Engineering height measurement method and system based on barometer as error compensation for inertial navigation system

Abstract

The invention innovatively provides an engineering height measurement method and system for an inertial navigation system based on a barometer as error compensation, engineering equipment is modeled, the modeling comprises the shape and characteristics of the equipment and the installation position of the inertial navigation system, different sensor parameters are adjusted through a simulation model, and the engineering height measurement accuracy is improved. Observing a starting jumping rule and a stopping jumping rule of the sensor, analyzing error performance of the sensor under different moving distances, and obtaining an inertial navigation simulation model through a simulation training strategy; collecting data of an inertial navigation system and a barometer in real time; inputting the inertial navigation data into the simulation model, and performing simulation correction on the inertial navigation data according to the inertial navigation data and a simulation algorithm; and the error compensation module is used for acquiring the corrected inertial navigation data and barometer data, acquiring an inertial navigation height through an inertial navigation model, calculating and acquiring an engineering height through a data fusion algorithm, and performing error compensation on the inertial navigation model by utilizing the engineering height through an error compensation strategy.

Description

Technical Field

[0001] The present invention relates to an engineering height measurement system, and in particular to an engineering height measurement method and system based on an inertial navigation system using a barometer for error compensation. Background Art

[0002] At present, the measurement of the working height of construction machinery is based on mechanical and contact sensors, or the satellite positioning measurement method of RTK (GNSS) technology. Among them, the mechanical measurement method is customized for different construction machinery and equipment in terms of its size, dimensions, and installation method, and does not have universality and generality. The satellite positioning measurement method using RTK (GNSS) technology must rely on a satellite positioning system (Beidou / GPS), that is, it can only be used in open outdoor areas. Moreover, this form of positioning system is relatively high in terms of system complexity and price; when using a barometer for height measurement, the elevation accuracy is 50m, with a large error and is not suitable for the application scenarios of construction machinery; when using an inertial navigation method for measurement, the accumulated error drift is serious, and the accuracy is insufficient for long-term applications. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide an engineering height measurement method and system based on an inertial navigation system using a barometer for error compensation to overcome the above-mentioned defects in the existing technology.

[0004] To achieve the above purpose, the present invention provides the following technical solutions: An engineering height measurement method based on an inertial navigation system using a barometer for error compensation, including Steps of constructing a simulation model: Modeling the engineering equipment, including the shape, characteristics of the equipment, and the installation position of the inertial navigation system. Through the simulation model, adjust different sensor parameters, observe its starting and stopping jumping rules, and analyze the error performance of the sensor at different moving distances. Through the simulation training strategy, an inertial navigation simulation model; Steps of data collection: Real-time collection of data from the inertial navigation system and the barometer. The inertial navigation data includes the original data of the three-axis accelerometer and the three-axis gyroscope, and the barometer data includes the atmospheric pressure and temperature data; Steps of simulation correction: Input the inertial navigation data into the simulation model, obtain the error jumping rule and the error starting position according to the inertial navigation data, and perform simulation correction on the inertial navigation data according to the simulation algorithm; Steps of error compensation: Used to obtain the corrected inertial navigation data and barometer data, obtain the inertial navigation height through the inertial navigation model, calculate and obtain the engineering height through the data fusion algorithm, and perform error compensation on the inertial navigation model using the engineering height through the error compensation strategy.

[0005] Preferably, the simulation training strategy includes A trigger threshold component sub-step, which presets a motion control instruction set, simulates the operation trajectory of the device in three-dimensional space, calculates the position and speed of the device at different times, and compares the simulated results with the actual data of the sensor through the data collected by the sensor to calculate the trigger threshold of the sensor; An error jump law library sub-step, which adds different types of errors during the device simulation process, generates error simulation data, obtains a probability distribution model based on the error simulation data, and sets an error jump law library in the probability distribution model.

[0006] Preferably, the simulation model component step includes a simulation model verification sub-step, which is used to obtain the device state and environmental information in historical data, set corresponding initial conditions and parameters in the simulation model, compare the historical data with the output data of the simulation model, calculate the contribution degree of sensor parameters, kinematic parameters, etc. to the model error, obtain the parameters that need to be corrected according to the contribution degree, and correct the model based on the corrected parameters.

