Automatic boarding bridge approaching and withdrawing positioning precision optimization method and system based on Kalman filtering algorithm
By applying the Kalman filtering algorithm in the boarding bridge system, the positioning accuracy of the boarding bridge is optimized, the problem of insufficient docking accuracy in the existing technology is solved, and higher docking accuracy and safety are achieved.
Patent Information
- Application Number
- CN202411651333.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-19
- Publication Date
- 2025-02-28
AI Technical Summary
When facing complex environments and noise interference, the existing boarding bridge docking system is difficult to meet the requirements of high-precision docking, resulting in docking errors and safety hazards.
The automatic boarding bridge positioning accuracy optimization method based on the Kalman filtering algorithm is adopted. Through real-time data acquisition, data preprocessing and establishment of Kalman filtering models of sensors, accurate estimation and real-time control of the boarding bridge position and speed are achieved.
It improves the accuracy and safety of docking between the boarding bridge and the aircraft, reduces docking errors, and improves docking efficiency and system stability.
Smart Images
Figure CN119533480A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to a method and system for optimizing the positioning accuracy of a boarding bridge approaching and withdrawing, and in particular to a method and system for optimizing the positioning accuracy of an automated boarding bridge approaching and withdrawing based on a Kalman filtering algorithm. Background Art
[0002] This section merely provides background information related to the present disclosure and is not necessarily prior art.
[0003] Passenger boarding bridges are closed passages provided to airports to connect aircraft and terminal buildings. The use of boarding bridges can provide passengers with all-weather boarding and disembarking services. Manual operation of boarding bridges may lead to safety issues such as high docking error rate and collision with aircraft during docking.
[0004] Traditional boarding bridge docking technology mainly relies on manual driving. When docking with a boarding bridge manually, docking errors are prone to occur due to the driver's experience and operating level, resulting in inaccurate docking, and even causing the boarding bridge to collide with the aircraft, posing a safety hazard. Secondly, the driver needs to adjust the position of the boarding bridge at all times during the docking process. The operation is complicated and requires high standards from the driver, which can easily lead to fatigue operation, thereby increasing the probability of docking errors. Since manual driving requires repeated adjustments and confirmations, the entire docking process takes a long time, reducing the efficiency of passengers boarding and disembarking.
[0005] With the development of automation technology, some automated boarding bridge docking systems have been introduced to improve the accuracy and efficiency of docking. Automated boarding bridge systems use various sensors and algorithms to assist the docking process. In modern automated docking systems, laser rangefinders, infrared sensors, cameras and other sensors are widely used to obtain the relative position of the boarding bridge and the aircraft. In actual applications, these sensors are greatly affected by environmental factors such as light changes and weather conditions, and are prone to introduce noise, resulting in reduced docking accuracy. In order to improve docking accuracy, some systems have introduced filtering algorithms, such as digital filters and Fourier transform filters. These algorithms can handle the noise in sensor data to a certain extent, but they still have shortcomings when facing complex actual application scenarios and are difficult to meet the requirements of high-precision docking. Among the numerous positioning filtering algorithms, median filtering reduces the impact of fluctuation interference by taking the middle value of N sampling values, and has a good suppression effect on pulse interference caused by accidental factors, but has a poor filtering effect on rapidly changing parameters; particle filtering approximates the probability density function through random samples, and is superior in nonlinear and non-Gaussian system filtering, especially suitable for multi-modal processing, but the calculation complexity is high, and enough particles are needed to ensure the accuracy of the estimation; the composite filtering algorithm combines the characteristics of multiple filtering algorithms, and can suppress random interference and pulse interference, but the measurement speed is slow and occupies more memory. It is difficult to ensure real-time performance and has high calculation complexity, making it difficult to quickly respond to actual operation needs.
[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention
[0007] Purpose of the invention: The technical problem to be solved by the present invention is to provide a method and system for optimizing the positioning accuracy of an automated boarding bridge based on a Kalman filter algorithm in view of the deficiencies in the prior art.
