CN112304312B - Unmanned aerial vehicle attitude calculation method and system based on least square method and EKF - Google Patents
Unmanned aerial vehicle attitude calculation method and system based on least square method and EKF Download PDFInfo
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Abstract
The invention discloses a method and a system for resolving unmanned aerial vehicle attitude based on a least square method and an EKF (extended Kalman Filter), comprising S100, acquiring state parameter values of the flight state of an unmanned aerial vehicle based on an IMU (inertial measurement Unit) data sensor, wherein the state parameter values comprise an angular velocity value and a linear acceleration value; s200, fitting and interpolating calculation are carried out on the angular velocity value and the linear acceleration value based on a least square method, and a state space equation about the angular velocity and the acceleration is established; s300, importing IMU data subjected to least square calculation processing into an extended Kalman filtering algorithm module, and carrying out attitude calculation on the attitude of the unmanned aerial vehicle. According to the method, based on the angular velocity value and the acceleration value processed by the least square method, a state space equation and a measurement equation about the angular velocity and the acceleration are established, and based on the equations and the corresponding Jacobian matrix, an attitude estimation module is set up through an extended Kalman filtering algorithm theory, attitude resolving is carried out on the attitude of the unmanned aerial vehicle, and the influence of noise and mutation values in data on accuracy and stability of the resolved attitude is effectively reduced.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle attitude calculation method and system based on a least square method and EKF.
Background
The unmanned aerial vehicle is an airplane which does not contain a manned function and is controlled by radio or controlled by a program of the unmanned aerial vehicle. The flight control algorithm with good stability and high precision can not be widely applied in the fields of plant protection, military, geological exploration and the like. The flight control algorithm mainly comprises three algorithm modules, namely a strapdown inertial navigation system, a Kalman filtering algorithm and a flight control PID (proportion integration differentiation), and the quality of the algorithm is the key for realizing stable flight. The Kalman filtering algorithm is used for completing state parameter estimation of the unmanned aerial vehicle based on the IMU module, the GPS and the anemometer, and attitude calculation of the unmanned aerial vehicle in the state estimation is the basis for realizing stable control of the unmanned aerial vehicle.
The defects and shortcomings of the prior art are as follows:
at present, an unmanned aerial vehicle attitude resolving method mainly comprises the steps of establishing a space state equation and estimating unmanned attitude and position information based on an extended Kalman filtering algorithm. External data from a gyroscope and an accelerometer are adopted in the extended Kalman filtering algorithm, vibration and sound of the unmanned aerial vehicle influence the quality of IMU data acquisition, and noise or unsmooth exists in output position and attitude data, so that the stability and stability of the flight of the unmanned aerial vehicle are influenced, the quality and safety of an operation target and an operator are hidden, and popularization of unmanned products are influenced.
Disclosure of Invention
The unmanned aerial vehicle attitude calculation method and system based on the least square method and the EKF can solve the problems of noise and unsmooth data in attitude calculation, thereby improving the stable and stable performance of the flight state of the unmanned aerial vehicle and promoting the development and popularization of the unmanned aerial vehicle.
In order to realize the purpose, the invention adopts the following technical scheme:
a method for unmanned aerial vehicle attitude calculation based on a least square method and EKF comprises the following steps:
the method comprises the following steps:
s100, acquiring state parameter values of the flight state of the unmanned aerial vehicle based on an IMU data sensor, wherein the state parameter values comprise an angular velocity value and a linear acceleration value;
s200, fitting and interpolating calculation are carried out on the angular velocity value and the acceleration value based on a least square method, a state space equation and a measurement equation about the angular velocity and the acceleration are established, and an attitude estimation module is built through an extended Kalman filtering algorithm theory based on the equations and a corresponding Jacobian matrix;
s300, importing IMU data subjected to least square calculation processing into an attitude estimation module, and carrying out attitude calculation on the attitude of the unmanned aerial vehicle.