[0007] Preferably, the simulation algorithm is ; is the inertial navigation data after simulation correction, D is the uncorrected original data collected in real time, t0 is the start time of detection, t1 is the end time of the detection process, n is the number of acquisition points within the detection, is the displacement corresponding to the i-th sampling point, is the trigger threshold of the sensor, that is, the signal jumps when the displacement reaches this value, reflects the steepness of the signal jump, erf(z) is the error function, which is used to describe the influence of the error starting position on the correction, E is the position where the error occurs within the detection threshold, is the standard deviation for controlling the influence range of the error starting position, is the correction increment calculated for the i-th sampling point according to the error jump law and the error starting position.

[0008] Preferably, the data fusion algorithm is: ; is the engineering height, is the barometer height, is the inertial navigation height, is the dynamic weight, which is dynamically adjusted according to the reliability of the barometer data and the vertical acceleration of the accelerometer for ​

[0009] Preferably, the error compensation strategy includes: Height error estimation step: obtaining a height error value by using the engineering height and the barometer height, and using the height estimation value; Error correction step: performing real-time correction on the inertial navigation model according to the height error estimation value.

[0010] Preferably, a plurality of groups of barometers are arranged at different positions of the engineering equipment, and a barometric analysis step is further included, which is used to obtain the barometer data of the plurality of groups of barometers, compare the barometer data of each group, and eliminate abnormal data. Each sensor obtains a corresponding weight value according to the installation position, and obtains the historical data volatility of each barometer. The weight value is redistributed according to the volatility of each barometer, and the barometer data of each group is calculated as the atmospheric pressure according to the weight.

[0011] Preferably, in the barometric analysis step, a barometer reliability verification sub-step is further included, which is used to detect the barometer data of the barometer in the detection process in real time, preset a time window, calculate the mean value, variance and range of the barometer data within the time window, and judge whether there is an abnormality according to the mean value, variance and range. If there is an abnormality, obtain the abnormal point time and eliminate the data of the abnormal point in the error compensation algorithm.

[0012] An engineering height measurement system for an inertial navigation system based on a barometer for error compensation, including A simulation model component module, which models the engineering equipment, including the shape, characteristics of the equipment and the installation position of the inertial navigation system. Through the simulation model, different sensor parameters are adjusted, and the starting and stopping jumping rules are observed, and the error performance of the sensor at different moving distances is analyzed. Through the simulation training strategy, an inertial navigation simulation model; A data acquisition module, which collects the data of the inertial navigation system and the barometer in real time. The inertial navigation data includes the original data of the three-axis accelerometer and the three-axis gyroscope, and the barometer data includes the atmospheric pressure and the temperature data; A simulation correction module, which inputs the inertial navigation data into the simulation model, obtains the error jumping rule and the error starting position according to the inertial navigation data, and performs simulation correction on the inertial navigation data according to the simulation algorithm An error compensation module, which is used to obtain the corrected inertial navigation data and barometer data, obtain the inertial navigation height through the inertial navigation model, calculate and obtain the engineering height through the data fusion algorithm, and use the engineering height to perform error compensation on the inertial navigation model.