[0008] In order to solve the above technical problems, the present invention discloses a method and system for optimizing the positioning accuracy of an automated boarding bridge based on a Kalman filter algorithm, the method comprising the following steps:
[0009] Step 1: Use the sensor module to collect real-time data;
[0010] Step 2, using a data processing module to perform data preprocessing on the data collected in step 1;
[0011] Step 3, using the Kalman filter module to establish a Kalman filter model, inputting the preprocessed data in step 2, and obtaining a state estimate, i.e., the position and speed estimation data of the boarding bridge;
[0012] Step 4, using a control module to perform real-time control of the boarding bridge based on the position and speed estimates obtained in step 3;
[0013] Step 5: Use the display module to display data and issue warnings.
[0014] Furthermore, the real-time data collection described in step 1 includes:
[0015] Collect the relative position, distance and image data between the boarding bridge and the aircraft in real time.
[0016] Further, the data preprocessing described in step 2 includes:
[0017] Data filtering and denoising and data outlier processing to remove high-frequency noise and abnormal data; wherein, the data filtering and denoising uses a low-pass filter to remove high-frequency noise and a median filter to remove random noise; the data outlier processing uses a threshold method and statistical analysis method to identify and delete abnormal data points.
[0018] Furthermore, the Kalman filter model described in step 3 includes:
[0019] Step 3-1, set the position and speed of the boarding bridge wheel as its state variable [x x v x x y v y ], where (x x ,x y ) represents the position of the boarding bridge wheels, (v x ,v y ) represents the speed of the wheels of the aerobridge;
[0020] Step 3-2, initialize the state variables, observation matrix and covariance matrix, where the covariance matrix includes the process noise covariance matrix Q and the measurement noise covariance matrix R, which are initialized as follows:
[0021]
[0022]
[0023] Step 3-3, predict the state variables of the boarding bridge wheels based on the state prediction equation and update the error covariance matrix. The state prediction equation is as follows:
[0024]
[0025] in, represents the state variable of the boarding bridge wheel at time k, i.e. the current time, represents the state variable of the boarding bridge wheel at the previous moment k-1, and A is the state transfer matrix, which is used to derive the prediction of the current state from the state estimate at the previous moment. is the forecast error covariance;
[0026] Step 3-4, based on the observation matrix H and sensor measurement value z k and the Kalman gain K k Update the state estimate as follows:
[0027]
[0028] Among them, the sensor measurement value z k That is, the relative position, distance and image data between the boarding bridge and the aircraft preprocessed in step 2;
[0029] Step 3-5, compare the predicted value with the measured value through the observation matrix H, use the Kalman gain to update the state estimate and the error covariance matrix, and achieve the optimal estimation of the boarding bridge wheel state, as follows:
[0030]
[0031] Where I is the identity matrix, K k is the Kalman gain, which is used to adjust the weight between the measured value and the predicted value. is the forecast error covariance matrix, which represents the forecast error covariance obtained based on the previous state estimate before considering process noise.
[0032] Furthermore, the initialization state variables described in step 3-2 include:
[0033] [x x v x x y v y ]=[x(0)0y(0)0]
[0034] Among them, x(0) and y(0) represent the initial positions of the boarding bridge wheels in the horizontal and vertical directions, respectively.
[0035] Furthermore, the initialization of the observation matrix H described in step 3-2 includes:
[0036]
[0037] Furthermore, the real-time control of the boarding bridge described in step 4 includes:
[0038] According to the position and speed estimates output by the Kalman filter module, corresponding control instructions are generated to adjust the moving direction and speed of the boarding bridge in real time.
[0039] Furthermore, the data display and warning described in step 5, that is, the relative position, docking status and warning information of the boarding bridge and the aircraft are displayed in real time on the monitoring screen, and the specific method includes:
[0040] Step 5-1, pre-set the ideal position, i.e. the best docking point between the boarding bridge and the aircraft;
[0041] Step 5-2, using the sensor module to measure the real-time relative position between the end of the boarding bridge and the aircraft door;
[0042] Step 5-3, calculating the difference x between the real-time relative position and the ideal position;
[0043] Step 5-4, set a threshold. If the difference x exceeds the threshold, it is judged that the docking deviation exceeds the limit and an early warning is issued;
[0044] Step 5-5, monitor the status of the sensor module and issue an early warning when a fault occurs.
