An inertial navigation method for civil aviation

Abstract

The invention discloses an inertial navigation method for civil aviation, which relates to the field of inertial navigation technology, and includes the following steps: S1, obtaining static initialization data of a drone; S2, collecting dynamic flight data sets of the drone during flight, and generating a cruise control coefficient according to the dynamic flight data sets and lift assistance of the drone; S3, generating a predicted position of the drone according to the static initialization data and the cruise control coefficient of the drone. The invention can timely discover and correct deviations and anomalies during flight, avoid the drone from deviating from the scheduled route or other flight accidents, and thus improve the safety of flight.

Description

Technical Field

[0001] The present invention relates to the technical field of inertial navigation, and in particular to an inertial navigation method for civil aviation. Background Art

[0002] With the rapid development of drone technology, its application in the field of civil aviation is becoming more and more extensive. However, how to accurately and stably perform inertial navigation for drone positioning during flight, especially in complex environments or areas with weak satellite signals, has become an urgent problem to be solved. Traditional satellite navigation systems can provide accurate location information in open areas, but cannot work effectively in areas where satellite signals are limited, such as indoors and tunnels. Although the inertial navigation system can perform navigation calculations autonomously, errors will gradually accumulate over time, affecting navigation accuracy. Therefore, the present invention proposes an inertial navigation method for accurately predicting the position of a drone. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes an inertial navigation method for civil aviation.

[0004] The technical solution of the present invention is: a civil aviation inertial navigation method comprises the following steps:

[0005] S1. Obtain the static initialization data of the drone;

[0006] S2, collecting a dynamic flight data set of the UAV during the flight process, and generating a cruise control coefficient according to the dynamic flight data set and lift assistance amount of the UAV;

[0007] S3. Generate the predicted position of the UAV based on the static initialization data and cruise control coefficient of the UAV.

[0008] Furthermore, in S1, the static initialization data of the UAV includes an initial pitch angle and an initial yaw angle.

[0009] Furthermore, S2 includes the following sub-steps:

[0010] S21, collecting the acceleration value of the UAV during the flight process as a dynamic flight data set;

[0011] S22, collecting the wind speed of the UAV during the flight process, and calculating the lift assistance amount of the UAV;

[0012] S23, inputting the lift assistance amount of the UAV and the acceleration value of the flight process into the ridge regression cost function to obtain the cruise control coefficient of the UAV.

[0013] The beneficial effect of the above further scheme is: in the present invention, the wind speed data during the flight is collected, and combined with the flight status of the UAV, the lift assistance amount can be accurately calculated. Lift is an important parameter for UAV flight and directly affects the flight altitude and speed of the UAV. The lift assistance amount and the acceleration value are input into the ridge regression cost function to obtain the cruise control coefficient of the UAV. Ridge regression is a regularized linear regression model. By adding a regularization term to prevent overfitting and improve the generalization ability of the model, these two parameters are input into the ridge regression model, which can comprehensively consider the flight status of the UAV and external environmental factors, thereby generating a more accurate cruise control coefficient.

[0014] Furthermore, in S22, the lift assistance amount of the UAV The calculation formula is:

[0015] ;

[0016] In the formula, Indicates that the drone is in flight The wind speed at that moment, Indicates that the drone is in flight The wind direction at that moment, Indicates the time number of the flight process, represents the sweep angle of the drone, Indicates the time of the flight.

[0017] The sweep angle is used to describe the angle between the characteristic line of the wing surface and the reference axis. The sweep angle affects the drag characteristics and stability of the wing when flying at high speed.

[0018] Further, S23 includes the following sub-steps:

[0019] S231, combining the acceleration values ​​of every two adjacent moments during the flight process to generate a number of related samples;

[0020] S232, adding all relevant samples as the penalty vector of the ridge regression cost function;

[0021] S233. Obtain a cruise control coefficient according to the lift assistance amount of the UAV and the penalty vector of the ridge regression cost function.

