, the cost function needs to be transformed into matrix form. In the collaborative calibration control unit 3, when obtaining the optimal compensation coefficients using the QR decomposition method, the cost function is expressed in matrix form. ,in The coefficient matrix, Let be the vector of compensation coefficients to be determined. For the observation vector, This is the weight matrix. For the transpose operation, on the coefficient matrix Perform QR decomposition to obtain ,in It is an orthogonal matrix. It is an upper triangular matrix, and then it is solved... Obtain the optimal compensation coefficient vector The optimal compensation coefficient vector obtained by solving the problem Substituting back into the cost function, we calculate the value of the cost function and check whether the value meets the preset convergence condition. At the same time, through actual flight tests or simulation experiments, we can observe whether the system performance is improved after calibration using the compensation coefficient, thus avoiding the use of incorrect compensation coefficients for calibration and ensuring that the UAV measurement system can operate stably and accurately.

[0050] In the collaborative calibration control unit 3, a proportional-integral-derivative controller is used when converting the optimal compensation coefficient into flight control commands.

[0051] The optimal compensation coefficient is obtained to correct the deviation of the UAV in heading and altitude. It needs to be converted into specific heading angle and altitude adjustment amount in order to provide a clear target for subsequent control. The heading angle and altitude adjustment amount are calculated based on the optimal compensation coefficient. The heading angle and altitude adjustment amount are used as inputs to the proportional-integral-derivative controller. The corresponding control quantity is calculated through the proportional, integral and derivative links of the proportional-integral-derivative controller. The control quantity is converted into flight control commands and sent to the UAV.

[0052] For heading angle control, the output of the proportional element... ,in It is the heading angle proportionality factor. This refers to the heading angle error, which is the difference between the desired heading angle and the actual heading angle. For altitude control, this is the output of the proportional element. ,in It is the height ratio coefficient. This is the altitude error, which is the difference between the desired altitude and the actual altitude. The integration stage accumulates the error, and the output of the heading angle integration stage is... ,in It is the integral coefficient of the heading angle. It is the first The heading angle error at any given time, and the output of the altitude integration stage. ,in It is a high integral coefficient. It is the first The altitude error at any given time is adjusted by the differential element based on the rate of change of the error; the output of the heading angle differential element is... ,in These are the differential coefficients of the heading angle. It is the sampling time interval, the output of the highly differentiable element. ,in These are the altitude differential coefficients. Finally, the outputs of the three stages are added together to obtain the total control quantity, specifically the heading angle control quantity. Height control amount The three components of the proportional-integral-derivative (PI-DE) controller work together to comprehensively consider the current value, historical cumulative value, and trend of error, achieving more precise and stable control. According to the interface protocol and requirements of the UAV's flight control system, the heading angle control quantity and altitude control quantity are converted, and the converted flight control command is sent to the UAV's flight controller. After receiving the command, the flight controller drives the corresponding actuator to adjust the UAV's heading and altitude, enabling the UAV to fly at the desired heading and altitude, thus improving the overall performance of the UAV measurement system.

[0053] The adaptive measurement unit 4 controls the multispectral imager to automatically switch bands based on the track coordinates and temperature stratification parameters, and adjusts the lidar scanning angle based on the horizontal air pressure gradient field parameters to achieve synchronous inversion of the aerosol vertical profile.

[0054] In the adaptive measurement unit 4, when controlling the multispectral imager to automatically switch bands based on track coordinates and temperature stratification parameters, a spectral matching-based method is used.

[0055] A spectral library of typical ground features under different temperature stratifications is established. The current measurement area of ​​the UAV is determined based on the flight track coordinates, and the temperature stratification parameters of the area are obtained. Then, the spectral data collected by the multispectral imager is matched with the spectra in the spectral library. After the spectral matching calculation is completed, all calculated similarity values ​​are sorted, and the spectral record in the spectral library corresponding to the maximum value is found. The multispectral imager band information associated with this record is extracted, and a control command is sent to the multispectral imager to switch to the selected band for subsequent measurement operations. At the same time, the currently switched band information is displayed on the UAV's control system interface for easy monitoring by the operator. The band with the highest matching degree is selected as the current measurement band. In actual measurement tasks, clearer and more distinctive ground feature images can be obtained, which can significantly improve the quality of the final results, whether used in agricultural monitoring, environmental assessment, or geographic mapping.