[0013] The beneficial effects of the present invention are as follows: through the simulation model construction step, the error performance of the sensor under different conditions is deeply studied, and a detailed data basis is provided for the subsequent error correction. In the simulation correction step, the error jump law and the starting position can be accurately obtained, and the inertial navigation data can be corrected in a targeted manner, reducing the height measurement deviation caused by the error of the inertial navigation system itself; in the error compensation step, the barometer data and the corrected inertial navigation data are fused and calculated. The principle of the barometer measuring atmospheric pressure and indirectly calculating the height complements the inertial navigation system based on acceleration and angular velocity measurement. The data fusion algorithm fully integrates the information of the two, effectively makes up for the shortcomings of the inertial navigation system in height measurement, and significantly improves the accuracy of the final engineering height measurement; the shape, characteristics and installation position of the engineering equipment and the inertial navigation system are modeled, so that the simulation model is highly consistent with the actual engineering scene. In different application environments, such as different terrains and climate conditions, by adjusting the sensor parameters and observing their laws, the inertial navigation simulation model can be better adapted to various complex working conditions. This means that the height measurement method can operate stably in a variety of engineering scenes, reducing excessive interference from individual differences in equipment or environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is the overall flow chart of the present invention; Figure 2 It is a simulation training flow chart of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] It should be noted that when a component is referred to as being "fixed to" another component, it may be directly on the other component or there may also be a component centered. When a component is considered to be "connected to" another component, it may be directly connected to the other component or there may also be a component centered. When a component is considered to be "set on" another component, it may be directly set on the other component or there may also be a component centered. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs. The terms used in the description of the invention herein are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0018] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings: As Figure 1 - Figure 2 shown, the present invention provides an engineering altimetry method for an inertial navigation system based on a barometer for error compensation, including Steps of the simulation model component: Model the engineering equipment, including the shape, features of the equipment, and the installation position of the inertial navigation system. Through the simulation model, adjust different sensor parameters, observe its starting and stopping jitter rules, and analyze the error performance of the sensor at different moving distances. Through the simulation training strategy, the inertial navigation simulation model; Model the engineering equipment, accurately describe the shape and features of the equipment, and use 3D modeling software to construct the geometric model of the equipment, accurately present its structural details, determine the installation position of the inertial navigation system, and based on the coordinate system of the equipment, determine the specific coordinates of the inertial navigation system in the equipment to provide accurate spatial information for subsequent simulation analysis; By changing parameters such as the resolution and sensitivity of the sensor, observe the starting and stopping jitter rules of the sensor; For example, gradually increase the resolution of the accelerometer, and record the moments and positions where the sensor starts to generate signal jumps and stops jumping under different motion conditions; Analyze the error performance of the sensor at different moving distances, set various motion trajectories such as straight lines and curves for the equipment, measure and compare the deviation between the sensor measurement value and the actual value, and study the change trend of the error with the moving distance; Use machine learning algorithms (such as neural networks, genetic algorithms, etc.) to train the inertial navigation simulation model; Use a large amount of sensor data and corresponding real motion states in different motion scenarios as training samples to let the model learn the mapping relationship between sensor parameters and motion states; By continuously adjusting the parameters of the model (such as the weights of the neural network, the genetic factors of the genetic algorithm, etc.), optimize the performance of the model so that it can more accurately simulate the working process of the inertial navigation system.

[0019] The simulation training strategy includes Trigger threshold component sub-step. There is a preset motion control instruction set. Simulate the operating trajectory of the device in three-dimensional space, calculate the position and speed of the device at different times, and compare the simulated results with the actual data collected by the sensor to calculate the trigger threshold of the sensor; Before conducting simulation training, a series of motion control instruction sets need to be preset according to the actual engineering scenario. These instruction sets should cover various possible motion modes of the device, such as linear motion, curvilinear motion, accelerating motion, decelerating motion, etc.; For example, for a construction machinery equipment, the motion control instruction set may include instructions such as forward, backward, left turn, right turn, lift, lower, etc., and each instruction should also include specific motion parameters, such as motion speed, acceleration, motion time, etc.; Utilize kinematic and dynamic principles, combined with the preset motion control instruction set, to simulate the operating trajectory of the device in three-dimensional space. Professional simulation software (such as MATLAB, Simulink, etc.) can be used to achieve this process. By establishing a motion model of the device, calculate the position and speed of the device at different times according to the instruction set; While simulating the operation of the device, use virtual sensors to collect data during the simulation process, such as acceleration, angular velocity, position, etc. At the same time, install the same type of sensors on the actual device to collect data during the actual operation process. Compare the simulated data with the actual sensor data to find the differences between the two. By comparing the simulated results with the actual sensor data, analyze the response of the sensor under different motion states. When the difference between the simulated data and the actual data exceeds a certain range, it is considered that the sensor is triggered. According to the results of multiple simulations and actual tests, statistically analyze various parameters (such as position, speed, acceleration, etc.) when the sensor is triggered, and calculate the trigger threshold of the sensor. For example, statistical analysis methods can be used to calculate the average value, standard deviation, etc. of the parameters when the sensor is triggered to determine the range of the trigger threshold.

