CN117409150A - Method and system for improving track smoothness of three-dimensional display unmanned aerial vehicle - Google Patents
Method and system for improving track smoothness of three-dimensional display unmanned aerial vehicle Download PDFInfo
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Abstract
The invention discloses a method and a system for improving the track smoothness of a three-dimensional display unmanned aerial vehicle, wherein the method comprises the following steps: collecting track data of the unmanned aerial vehicle through a sensor; establishing a state space model describing the flight of the unmanned aerial vehicle based on a flight dynamics principle, wherein the state space model comprises a state equation and an observation equation; using a state space model to perform state estimation on the unmanned aerial vehicle, and predicting the state estimation and covariance matrix of the next state through a state equation to realize a prediction step of a Kalman filter, thereby reducing the influence of a sensor measurement error; the final state estimation and covariance matrix are calculated through the observation equation and the predicted state estimation, so that the updating step of the Kalman filter is realized, and the state of the unmanned aerial vehicle is estimated more accurately; and correcting the track data of the unmanned aerial vehicle in real time based on the final state estimation, generating a smooth track for presenting on the three-dimensional display, and improving the visual attractiveness, accuracy and fluency of the track.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle track processing, in particular to a method and a system for improving three-dimensional display unmanned aerial vehicle track smoothness.
Background
Unmanned aerial vehicle technology has gained huge development in each field to wide application in fields such as military affairs, civilian and scientific research. The smoothness of the flight path of the drone is critical to its successful performance of the task. In a three-dimensional display, the smoothed track not only enhances the visual appeal of the drone, but also provides more accurate and reliable information to operators, monitors, and system controllers.
However, the current method has some key problems in improving the track smoothness of the three-dimensional display of the unmanned aerial vehicle, such as: the problem of track discontinuity is not solved. As a result, the displayed unmanned aerial vehicle track often presents a convoluted or jagged path, which presents visual discomfort and confusion to the observer. Such discontinuities not only reduce the visual quality of the flight path, but may also adversely affect navigation and control of the drone. Some existing methods, while capable of smoothing the track as a whole, fail to adequately address localized curves or turns, resulting in unnatural saw teeth or angular changes in the curve of the unmanned aerial vehicle track that make the overall track appear insufficiently smooth and fluent.
Another challenge is computational cost and adaptability. Some methods may require complex computations and significant computational resources, which increase the difficulty and cost of applying these methods in real-time. Furthermore, some methods may lack adaptability to different flight environments and tasks, making them perform poorly in different situations.
Finally, sensor noise is also an important issue. In actual flight, the sensor data of the unmanned aerial vehicle may be disturbed by noise, and some methods fail to effectively process the noise, resulting in instability of track smoothness. Thus, the current approach requires more powerful noise suppression and data filtering strategies to improve track smoothness.
In view of the increasing use of unmanned aerial vehicles, particularly in the fields of logistics, agriculture, searching, rescue, etc., there is an urgent need to develop more efficient methods to solve the above problems. The unmanned aerial vehicle system management method and system can improve user experience of the unmanned aerial vehicle system, enhance performance and safety of the unmanned aerial vehicle system, and lay a solid foundation for future development of unmanned aerial vehicle technology.
Disclosure of Invention
Aiming at the problems of track discontinuity, calculation cost and adaptability of three-dimensional display of an unmanned aerial vehicle, instability of track smoothness and the like, a method and a system for improving the track smoothness of the three-dimensional display unmanned aerial vehicle are provided, the flight of the unmanned aerial vehicle is modeled as a linear system, final state estimation of the unmanned aerial vehicle is realized through a Kalman filter, track data of the unmanned aerial vehicle are corrected in real time, and a smooth track for displaying on the three-dimensional display is generated.