[0022] The beneficial effect of the above further scheme is: in the present invention, S231 generates relevant samples by combining the acceleration values ​​at every two adjacent moments, making full use of the dynamic data during the flight of the UAV, and considering not only the acceleration value itself, but also the changing trend of the acceleration value over time; S232 adds all relevant samples as the penalty vector of the ridge regression cost function, which actually introduces a regularization mechanism when constructing the ridge regression model, which helps to prevent the model from overfitting, so that the generated cruise control coefficient is more accurate.

[0023] In S231, the first correlation vector The expression is: , where represents the acceleration value at the first moment, Indicates the acceleration value at the second moment; the second related vector The expression is: ; In the formula, represents the acceleration value at the third moment, Represents the acceleration value at the 4th moment; and so on, we get several related vectors.

[0024] Furthermore, in S233, the ridge regression cost function The expression is:

[0025] ;

[0026] In the formula, represents the regularization coefficient of the ridge regression cost function, Indicates The value of the first element in the relevant samples, Indicates The value of the second element in the relevant samples, represents the penalty vector of the ridge regression cost function, represents the L2 norm, Indicates Related samples, Indicates the relevant sample number, represents the number of relevant samples, Indicates the amount of lift assist for the drone.

[0027] Furthermore, S3 includes the following sub-steps:

[0028] S31, generating a heading value according to an initial pitch angle and an initial yaw angle of the UAV;

[0029] S32, calculating the heading weight of the UAV according to the heading value and the cruise control coefficient;

[0030] S33, the heading weight of the UAV is used as the difference order of the ARIMA model;

[0031] S34, inputting the acceleration value of the UAV during the flight process into the ARIMA model to obtain the predicted acceleration value of the UAV at the next moment;

[0032] S35. Generate the predicted position of the UAV using the Simpson method according to the acceleration value of the UAV during the flight and the predicted acceleration value at the next moment.

[0033] The beneficial effect of the above further scheme is: in the present invention, by taking the heading weight as the differential order, the ARIMA model can be better adapted to the flight characteristics of the UAV, and the accuracy and stability of the prediction can be improved. Acceleration is an important parameter in the flight process of the UAV, which reflects the speed change of the UAV. By inputting the acceleration value into the ARIMA model, the future acceleration of the UAV can be predicted, and the position prediction can be completed.

[0034] In S35, a numerical integration method may be used to integrate the acceleration data over time to obtain velocity data, and then the velocity data may be integrated to obtain position data. There are many numerical integration methods, such as the trapezoidal method, the Simpson method, and the like.

[0035] Furthermore, in S31, the heading value The calculation formula is:

[0036] ;

[0037] In the formula, Indicates the initial pitch angle of the drone, represents the initial yaw angle of the drone,

[0038] Furthermore, in S32, the heading weight of the drone The calculation formula is:

[0039] ;

[0040] In the formula, represents the cruise control coefficient, Indicates the heading value, Indicates rounding up. Represents an exponent.

[0041] The beneficial effects of the present invention are as follows: the present invention obtains the static initialization data of the UAV, which can ensure that the UAV has accurate initial attitude information before starting the flight, which helps to reduce the error accumulation in the subsequent navigation process, thereby improving the overall navigation accuracy; in addition, the dynamic flight data set collected by the present invention includes the state information of the UAV during the flight, and generates a cruise control coefficient in combination with the lift assist amount. The cruise control coefficient reflects the dynamic changes of the UAV during the flight, and can more accurately predict the flight acceleration of the UAV; finally, the predicted position is generated according to the static initialization data and the cruise control coefficient of the UAV, which has high accuracy; therefore, the present invention can timely discover and correct deviations and anomalies in the flight process, avoid the UAV from deviating from the scheduled route or other flight accidents, thereby improving the safety of the flight. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Flowchart of the inertial navigation method for civil aviation. DETAILED DESCRIPTION

[0043] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0044] like Figure 1 As shown, the present invention provides an inertial navigation method for civil aviation, comprising the following steps:

[0045] S1. Obtain the static initialization data of the drone;

[0046] S2, collecting a dynamic flight data set of the UAV during the flight process, and generating a cruise control coefficient according to the dynamic flight data set and lift assistance amount of the UAV;

[0047] S3. Generate the predicted position of the UAV based on the static initialization data and cruise control coefficient of the UAV.