[0020] Error Jitter Law Library Sub-step. During the device simulation process, different types of errors are added, and error simulation data is generated. Based on the error simulation data, a probability distribution model is obtained. According to the probability distribution model, an error jitter law library is set up within the probability distribution model. During the device simulation process, in order to more realistically simulate the actual situation, different types of errors need to be added. The error types can include sensor measurement errors, system noise, external interference, etc. For example, sensor measurement errors can be simulated by adding random noise to the simulation data, and system noise can be introduced by establishing a noise model. By adding these errors, a large amount of error simulation data is generated. Statistical analysis is performed on the generated error simulation data to determine the probability distribution model of the errors. Common probability distribution models include normal distribution, uniform distribution, Poisson distribution, etc. Statistical software (such as SPSS, Origin, etc.) can be used to fit the error data to find the most suitable probability distribution model. For example, if the error data shows the characteristics of a bell-shaped curve, it can be considered that the errors follow a normal distribution. According to the obtained probability distribution model, an error jitter law library is established. The error jitter law library should include the probability distribution parameters (such as mean, standard deviation, etc.) of different types of errors and the change laws of errors under different conditions. For example, for normally distributed errors, their mean and standard deviation can be recorded, as well as how the errors change with factors such as time, position, and speed. Through the error jitter law library, errors can be predicted and compensated in practical applications, improving the reliability and accuracy of the system.

[0021] Data Acquisition Step. The data of the inertial navigation system and the barometer are collected in real time. The inertial navigation data includes the raw data of the three-axis accelerometer and the three-axis gyroscope, and the barometer data includes the atmospheric pressure and temperature data. Using the three-axis accelerometer and the three-axis gyroscope in the inertial navigation system, the acceleration and angular velocity information of the device are collected in real time. Data is collected at a fixed sampling frequency (such as 100Hz, 1000Hz, etc., determined according to actual needs) to ensure that the acquired data can accurately reflect the change of the device's motion state. The collected raw data is preprocessed, including filtering (such as using Kalman filtering, low-pass filtering, etc.) to remove noise interference, and coordinate transformation to convert the relative coordinate system data measured by the sensor into unified engineering coordinate system data. The atmospheric pressure and temperature data are measured in real time through the barometer. Similarly, it is collected at a suitable sampling frequency. Considering that the changes in air pressure and temperature are relatively slow, the sampling frequency can be lower than that of the inertial navigation data, such as about 10Hz. The barometer data is calibrated to compensate for the influence of temperature changes on the air pressure measurement accuracy. According to the temperature compensation formula of the barometer or the temperature-pressure correction curve obtained through experiments, the collected air pressure and temperature data are converted into accurate air pressure values.

