Unmanned aerial vehicle course determination method and device based on GNSS single antenna

Abstract

The invention discloses an unmanned aerial vehicle course determination method and device based on a GNSS (Global Navigation Satellite System) single antenna. The method comprises the following steps: performing data fusion processing on first acceleration information and first angular rate information of an inertial measurement unit at the current moment, and determining a target gyroscope zero offset value of the unmanned aerial vehicle; and acquiring first speed information of the GNSS single antenna at the current moment, and performing data fusion processing on the first speed information, the zero offset value of the target gyroscope, the first acceleration information and the first angular rate information to determine a target course angle of the unmanned aerial vehicle at the next moment so as to adjust the course of the unmanned aerial vehicle according to the target course angle. According to the method, the zero offset value of the target gyroscope of the unmanned aerial vehicle is accurately obtained by fusing the information of the inertial measurement unit of the unmanned aerial vehicle, and the information of the GNSS single antenna of the unmanned aerial vehicle is further adjusted and fused through the zero offset value of the target gyroscope and the information of the inertial measurement unit, so that the course angle of the unmanned aerial vehicle is accurately estimated. The accuracy and the stability of generating the course of the unmanned aerial vehicle are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and particularly to a method and device for determining the heading of an unmanned aerial vehicle based on a GNSS single antenna. Background Art

[0002] Unmanned aerial vehicles have many unique advantages such as low cost, low loss, good safety, and reusability. The applications of unmanned aerial vehicles are becoming more and more extensive. Therefore, it is very important to ensure the accuracy of the heading of the unmanned aerial vehicle during flight.

[0003] Currently, for small unmanned aerial vehicles, heading determination generally uses RTK dual-antenna orientation and electronic magnetic compasses. RTK dual-antenna orientation uses the precise relative position of two GNSS antennas to calculate the heading angle through the relationship between the baseline vector and the earth coordinate system. It requires RTK technology to provide a high-precision relative position. The longer the baseline, the higher the orientation accuracy. Therefore, RTK dual-antenna orientation requires an increase in the design size of the unmanned aerial vehicle and has a relatively high hardware cost. Further, the electronic magnetic compass uses the direction of the geomagnetic field to measure the components of the earth's magnetic field in different directions to calculate the heading angle, relying on the intensity and direction of the geomagnetic field. The built-in magnetometer can directly measure the magnetic field components, but the electronic magnetic compass is easily affected by surrounding metal objects, current, or strong magnetic fields, resulting in inaccurate heading determination. Therefore, it is very important to reduce the cost while ensuring the heading accuracy of the unmanned aerial vehicle. Summary of the Invention

[0004] The present invention provides a method and device for determining the heading of an unmanned aerial vehicle based on a GNSS single antenna to improve the accuracy and stability of generating the heading of the unmanned aerial vehicle.

[0005] According to one aspect of the present invention, there is provided a method for determining the heading of an unmanned aerial vehicle based on a GNSS single antenna, characterized in that the unmanned aerial vehicle is configured with a GNSS single antenna and an inertial measurement unit, and the method includes:

[0006] Obtaining the first acceleration information and the first angular rate information of the inertial measurement unit at the current moment, and determining the target gyroscope zero bias value of the unmanned aerial vehicle by performing data fusion processing on the first acceleration information and the first angular rate information;

[0007] Obtaining the first velocity information of the GNSS single antenna at the current moment, and determining the target heading angle of the unmanned aerial vehicle at the next moment by performing data fusion processing on the first velocity information, the target gyroscope zero bias value, the first acceleration information, and the first angular rate information, so as to adjust the heading of the unmanned aerial vehicle according to the target heading angle; the first velocity information is the velocity in a preset reference coordinate system.

[0008] According to another aspect of the present invention, there is provided a UAV heading determination device based on a GNSS single antenna, characterized in that the UAV is configured with a GNSS single antenna and an inertial measurement unit, and the device includes:

[0009] A first data fusion module, configured to obtain first acceleration information and first angular rate information of the inertial measurement unit at the current moment, and determine a target gyroscope zero bias value of the UAV by performing data fusion processing on the first acceleration information and the first angular rate information;

[0010] A second data fusion module, configured to obtain first velocity information of the GNSS single antenna at the current moment, and determine a target heading angle of the UAV at the next moment by performing data fusion processing on the first velocity information, the target gyroscope zero bias value, the first acceleration information, and the first angular rate information, so as to adjust the heading of the UAV according to the target heading angle; the first velocity information is the velocity in a preset reference coordinate system.