[0048] In the embodiment of the present invention, in S1, the static initialization data of the UAV includes an initial pitch angle and an initial yaw angle.

[0049] In this embodiment of the present invention, S2 includes the following sub-steps:

[0050] S21, collecting the acceleration value of the UAV during the flight process as a dynamic flight data set;

[0051] S22, collecting the wind speed of the UAV during the flight process, and calculating the lift assistance amount of the UAV;

[0052] S23, inputting the lift assistance amount of the UAV and the acceleration value of the flight process into the ridge regression cost function to obtain the cruise control coefficient of the UAV.

[0053] In the present invention, the wind speed data during the flight is collected, and combined with the flight status of the UAV, the lift assistance amount can be accurately calculated. Lift is an important parameter for UAV flight and directly affects the flight altitude and speed of the UAV. The lift assistance amount and the acceleration value are input into the ridge regression cost function to obtain the cruise control coefficient of the UAV. Ridge regression is a regularized linear regression model. By adding regularization terms to prevent overfitting and improve the generalization ability of the model, these two parameters are input into the ridge regression model, which can comprehensively consider the flight status of the UAV and external environmental factors, thereby generating a more accurate cruise control coefficient.

[0054] In the embodiment of the present invention, in S22, the lift assistance amount of the drone The calculation formula is:

[0055] ;

[0056] In the formula, Indicates that the drone is in flight The wind speed at that moment, Indicates that the drone is in flight The wind direction at that moment, Indicates the time number of the flight process, represents the sweep angle of the drone, Indicates the time of the flight.

[0057] The sweep angle is used to describe the angle between the characteristic line of the wing surface and the reference axis. The sweep angle affects the drag characteristics and stability of the wing when flying at high speed.

[0058] In this embodiment of the present invention, S23 includes the following sub-steps:

[0059] S231, combining the acceleration values ​​of every two adjacent moments during the flight process to generate a number of related samples;

[0060] S232, adding all relevant samples as the penalty vector of the ridge regression cost function;

[0061] S233. Obtain a cruise control coefficient according to the lift assistance amount of the UAV and the penalty vector of the ridge regression cost function.

[0062] In the present invention, S231 generates relevant samples by combining the acceleration values ​​at every two adjacent moments, making full use of the dynamic data of the UAV during flight, and considering not only the acceleration value itself but also the changing trend of the acceleration value over time; S232 adds all relevant samples as the penalty vector of the ridge regression cost function, which actually introduces a regularization mechanism when constructing the ridge regression model, which helps to prevent the model from overfitting, so that the generated cruise control coefficient is more accurate.

[0063] In S231, the first correlation vector The expression is: , where represents the acceleration value at the first moment, Indicates the acceleration value at the second moment; the second related vector The expression is: ; In the formula, represents the acceleration value at the third moment, Represents the acceleration value at the 4th moment; and so on, we get several related vectors.

[0064] In the embodiment of the present invention, in S233, the ridge regression cost function The expression is:

[0065] ;

[0066] In the formula, represents the regularization coefficient of the ridge regression cost function, Indicates The value of the first element in the relevant samples, Indicates The value of the second element in the relevant samples, represents the penalty vector of the ridge regression cost function, represents the L2 norm, Indicates Related samples, Indicates the relevant sample number, represents the number of relevant samples, Indicates the amount of lift assist for the drone.

[0067] In this embodiment of the present invention, S3 includes the following sub-steps:

[0068] S31, generating a heading value according to an initial pitch angle and an initial yaw angle of the UAV;

[0069] S32, calculating the heading weight of the UAV according to the heading value and the cruise control coefficient;

[0070] S33, the heading weight of the UAV is used as the difference order of the ARIMA model;

[0071] S34, inputting the acceleration value of the UAV during the flight process into the ARIMA model to obtain the predicted acceleration value of the UAV at the next moment;

[0072] S35. Generate the predicted position of the UAV using the Simpson method according to the acceleration value of the UAV during the flight and the predicted acceleration value at the next moment.