[0022] Simulation correction steps: Input inertial navigation data into the simulation model. According to the inertial navigation data, obtain the error fluctuation law and the error starting position, and perform simulation correction on the inertial navigation data according to the simulation algorithm; Input the real-time collected inertial navigation data into the pre-constructed simulation model, and the model calculates the motion trajectory and attitude change of the device according to the input data. In this process, by comparing the model calculation results with the preset standard motion state, obtain the error fluctuation law and the error starting position; For example, when the deviation between the device position calculated by the model and the actual set position exceeds a certain threshold, record the motion parameters and time point at this time as the error starting position; At the same time, analyze the change frequency and amplitude of the error in different motion stages, and summarize the error fluctuation law; Based on the obtained error information, use a suitable simulation algorithm to correct the inertial navigation data. For example, use the method based on model predictive control to calculate the error trend according to the error fluctuation law and correct the error of the inertial navigation system data; For the error starting position, reduce the impact of the error on the subsequent motion state calculation by compensating or correcting the sensor data at this position; The data of the accelerometer and gyroscope can be weighted adjusted or correction terms can be added according to the type and magnitude of the error; The steps of the simulation model components include a simulation model verification sub-step. The simulation model verification sub-step is used to obtain the device status and environmental information in historical data, set the corresponding initial conditions and parameters in the simulation model, compare the historical data with the output data of the simulation model, calculate the contribution degree of sensor parameters, kinematic parameters, etc. to the model error, obtain the parameters that need to be corrected according to the contribution degree, and correct the model based on the corrected parameters; accurately set the corresponding initial conditions in the simulation model according to the initial device status marked in the historical data; adjust various parameters in the simulation model with reference to the actual working conditions reflected in the historical data. For kinematic parameters, set reasonable translation, rotation range and other parameters according to the actual movement range of the device under different working conditions. For sensor parameters, adjust the zero bias and scale factor of the accelerometer, the angular random walk parameter of the gyroscope, etc. according to the performance of the sensor in the historical data. For example, if the historical data shows that the zero bias of the accelerometer has a certain drift in a specific environment, adjust the zero bias parameter of the accelerometer in the simulation model; accurately match and compare the output data of the simulation model with the corresponding historical data. For time series data, ensure that the data is compared at the same time node. For example, compare the accelerometer data output by the simulation model with the accelerometer measurement value in the historical data at the t-th second after the device starts; accurately match and compare the output data of the simulation model with the corresponding historical data. For time series data, ensure that the data is compared at the same time node. For example, compare the accelerometer data output by the simulation model with the accelerometer measurement value in the historical data at the t-th second after the device starts; by changing a single parameter (such as sensor parameters, kinematic parameters, etc.) in the simulation model, observe the change of the model output error, so as to analyze the sensitivity of each parameter to the model error. For example, gradually increase the moment of inertia parameter of the joint, and at the same time monitor the change of the position error output by the model, and calculate the ratio of the change rate of the moment of inertia parameter to the change rate of the position error to quantify the influence degree of the moment of inertia on the position error; based on the results of the parameter sensitivity analysis, calculate the contribution degree of each parameter to the model error. The normalization method can be used to take the ratio of the sensitivity of each parameter to the sum of the sensitivities of all parameters as the contribution degree of this parameter; according to the calculation results of the contribution degree, determine the parameters with a greater contribution to the model error as the parameters that need to be corrected. Usually, the parameters with a contribution degree exceeding a certain threshold (such as 0.2) are selected as the key correction objects. For example, if the contribution degree of the scale factor of the accelerometer is 0.3, which is greater than the threshold 0.2, then the scale factor of the accelerometer is listed as the parameter that needs to be corrected; according to the determined type of parameters that need to be corrected and the characteristics of the model, select a suitable correction method. For parameters with a clear mathematical relationship, such as the zero bias and scale factor of the accelerometer, the key parameter identification correction method can be used, and more accurate parameter values can be calculated by using historical data combined with optimization algorithms such as the least squares method.For cases where the error pattern is relatively complex and difficult to describe with a simple mathematical formula, a machine learning-based correction method can be adopted. Historical data is used to train a machine learning model to predict and correct errors.

[0023] The simulation algorithm is ; is the inertial navigation data after simulation correction, D is the uncorrected original data collected in real time, t0 is the start time of detection, t1 is the end time of the detection process, n is the number of acquisition points within the detection, is the displacement corresponding to the i-th sampling point, is the trigger threshold of the sensor, that is, when the displacement reaches this value, the signal jumps, reflects the steepness of the signal jump, erf(z) is the error function ( ), which is used to describe the influence of the error starting position on the correction, E is the position where the error occurs within the detection threshold, is the standard deviation that controls the influence range of the error starting position, is the correction increment calculated for the i-th sampling point according to the error jump pattern and the error starting position.

[0024] The error compensation steps are used to obtain the corrected inertial navigation data and barometer data. The inertial navigation altitude is obtained through the inertial navigation model, and the engineering altitude is obtained by calculation and through the data fusion algorithm. The inertial navigation model is compensated for errors using the engineering altitude through the error compensation strategy; the inertial navigation data after simulation correction is fused with the barometer data; the data fusion algorithm is used to comprehensively process the two types of data by combining the dynamic model of the inertial navigation system and the measurement characteristics of the barometer; the engineering altitude is calculated from the fused result, and the altitude value is initially calculated based on the barometric pressure value measured by the barometer using the conversion relationship between pressure and altitude (such as the international standard atmosphere model formula); then, in combination with the acceleration and angular velocity information measured by the inertial navigation system, the initial altitude value is optimized and corrected to obtain a more accurate engineering altitude; based on the obtained engineering altitude, the inertial navigation model is compensated using the compensation strategy; for example, the difference between the engineering altitude and the altitude calculated by the inertial navigation model is calculated and used as the compensation amount to be fed back into the inertial navigation model. By adjusting the parameters of the model (such as the zero bias and scale factor of the accelerometer and gyroscope) or correcting the calculation process of the model (such as adding a compensation term to the motion equation), the output of the inertial navigation model is made closer to the true engineering altitude. The above process is continuously repeated to continuously optimize the inertial navigation model and improve the accuracy and reliability of engineering height measurement.