Further, the S200 specifically includes:
step 1:
acquiring flight state parameter values of the unmanned aerial vehicle through three groups of IMU sensors, taking angular velocity acquired by a gyroscope and linear acceleration acquired by an accelerometer as state input quantity for attitude calculation, and setting a data acquisition period of an inertial sensor;
wherein the content of the first and second substances,
the gyroscope acquisition data is defined as follows,
Gyro_ang_vel[3][3]＝{Vel_gyro_rx1,Vel_gyro_ry1,Vel_gyro_rz1；Vel_gyro_rx2,Vel_gyro_ry2,Vel_gyro_rz2；Vel_gyro_rx3,Vel_gyro_ry3,Vel_gyro_rz3；}
the accelerometer acquisition data is defined as follows,
Acc_line_acc[3][3]＝{Acc_x1,Acc_y1,Acc_z1；Acc_x2,Acc_y2,Acc_z2；Acc_x3,Acc_y3,Acc_z3；}
wherein the data of the first set of IMUs is defined as follows,
vel _ gyro _ rx1, namely the angular speed value of the engine body around the X axis of an engine body coordinate system, wherein the unit is rad/s;
vel _ gyro _ ry1, namely the angular speed value of the body around the Y axis of a body coordinate system, wherein the unit is rad/s;
vel _ gyro _ rz1, namely the angular velocity value of the body around the Z axis of a body coordinate system, wherein the unit is rad/s;
acc _ X1, the linear velocity value of the machine body along the X axis of the machine body coordinate system, the unit is m/s 2;
acc _ Y1, the linear velocity value of the machine body along the Y axis of the machine body coordinate system, the unit is m/s ^ 2;
acc _ Z1, the linear velocity value of the machine body along the Z axis of the machine body coordinate system, the unit is m/s ^ 2;
dt is a data acquisition period, dt is set to be 0.02, and the unit is s;
step 2:
dividing the state parameter values of 10 periods into calculation units M, and performing data preprocessing on angular velocity values Vel _ gyro _ rx1 around the X axis of a body coordinate system;
wherein:
the value of each sensor acquired in 10 sampling periods is one calculation unit, and the mth calculation unit is defined as W ═ W (W ═ W) 10*m+0 ,w 10*m+1 ,w 10*m+2 ...w 10*m+9 )，
Wherein:
w＝(Vel_gyro_rx,Vel_gyro_ry,Vel_gyro_rz,Acc_x,Acc_y,Acc_z)，
M＝(Vel_gyro_rx 10*m+0 ,Vel_gyro_rx 10*m+1 ,Vel_gyro_rx 10*m+2 ...Vel_gyro_rx 10*m+9 )，Time＝(0.00,0.02,...0.18)，
N＝10；
and step 3:
a linear regression equation of M with respect to Time is calculated based on the least square method,
the regression curve equation is calculated by using the least square method for the angular velocity around the X axis in the calculation unit W as follows:
is provided with
Solving the notations by the least squares coefficients can yield:
the m group of linear regression equations is determined, then the state parameter value of the corresponding point of each time value is calculated based on the equation, the other five state parameters of the IMU are calculated as above, the data values of the other two groups are calculated as follows, the state parameter values of the three groups of IMU after least square calculation are obtained, and the following steps are referred specifically;
and 4, step 4: the calculating unit calculates the angular velocity value of the corresponding moment according to the time parameter value
Wherein:
and 5: similarly performing operation calculation in the steps 2, 3 and 4 on Y-axis angular velocity data Vel _ gyro _ ry1, Z-axis angular velocity data Vel _ gyro _ rz1, X-axis acceleration data Acc _ X1, Y-axis acceleration data Acc _ Y1 and Z-axis acceleration data Acc _ Z1 acquired by the first group of IMUs to acquire corresponding calculated state parameter values;
step 6: and (5) similarly adopting the operation rooms of the step (2), the step (3), the step (4) and the step (5) for the data of the second group and the third group to obtain the corresponding calculated state parameter values.
On the other hand, the invention also discloses an unmanned aerial vehicle attitude calculation system based on a least square method and EKF, which comprises the following units:
the data acquisition unit is used for acquiring state parameter values of the flight state of the unmanned aerial vehicle based on the IMU data sensor, wherein the state parameter values comprise an angular velocity value and a linear acceleration value;
the data processing unit is used for performing fitting and interpolation calculation on the angular velocity value and the acceleration value based on a least square method, establishing a state space equation and a measurement equation related to the angular velocity and the acceleration, and building an attitude estimation module based on the equations and a corresponding Jacobian matrix through an extended Kalman filtering algorithm theory;
and the unmanned aerial vehicle attitude calculation unit is used for importing IMU data subjected to least square calculation into the attitude estimation module and calculating the attitude of the unmanned aerial vehicle.