In order to achieve the above object, the present invention is realized by the following technical scheme:
a method of improving three-dimensional display unmanned aerial vehicle track smoothing, the method comprising:
collecting flight path data of the unmanned aerial vehicle through a sensor, wherein the flight path data comprise position, speed and gesture information of the unmanned aerial vehicle;
establishing a state space model describing the flight of the unmanned aerial vehicle based on a flight dynamics principle, wherein the state space model comprises a state equation and an observation equation;
the state space model is used for carrying out state estimation on the unmanned aerial vehicle, and the method specifically comprises the following steps: predicting a state estimation and covariance matrix of the next state through a state equation, so as to realize a prediction step of the Kalman filter; calculating a final state estimation and a covariance matrix through an observation equation and a predicted state estimation, and realizing the updating step of the Kalman filter;
and correcting the track data of the unmanned aerial vehicle in real time based on the final state estimation, and generating a smooth track for presenting on the three-dimensional display.
As a preferred embodiment of the present invention, the state space model is expressed as:
；
in the method, in the process of the invention,is an unmanned planekThree-dimensional position of time of day->Is an unmanned planekThree-dimensional speed of time,/->Representation ofkA state vector of time;
wherein the state vectorAt the position ofkThe evolution of time is represented by the following state equation:
；
in the method, in the process of the invention,representation ofkFinal state estimation at time +1,Ain the form of a state transition matrix,Bfor controlling the input matrix>For control input +.>Is process noise;
the measurement of the sensor is described by the observation equation, which is as follows:
；
in the method, in the process of the invention,is thatkThe measured value of the time sensor,Hfor observing matrix +.>To observe noise.
As a preferred embodiment of the present invention, the predicting step predicts the state estimation and covariance matrix of the next state using a state equation, the formula being:
；
；
in the method, in the process of the invention,representation ofkThe state vector of the moment of time,Ain the form of a state transition matrix,Bfor controlling the input matrix>Is a control input; />Representing predictionskState estimation at +1; />Representing predictionskCovariance matrix of state estimation at +1 time, +.>Is thatkCovariance matrix of state estimation at a moment in time,Qcovariance matrix of process noise;
the updating step calculates a final state estimate and covariance matrix using the observation equation and the predicted state estimate, the formula being:
；
；
；
in the method, in the process of the invention,in order for the kalman gain to be achieved,Hin order to observe the matrix,Rfor the covariance matrix of the observation noise, +.>Is thatkObservation value measured by sensor at +1 moment, < >>Representation ofkFinal state estimation at +1 time, +.>Is thatkCovariance matrix of final state estimate at time +1,Iis an identity matrix.
As a preferred solution of the present invention, the real-time correction of the track data of the unmanned aerial vehicle based on the final state estimation generates a smooth track for presenting on the three-dimensional display, specifically includes: from the estimatedkFinal state estimation at time +1Updating track data of the unmanned aerial vehicle in real time, wherein the track data comprises three-dimensional positions of the unmanned aerial vehicle>And three-dimensional speed->And performing difference and rendering, realizing a smooth track by minimizing the curvature or slope of the track at the connection point, and generating an unmanned aerial vehicle track for presenting on a three-dimensional display based on the smooth track.
A system for improving three-dimensional display drone track smoothing, the system comprising:
the data collection module is used for collecting track data of the unmanned aerial vehicle through the sensor;
the EKF algorithm module comprises a model building unit, a prediction unit and an updating unit;
the model building unit is used for building a state space model describing the unmanned aerial vehicle flight based on a dynamic principle;
the prediction unit is used for predicting the estimation and covariance matrix of the next state and realizing the prediction step of the Kalman filter;
the updating unit is used for calculating the final state estimation and covariance matrix and realizing the updating step of the Kalman filter;
the data correction module is used for correcting the track data of the unmanned aerial vehicle in real time based on the final state estimation, and comprises the three-dimensional position of the unmanned aerial vehicleAnd three-dimensional speed->；
And the smooth track module is used for carrying out difference and rendering on the corrected track data, realizing a smooth track by minimizing the curvature or slope of the track at the connecting point, and generating an unmanned plane track for presenting on three-dimensional display based on the smooth track.