[0011] According to another aspect of the present invention, there is provided an electronic device, and the electronic device includes:

[0012] At least one processor; and

[0013] A memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the UAV heading determination method based on a GNSS single antenna according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, there is provided a computer-readable storage medium, and the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the UAV heading determination method based on a GNSS single antenna according to any embodiment of the present invention is implemented.

[0016] The technical solution of the embodiment of the present invention realizes the accurate determination of the target gyro zero bias value of the unmanned aerial vehicle (UAV) by obtaining the first acceleration information and the first angular rate information of the inertial measurement unit at the current moment, and through data fusion processing of the first acceleration information and the first angular rate information, so as to facilitate the subsequent adjustment of the heading angle by using the gyro zero bias; obtaining the first velocity information of the GNSS single antenna at the current moment, where the first velocity information is the velocity in the north-east-earth reference coordinate system, and the GNSS single antenna has higher measurement accuracy for the velocity information. Further, through data fusion processing of the first velocity information, the target gyro zero bias value, the first acceleration information and the first angular rate information, the addition of the target gyro zero bias value avoids inaccurate determination of the heading angle caused by the gyro zero bias, and by combining the first velocity information, the first acceleration information and the first angular rate information for data fusion processing, the target heading angle of the UAV at the next moment can be determined more accurately, so as to adjust the heading of the UAV according to the target heading angle, and improve the accuracy and stability of generating the UAV heading.

[0017] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Brief Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0019] Figure 1 is a flowchart of a method for determining the heading of a UAV based on a GNSS single antenna according to an embodiment of the present invention;

[0020] Figure 2 is a flowchart of another method for determining the heading of a UAV based on a GNSS single antenna according to an embodiment of the present invention;

[0021] Figure 3 is a flowchart of another method for determining the heading of a UAV based on a GNSS single antenna according to an embodiment of the present invention;

[0022] Figure 4 is a schematic structural diagram of a device for determining the heading of a UAV based on a GNSS single antenna according to an embodiment of the present invention;

[0023] Figure 5It is a schematic structural diagram of an electronic device for implementing the method for determining the heading of an unmanned aerial vehicle based on a GNSS single antenna according to an embodiment of the present invention. Detailed implementation manners

[0024] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0026] Embodiment 1

[0027] Figure 1 It is a flowchart of a method for an unmanned aerial vehicle based on a GNSS single antenna according to an embodiment of the present invention. This embodiment is applicable to the situation of estimating the heading of an unmanned aerial vehicle. This method can be executed by a device for determining the heading of an unmanned aerial vehicle based on a GNSS single antenna. The device for determining the heading of an unmanned aerial vehicle based on a GNSS single antenna can be implemented in the form of hardware and / or software, and the device for determining the heading of an unmanned aerial vehicle based on a GNSS single antenna can be configured in any electronic device with network communication functions.

[0028] The unmanned aerial vehicle of the present invention is configured with a GNSS single antenna and an inertial measurement unit. The GNSS single antenna is an antenna device for receiving signals of the Global Navigation Satellite System (GNSS). The Inertial Measurement Unit (IMU) is a device that can measure acceleration and angular velocity, and then calculate the attitude and motion state. The inertial measurement unit consists of an accelerometer and a gyroscope. The accelerometer is a sensor for measuring the acceleration of an object, and the gyroscope is used to measure the angular velocity around an axis.

[0029] AsFigure 1 As shown in the figure, the method for determining the heading of an unmanned aerial vehicle (UAV) based on a GNSS single antenna includes the following process:

[0030] S110. Obtain the first acceleration information and the first angular rate information of the inertial measurement unit at the current moment, and determine the target gyroscope zero bias value of the UAV by performing data fusion processing on the first acceleration information and the first angular rate information.

[0031] Among them, the first acceleration information is the acceleration measurement value of the UAV at the current moment obtained by the accelerometer of the inertial measurement unit. The first angular rate information is the gyroscope measurement value of the UAV at the current moment obtained by the gyroscope of the inertial measurement unit, and the gyroscope measurement value is the triaxial measurement value of the gyroscope.