According to the technical scheme, the unmanned aerial vehicle attitude calculation method based on the least square method and the EKF establishes a state space equation and a measurement equation about the angular velocity and the acceleration based on the angular velocity value and the acceleration value processed by the least square method, and establishes an attitude estimation module based on the equations and the corresponding Jacobian matrix through an extended Kalman filtering algorithm theory to calculate the attitude of the unmanned aerial vehicle, so that the influence of noise and mutation values in data on the accuracy and stability of the calculated attitude is effectively reduced.
Specifically, the beneficial effects of the invention are as follows:
(1) the stability of attitude resolving of extended Kalman filtering is improved;
(2) the influence of the inaccuracy of the covariance of the extended Kalman filtering algorithm and the model of the noise error variance on the accuracy of the attitude estimation is reduced;
(3) the least square method is combined with the IMU data preprocessing and the extended Kalman algorithm, the influence of data noise on later estimation is effectively reduced, the precision and the stability are effectively improved, and the basis is set for the stable flight cushion of the unmanned aerial vehicle.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention.
As shown in fig. 1, the method for resolving the attitude of the unmanned aerial vehicle based on the least square method and the EKF according to this embodiment is mainly applicable to the field of unmanned aerial vehicles with multiple rotors, and requires that the unmanned aerial vehicle has at least three sets of 6-axis IMU data acquisition capabilities, and includes the following steps:
s100, acquiring state parameter values of the flight state of the unmanned aerial vehicle based on an IMU data sensor, wherein the state parameter values comprise an angular velocity value and a linear acceleration value;
s200, fitting and interpolating calculation are carried out on the angular velocity value and the acceleration value based on a least square method, a state space equation and a measurement equation about the angular velocity and the acceleration are established, and an attitude estimation module is built through an extended Kalman filtering algorithm theory based on the equations and a corresponding Jacobian matrix;
s300, importing IMU data subjected to least square calculation processing into an attitude estimation module, and performing attitude calculation on the attitude of the unmanned aerial vehicle.
The following is a detailed description:
step 1:
acquiring angular velocity values and linear acceleration values of the flight state of the unmanned aerial vehicle based on the three groups of IMU data sensors;
the method comprises the steps that flight state parameter values of the unmanned aerial vehicle are collected through three groups of IMU sensors (namely, three-axis gyroscopes and three-axis accelerometer integrated chips), angular velocities collected by the gyroscopes and linear accelerations collected by the accelerometers are used as state input quantities for attitude calculation, and data collection periods of inertial sensors are set;
wherein the content of the first and second substances,
the gyroscope acquisition data is defined as follows,
Gyro_ang_vel[3][3]＝{Vel_gyro_rx1,Vel_gyro_ry1,Vel_gyro_rz1；Vel_gyro_rx2,Vel_gyro_ry2,Vel_gyro_rz2；Vel_gyro_rx3,Vel_gyro_ry3,Vel_gyro_rz3；}
the accelerometer acquisition data is defined as follows,
Acc_line_acc[3][3]＝{Acc_x1,Acc_y1,Acc_z1；Acc_x2,Acc_y2,Acc_z2；Acc_x3,Acc_y3,Acc_z3；}
wherein the data of the first set of IMUs is defined as follows,
vel _ gyro _ rx1, namely the angular speed value of the body around the X axis of a body coordinate system, and the unit is rad/s;
vel _ gyro _ ry1, namely the angular speed value of the machine body around the Y axis of the machine body coordinate system, and the unit is rad/s;
vel _ gyro _ rz1 angular speed value of the machine body around the Z axis of the machine body coordinate system, and the unit is rad/s;
acc _ X1, the linear velocity value of the machine body along the X axis of the machine body coordinate system, the unit is m/s 2;
acc _ Y1, the linear velocity value of the machine body along the Y axis of the machine body coordinate system, the unit is m/s ^ 2;
acc _ Z1, the linear velocity value of the machine body along the Z axis of the machine body coordinate system, the unit is m/s ^ 2;
dt is the data acquisition period, and dt is set to 0.02 and is expressed in s.