As a preferred embodiment of the present invention, the state space model is expressed as:
；
in the method, in the process of the invention,is an unmanned planekThree-dimensional position of time of day->Is an unmanned planekThree-dimensional speed of time,/->Representation ofkA state vector of time;
wherein the state vectorAt the position ofkThe evolution of time is represented by the following state equation:
；
in the method, in the process of the invention,representation ofkFinal state estimation at time +1,Ain the form of a state transition matrix,Bin order to control the input matrix,for control input +.>Is process noise;
the measurement of the sensor is described by the observation equation, which is as follows:
；
in the method, in the process of the invention,is thatkThe measured value of the time sensor,Hfor observing matrix +.>To observe noise.
As a preferred embodiment of the present invention, the prediction unit predicts the state estimation and covariance matrix of the next state using a state equation, where:
；
；
in the method, in the process of the invention,representing predictionskState estimation at +1 time, +.>Representing predictionskCovariance matrix of state estimation at +1 time, +.>Is thatkCovariance matrix of state estimation of time instant, +.>Covariance matrix of process noise;
the updating unit calculates an updated state vector and covariance matrix by using an observation equation and a predicted state estimate, and the formula is as follows:
；
；
；
in the method, in the process of the invention,in order for the kalman gain to be achieved,Rfor the covariance matrix of the observation noise, +.>Is thatkObservation value measured by sensor at +1 moment, < >>A covariance matrix for the final state estimate at time k +1,Iis an identity matrix.
A storage medium containing computer executable instructions which when executed by a computer processor are for performing a method of improving three-dimensional display drone track smoothing as described above.
Compared with the prior art, the invention has the beneficial effects that: the unmanned aerial vehicle flight modeling is a linear system, the unmanned aerial vehicle state is estimated by using a Kalman filter, the unmanned aerial vehicle state is estimated by prediction and updating steps based on a flight dynamics principle and a mathematical model, and the influence of noise and instability measured by a sensor can be reduced, so that the visual effect of a flight path is improved, the smoothness and stability of the three-dimensional display unmanned aerial vehicle flight path can be remarkably improved, the unmanned aerial vehicle flight modeling method can be widely applied to unmanned aerial vehicle application, the visual effect and accuracy of flight data are improved, and the visual attraction, accuracy and fluency of the flight path are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flow chart of a method in an embodiment of the invention;
fig. 2 is a system modular block diagram in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which are obtained by a person skilled in the art based on the described embodiments of the invention, fall within the scope of protection of the invention.
As shown in fig. 1, in one embodiment of the present invention, a method for improving track smoothing of a three-dimensional display unmanned aerial vehicle is provided, which includes data acquisition, mathematical model establishment, kalman filtering implementation, real-time updating of track data, parameter adjustment and performance evaluation. By the method, the visual effect and the accuracy of the unmanned aerial vehicle track can be remarkably improved, so that better flight data performance can be obtained in various applications.
The method specifically comprises the following steps:
s1: collecting track data of the unmanned aerial vehicle through a sensor, wherein the track data comprise position, speed, gesture information and the like;
sensors such as GPS, inertial Measurement Units (IMUs), altimeters, etc. periodically measure the position, speed, etc. of the drone and transmit these flight path data to the control system.
S2: establishing a state space model describing the flight of the unmanned aerial vehicle based on a flight dynamics principle, wherein the state space model comprises a state equation and an observation equation;
in one embodiment, the state space model is expressed as:
；
in the method, in the process of the invention,is an unmanned planekThree-dimensional position of time of day->Is an unmanned planekThree-dimensional speed of time,/->Representation ofkA state vector of time;
wherein the state vectorAt the position ofkThe evolution of time can be expressed by the following state equation:
；
in the method, in the process of the invention,representation ofkFinal state estimation at time +1,Ain the form of a state transition matrix,Bin order to control the input matrix,for control input +.>Is process noise;
the measurement of the sensor is described by the observation equation, which is as follows:
；
in the method, in the process of the invention,is thatkThe measured value of the time sensor,Hfor observing matrix +.>To observe noise.