[0032] The data fusion processing of the present invention refers to comprehensively processing data from various different types of sensors and other relevant data sources carried by the UAV to obtain more accurate, more complete, and more reliable information, so as to support the UAV to perform more effective operations in aspects such as flight control, environmental perception, target recognition, and mission decision-making. The data fusion processing of this application is to perform fusion processing on the first acceleration information and the first angular rate information of the inertial measurement unit of the UAV to accurately obtain the target gyroscope zero bias value of the UAV.

[0033] The total dimension of the data for the data fusion processing of the present invention is seven-dimensional, that is, the total dimension of the first acceleration information and the first angular rate information is 7D, so as to greatly reduce the computational complexity, require less CPU resources to be configured, and be suitable for the application of low-cost embedded single-chip microcontrollers.

[0034] The methods of data fusion processing may include, but are not limited to, complementary filtering method, neural network fusion method, and Kalman filtering algorithm.

[0035] 1) The complementary filtering method may be a method of performing data fusion by utilizing the complementary characteristics of different sensors. For example, the accelerometer in the inertial measurement unit can measure the acceleration of the UAV, but there are problems of noise and drift; while the gyroscope in the inertial measurement unit can measure the angular velocity of the UAV, and drift will also occur after long-term use. The complementary filter fuses the data of the two according to the characteristics that the accelerometer is more accurate in the low-frequency band and the gyroscope is more accurate in the high-frequency band, and processes the data of the accelerometer and the gyroscope through a low-pass filter and a high-pass filter respectively to obtain the target gyroscope zero bias value of the UAV.

[0036] 2) The neural network fusion method means that the neural network has a powerful non - linear mapping ability and learning ability, and can automatically learn the complex relationships between different data sources. In the data fusion of unmanned aerial vehicles (UAVs), a multi - layer neural network can be constructed. The data from different sensors are used as the nodes of the input layer. Through a series of non - linear transformations and calculations in the hidden layer, the fused result is obtained in the output layer. The neural network is trained with a large amount of training data so that it can adapt to different flight scenarios and data patterns.

[0037] 3) The Kalman filter algorithm is an algorithm that uses the linear system state equation to optimally estimate the system state through the system input - output observation data.

[0038] S120. Obtain the first velocity information of the GNSS single antenna at the current moment. By performing data fusion processing on the first velocity information, the target gyro zero - bias value, the first acceleration information, and the first angular rate information, determine the target heading angle of the UAV at the next moment, so as to adjust the heading of the UAV according to the target heading angle; the first velocity information is the velocity in the preset reference coordinate system.

[0039] Among them, the first velocity information can be the velocity measurement value of the UAV obtained by the GNSS single antenna at the current moment. The total dimension of the data for data fusion processing is seven - dimensional, that is, the total dimension of the first velocity information, the target gyro zero - bias value, the first acceleration information, and the first angular rate information is seven - dimensional. This can greatly reduce the computational complexity, requires less CPU resources to be configured, and is suitable for the application of low - cost embedded single - chip microcontrollers. The methods of data fusion processing can include but are not limited to complementary filtering method, neural network fusion method, and Kalman filter algorithm. Through data fusion processing, the UAV can make more full use of the acquired data resources, improve its own performance and intelligence level, and better adapt to complex and changeable flight environments and diverse mission requirements.

[0040] Among them, the preset reference coordinate system can be understood as a three - dimensional space coordinate system, and can be any reference coordinate system related to the geodetic coordinate system; the preset reference coordinate system is preferably the North - East - Down (NED) reference coordinate system. The NED reference coordinate system is a geographical coordinate system commonly used to describe local positions and attitudes. The axis definitions of the North - East - Down (NED) reference coordinate system: x - axis: points to the north, that is, along the meridian direction of the earth's surface towards the north pole, and is used to represent the northward displacement in the horizontal direction. y - axis: points to the east, perpendicular to the x - axis and along the latitude direction of the earth's surface, and is used to describe the eastward displacement in the horizontal direction. z - axis: points downward, perpendicular to the earth's surface downward (towards the center of the earth), and is used to represent the displacement in the vertical direction, that is, the change in height.