[0025] The data fusion algorithm is: ; is the engineering height, is the barometer height, is the inertial navigation height, is the dynamic weight, which is dynamically adjusted according to the reliability of barometer data and the vertical acceleration of the accelerometer for dynamic adjustment.

[0026] The error compensation strategy includes: Height error estimation step: Obtain the height error value by using the engineering height and the barometer height, and use the height estimation value; More accurate height estimation values can be obtained by statistically analyzing the height error values over a period of time, such as calculating the mean, variance, etc. For example, using the sliding window averaging method, calculate the average value of the height error values within the window as the current height estimation value to reduce the influence of single measurement errors; Error correction step, perform real-time correction on the inertial navigation model according to the height error estimation value; Correction method based on feedback control: The PID controller calculates the control quantity according to the height error estimation value and performs real-time correction on the inertial navigation model. The proportional term is adjusted according to the magnitude of the current error, the integral term is used to eliminate the steady-state error of the system, and the derivative term is adjusted in advance according to the change rate of the error; By adjusting the parameters of the PID controller (proportional coefficient, integral coefficient, and derivative coefficient), the output of the inertial navigation model can quickly and stably track the engineering height; Fuzzy control is a control method based on fuzzy logic, which does not require an accurate mathematical model. According to the height error estimation value and the error change rate, define fuzzy rules and fuzzy membership functions, convert the input exact values into fuzzy quantities, obtain the fuzzy control quantity through fuzzy reasoning, and then convert it into an exact control quantity to correct the inertial navigation model. Fuzzy control has strong robustness and can adapt to the uncertainty and nonlinear characteristics of the system.

[0027] During the movement of the inertial navigator, use the barometer to collect the air pressure at different heights in real time, calculate the height error in real time, obtain the barometer height at different moments, and correct the inertial navigation according to the barometer data collected at different moments to improve the accuracy of the current height measurement system, ensure that the detected output data approaches the actual data, and improve the detection accuracy.

[0028] The model-based calibration method uses the height error estimate as the observation value and employs the Kalman filter algorithm to estimate and correct the state of the inertial navigation model. The Kalman filter adjusts the state estimate of the model through two steps, prediction and update, to make it approach the true value. In the prediction step, the state at the next moment is predicted according to the dynamic equation of the inertial navigation model; in the update step, the predicted state is corrected according to the height error estimate. For a non-linear inertial navigation model, EKF and UKF are more suitable calibration methods. EKF linearizes the non-linear model and then applies the Kalman filter algorithm; UKF uses a deterministic sampling strategy, which can handle non-linear problems more accurately and avoid the errors caused by linearization in EKF.

[0029] Several groups of barometers are set at different positions of the engineering equipment. There is also a barometric pressure analysis step for obtaining the barometer data of several groups of barometers, comparing the barometer data of each group, and eliminating abnormal data. Each sensor obtains the corresponding weight value according to the installation position and obtains the historical data volatility of each barometer. The weight value is redistributed according to the volatility of each barometer, and the barometer data of each group is calculated as the atmospheric pressure according to the weight. Multiple groups of barometers are set on the engineering equipment. By using multiple groups of barometers, the stability of barometer detection is improved. At the same time, the influence of the barometer installation position on data acquisition and detection is reduced. The barometer data obtained are compared. The comparison can be carried out by calculating the difference between adjacent barometer data or calculating the statistical quantities (such as mean, median, etc.) of all barometer data.