S3: the method for estimating the state of the unmanned aerial vehicle by using the state space model specifically comprises the following steps: predicting a state estimation and covariance matrix of the next state through a state equation, so as to realize a prediction step of the Kalman filter; calculating a final state estimation and a covariance matrix through an observation equation and a predicted state estimation, and realizing the updating step of the Kalman filter;
in a specific embodiment, the flow of step S3 includes:
s31: the predicting step predicts the state estimate and covariance matrix for the next state using the state equation, the formula:
；
；
in the method, in the process of the invention,representing predictionskState estimation at +1; />Representing pre-emphasisMeasured bykCovariance matrix of state estimation at +1 time, +.>Is thatkCovariance matrix of state estimation of time instant, +.>Covariance matrix of process noise;
s32: the updating step calculates a final state estimate and covariance matrix using the observation equation and the predicted state estimate, the formula being:
；
；
；
in the method, in the process of the invention,in order for the kalman gain to be achieved,Rfor the covariance matrix of the observation noise, +.>Is thatkObservation value measured by sensor at +1 moment, < >>Is thatkCovariance matrix of final state estimate at time +1,Iis an identity matrix.
State transition matrixAControl input matrixBObservation matrixHCovariance matrix of process noiseQCovariance matrix of observed noiseRWill be determined according to the specific drone and sensor configuration.
S4: correcting the track data of the unmanned aerial vehicle in real time based on the final state estimation, and generating a smooth track for presenting on a three-dimensional display;
in one embodiment, step S4 specifically includes: from the estimatedkFinal state estimation at time +1Updating track data of the unmanned aerial vehicle in real time, wherein the track data comprises three-dimensional positions of the unmanned aerial vehicle>And three-dimensional speedAnd performing difference and rendering, realizing a smooth track by minimizing the curvature or slope of the track at the connection point, and generating the unmanned aerial vehicle track for presenting on the three-dimensional display based on the smooth track.
Once the update step of the kalman filter is completed, the final state estimate can be usedTo update the flight path data of the drone in real time, which updated data may be used in a three-dimensional display or navigation system to provide smoother and accurate flight path information.
In practical applications, we need to adjust parameters in the model according to specific unmanned aerial vehicle and sensor configurations, which may require offline or online debugging according to actual flight data; meanwhile, performance evaluation is needed to ensure that the Kalman filter can meet the requirements of precision and stability.
As shown in fig. 2, another embodiment of the present invention provides a system for improving track smoothness of a three-dimensional display unmanned aerial vehicle, including:
the data collection module is used for collecting track data of the unmanned aerial vehicle through the sensor;
the EKF algorithm module comprises a model building unit, a prediction unit and an updating unit;
the model building unit is used for building a state space model describing the unmanned aerial vehicle flight based on the action mechanics principle;
the prediction unit is used for predicting the estimation and covariance matrix of the next state and realizing the prediction step of the Kalman filter;
the updating unit is used for calculating the final state estimation and covariance matrix and realizing the updating step of the Kalman filter;
the data correction module is used for correcting the track data of the unmanned aerial vehicle in real time based on the final state estimation, and comprises the three-dimensional position of the unmanned aerial vehicleAnd three-dimensional speed->；
And the smooth track module is used for carrying out difference and rendering on the corrected track data, realizing a smooth track by minimizing the curvature or slope of the track at the connecting point, and generating an unmanned plane track for presenting on three-dimensional display based on the smooth track.
In one embodiment, the state space model is expressed as:
；
in the method, in the process of the invention,is an unmanned planekThree-dimensional position of time of day->Is an unmanned planekThree-dimensional speed of time,/->Representation ofkA state vector of time;
wherein the state vectorAt the position ofkThe evolution of time is represented by the following state equation:
；
in the method, in the process of the invention,representation ofkFinal state estimation at time +1,Ain the form of a state transition matrix,Bin order to control the input matrix,for control input +.>Is process noise;
the measurement of the sensor is described by the observation equation, which is as follows:
；
in the method, in the process of the invention,is thatkThe measured value of the time sensor,Hfor observing matrix +.>To observe noise.