[0045] Furthermore, the method for calculating the difference x between the real-time relative position and the ideal position described in step 5-3 is as follows:
[0046]
[0047] Where x is the difference between the real-time relative position and the ideal position, (x r ,y r ,z r ) is the real-time relative position coordinate, (x i ,y i ,z i ) are the ideal position coordinates.
[0048] The present invention also proposes an automatic boarding bridge positioning accuracy optimization system based on a Kalman filter algorithm, which is used to implement the above method, including:
[0049] Sensor module, used for real-time data collection;
[0050] A data processing module, used for preprocessing the data collected by the sensor module;
[0051] A Kalman filter module is used to establish a Kalman filter model, input the pre-processed data in the data processing module, and obtain state estimation, i.e., the position and speed estimation data of the boarding bridge;
[0052] A control module for controlling the boarding bridge in real time based on the position and velocity estimates obtained by the Kalman filter module;
[0053] Display module, for data display and early warning.
[0054] Beneficial effects:
[0055] 1. The method of the present invention reduces the influence of noise and environmental interference on sensor data during the docking and withdrawal process of the automated boarding bridge, improves positioning accuracy, and further improves the accuracy and safety of docking between the boarding bridge and the aircraft.
[0056] 2. The present invention optimizes the sensor data by applying the Kalman filter algorithm, thereby improving the accuracy of position estimation, thereby effectively reducing docking errors and improving docking efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS
[0057] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more clear.
[0058] Figure 1 It is a block diagram of the overall structure of the system of the present invention.
[0059] Figure 2 It is a data processing flow chart of the present invention.
[0060] Figure 3 This is a flow chart of the Kalman filtering method of the present invention.
[0061] Figure 4 Figure 1 shows the trajectory of the automated boarding bridge before and after using the Kalman filter.
[0062] Figure 5 The peak error and root mean square error measurement results after using the Kalman filter positioning algorithm. DETAILED DESCRIPTION
[0063] The general idea of the present invention is as follows: During the process of docking and undocking of the automated boarding bridge, the positioning accuracy is not high due to the influence of noise and environmental interference on the sensor data, which in turn affects the accuracy and safety of docking between the boarding bridge and the aircraft. By applying the Kalman filter algorithm, the sensor data is optimized and processed, the accuracy of position estimation is improved, thereby effectively reducing docking errors and improving docking efficiency and safety. As a widely used control algorithm, the Kalman filter algorithm has strong real-time performance and high robustness. Its superiority has been demonstrated in many fields. It can provide the optimal estimate of the system state by fusing the measured value and the system model, and can achieve good results even in the presence of noise and uncertainty. During the process of docking and undocking of the boarding bridge, the Kalman filter algorithm can process data in real time during the process of tracking the boarding bridge's approaching and undocking trajectory, and can be applied to scenarios with rapid response. Since the Kalman filter has a strong resistance to noise, it can still provide a relatively accurate estimate when the sensor data quality is not high. According to the "Civil Airport Unmanned Passenger Boarding Bridge Testing Specifications", it can ensure that the docking accuracy of the automated boarding bridge in actual operation meets the established standards, and verify the effectiveness of the Kalman filter docking and undocking positioning accuracy optimization method. It ensures the safety of passengers and the operating efficiency of the boarding bridge, and helps promote the further development and improvement of automated boarding bridge technology.
[0064] The specific technical solution of the present invention is as follows: a method and system for optimizing the positioning accuracy of an automated boarding bridge for approaching and withdrawing aircraft based on a Kalman filter algorithm, such as Figure 1 As shown in the figure, the relative position data between the boarding bridge and the aircraft is collected in real time through sensors, and the data is preprocessed to remove noise and outliers. Then, the Kalman filter algorithm is used to predict and update these data to obtain the optimal estimate of the boarding bridge position. Finally, the control system adjusts the position of the boarding bridge in real time to ensure accurate and safe docking, and the docking status and related information are displayed to the operator in real time. This method effectively improves the docking accuracy and safety of the boarding bridge, adapts to complex application scenarios, and ensures the stability and reliability of the system.