[0030] Abnormal data elimination: Set a reasonable threshold range. When the data of a certain barometer differs from the data of other barometers by more than this threshold, determine that this data is abnormal data and eliminate it. For example, the mean and standard deviation of all barometer data can be calculated. When the data of a certain barometer , when (b is a preset coefficient, usually taking 2 or 3), it is considered that the data is abnormal, and this data point is excluded; each sensor obtains the corresponding weight value according to its installation position; due to different installation positions, the contribution degree of the barometer to the measurement of the overall atmospheric pressure may be different; for example, the barometer installed at a key part of the device or in a position less affected by external interference may be given a higher weight; these weight values can be determined in advance through experience or experiments; obtain the historical data volatility of each barometer; the volatility can be measured by calculating statistical quantities such as the standard deviation, variance or range of the historical data; for example, calculate the standard deviation of the measurement data of a barometer over a period of time in the past, and the larger the standard deviation, the greater the volatility of the data of this barometer; reallocate the weight values according to the volatility of each barometer; the data of the barometer with smaller volatility is relatively more reliable and should be given a higher weight; while the data of the barometer with larger volatility has lower reliability and should be given a lower weight; the weight adjustment can be carried out in a linear or non-linear manner; calculate the weighted values of each barometer data according to the reallocated weights to obtain the final atmospheric pressure value.

[0031] In the barometric analysis step, there is also a barometer reliability verification sub-step, which is used to detect the barometer data during the detection process of the barometer in real time, and a time window is preset, calculate the mean value, variance and range of the barometer data within the time window, and judge whether there is an abnormality according to the mean value, variance and range. If there is an abnormality, obtain the time of the abnormal point and exclude the data of this abnormal point in the error compensation algorithm; detect the barometer data during the detection process of the barometer in real time. At the same time, a time window is preset, and this time window can be set according to actual needs, such as set to 5s, 10s, etc.; within each event window, calculate the mean value, variance and range of the barometer data.

[0032] An engineering altimetry system for an inertial navigation system based on a barometer for error compensation, including A simulation model component module that models the engineering equipment, including the shape, characteristics of the equipment and the installation position of the inertial navigation system. Through the simulation model, adjust different sensor parameters, observe its starting jump law and ending jump law, and analyze the error performance of the sensor at different moving distances. Through the simulation training strategy, an inertial navigation simulation model; A data acquisition module that collects the data of the inertial navigation system and the barometer in real time. The inertial navigation data includes the original data of the three-axis accelerometer and the three-axis gyroscope, and the barometer data includes the atmospheric pressure and temperature data; A simulation correction module that inputs the inertial navigation data into the simulation model, obtains the error jump law and the error starting position according to the inertial navigation data, and corrects the inertial navigation data according to the simulation algorithm An error compensation module is used to obtain the corrected inertial navigation data and barometric data, obtain the inertial navigation altitude through the inertial navigation model, calculate and obtain the engineering altitude through a data fusion algorithm, and use the engineering altitude to perform error compensation on the inertial navigation model.

[0033] The above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the concept of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. An engineering height measurement method using an inertial navigation system based on a barometer as error compensation, characterized in that: include Simulation model construction steps: Model the engineering equipment, including the shape, features and installation position of the inertial navigation system. Through the simulation model, adjust different sensor parameters, observe the starting and ending beating rules, and analyze the error performance of the sensor at different moving distances. Correct the inertial navigation simulation model through simulation training strategies. Data collection step: real-time collection of inertial navigation system and barometer data. The inertial navigation data includes the original data of the three-axis accelerometer and the three-axis gyroscope, and the barometer data includes atmospheric pressure and temperature data. The simulation correction step includes inputting the inertial navigation data into the simulation model, obtaining the error jump law and the error starting position according to the inertial navigation data, and performing simulation correction on the inertial navigation data according to the simulation algorithm; The error compensation step is used to obtain the corrected inertial navigation data and barometer data, obtain the inertial navigation altitude through the inertial navigation model, calculate and obtain the engineering altitude through the data fusion algorithm, and use the engineering altitude to compensate the error of the inertial navigation model through the error compensation strategy.

2. The engineering height measurement method of an inertial navigation system based on a barometer as error compensation according to claim 1, characterized in that: Simulation training strategies include The trigger threshold component sub-step is preset with a motion control instruction set, which summarizes the running trajectory of the simulated device in three-dimensional space, calculates the position and speed of the device at different times, and compares the simulation results with the actual data of the sensor through the data collected by the sensor to calculate the trigger threshold of the sensor; The error jump law library sub-step adds different types of errors during the equipment simulation process, generates error simulation data, and obtains a probability distribution model based on the error simulation data. According to the probability distribution model, an error jump law library is set in the probability distribution model.