In one embodiment, the prediction unit predicts the state estimate and covariance matrix for the next state using the state equation, as:
；
；
in the method, in the process of the invention,representing predictionskState estimation at +1 time, +.>Representing predictionskCovariance matrix of state estimation at +1 time, +.>Is thatkCovariance matrix of state estimation of time instant, +.>Covariance matrix of process noise;
the updating unit calculates an updated state vector and covariance matrix by using an observation equation and a predicted state estimate, wherein the formula is as follows:
；
；
；
in the method, in the process of the invention,in order for the kalman gain to be achieved,Rfor the covariance matrix of the observation noise, +.>Is thatkObservation value measured by sensor at +1 moment, < >>Covariance matrix estimated for final state at time k+1,/>Is an identity matrix.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, may be embodied in whole or in part in the form of a computer program product comprising one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. Computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. The storage medium may be a read-only memory, a magnetic or optical disk, or the like. Accordingly, embodiments of the present invention also provide a storage medium containing computer executable instructions which, when executed by a computer processor, are configured to perform a method of improving three-dimensional display drone track smoothing as described above.
In summary, the unmanned aerial vehicle flight modeling is a linear system, the unmanned aerial vehicle state is estimated by using the Kalman filter, the unmanned aerial vehicle state is estimated by the prediction and updating steps based on the flight dynamics principle and the mathematical model, and the influence of noise and instability measured by the sensor can be reduced, so that the visual effect of the flight path is improved, the smoothness and stability of the three-dimensional display unmanned aerial vehicle flight path can be obviously improved, the unmanned aerial vehicle flight modeling method can be widely applied to unmanned aerial vehicle application, the visual effect and accuracy of flight data are improved, and the visual attraction, accuracy and fluency of the flight path are improved.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think of various changes or substitutions within the technical scope of the present application, and these should be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A method of improving track smoothness of a three-dimensional display unmanned aerial vehicle, the method comprising:
collecting flight path data of the unmanned aerial vehicle through a sensor, wherein the flight path data comprise position, speed and gesture information of the unmanned aerial vehicle;
establishing a state space model describing the flight of the unmanned aerial vehicle based on a flight dynamics principle, wherein the state space model comprises a state equation and an observation equation;
the state space model is used for carrying out state estimation on the unmanned aerial vehicle, and the method specifically comprises the following steps: predicting a state estimation and covariance matrix of the next state through a state equation, so as to realize a prediction step of the Kalman filter; calculating a final state estimation and a covariance matrix through an observation equation and a predicted state estimation, and realizing the updating step of the Kalman filter;
and correcting the track data of the unmanned aerial vehicle in real time based on the final state estimation, and generating a smooth track for presenting on the three-dimensional display.
2. A method of improving three-dimensional display drone track smoothing as claimed in claim 1, wherein the state space model is expressed as:
；
in the method, in the process of the invention,is an unmanned planekThree-dimensional position of time of day->Is an unmanned planekThree-dimensional speed of time,/->Representation ofkA state vector of time;
wherein the state vectorAt the position ofkThe evolution of time is represented by the following state equation:
；
in the method, in the process of the invention,representation ofkFinal state estimation at time +1,Ain the form of a state transition matrix,Bfor controlling the input matrix>For control input +.>Is process noise;
the measurement of the sensor is described by the observation equation, which is as follows:
；
in the method, in the process of the invention,is thatkThe measured value of the time sensor,Hfor observing matrix +.>To observe noise.