[0065] As a preferred solution of the Kalman filter positioning accuracy optimization method for automated boarding bridges described in the present invention, the present invention mainly comprises five parts: a sensor module, a data processing module, a Kalman filter module, a control module and a display module.
[0066] Sensor module: used to collect the relative position data between the boarding bridge and the aircraft in real time. Each sensor module collects the relative position data between the boarding bridge and the aircraft in real time and transmits the data to the data processing module.
[0067] Data processing module: pre-processes the data collected by the sensor, removes obvious noise and outliers, and ensures that the data input to the Kalman filter has high quality.
[0068] Kalman filter module: Establish the Kalman positioning filter algorithm model. Define the position and speed of the boarding bridge wheel as state variables, initialize the state variables and initial covariance matrix of the Kalman filter, and set the initial conditions. After the initialization is completed, the wheel state vector is predicted based on the Kalman filter algorithm model, and the state equation is established. These variables are predicted through the state transfer matrix A, and the initial state and uncertainty are determined by the initial covariance matrix P 0 Description. At the end of each time step Δt, the filter uses the prediction error covariance P 0 And the process noise covariance matrix Q is used to update the state prediction. According to the preprocessed sensor data, the observation model is established. The predicted state vector is compared with the actual measurement value z of the sensor through the observation matrix H. k Correspondingly, the residual is calculated and the Kalman gain K is used k To fuse observation data and update state estimation and error covariance P k This process iterates through prediction and update steps to achieve a real-time optimal estimate of the relative position of the aerobridge and aircraft, maintaining a high degree of accuracy even in the presence of measurement noise and system uncertainties.
[0069] Control module: Based on the optimal estimation data after Kalman filtering, the control module calculates the adjustment instructions of the boarding bridge in real time. It controls the movement of the boarding bridge so that it can be accurately docked at the aircraft door, ensuring the safety and accuracy of the docking process.
[0070] Display module: The relative position of the boarding bridge and the aircraft, docking status, warning information and other key data and status information during the docking process are displayed in real time on the monitoring screen for reference and management by operators.
[0071] As a preferred solution of the Kalman filter positioning accuracy optimization method for automated boarding bridges described in the present invention, the sensor module of the system includes a laser rangefinder, an infrared sensor and a camera. The laser rangefinder is used to accurately measure the distance between the boarding bridge and the aircraft. The laser rangefinder is installed at the end of the boarding bridge and can provide millimeter-level distance measurement accuracy. The infrared sensor is used to detect the relative position change between the boarding bridge and the aircraft. The infrared sensor is installed at multiple positions of the boarding bridge to provide multi-point position information. The camera is used to obtain image data of the boarding bridge and the aircraft to assist in position detection. The camera is installed on both sides of the boarding bridge to provide real-time video monitoring images. They are installed at key positions of the boarding bridge to ensure that the relative position between the boarding bridge and the aircraft can be fully covered. The sensor module transmits the collected data to the data processing module via the data bus.
[0072] As a preferred solution of the Kalman filter positioning accuracy optimization method for automated boarding bridges described in the present invention, the data processing module pre-processes the data collected by the sensor, such as Figure 2 As shown, it includes filtering denoising, outlier detection, etc. Filtering uses a low-pass filter to remove high-frequency noise. The filter parameters are adjusted according to the characteristics of the environmental noise to ensure good filtering effect. Denoising uses algorithms such as median filtering to remove random noise in the data. The median filter can effectively eliminate burst noise and retain valid signals. Outlier detection mainly identifies and removes obviously abnormal data points to ensure data quality. Outlier detection algorithms include threshold method and statistical analysis method. The threshold method identifies outliers by setting a fixed threshold. When the data point exceeds this threshold, it is considered abnormal. The statistical analysis method is more complex and usually includes the use of statistical models to evaluate whether the data points conform to the expected distribution (Leong HS, Zhang T, Corrigan A, et al. Hit screening with multivariate robust outlier detection [J]. PloS one, 2024, 19 (9): e0310433.). The preprocessed data is transmitted to the Kalman filter module.