Step 2:
dividing the state parameter values of 10 periods into calculation units M, and performing data preprocessing on angular velocity values Vel _ gyro _ rx1 around the X axis of a body coordinate system;
wherein:
the value of each sensor acquired in 10 sampling periods is one calculation unit, and the mth calculation unit may be defined as W ═ W (W ═ W) 10*m+0 ,w 10*m+1 ,w 10*m+2 ...w 10*m+9 )，
Wherein:
w＝(Vel_gyro_rx,Vel_gyro_ry,Vel_gyro_rz,Acc_x,Acc_y,Acc_z)，
M＝(Vel_gyro_rx 10*m+0 ,Vel_gyro_rx 10*m+1 ,Vel_gyro_rx 10*m+2 ...Vel_gyro_rx 10*m+9 )，Time＝(0.00,0.02,...0.18)，
N＝10。
and step 3:
a linear regression equation of M over Time is calculated based on a least squares method,
the regression curve equation is calculated by using the least square method for the angular velocity around the X axis in the calculation unit W as follows:
is provided with
Solving the notations by the least squares coefficients can yield:
the m group of linear regression equations is determined, then the state parameter value of the corresponding point of each time value is calculated based on the equation, the other five state parameters of the IMU are calculated as above, the data values of the other two groups are calculated as follows, the state parameter values of the three groups of IMU after least square calculation are obtained, and the following steps are referred specifically;
and 4, step 4: the calculating unit calculates the angular velocity value of the corresponding moment according to the time parameter value
Wherein:
and 5: similarly, performing operation calculation in the steps 2, 3 and 4 on Y-axis angular velocity data Vel _ gyro _ ry1, Z-axis angular velocity data Vel _ gyro _ rz1, X-axis acceleration data Acc _ X1, Y-axis acceleration data Acc _ Y1 and Z-axis acceleration data Acc _ Z1 acquired by the first group of IMUs to acquire corresponding calculated state parameter values;
step 6: similarly adopting the operation rooms of the step 2, the step 3, the step 4 and the step 5 for the data of the second group and the third group to obtain corresponding calculated state parameter values;
and 7: three groups of IMU data, namely Gyro _ ang _ vel [3] [3] and Acc _ line _ Acc [3] [3], which are calculated and processed by a least square method are led into the extended Kalman filtering algorithm module to realize the attitude parameter calculation of the unmanned aerial vehicle, and a foundation is provided for the stable control of the unmanned aerial vehicle.
According to the method, the state space equation and the measurement equation related to the angular velocity and the acceleration are established based on the angular velocity value and the linear acceleration value processed by the least square method, the extended Kalman filtering algorithm module is constructed based on the equations and the corresponding Jacobian matrix by adopting the extended Kalman algorithm theory, the attitude of the unmanned aerial vehicle is solved, and the influence of noise and mutation values in data on the accuracy and stability of the solved attitude is effectively reduced.
On the other hand, the invention also discloses an unmanned aerial vehicle attitude calculation system based on a least square method and EKF, which comprises the following units:
the data acquisition unit is used for acquiring state parameter values of the flight state of the unmanned aerial vehicle based on the IMU data sensor, wherein the state parameter values comprise an angular velocity value and a linear acceleration value;
the data processing unit is used for performing fitting and interpolation calculation on the angular velocity value and the acceleration value based on a least square method, establishing a state space equation and a measurement equation related to the angular velocity and the acceleration, and building an attitude estimation module through an extended Kalman filtering algorithm theory based on the equations and a corresponding Jacobian matrix;
and the unmanned aerial vehicle attitude calculation unit is used for importing IMU data subjected to least square calculation into the attitude estimation module and calculating the attitude of the unmanned aerial vehicle.