[0041] The technical solution of the embodiment of the present invention obtains the first acceleration information and the first angular rate information of the inertial measurement unit at the current moment, and through data fusion processing of the first acceleration information and the first angular rate information, accurately determines the target gyroscope zero bias value of the drone, so as to facilitate subsequent adjustment of the heading angle using the gyroscope zero bias; obtains the first velocity information of the GNSS single antenna at the current moment, and the first velocity information is the velocity in the north-east-earth reference coordinate system. The GNSS single antenna has higher measurement accuracy for velocity information. Further, through data fusion processing of the first velocity information, the target gyroscope zero bias value, the first acceleration information, and the first angular rate information, the addition of the target gyroscope zero bias value avoids inaccurate determination of the heading angle caused by the gyroscope zero bias, and combines the first velocity information, the first acceleration information, and the first angular rate information for data fusion processing, which can more accurately determine the target heading angle of the drone at the next moment, so as to adjust the heading of the drone according to the target heading angle, improving the accuracy and stability of generating the drone heading.

[0042] Embodiment 2

[0043] Figure 2 It is a flowchart of another method for determining the heading of a drone based on a GNSS single antenna provided by an embodiment of the present invention. The technical solution of this embodiment further optimizes the process of S110 in the foregoing embodiment on the basis of the above embodiment. Optionally, an error-state Kalman filter algorithm is used to fuse the first acceleration information and the first angular rate information of the inertial measurement unit to obtain the target gyroscope zero bias value of the drone. This embodiment can be combined with each optional solution in one or more of the above embodiments. As Figure 2 shown, the method for determining the heading of a drone based on a GNSS single antenna of the present invention includes:

[0044] S210. Obtain the first gyroscope zero bias of the drone at the current moment, and determine the first nominal state of the drone at the current moment according to the first angular rate information and the first gyroscope zero bias.

[0045] Wherein, the first nominal state is the state value predicted for the state of the drone in an ideal state. That is, the first nominal state is the gyroscope zero bias value and the angular rate information of the drone in an ideal state.

[0046] Specifically, the first gyroscope zero bias may be the gyroscope zero bias determined at the previous moment. Determine the first quaternion of the drone at the current moment according to the first angular rate information, and construct the first nominal state of the drone at the current moment according to the first quaternion and the first gyroscope zero bias.

[0047] Correspondingly, determine the first quaternion q of the drone at the current moment according to the first angular rate information k1 , which can be expressed by the following formula:

[0048]

[0049] Among them, ; ω x 、ω y 、ω z are the first angular rate information, that is, the three-axis measurement values of the gyroscope. α is the rotation angle generated by the three-axis rotation of the gyroscope within dt time. .

[0050] The first nominal state x k1 can be expressed as: . is the first gyroscope bias.

[0051] S220. Determine the first error state of the UAV at the current moment, and determine the first covariance matrix of the first error state. The first error state is a zero matrix with six rows and one column.

[0052] Specifically, the first error state is the predicted value of the error state, which can be the difference between the true state value of the UAV at the current moment and the first nominal state. That is, the first error state can be expressed as:

[0053]

[0054] Among them, is the Euler angle change amount generated by the change ∆q of the quaternion within dt time; is the change of the gyroscope bias.

[0055] However, in order to ensure the accuracy of the subsequent update of the error state and the nominal state in this application, the first error state is reset in this application and reset to a zero matrix with six rows and one column, that is .

[0056] Furthermore, determining the first covariance matrix of the first error state includes steps A1 - A2:

[0057] Step A1. Obtain the first process noise of the UAV at the current moment. The first process noise is determined by the gyroscope measurement noise and the gyroscope bias noise.

[0058] Among them, the first process noise can be expressed as: ; is the gyroscope measurement noise, is the gyroscope bias noise.

[0059] Step A2. Determine the first state transition matrix of the first error state, and determine the first covariance matrix of the first error state according to the first state transition matrix and the first process noise.

[0060] Specifically, the first state transition matrix can be determined according to the state transition matrix of , and the state transition matrix from to . The first state transition matrix can be expressed as:

[0061] ;

[0062]

[0063] Wherein, is the state transition matrix of , is the state transition matrix from to , is the identity matrix, ω x , ω y , ω z are the first angular rate information, that is, the triaxial measurement values of the gyroscope. α is the rotation angle generated by the triaxial rotation of the gyroscope within dt time.

[0064] Further, according to the first state transition matrix and the first process noise, the first covariance matrix of the first error state is determined. The first covariance matrix can be expressed as:

[0065] .

[0066] S230. Determine the first Kalman gain according to the first covariance matrix, and determine the second error state according to the first Kalman gain and the first acceleration information.