[0073] In this paper, a method for optimizing the positioning accuracy of automated boarding bridges is proposed, using the Kalman filter algorithm for core processing. The algorithm consists of two main processes: system propagation and observation update. Each process performs ten steps in each iteration, such as Figure 3As shown. Steps 1 to 4 involve the system propagation process, which starts with initializing the position and speed of the boarding bridge as the initial estimate of the algorithm. Then, the state at the next moment is predicted by the state transfer matrix, and the error covariance matrix is updated using the system noise estimate to prepare for the subsequent observation data integration. Steps 5 to 10 constitute the observation update process. In this stage, the state estimate and the error covariance matrix are adjusted by using the newly obtained sensor data, focusing on the relative position and distance data between the boarding bridge and the aircraft, and calculating the Kalman gain. This gain is the key, which determines the weight ratio of the predicted and observed data in the state estimation. Through this continuous system propagation and observation update process, the Kalman filter algorithm effectively copes with sensor noise and environmental interference, ensuring the accuracy and stability of the boarding bridge motion control. In addition, the real-time processing capability of the algorithm can quickly respond to changes in the position of the aircraft, greatly improving the safety and reliability of the system. As a preferred solution of the Kalman filter positioning accuracy optimization method for the automated boarding bridge described in the present invention, wherein: the position adjustment control calculates the direction and distance that the boarding bridge needs to move according to the data output by the Kalman filter. Generate the movement command of the boarding bridge and transmit it to the actuator through the control bus. Control the movement of the boarding bridge in real time to ensure accurate docking.
[0074] The display module displays the distance, angle and other key parameters of the real-time relative position between the boarding bridge and the aircraft, as well as docking status and warning information such as docking deviation exceeding the limit and sensor failure, which is convenient for operators to monitor and manage.
[0075] Example:
[0076] like Figures 1 to 3 As shown, the present invention provides a method for suppressing front wheel corner noise disturbance suitable for high-speed scenarios. In order to ensure that the automated boarding bridge can accurately park at the designated anchor position or successfully dock with the aircraft cabin door, attention must be paid to the stability of the bridge body. Since the length of the boarding bridge far exceeds the anchor position diameter, even a slight jitter of the bridge body may seriously affect its parking and docking accuracy. In order to reduce the trajectory jitter during the withdrawal and docking process, a positioning filtering algorithm is used to enhance the stability of the boarding bridge movement.
[0077] The Kalman filter algorithm, as an efficient recursive algorithm, can optimize the performance of the boarding bridge in the trajectory tracking process in real time. By integrating the Kalman filter algorithm into the motion control system of the automated boarding bridge, not only can the accuracy of trajectory tracking be significantly improved, but also the robustness of the entire system can be greatly improved. The application of this algorithm ensures that the boarding bridge can still maintain accurate trajectory tracking in the face of environmental changes and dynamic disturbances, thereby ensuring the safety and reliability of the docking process. The following is the specific implementation of the Kalman filter algorithm in the automated boarding bridge system.
[0078] (1) Initialization
[0079] In the process of the movement of the automated boarding bridge, due to the lack of sufficient prior knowledge, the present invention chooses to make an unbiased estimate of each state variable, that is, it is assumed that each state variable has the same importance and uncertainty. This estimation method simplifies the expression of the uncertainty of the system state. Therefore, the present invention adopts the unit matrix P 0 To initialize the covariance matrix, we can quantify the uncertainty of the system state estimate.