3. The engineering height measurement method of an inertial navigation system based on a barometer as error compensation according to claim 1, characterized in that: The simulation model component step includes a simulation model verification sub-step, which is used to obtain device status and environmental information in historical data, set corresponding initial conditions and parameters in the simulation model, compare historical data with output data of the simulation model, calculate the contribution of sensor parameters, kinematic parameters, etc. to model errors, obtain parameters that need to be corrected according to the contribution, and correct the model based on the corrected parameters.

4. The engineering height measurement method of an inertial navigation system based on a barometer as error compensation according to claim 1, characterized in that: The simulation algorithm is ； is the simulated corrected inertial navigation data, D is the real-time collected uncorrected raw data, t0 is the detection start time, t1 is the detection process end time, n is the number of collection points in the detection, is the displacement corresponding to the i-th sampling point, is the trigger threshold of the sensor, that is, the signal jumps when the displacement reaches this value. It reflects the steepness of the signal jump. Erf(z) is the error function, which is used to describe the influence of the error starting position on the correction. E is the position where the error occurs within the detection threshold. is the standard deviation of the influence range of the starting position of the control error, It is the correction increment calculated for the i-th sampling point according to the error jump law and the error starting position.

5. The engineering height measurement method of an inertial navigation system based on a barometer as error compensation according to claim 1, characterized in that: The data fusion algorithm is: ； is the engineering height, is the barometer altitude, is the inertial navigation altitude, is a dynamic weight, based on the reliability of the barometer data and the vertical acceleration of the accelerometer Make dynamic adjustments.

6. The engineering height measurement method of an inertial navigation system based on a barometer as error compensation according to claim 1, characterized in that: The error compensation strategy includes: Height error estimation steps: Use the engineering height and barometer height to obtain the height error value, and use the height estimation value; The error correction step performs real-time correction on the inertial navigation model based on the height error estimate.

7. The engineering height measurement method of an inertial navigation system based on a barometer as error compensation according to claim 1, characterized in that: Several groups of barometers are arranged at different positions of the engineering equipment, and a pressure analysis step is also included, which is used to obtain barometer data of several groups of the barometers, compare the data of each barometer, and eliminate abnormal data. Each sensor obtains a corresponding weight value according to the installation position, and obtains the volatility of the historical data of each barometer. The weight value is redistributed according to the volatility of each barometer, and the data of each barometer is calculated as the atmospheric pressure according to the weight.

8. The engineering height measurement method of an inertial navigation system based on a barometer as error compensation according to claim 7, characterized in that: The air pressure analysis step also includes a barometer reliability verification sub-step, which is used to detect the barometer data of the barometer during the detection process in real time, and a time window is preset to calculate the mean, variance and range of the barometer data within the time window, and based on the mean, variance and range, it is determined whether there is an abnormality. If there is an abnormality, the time of the abnormal point is obtained, and the data of the abnormal point is eliminated in the error compensation algorithm.

9. An engineering altimetry system using an inertial navigation system as an error compensation barometer, characterized in that: include The simulation model component module models the engineering equipment, including the shape, features and installation position of the inertial navigation system. Through the simulation model, different sensor parameters are adjusted to observe the starting and ending beating rules, and the error performance of the sensor at different moving distances is analyzed. Through the simulation training strategy, the inertial navigation simulation model is constructed. Data acquisition module, real-time acquisition of inertial navigation system and barometer data, inertial navigation data includes the original data of three-axis accelerometer and three-axis gyroscope, barometer data includes atmospheric pressure and temperature data; The simulation correction module inputs the inertial navigation data into the simulation model, obtains the error jump law and the error starting position according to the inertial navigation data, and performs simulation correction on the inertial navigation data according to the simulation algorithm. The error compensation module is used to obtain the corrected inertial navigation data and barometer data, obtain the inertial navigation altitude through the inertial navigation model, calculate and obtain the engineering altitude through the data fusion algorithm, and use the engineering altitude to perform error compensation on the inertial navigation model.

Images