A speed estimation method for civil air traffic control secondary radar

Abstract

The application belongs to the technical field of civil air traffic control, and discloses a speed estimation method for civil air traffic control secondary radar. The application effectively reduces the influence of noise and error on speed estimation through linear fitting of a sliding window, and improves the accuracy of speed measurement. The optimized speed estimation method makes the speed change more stable, avoids the problem of excessive speed fluctuation in the traditional method, and is more in line with the actual situation. Through error correction and real-time data processing, the reliability of speed estimation is significantly improved, and the possibility of misjudgment is reduced. The improvement of the accuracy and reliability of speed estimation helps air traffic controllers more accurately judge the flight state of the aircraft and make timely decisions, thereby ensuring flight safety.

Description

Technical Field

[0001] This invention relates primarily to the field of civil air traffic control technology, and in particular to a speed estimation method for secondary radars used in civil air traffic control. Background Technology

[0002] As a key technological support for the civil air traffic control system, the secondary radar system works primarily based on an interrogation-response mechanism: the system transmits interrogation pulse signals in a specific frequency band through an antenna, the airborne transponder immediately responds after receiving the signal, and the ground-based secondary radar system performs real-time analysis and data processing on the received response signal to accurately obtain key flight parameters such as the aircraft's SSR Code, flight status, three-dimensional spatial position, and velocity vector.

[0003] However, under current technological conditions, civilian air traffic control secondary radar systems still face significant technical bottlenecks in airspeed estimation, mainly manifested in insufficient accuracy and large error fluctuations in airspeed measurement. This technical deficiency not only restricts air traffic controllers from accurately determining aircraft speed parameters but also poses potential risks to critical safety aspects such as maintaining flight separation and conflict warning, thereby threatening the overall safety of aviation operations. Especially in high-density airspace environments, this technical shortcoming may further amplify safety risks and pose a severe challenge to air traffic control command and decision-making. Summary of the Invention

[0004] The purpose of this invention is to provide a speed estimation method for civil air traffic control secondary radar. By using sliding window linear fitting on civil aircraft, this method solves the problems of insufficient accuracy and large error fluctuation in existing speed measurement technologies, improves the accuracy and reliability of speed measurement, and thus ensures flight safety.

[0005] To achieve the above objectives, this invention provides a speed estimation method for civilian air traffic control secondary radar, comprising the following steps: Step 1: At time k, the aircraft's three-dimensional velocities are calculated by the secondary radar's information processing module. , and To obtain the aircraft's speed ; Step 2: Take n time points backward from time k, the speed sequence from time (kn) to time k is as follows: By performing a linear fit on the speed sequence from time (kn) to time k, the new speed at time k is obtained. ; Step 3: At time k+1, take n time steps backward and repeat step 2 using a sliding window method to obtain the new speed at time k+1. ; Step 4: When k ≥ n, perform the operations of Step 1 to Step 3; When k < n, do not perform the operations of Step 1 to Step 3.

[0006] Furthermore, in Step 2, performing linear fitting on the speed sequence from time (k - n) to time k includes: S1: Assume the change of speed is linearly varying with time k within the time period , then (1); S2: If n is a natural number greater than 1, then (2); S3: Convert formula (2) into matrix form, which is (3); S4: Let , , , then formula (3) can be converted to (4); S5: Solve formula (4) to obtain A, which is (5); S6: According to formula (5), obtain and in formula (1). Substitute , and k into formula (1), and the new speed at time k can be obtained .

[0007] Furthermore, in Step 2, when sliding the window each time, only calculate the influence of the newly added data points and the removed data points on the linear fitting result, without recalculating the linear fitting result of the entire sequence.

[0008] Furthermore, before Step 1, there is also Step T1: After the secondary radar information processing module in the radar tracking system obtains the aircraft's track information, use the collaborative positioning algorithm, track initiation algorithm, data association algorithm, and tracking filtering algorithm to process the track information, and complete data preprocessing, track initiation, data association, and tracking filtering operations.

[0009] Furthermore, for the selection of the window size n in Step 2 to Step 4, use the extended Kalman filter. Expand the nonlinear radar tracking system using the Taylor series and omit the high-order terms above the second order to obtain a linearized radar tracking system. Use the Kalman filter framework for filtering, and use the information in the Kalman filter to determine whether the current speed of the aircraft changes violently.

[0010] Furthermore, for the selection of the window size n in Step 2 to Step 4, the steps of the extended Kalman filter include: T2: Let the nonlinear state transition equation of the radar tracking system be... (6) The nonlinear observation equation of the radar tracking system is: (7), of which and It is a nonlinear function. and These are process noise and observation noise, respectively. T3: Extended Kalman filtering is used. Extended Kalman filtering linearizes the nonlinear function through Taylor series expansion, including: K1: Predicted State: The equation for the predicted state is... Formula (6) is in The Jacobian matrix at point is Formula (7) in The Jacobian matrix at point is , This represents the prior state estimate at the current moment; K2: Prediction Error Covariance Matrix: The equation for the prediction error covariance matrix is ​​as follows: ,in and Let these be the covariance matrices of the prior and posterior state estimates, respectively. and Let represent the covariance moments of process noise and observation noise, respectively; K3: Define the normalized residual at time k: The equation for the normalized residual at time k is... Its value range is {0,1}.