[0080] For the relative position and speed between the boarding bridge and the aircraft, the state variables are first initialized. According to the initial position and speed information of the boarding bridge, the state vector is initially estimated. The initial value of the state vector is set as:
[0081] [x x v x x y v y ]=[x(0)0y(0)0]
[0082] Among them, x(0) and y(0) represent the initial positions of the boarding bridge in the horizontal and vertical directions respectively, and 0 represents that the initial velocity is zero. This initialization method ensures that the estimation of the present invention is both concise and reasonable in the absence of specific prior information.
[0083] Assume that the process noise covariance matrix is Q, which captures the uncertainty of the trajectory of the boarding bridge during motion. Q reflects the random fluctuations in the trajectory tracking process, which are derived from the dynamic characteristics of the boarding bridge system itself or external interference factors. Q is a diagonal matrix, that is:
[0084]
[0085] Assume that the measurement noise covariance matrix is R, which describes the uncertainty in the sensor measurement process. R quantifies the random noise in the sensor measurement data. The R matrix is:
[0086]
[0087] These two matrices, Q and R, together determine how the Kalman filter weighs the relative importance between predictions (based on prior knowledge) and observations (based on a posteriori knowledge). By precisely adjusting Q and R, the Kalman filter can adapt to different system characteristics and observation conditions, thereby providing accurate and reliable state estimates.
[0088] The observation matrix H is:
[0089]
[0090] Where Q describes the uncertainty in the process, R describes the impact of measurement noise, and H determines how to extract the part that directly corresponds to the measurement value from the complete state vector (position and velocity). It directly affects the calculation of the Kalman gain and the state update.
[0091] (2) Prediction step:
[0092] After initialization, the wheel state vector is realized based on the Kalman filter algorithm model The prediction of the wheel at time k is At time k-1, the state vector of the wheel is The state prediction equation is expressed as:
[0093]
[0094] Where: is the state estimation of the previous moment, A is the state transfer matrix of the system, is the prior state estimate at the current moment.
[0095] The forecast error covariance matrix is:
[0096]
[0097] in, is the prior error covariance matrix at time k, is the error covariance matrix at time k-1, A is the state transfer matrix, and Q is the noise covariance matrix.
[0098] (3) Update step
[0099] Next is the update phase, which takes the wheel trajectory of the boarding bridge as the research object and combines the prior state estimation at the current moment and the actual measured value z of the sensor k , get the posterior state estimate at time k The calculation equation is:
[0100]
[0101] In the formula, K k is the Kalman gain at the current moment, which is used to balance the weights of the predicted value and the measured value.
[0102] The calculation equation of Kalman gain is:
[0103]
[0104] In the formula, is the prior error covariance matrix, H is the observation matrix, and R is the measurement noise covariance matrix.
[0105] Finally, the posterior error covariance matrix is updated, and the calculation equation is:
[0106]
[0107] Where H is the observation matrix, is the prior error covariance matrix, is the posterior error covariance matrix, which is used in the next prediction step.
[0108] Through the above prediction and update steps, the Kalman filter algorithm can process sensor data in real time and provide the best estimate of the relative position between the boarding bridge and the aircraft. The data processing module of the boarding bridge implements the Kalman filter algorithm through programming, and processes the data of the laser rangefinder, infrared sensor and camera in real time to ensure that the boarding bridge can be accurately connected to the aircraft door and provide a safe and stable passage for passengers to get on and off the aircraft.
[0109] In order to verify the effectiveness of the Kalman filter positioning accuracy optimization algorithm for automated boarding bridges, it is necessary to test the Kalman filter positioning filter algorithm for automated boarding bridges.
[0110] The X-axis is selected as the x-coordinate of the boarding bridge's trajectory, and the Y-axis is selected as the y-coordinate of the boarding bridge's trajectory. The proposed method is used for filtering. The filtering effect is shown in the figure below. Figure 4 shown.
[0111] Figure 4 This is the trajectory diagram of the automated boarding bridge before and after using the Kalman filter. It can be seen from the figure that the use of the Kalman filter positioning algorithm has a good filtering effect on the original trajectory of the automated boarding bridge, and improves the stability of the boarding bridge movement.