[0067] Specifically, obtain the first measurement noise of the UAV at the current moment. The first measurement noise is determined by the measurement noise of the first acceleration information of the inertial measurement unit; determine the first Kalman gain according to the first measurement noise and the first covariance matrix.

[0068] Wherein, the first measurement noise R 1 can be expressed as , and the first Kalman gain can be expressed as:

[0069]

[0070] Wherein, H 1 is the observation matrix of the acceleration observation relative to the error state, H x1 is the observation matrix of the acceleration observation relative to the nominal state, and X x1 is the observation matrix of the nominal state relative to the error state.

[0071] Further, based on the first Kalman gain and the first acceleration information z k1 determine the second error state, where the second error state can be expressed as:

[0072] .

[0073] S240. Update the first nominal state according to the second error state to determine the target gyroscope zero bias value of the UAV.

[0074] Specifically, the change in the gyroscope zero bias of the second error state is used to update the first gyroscope zero bias of the first nominal state to determine the target gyroscope zero bias value of the UAV. The target gyroscope zero bias value can be expressed as: . .

[0075] S250. Obtain the first velocity information of the GNSS single antenna at the current moment. By performing data fusion processing on the first velocity information, the target gyroscope zero bias value, the first acceleration information, and the first angular rate information, determine the target heading angle of the UAV at the next moment, so as to adjust the heading of the UAV according to the target heading angle; the first velocity information is the velocity in the north-east-down reference coordinate system.

[0076] The technical solution of the embodiment of the present invention obtains the first gyroscope zero bias of the UAV at the current moment, determines the first nominal state of the UAV at the current moment according to the first angular rate information and the first gyroscope zero bias; further determines the first error state of the UAV at the current moment, and determines the first covariance matrix of the first error state, where the first error state is a zero matrix with six rows and one column; then determines the first Kalman gain according to the first covariance matrix, and determines the second error state according to the first Kalman gain and the first acceleration information; finally, updates the first nominal state according to the second error state to determine the target gyroscope zero bias value of the UAV, realizing the accurate determination of the gyroscope zero bias, so as to facilitate the subsequent adjustment of the heading angle using the gyroscope zero bias; further, by performing data fusion processing on the obtained first velocity information, target gyroscope zero bias value, first acceleration information, and first angular rate information at the current moment, the addition of the target gyroscope zero bias value avoids inaccurate determination of the heading angle caused by the gyroscope zero bias, and combines the first velocity information, first acceleration information, and first angular rate information for data fusion processing, which can more accurately determine the target heading angle of the UAV at the next moment, so as to adjust the heading of the UAV according to the target heading angle, improving the accuracy and stability of generating the UAV heading.

[0077] Embodiment III

[0078] Figure 3The figure is a flowchart of another method for determining the heading of an unmanned aerial vehicle (UAV) based on a GNSS single antenna provided by an embodiment of the present invention. The technical solution of this embodiment further optimizes the process of S120 in the foregoing embodiment. Optionally, an error state Kalman filter algorithm is used to perform data fusion processing on the first velocity information, the target gyroscope zero bias value, the first acceleration information, and the first angular rate information to obtain the target heading angle of the UAV at the next moment, and the total dimension of the data for the data fusion processing is seven-dimensional. This embodiment can be combined with each optional solution in one or more of the foregoing embodiments. As Figure 3 shown, the method for determining the heading of a UAV based on a GNSS single antenna according to the present invention includes:

[0079] S310. Obtain the first acceleration information and the first angular rate information of the inertial measurement unit at the current moment, and determine the target gyroscope zero bias value of the UAV by performing data fusion processing on the first acceleration information and the first angular rate information.

[0080] S320. Determine the second nominal state of the UAV at the current moment according to the target gyroscope zero bias value, the first angular rate information, and the first velocity information.

[0081] Wherein, the second nominal state is the angular rate information and the velocity information of the UAV in an ideal state.

[0082] Specifically, determining the second nominal state of the UAV at the current moment according to the target gyroscope zero bias value, the first angular rate information, and the first velocity information includes steps B1 - B3:

[0083] Step B1. Determine the second angular rate information according to the target gyroscope zero bias value and the first angular rate information.

[0084] Wherein, the target gyroscope zero bias value ; ω x , ω y , ω z are the first angular rate information, and the second angular rate information .