[0011] Furthermore, when 0.8 ≤ When n ≤ 1, it is assumed that the aircraft's speed changes greatly, and the window size n can be reduced to respond quickly to the changes; When 0≤ When the value is less than 0.8, the aircraft's speed is considered to change little, and the window size n can be increased to improve the accuracy of speed estimation.

[0012] Beneficial effects: This invention provides a speed estimation method for civil air traffic control secondary radar. Through sliding window linear fitting, it effectively reduces the impact of noise and error on speed estimation, improving the accuracy of speed measurement. The optimized speed estimation method makes speed changes smoother, avoiding the problem of excessive speed fluctuations in traditional methods, and is more in line with actual conditions. Through error correction and real-time data processing, the reliability of speed estimation is significantly improved, reducing the possibility of misjudgment. The improved accuracy and reliability of speed estimation help air traffic controllers to more accurately judge the flight status of aircraft and make timely decisions, thereby ensuring flight safety. Attached Figure Description

[0013] Figure 1 This is an embodiment of the present invention relating to an airport secondary air traffic control radar's speed estimation diagram of a civil aircraft during its landing phase, including the original speed and the speed estimated using the new method; Figure 2 This is a flowchart of the target tracking process involved in an embodiment of the present invention. Detailed Implementation

[0014] The preferred mechanisms and implementation methods of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0015] like Figure 1 As shown, this invention discloses a technical solution for airspeed estimation method for civilian air traffic control secondary radar. Figure 2 This is a flowchart of the target tracking process involved in an embodiment of the present invention. Example

[0016] This invention provides a speed estimation method for civilian air traffic control secondary radar, comprising the following steps: Step 1: At time k, the aircraft's three-dimensional velocities are calculated by the secondary radar's information processing module. , and To obtain the aircraft's speed ; Step 2: Take n time points backward from time k, the speed sequence from time (kn) to time k is as follows: ; Considering the stable flight and minimal maneuverability of civil aircraft, a linear fit is performed on the airspeed sequence from time (kn) to time k to obtain the new airspeed at time k. ; Because of the linear fitting, the speed is assumed to be... Changes, over a period of time The inner dimension follows a linear change over time k, i.e. (1); Assuming n=10, we can obtain (2); Formula (2) can be written in matrix form as follows: (3); make , , Then formula (3) can be written as (4); Solving formula (4) yields A. (5); From formula (5), we can obtain the formula in formula (1). and ,Will , Substitute \(h\) and \(k\) into formula (1), and the new speed at time \(k\) can be obtained. ; Step 3: At time \(k + 1\), take the previous \(n\) time instants, and using the sliding window method, repeat Step 2 to obtain the new speed at time \(k + 1\). ; Step 4: When \(k\geq n\), perform the operations of Step 1 to Step 3; When \(k < n\), do not perform the operations of Step 1 to Step 3.

[0017] Before Step 1, there is also Step T1: After the secondary radar information processing module in the radar tracking system obtains the target information of the aircraft, use the collaborative positioning algorithm, track initiation algorithm, data association algorithm, and tracking filtering algorithm to process the target information, and complete the operations of data preprocessing, track initiation, data association, and tracking filtering. The process is as Figure 2 shown.

[0018] In Step 2, each time the sliding window is moved, only calculate the influence of the newly added data points and the removed data points on the linear fitting result, and do not recalculate the linear fitting result of the entire sequence.

[0019] In the selection of the window size \(n\) in Steps 2 to Step 4, the extended Kalman filter is used for the tracking filtering algorithm. The extended Kalman filter is an extended form of the standard Kalman filter proposed for the nonlinear motion of the target. This algorithm enables the Kalman filter algorithm to be better applied in real life by introducing a linearization approximation method in the nonlinear model. It is a high-efficiency recursive filter. The extended Kalman linearizes the nonlinear radar tracking system by using the Taylor series expansion and omitting the high-order terms above the second order, obtaining a linearized radar tracking system, and performs filtering using the Kalman filter framework. Use the information in the Kalman filtering to determine whether the current speed of the aircraft changes violently.

[0020] In the selection of the window size \(n\) in Steps 2 to Step 4, the steps of the extended Kalman wave include: T2: Let the nonlinear state transition equation of the radar tracking system be (6), and the nonlinear observation equation of the radar tracking system be (7), where and are nonlinear functions, and are the process noise and the observation noise respectively; T3: Use the extended Kalman filter, and the extended Kalman filter linearizes the nonlinear function through the Taylor series expansion, including: K1: Predict the state: The equation for predicting the state is , where formula (6) is at The Jacobian matrix at point is Formula (7) in The Jacobian matrix at point is , This represents the prior state estimate at the current moment; K2: Prediction Error Covariance Matrix: The equation for the prediction error covariance matrix is ​​as follows: ,in and Let these be the covariance matrices of the prior and posterior state estimates, respectively. and Let represent the covariance moments of process noise and observation noise, respectively; K3: Define the normalized residual at time k: The equation for the normalized residual at time k is... Its value range is {0,1}.