It is understood that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, the example and the beneficial effects of the related contents can refer to the corresponding parts in the method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (2)
1. A method for resolving unmanned aerial vehicle attitude based on least square method and EKF is characterized in that: the method comprises the following steps:
s100, acquiring state parameter values of the flight state of the unmanned aerial vehicle based on an IMU data sensor, wherein the state parameter values comprise an angular velocity value and a linear acceleration value;
s200, fitting and interpolating calculation are carried out on the angular velocity value and the acceleration value based on a least square method, a state space equation and a measurement equation about the angular velocity and the acceleration are established, and an attitude estimation module is built through an extended Kalman filtering algorithm theory based on the equations and a corresponding Jacobian matrix;
s300, importing IMU data subjected to least square calculation processing into an attitude estimation module, and performing attitude calculation on the attitude of the unmanned aerial vehicle;
the S200 specifically includes:
step 1:
acquiring flight state parameter values of the unmanned aerial vehicle through three groups of IMU sensors, taking angular velocity acquired by a gyroscope and linear acceleration acquired by an accelerometer as state input quantity for attitude calculation, and setting a data acquisition period of an inertial sensor;
wherein, the first and the second end of the pipe are connected with each other,
the gyroscope acquisition data is defined as follows,
Gyro_ang_vel[3][3]＝{Vel_gyro_rx1,Vel_gyro_ry1,Vel_gyro_rz1；Vel_gyro_rx2,Vel_gyro_ry2,Vel_gyro_rz2；Vel_gyro_rx3,Vel_gyro_ry3,Vel_gyro_rz3；}
the accelerometer acquisition data is defined as follows,
Acc_line_acc[3][3]＝{Acc_x1,Acc_y1,Acc_z1；Acc_x2,Acc_y2,Acc_z2；Acc_x3,Acc_y3,Acc_z3；}
wherein the data of the first set of IMUs is defined as follows,
vel _ gyro _ rx1, namely the angular speed value of the body around the X axis of a body coordinate system, and the unit is rad/s;
vel _ gyro _ ry1, namely the angular speed value of the machine body around the Y axis of the machine body coordinate system, and the unit is rad/s;
vel _ gyro _ rz1 angular speed value of the machine body around the Z axis of the machine body coordinate system, and the unit is rad/s;
acc _ X1, the linear velocity value of the machine body along the X axis of the machine body coordinate system, the unit is m/s ^ 2;
acc _ Y1, the linear velocity value of the machine body along the Y axis of the machine body coordinate system, the unit is m/s ^ 2;
acc _ Z1, the linear velocity value of the machine body along the Z axis of the machine body coordinate system, the unit is m/s ^ 2;
dt is a data acquisition period, dt is set to be 0.02, and the unit is s;
and 2, step:
dividing the state parameter values of 10 periods into calculation units M, and performing data preprocessing on angular velocity values Vel _ gyro _ rx1 around the X axis of a body coordinate system;
the value of each sensor acquired in 10 sampling periods is one calculation unit, and the mth calculation unit is defined as W ═ W (W ═ W) 10*m+0 ,w 10*m+1 ,w 10*m+2 ...w 10*m+9 )，
Wherein:
w＝(Vel_gyro_rx,Vel_gyro_ry,Vel_gyro_rz,Acc_x,Acc_y,Acc_z)，
M＝(Vel_gyro_rx 10*m+0 ,Vel_gyro_rx 10*m+1 ,Vel_gyro_rx 10*m+2 ...Vel_gyro_rx 10*m+9 )，Time＝(0.00,0.02,...0.18)，
N＝10；
and step 3:
a linear regression equation of M with respect to Time is calculated based on the least square method,
the regression curve equation is calculated by using the least square method for the angular velocity around the X axis in the calculation unit W as follows:
is provided with
Solving the formula by using the least square method coefficients:
the m-th group of linear regression equations is determined, then the state parameter value of the corresponding point of each time value is calculated based on the equations, the five other state parameters of the IMU are calculated as above, the data values of the other two groups are calculated as below, the state parameter values of the three groups of IMU after least square calculation are obtained, and the following steps are referred to specifically;
and 4, step 4: the calculating unit calculates the angular velocity value of the corresponding moment according to the time parameter value
Wherein:
and 5: similarly performing operation calculation in the steps 2, 3 and 4 on Y-axis angular velocity data Vel _ gyro _ ry1, Z-axis angular velocity data Vel _ gyro _ rz1, X-axis acceleration data Acc _ X1, Y-axis acceleration data Acc _ Y1 and Z-axis acceleration data Acc _ Z1 acquired by the first group of IMUs to acquire corresponding calculated state parameter values;
step 6: and (5) similarly adopting the operation rooms of the step (2), the step (3), the step (4) and the step (5) for the data of the second group and the third group to obtain the corresponding calculated state parameter values.
2. A system for unmanned aerial vehicle attitude solution based on least square method and EKF realizes the method for unmanned aerial vehicle attitude solution based on least square method and EKF of claim 1, which is characterized in that: the method comprises the following units:
the data acquisition unit is used for acquiring state parameter values of the flight state of the unmanned aerial vehicle based on the IMU data sensor, wherein the state parameter values comprise an angular velocity value and a linear acceleration value;
the data processing unit is used for performing fitting and interpolation calculation on the angular velocity value and the acceleration value based on a least square method, establishing a state space equation and a measurement equation related to the angular velocity and the acceleration, and building an attitude estimation module based on the equations and a corresponding Jacobian matrix through an extended Kalman filtering algorithm theory;
and the unmanned aerial vehicle attitude calculation unit is used for importing IMU data subjected to least square calculation into the attitude estimation module and calculating the attitude of the unmanned aerial vehicle.
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