[0085] Step B2. Determine the second velocity information of the UAV according to the first velocity information and the first acceleration information; the second velocity information is three-dimensional information.

[0086] Wherein, the second velocity information is three-dimensional information, that is, the velocity information in three directions in the north-east-down reference coordinate system. The second velocity information , the second velocity information can be expressed as:

[0087]

[0088] Wherein, is the first acceleration information, is the first acceleration information, and g is the acceleration due to gravity, the rotation matrix from the north-east local reference coordinate system to the body coordinate system.

[0089] Step B3: Construct the second nominal state of the UAV at the current moment according to the second angular rate information and the second velocity information.

[0090] Specifically, determine the second quaternion of the second nominal state according to the second angular rate information, and further construct the second nominal state of the UAV at the current moment according to the second quaternion and the second velocity information.

[0091] Among them, the second quaternion is four-dimensional information, and the second quaternion , and the second quaternion q k2 can be expressed as ;

[0092] ;

[0093] Among them, ω x , ω y , ω z are the first angular rate information, that is, the three-axis measurement values of the gyroscope. α is the rotation angle generated by the three-axis rotation of the gyroscope within the dt time.

[0094] Correspondingly, the second nominal state x k2 can be expressed as: .

[0095] S330: Determine the second error state of the UAV at the current moment, and determine the second covariance matrix of the second error state. The second error state is a six-row and one-column zero matrix.

[0096] Specifically, the second error state is the predicted value of the error state, and can be the difference between the true state value of the UAV at the current moment and the second nominal state, that is, the second error state can be expressed as:

[0097]

[0098] Among them, ∆q is the change in the Euler angle caused by the change in the quaternion; is the change in the second velocity information.

[0099] However, for the accuracy of subsequent updates of the error state and the nominal state in this application, the first error state in this application is reset and reset to a six-row and one-column zero matrix, that is .

[0100] Further, determining the second covariance matrix of the second error state includes steps C1 - C2:

[0101] Step C1: Obtain the second process noise of the UAV at the current moment. The second process noise is determined by the measurement noise of the gyroscope and the measurement noise of the first acceleration information of the inertial measurement unit.

[0102] Among them, the second process noise can be expressed as: ; is the measurement noise of the gyroscope, is the measurement noise of the first acceleration information, that is, the measurement noise of the accelerometer.

[0103] Step C2: Determine the second state transition matrix of the second error state, and determine the second covariance matrix of the second error state according to the second state transition matrix and the second process noise.

[0104] Specifically, the second state transition matrix can be determined according to 's state transition matrix and 's state transition matrix. The second state transition matrix can be expressed as:

[0105]

[0106] Among them, F R is 's state transition matrix, F V is 's state transition matrix, is the identity matrix, is the first acceleration information.

[0107] Further, determine the second covariance matrix of the second error state according to the second state transition matrix and the second process noise. The second covariance matrix can be expressed as:

[0108] .

[0109] S340: Obtain the second measurement noise of the UAV at the current moment, and determine the second Kalman gain according to the second measurement noise and the second covariance matrix; the second measurement noise is determined by the measurement noise of the first velocity information of the GNSS single antenna.

[0110] Among them, the second measurement noise R 2 can be expressed as , is the measurement noise of the first velocity information of the GNSS single antenna.

[0111] The second Kalman gain can be expressed as:

[0112]

[0113] Among them, H 2 is the observation matrix of the speed observation relative to the error state, and H x2 is the observation matrix of the speed observation relative to the nominal state, and X x2 is the observation matrix of the nominal state relative to the error state.

[0114] S350. Determine the second error state according to the second Kalman gain and the first speed information.

[0115] Among them, the second error state can be expressed as:

[0116] .

[0117] Among them, is the first speed information.

[0118] S360. Update the second nominal state according to the second error state to determine the target heading angle of the UAV at the next moment.

[0119] Specifically, the change ∆q of the second quaternion of the second error state updates the q of the second nominal state k2 to determine the target heading angle of the UAV at the next moment. The target heading angle q k can be expressed as: .