[0021] When 0.8≤ When n ≤ 1, it is assumed that the aircraft's speed changes greatly, and the window size n can be reduced to respond quickly to the changes; When 0≤ When the value is less than 0.8, the aircraft's speed is considered to change little, and the window size n can be increased to improve the accuracy of speed estimation.

[0022] Based on this embodiment, the measured data of civil aircraft entering and landing at an airport are verified using a secondary air traffic control radar at a certain airport. Figure 1 The image shows the aircraft's speed during landing. The original speed is represented by the blue circle, while the speed after using the new method is represented by the red star. The horizontal axis represents time (s), and the vertical axis represents airspeed (km / h). It can be seen that the aircraft's speed fluctuated excessively before optimization, clearly not reflecting reality. After adopting the new method, the aircraft's speed decreased smoothly, effectively improving the accuracy of ground air traffic controllers' speed assessment and ensuring flight safety.

[0023] This invention provides a speed estimation method for civil air traffic control secondary radar. By using sliding window linear fitting on civil aircraft, it solves the problems of insufficient accuracy and large error fluctuation in existing speed measurement technologies, improves the accuracy and reliability of speed measurement, and thus ensures flight safety.

[0024] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. However, any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A speed estimation method for civilian air traffic control secondary radar, characterized in that, It includes the following steps: Step 1: At time k, the aircraft's three-dimensional velocities are calculated by the secondary radar's information processing module. , and To obtain the aircraft's speed ; Step 2: Take n time points backward from time k, the speed sequence from time (kn) to time k is as follows: By performing a linear fit on the speed sequence from time (kn) to time k, the new speed at time k is obtained. ; Step 3: At time k+1, take n time steps backward and repeat step 2 using a sliding window method to obtain the new speed at time k+1. ; Step 4: When k ≥ n, perform the operations of Step 1 to Step 3; When k < n, do not perform the operations of Step 1 to Step 3; For the selection of the window size n in Steps 2 to 4, the Extended Kalman Filter is adopted. The nonlinear radar tracking system is expanded using the Taylor series, and the high-order terms above the second order are omitted to obtain a linearized radar tracking system. The Kalman filter framework is used for filtering, and the information in the Kalman filter is utilized to determine whether the current flight speed of the aircraft changes violently; For the selection of the window size n in Steps 2 to 4, the steps of the Extended Kalman Wave include: T2: Let the nonlinear state transition equation of the radar tracking system be... (6) The nonlinear observation equation of the radar tracking system is: (7), of which and It is a nonlinear function. and These are process noise and observation noise, respectively. T3: The Extended Kalman Filter is adopted. The Extended Kalman Filter linearizes the nonlinear function through the Taylor series expansion, including: K1: Predicted State: The equation for the predicted state is... Formula (6) is in The Jacobian matrix at point is Formula (7) in The Jacobian matrix at point is , This represents the prior state estimate at the current moment; K2: Prediction Error Covariance Matrix: The equation for the prediction error covariance matrix is ​​as follows: ,in and Let these be the covariance matrices of the prior and posterior state estimates, respectively. and Let represent the covariance moments of process noise and observation noise, respectively; K3: Define the normalized residual at time k: The equation for the normalized residual at time k is... Its value range is {0,1}; When 0.8≤ When n ≤ 1, it is assumed that the aircraft's speed changes greatly, and the window size n can be reduced to respond quickly to the changes; When 0≤ When the value is less than 0.8, the aircraft's speed is considered to change little, and the window size n can be increased to improve the accuracy of speed estimation.

2. The airspeed estimation method for civil air traffic control secondary radar according to claim 1, characterized in that, In Step 2, a linear fitting is performed on the flight speed sequence from the (k - n) moment to the k moment, including: S1: Assuming speed Changes, over a period of time If the inner dimension follows a linear change over time k, then... (1); S2: If n is a natural number greater than 1, then (2); S3: Convert formula (2) into matrix form, which is... (3); S4: Order , , Then formula (3) can be converted to (4); S5: Solving formula (4), we can obtain A, which is... (5); S6: According to formula (5), we can obtain the formula (1) and ,Will , Substituting k into formula (1), we can obtain the new speed at time k. .

3. The airspeed estimation method for civil air traffic control secondary radar according to claim 1, characterized in that, In Step 2, when sliding the window each time, only calculate the influence of the newly added data points and the removed data points on the linear fitting result, without recalculating the linear fitting result of the entire sequence.

4. The airspeed estimation method for civil air traffic control secondary radar according to claim 1, characterized in that, Before Step 1, there is also Step T1: After the secondary radar information processing module in the radar tracking system obtains the target information of the aircraft, use the collaborative positioning algorithm, track initiation algorithm, data association algorithm, and tracking filtering algorithm to process the target information, and complete operations including data preprocessing, track initiation, data association, and tracking filtering.

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