[0112] The original trajectory of the automated boarding bridge was experimentally recorded, 14 sets of successful docking data were selected, and the root mean square error before and after filtering was calculated. The experimental results are shown in Table 1.
[0113] Table 1 Experimental data table
[0114]
[0115]
[0116] Quantification using error positive peak EPP, error negative peak ENP, and root mean square error RMSE Figure 4 The test results are calculated for the 14 sets of data, and the positive peak value and negative peak value of the error of the x-coordinate and y-coordinate are averaged, and the root mean square error of the 14 sets of data is plotted. The smaller the peak error and the root mean square error, the better the filtering effect.
[0117] Depend on Figure 5It can be seen that the positive peak error, negative peak error and root mean square error of the Kalman filter positioning algorithm are all lower than the original trajectory data, indicating that the filtering algorithm has excellent effect and is suitable for automated boarding bridge positioning.
[0118] Formula summary:
[0119] A priori state estimate:
[0120] Prior error covariance matrix:
[0121] Kalman gain:
[0122] Posterior state estimate:
[0123] Posterior error covariance matrix:
[0124] In a specific implementation, the present application provides a computer storage medium and a corresponding data processing unit, wherein the computer storage medium can store a computer program, and when the computer program is executed by the data processing unit, the invention content of the method and system for optimizing the positioning accuracy of the automated boarding bridge docking and withdrawal based on the Kalman filter algorithm provided by the present invention and some or all of the steps in each embodiment. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.
[0125] Those skilled in the art can clearly understand that the technical solutions in the embodiments of the present invention can be implemented by means of computer programs and their corresponding general hardware platforms. Based on such an understanding, the technical solutions in the embodiments of the present invention can be essentially or partly contributed to the prior art in the form of computer programs, i.e., software products, which can be stored in a storage medium and include several instructions for enabling a device including a data processing unit (which can be a personal computer, a server, a single-chip microcomputer, an MCU or a network device, etc.) to execute the methods described in various embodiments of the present invention or certain parts of the embodiments.
[0126] The present invention provides an idea and method for optimizing the positioning accuracy of an automated boarding bridge and its system based on a Kalman filter algorithm. There are many methods and approaches to implement the technical solution. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented using existing technologies.
Claims
1. A method for optimizing the positioning accuracy of automated boarding bridge docking and withdrawal based on Kalman filter algorithm, characterized in that: The method comprises the following steps: Step 1: Use the sensor module to collect real-time data; Step 2, using a data processing module to perform data preprocessing on the data collected in step 1; Step 3, using the Kalman filter module to establish a Kalman filter model, inputting the preprocessed data in step 2, and obtaining a state estimate, i.e., the position and speed estimation data of the boarding bridge; Step 4, using a control module to perform real-time control of the boarding bridge based on the position and speed estimates obtained in step 3; Step 5: Use the display module to display data and issue warnings.
2. The method for optimizing the positioning accuracy of automated boarding bridge docking and withdrawal based on Kalman filter algorithm according to claim 1 is characterized in that: Perform real-time data collection as described in step 1, including: Collect the relative position, distance and image data between the boarding bridge and the aircraft in real time.
3. The method for optimizing the positioning accuracy of automated boarding bridge docking and withdrawal based on Kalman filter algorithm according to claim 2 is characterized in that: The data preprocessing described in step 2 includes: Data filtering and denoising and data outlier processing to remove high-frequency noise and abnormal data; wherein, the data filtering and denoising uses a low-pass filter to remove high-frequency noise and a median filter to remove random noise; the data outlier processing uses a threshold method and statistical analysis method to identify and delete abnormal data points.