3. The method of claim 1, wherein the predicting step predicts the state estimate and covariance matrix for the next state using a state equation, the formula being:
；
；
in the method, in the process of the invention,representation ofkThe state vector of the moment of time,Ain the form of a state transition matrix,Bfor controlling the input matrix>Is a control input;representing predictionskState estimation at +1; />Representing predictionskCovariance matrix of state estimation at +1 time, +.>Is thatkCovariance matrix of state estimation of time instant, +.>Covariance matrix of process noise;
the updating step calculates a final state estimate and covariance matrix using the observation equation and the predicted state estimate, the formula being:
；
；
；
in the method, in the process of the invention,in order for the kalman gain to be achieved,Hin order to observe the matrix,Rfor the covariance matrix of the observation noise, +.>Is thatkObservation value measured by sensor at +1 moment, < >>Representation ofkFinal state estimation at +1 time, +.>Is thatkCovariance matrix of final state estimate at time +1,Iis an identity matrix.
4. The method for improving the track smoothness of a three-dimensional display unmanned aerial vehicle according to claim 1, wherein the step of correcting the track data of the unmanned aerial vehicle in real time based on the final state estimation to generate a smooth track for presentation on the three-dimensional display specifically comprises: from the estimatedkFinal state estimation at time +1Updating track data of the unmanned aerial vehicle in real time, wherein the track data comprises three-dimensional positions of the unmanned aerial vehicle>And three-dimensional speed->And performing difference and rendering, realizing a smooth track by minimizing the curvature or slope of the track at the connection point, and generating an unmanned aerial vehicle track for presenting on a three-dimensional display based on the smooth track.
5. A system for improving track smoothness of a three-dimensional display drone, the system comprising:
the data collection module is used for collecting track data of the unmanned aerial vehicle through the sensor;
the EKF algorithm module comprises a model building unit, a prediction unit and an updating unit;
the model building unit is used for building a state space model describing the unmanned aerial vehicle flight based on a dynamic principle;
the prediction unit is used for predicting the estimation and covariance matrix of the next state and realizing the prediction step of the Kalman filter;
the updating unit is used for calculating the final state estimation and covariance matrix and realizing the updating step of the Kalman filter;
the data correction module is used for correcting the track data of the unmanned aerial vehicle in real time based on the final state estimation, and comprises the three-dimensional position of the unmanned aerial vehicleAnd three-dimensional speed->；
And the smooth track module is used for carrying out difference and rendering on the corrected track data, realizing a smooth track by minimizing the curvature or slope of the track at the connecting point, and generating an unmanned plane track for presenting on three-dimensional display based on the smooth track.
6. The system for improving track smoothing of a three-dimensional display drone of claim 5, wherein the state space model is represented as:
；
in the method, in the process of the invention,is an unmanned planekThree-dimensional position of time of day->Is an unmanned planekThree-dimensional speed of time,/->Representation ofkA state vector of time;
wherein the state vectorAt the position ofkThe evolution of time is represented by the following state equation:
；
in the method, in the process of the invention,representation ofkFinal state estimation at time +1,Ain the form of a state transition matrix,Bfor controlling the input matrix>For control input +.>Is process noise;
the measurement of the sensor is described by the observation equation, which is as follows:
；
in the method, in the process of the invention,is thatkThe measured value of the time sensor,Hfor observing matrix +.>To observe noise.
7. The system for improving track smoothing of a three-dimensional display drone of claim 6, wherein the prediction unit predicts the state estimate and covariance matrix for the next state using a state equation, the formula being:
；
；
in the method, in the process of the invention,representing predictionskState estimation at +1 time, +.>Representing predictionskCovariance matrix of state estimation at +1 time, +.>Is thatkCovariance matrix of state estimation of time instant, +.>Covariance matrix of process noise;
the updating unit calculates an updated state vector and covariance matrix by using an observation equation and a predicted state estimate, and the formula is as follows:
；
；
；
in the method, in the process of the invention,in order for the kalman gain to be achieved,Rfor the covariance matrix of the observation noise, +.>Is thatkObservation value measured by sensor at +1 moment, < >>A covariance matrix for the final state estimate at time k +1,Iis an identity matrix.
8. A storage medium containing computer executable instructions which, when executed by a computer processor, are for performing a method of improving track smoothing of a three-dimensional display drone as claimed in any one of claims 1 to 4.
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