[0120] The technical solution of the embodiment of the present invention obtains the first acceleration information and the first angular rate information of the inertial measurement unit at the current moment, and through data fusion processing of the first acceleration information and the first angular rate information, accurately determines the target gyroscope zero bias value of the drone, so as to facilitate subsequent adjustment of the heading angle using the gyroscope zero bias; further, according to the target gyroscope zero bias value, the first angular rate information and the first velocity information, determines the second nominal state of the drone at the current moment. The addition of the target gyroscope zero bias value avoids inaccurate determination of the heading angle caused by the gyroscope zero bias. Then, determines the second error state of the drone at the current moment, and determines the second covariance matrix of the second error state, obtains the second measurement noise of the drone at the current moment, and determines the second Kalman gain according to the second measurement noise and the second covariance matrix; determines the second error state according to the second Kalman gain and the first velocity information, and finally updates the second nominal state according to the second error state to determine the target heading angle of the drone at the next moment. That is, the embodiment of the present invention also combines the first velocity information, the first acceleration information and the first angular rate information for data fusion processing, which can more accurately determine the target heading angle of the drone at the next moment, so as to adjust the heading of the drone according to the target heading angle, improving the accuracy and stability of generating the drone heading.

[0121] Embodiment 4

[0122] Figure 4 FIG. shows a schematic structural diagram of a drone heading determination device based on a GNSS single antenna provided by an embodiment of the present invention. This embodiment is applicable to the situation of predicting the heading of a drone. The drone heading determination device based on a GNSS single antenna can be implemented in the form of hardware and / or software, and can be configured in any electronic device with network communication functions. The drone of the present invention is configured with a GNSS single antenna and an inertial measurement unit, as Figure 4 shown, the drone heading determination device based on a GNSS single antenna includes:

[0123] A first data fusion module 410, configured to obtain the first acceleration information and the first angular rate information of the inertial measurement unit at the current moment, and determine the target gyroscope zero bias value of the drone through data fusion processing of the first acceleration information and the first angular rate information;

[0124] The second data fusion module 420 is configured to obtain the first velocity information of the GNSS single antenna at the current moment, and determine the target heading angle of the drone at the next moment by performing data fusion processing on the first velocity information, the target gyroscope zero bias value, the first acceleration information, and the first angular rate information, so as to adjust the heading of the drone according to the target heading angle; the first velocity information is the velocity in a preset reference coordinate system.

[0125] Based on the above embodiments, optionally, the total data dimension of the data fusion processing is seven - dimensional.

[0126] Based on the above embodiments, optionally, the first data fusion module includes:

[0127] The first nominal state determination unit is configured to obtain the first gyroscope zero bias of the drone at the current moment, and determine the first nominal state of the drone at the current moment according to the first angular rate information and the first gyroscope zero bias;

[0128] The first covariance matrix determination unit is configured to determine the first error state of the drone at the current moment, and determine the first covariance matrix of the first error state, where the first error state is a six - row and one - column zero matrix;

[0129] The second error state determination unit is configured to determine the first Kalman gain according to the first covariance matrix, and determine the second error state according to the first Kalman gain and the first acceleration information;

[0130] The gyroscope zero bias determination unit is configured to update the first nominal state according to the second error state to determine the target gyroscope zero bias value of the drone.

[0131] Based on the above embodiments, optionally, the first nominal state determination unit is configured to: determine the first quaternion of the drone at the current moment according to the first angular rate information, and construct the first nominal state of the drone at the current moment according to the first quaternion and the first gyroscope zero bias.

[0132] Based on the above embodiments, optionally, the first covariance matrix determination unit is configured to: obtain the first process noise of the drone at the current moment, where the first process noise is determined by the gyroscope measurement noise and the gyroscope zero bias noise; determine the first state transition matrix of the first error state, and determine the first covariance matrix of the first error state according to the first state transition matrix and the first process noise.

[0133] Based on the above embodiments, optionally, the second error state determination unit includes a first Kalman gain determination subunit, configured to: obtain the first measurement noise of the UAV at the current moment, where the first measurement noise is determined by the measurement noise of the first acceleration information of the inertial measurement unit; determine the first Kalman gain according to the first measurement noise and the first covariance matrix.

[0134] Based on the above embodiments, optionally, the second data fusion module includes:

[0135] A second nominal state determination unit, configured to determine the second nominal state of the UAV at the current moment according to the target gyroscope zero bias value, the first angular rate information, and the first velocity information;

[0136] A second covariance matrix determination unit, configured to determine the second error state of the UAV at the current moment and determine the second covariance matrix of the second error state, where the second error state is a six-row and one-column zero matrix;

[0137] A second Kalman gain determination unit, configured to obtain the second measurement noise of the UAV at the current moment, and determine the second Kalman gain according to the second measurement noise and the second covariance matrix; the second measurement noise is determined by the measurement noise of the first velocity information of the GNSS single antenna;

[0138] A second error state determination unit, configured to determine the second error state according to the second Kalman gain and the first velocity information;

[0139] A heading angle determination unit, configured to update the second nominal state according to the second error state and determine the target heading angle of the UAV at the next moment.