[0056] In the adaptive measurement unit 4, a fuzzy control algorithm is used to adjust the lidar scanning angle based on the horizontal air pressure gradient field parameters.

[0057] First, the range of values ​​for the horizontal pressure gradient field parameters is determined. Based on this range, the horizontal pressure gradient field parameters are divided into different fuzzy levels, each corresponding to a fuzzy subset, which can be represented by fuzzy linguistic variables. The number of levels and interval boundaries can be adjusted according to the accuracy requirements and system complexity of the actual application scenario. Based on different fuzzy levels, the scanning angle adjustment of the lidar is determined through a fuzzy rule base. The fuzzy rule base is designed based on the influence of the horizontal pressure gradient field on the vertical profile distribution of aerosols. In actual atmospheric detection missions, the lidar can flexibly adjust the scanning angle according to the real-time horizontal pressure gradient field, effectively acquiring aerosol vertical profile data. Whether under stable meteorological conditions or complex and changeable weather conditions, it can ensure the quality and integrity of data acquisition.

[0058] In this invention, meteorological parameters such as three-dimensional wind speed vector are collected by the low-altitude meteorological sensing unit 1. The positioning fusion calculation unit 2 uses an atmospheric delay correction model and fuzzy adaptive Kalman filtering to compensate for atmospheric delay error and adjust the positioning noise matrix to output accurate track coordinates. The collaborative calibration control unit 3 establishes the relationship between humidity gradient and positioning altitude error, obtains the optimal compensation coefficient and converts it into flight control commands. The adaptive measurement unit 4 controls the multispectral imager to switch bands and adjust the lidar scanning angle based on the track and meteorological parameters to achieve synchronous inversion of aerosol vertical profiles, thereby improving the positioning accuracy and measurement data reliability of the UAV under complex meteorological conditions.

[0059] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A UAV measurement system based on low-altitude meteorological high-precision positioning, characterized in that, Includes the following collaborative units: The low-altitude meteorological sensing unit (1) collects three-dimensional wind speed vector, temperature stratification, absolute humidity and horizontal pressure gradient field through a sensor array; The positioning fusion calculation unit (2) receives the three-dimensional wind speed vector and temperature stratification parameters, and substitutes the temperature and humidity gradient parameters into the refractive index calculation formula through the preset atmospheric delay correction model to calculate the ionosphere / troposphere joint correction amount. Based on the horizontal pressure gradient field, it uses Kalman filtering to adjust the positioning noise matrix and outputs the track coordinates. The collaborative calibration control unit (3) establishes a linear relationship between the humidity gradient and the positioning altitude error based on the track coordinates and absolute humidity parameters, and constructs a cost function that includes the heading angle deviation and humidity gradient observation values. It obtains the optimal compensation coefficient through the QR decomposition method and then converts the optimal compensation coefficient into flight control commands. The adaptive measurement unit (4) controls the multispectral imager to automatically switch bands based on the track coordinates and temperature stratification parameters, and adjusts the laser radar scanning angle based on the horizontal air pressure gradient field parameters to achieve synchronous inversion of the aerosol vertical profile.

2. The UAV measurement system based on low-altitude meteorological high-precision positioning according to claim 1, characterized in that, In the positioning fusion computing unit (2), the atmospheric delay correction model adopts a hierarchical modeling approach: The low-altitude meteorological environment is stratified according to altitude. The temperature and humidity gradient parameters within each stratum are considered to be uniformly distributed. For each stratum, the temperature and humidity gradient parameters are substituted into the refractive index calculation formula to calculate the ionospheric / tropospheric delay correction for each stratum. Finally, the corrections for each stratum are summed to obtain the total ionospheric / tropospheric joint correction.