4. The method for optimizing the positioning accuracy of automated boarding bridge docking and withdrawal based on Kalman filter algorithm according to claim 3 is characterized in that: The Kalman filter model described in step 3 includes: Step 3-1, set the position and speed of the boarding bridge wheel as its state variable [x x v x x y v y ], where (x x ,x y ) represents the position of the boarding bridge wheels, (v x ,v y ) represents the speed of the wheels of the aerobridge; Step 3-2, initialize the state variables, observation matrix and covariance matrix, where the covariance matrix includes the process noise covariance matrix Q and the measurement noise covariance matrix R, which are initialized as follows: Step 3-3, predict the state variables of the boarding bridge wheels based on the state prediction equation and update the error covariance matrix. The state prediction equation is as follows: in, represents the state variable of the boarding bridge wheel at time k, i.e. the current time, represents the state variable of the boarding bridge wheel at the previous moment k-1, and A is the state transfer matrix, which is used to derive the prediction of the current state from the state estimate at the previous moment. is the forecast error covariance; Step 3-4, based on the observation matrix H and sensor measurement value z k and the Kalman gain K k Update the state estimate as follows: Among them, the sensor measurement value z k That is, the relative position, distance and image data between the boarding bridge and the aircraft preprocessed in step 2; Step 3-5, compare the predicted value with the measured value through the observation matrix H, use the Kalman gain to update the state estimate and the error covariance matrix, and achieve the optimal estimation of the boarding bridge wheel state, as follows: Where I is the identity matrix, K k is the Kalman gain, which is used to adjust the weight between the measured value and the predicted value. is the forecast error covariance matrix, which represents the forecast error covariance obtained based on the previous state estimate before considering process noise.
5. The method for optimizing the positioning accuracy of automated boarding bridge docking and withdrawal based on Kalman filter algorithm according to claim 4 is characterized in that: Initialize the state variables described in step 3-2, including: [x x v x x y v y ]=[x(0)0y(0)0] Among them, x(0) and y(0) represent the initial positions of the boarding bridge wheels in the horizontal and vertical directions, respectively.
6. The method for optimizing the positioning accuracy of automated boarding bridge docking and withdrawal based on Kalman filter algorithm according to claim 5 is characterized in that: The initialization observation matrix H described in step 3-2 includes:
7. The method for optimizing the positioning accuracy of automated boarding bridge docking and withdrawal based on Kalman filter algorithm according to claim 6, characterized in that: The real-time control of the boarding bridge described in step 4 includes: According to the position and speed estimates output by the Kalman filter module, corresponding control instructions are generated to adjust the moving direction and speed of the boarding bridge in real time.
8. The method for optimizing the positioning accuracy of automated boarding bridge docking and withdrawal based on Kalman filter algorithm according to claim 7, characterized in that: The data display and warning described in step 5, that is, the relative position, docking status and warning information of the boarding bridge and the aircraft are displayed in real time on the monitoring screen. The specific method includes: Step 5-1, pre-set the ideal position, i.e. the best docking point between the boarding bridge and the aircraft; Step 5-2, using the sensor module to measure the real-time relative position between the end of the boarding bridge and the aircraft door; Step 5-3, calculating the difference x between the real-time relative position and the ideal position; Step 5-4, set a threshold. If the difference x exceeds the threshold, it is judged that the docking deviation exceeds the limit and an early warning is issued; Step 5-5, monitor the status of the sensor module and issue an early warning when a fault occurs.
9. The method for optimizing the positioning accuracy of automated boarding bridge docking and withdrawal based on Kalman filter algorithm according to claim 8, characterized in that: The calculation method of the difference x between the real-time relative position and the ideal position described in step 5-3 is as follows: Where x is the difference between the real-time relative position and the ideal position, (x r ,y r ,z r ) is the real-time relative position coordinate, (x i ,y i ,z i ) are the ideal position coordinates.
10. An automatic boarding bridge positioning accuracy optimization system based on Kalman filter algorithm, characterized in that: Used to implement any of the methods described in claims 1-9, comprising: Sensor module, used for real-time data collection; A data processing module, used for preprocessing the data collected by the sensor module; A Kalman filter module is used to establish a Kalman filter model, input the pre-processed data in the data processing module, and obtain state estimation, i.e., the position and speed estimation data of the boarding bridge; A control module for controlling the boarding bridge in real time based on the position and velocity estimates obtained by the Kalman filter module; Display module, for data display and early warning.