[0073] In the present invention, by taking the heading weight as the differential order, the ARIMA model can better adapt to the flight characteristics of the drone and improve the accuracy and stability of the prediction. Acceleration is an important parameter in the flight process of the drone, which reflects the speed change of the drone. By inputting the acceleration value into the ARIMA model, the future acceleration of the drone can be predicted and the position prediction can be completed.

[0074] In S35, a numerical integration method may be used to integrate the acceleration data over time to obtain velocity data, and then the velocity data may be integrated to obtain position data. There are many numerical integration methods, such as the trapezoidal method, the Simpson method, and the like.

[0075] In the embodiment of the present invention, in S31, the heading value The calculation formula is:

[0076] ;

[0077] In the formula, Indicates the initial pitch angle of the drone, represents the initial yaw angle of the drone,

[0078] In the embodiment of the present invention, in S32, the heading weight of the drone The calculation formula is:

[0079] ;

[0080] In the formula, represents the cruise control coefficient, Indicates the heading value, Indicates rounding up. Represents an exponent.

[0081] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. An inertial navigation method for civil aviation, characterized in that: The following steps are involved: S1. Obtain the static initialization data of the drone; S2, collecting a dynamic flight data set of the UAV during the flight process, and generating a cruise control coefficient according to the dynamic flight data set and lift assistance amount of the UAV; S3, generating a predicted position of the UAV according to the static initialization data and cruise control coefficient of the UAV; In S1, the static initialization data of the drone includes an initial pitch angle and an initial yaw angle; The S2 comprises the following sub-steps: S21, collecting the acceleration value of the UAV during the flight process as a dynamic flight data set; S22, collecting the wind speed of the UAV during the flight process, and calculating the lift assistance amount of the UAV; S23, inputting the lift assistance amount of the UAV and the acceleration value of the flight process into the ridge regression cost function to obtain the cruise control coefficient of the UAV; In S22, the lift assistance amount of the UAV The calculation formula is: ； In the formula, Indicates that the drone is in flight The wind speed at that moment, Indicates that the drone is in flight The wind direction at that moment, Indicates the time number of the flight process. represents the sweep angle of the drone, Indicates the number of moments during the flight; The S23 comprises the following sub-steps: S231, combining the acceleration values ​​of every two adjacent moments during the flight process to generate a number of related samples; S232, adding all relevant samples as the penalty vector of the ridge regression cost function; S233, obtaining a cruise control coefficient according to the lift assistance amount of the UAV and the penalty vector of the ridge regression cost function; In S233, the ridge regression cost function The expression is: ； In the formula, represents the regularization coefficient of the ridge regression cost function, Indicates The value of the first element in the relevant samples, Indicates The value of the second element in the relevant samples, represents the penalty vector of the ridge regression cost function, represents the L2 norm, Indicates Related samples, Indicates the relevant sample number, represents the number of relevant samples, Indicates the lift assist amount of the drone; The S3 comprises the following sub-steps: S31, generating a heading value according to an initial pitch angle and an initial yaw angle of the UAV; S32, calculating the heading weight of the UAV according to the heading value and the cruise control coefficient; S33, the heading weight of the UAV is used as the difference order of the ARIMA model; S34, inputting the acceleration value of the UAV during the flight process into the ARIMA model to obtain the predicted acceleration value of the UAV at the next moment; S35, generating a predicted position of the UAV using the Simpson method according to the acceleration value of the UAV during the flight process and the predicted acceleration value at the next moment; In S31, the heading value The calculation formula is: ； In the formula, Indicates the initial pitch angle of the drone, Indicates the initial yaw angle of the drone; In S32, the heading weight of the UAV The calculation formula is: ； In the formula, represents the cruise control coefficient, Indicates the heading value, Indicates rounding up. Represents an exponent.

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