3. The UAV measurement system based on low-altitude meteorological high-precision positioning according to claim 2, characterized in that, In the positioning fusion calculation unit (2), a fuzzy adaptive mechanism is introduced when adjusting the positioning noise matrix using Kalman filtering: Based on the range and rate of change of the horizontal pressure gradient field parameters, the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter are dynamically adjusted by a fuzzy rule base. The fuzzy rule base divides the horizontal pressure gradient field into different fuzzy levels according to the magnitude and trend of the horizontal pressure gradient field, and sets corresponding adjustment coefficients for each level.

4. The UAV measurement system based on low-altitude meteorological high-precision positioning according to claim 3, characterized in that, In the positioning fusion calculation unit (2), a track smoothing algorithm based on particle filtering is used when outputting track coordinates: A set of particles is generated based on the preliminary track coordinates obtained by Kalman filtering. Each particle represents a track state. The weight of each particle is then calculated based on the horizontal pressure gradient field and the joint correction of the ionosphere / troposphere. Particles whose weights exceed the preset weight threshold are retained through a resampling process. Finally, the retained particles are weighted and averaged to obtain the smoothed track coordinates.

5. The UAV measurement system based on low-altitude meteorological high-precision positioning according to claim 1, characterized in that, In the collaborative calibration control unit (3), the least squares method was used for parameter estimation when establishing the linear relationship between the humidity gradient and the positioning height error: Collect flight path coordinates and absolute humidity parameters under different flight conditions, calculate humidity gradient and positioning altitude error, and then fit the linear equation between humidity gradient and positioning altitude error using the least squares method to obtain the optimal parameter estimates.

6. The UAV measurement system based on low-altitude meteorological high-precision positioning according to claim 5, characterized in that, In the collaborative calibration control unit (3), the constructed cost function adopts the form of weighted least squares: Cost function ;in This represents the actual heading angle deviation. This is the estimated value of the heading angle deviation. These are humidity gradient observations. This is an estimate of the humidity gradient. These are weighting coefficients, set based on historical data. This represents the number of samples.

7. The UAV measurement system based on low-altitude meteorological high-precision positioning according to claim 6, characterized in that, In the collaborative calibration control unit (3), when obtaining the optimal compensation coefficient through the QR decomposition method, the cost function is expressed in matrix form. ,in The coefficient matrix, Let be the vector of compensation coefficients to be determined. For the observation vector, This is the weight matrix. For the transpose operation, on the coefficient matrix Perform QR decomposition to obtain ,in It is an orthogonal matrix. It is an upper triangular matrix, and then it is solved... Obtain the optimal compensation coefficient vector .

8. The UAV measurement system based on low-altitude meteorological high-precision positioning according to claim 7, characterized in that, In the collaborative calibration control unit (3), a proportional-integral-derivative controller is used when converting the optimal compensation coefficient into flight control commands: The heading angle and altitude adjustment are calculated based on the optimal compensation coefficient. The heading angle and altitude adjustment are used as inputs to the proportional-integral-derivative (PID) controller. The corresponding control quantities are calculated through the proportional, integral, and derivative components of the PID controller. The control quantities are then converted into flight control commands and sent to the UAV.

9. The UAV measurement system based on low-altitude meteorological high-precision positioning according to claim 1, characterized in that, In the adaptive measurement unit (4), when controlling the multispectral imager to automatically switch bands based on the track coordinates and temperature stratification parameters, a spectral matching-based method is used: Establish a spectral library of typical ground features under different temperature stratifications, determine the current measurement area of ​​the UAV based on the flight track coordinates, obtain the temperature stratification parameters of the area, and then match the spectral data collected by the multispectral imager with the spectra in the spectral library, selecting the band with the highest matching degree as the current measurement band.

10. The UAV measurement system based on low-altitude meteorological high-precision positioning according to claim 9, characterized in that, In the adaptive measurement unit (4), a fuzzy control algorithm is used when adjusting the lidar scanning angle based on the horizontal air pressure gradient field parameters: The horizontal pressure gradient field parameters are divided into different fuzzy levels. Based on the different fuzzy levels, the scanning angle adjustment of the lidar is determined by the fuzzy rule library. The fuzzy rule library is designed according to the influence of the horizontal pressure gradient field on the vertical profile distribution of aerosols.

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