[0140] Based on the above embodiments, optionally, the second nominal state determination unit is configured to: determine the second angular rate information according to the target gyroscope zero bias value and the first angular rate information; determine the second velocity information of the UAV according to the first velocity information and the first acceleration information; the second velocity information is three-dimensional information; construct the second nominal state of the UAV at the current moment according to the second angular rate information and the second velocity information.

[0141] Based on the above embodiments, optionally, the second covariance matrix determination unit is configured to: obtain the second process noise of the UAV at the current moment, where the second process noise is determined by the gyroscope measurement noise and the measurement noise of the first acceleration information of the inertial measurement unit; determine the second state transition matrix of the second error state, and determine the second covariance matrix of the second error state according to the second state transition matrix and the second process noise.

[0142] The drone heading determination device based on a GNSS single antenna provided by an embodiment of the present invention can execute the drone heading determination method based on a GNSS single antenna provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution of the method.

[0143] Embodiment 5

[0144] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0145] Figure 5 The structure diagram of the electronic device that can be used for the drone heading determination method based on a GNSS single antenna according to an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device (such as a helmet, glasses, a watch, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0146] As Figure 5 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0147] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0148] The processor 11 may be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for determining the heading of a drone based on a GNSS single antenna.

[0149] In some embodiments, the method for determining the heading of a drone based on a GNSS single antenna may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining the heading of a drone based on a GNSS single antenna described above may be executed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for determining the heading of a drone based on a GNSS single antenna in any other suitable manner (e.g., by means of firmware).

[0150] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), systems-on-a-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0151] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processors of general-purpose computers, special-purpose computers, or other programmable data processing devices such that the computer programs, when executed by the processors, cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0152] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0153] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic, speech, or tactile input).

[0154] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0155] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is created by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0156] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0157] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for determining the heading of an unmanned aerial vehicle based on a single GNSS antenna, characterized in that: The UAV is equipped with a GNSS single antenna and an inertial measurement unit, and the method comprises: Acquire first acceleration information and first angular velocity information of the inertial measurement unit at a current moment, and determine a target gyroscope zero bias value of the drone by performing data fusion processing on the first acceleration information and the first angular velocity information; The first velocity information of the GNSS single antenna at the current moment is obtained, and the target heading angle of the UAV at the next moment is determined by performing data fusion processing on the first velocity information, the target gyroscope zero bias value, the first acceleration information and the first angular rate information, so as to adjust the heading of the UAV according to the target heading angle; the first velocity information is the velocity in a preset reference coordinate system.

2. The method according to claim 1, characterized in that Determining a target gyroscope zero bias value of the UAV by performing data fusion processing on the first acceleration information and the angular rate information includes: Obtaining a first gyroscope bias of the drone at a current moment, and determining a first nominal state of the drone at a current moment according to the first angular rate information and the first gyroscope bias; the first nominal state is the gyroscope bias value and angular rate information of the drone under an ideal state; Determine a first error state of the drone at a current moment, and determine a first covariance matrix of the first error state, wherein the first error state is a zero matrix with six rows and one column; Determine a first Kalman gain according to the first covariance matrix, and determine a second error state according to the first Kalman gain and the first acceleration information; The first nominal state is updated according to the second error state to determine a target gyroscope zero bias value of the UAV.

3. The method according to claim 2, characterized in that Determining a first nominal state of the drone at a current moment according to the first angular rate information and the first gyroscope zero bias includes: A first quaternion of the drone at a current moment is determined according to the first angular rate information, and a first nominal state of the drone at a current moment is constructed according to the first quaternion and the first gyroscope zero bias.

4. The method according to claim 3, characterized in that Determining a first covariance matrix of the first error state includes: Acquire a first process noise of the drone at a current moment, where the first process noise is determined by gyroscope measurement noise and gyroscope zero bias noise; A first state transfer matrix of the first error state is determined, and a first covariance matrix of the first error state is determined according to the first state transfer matrix and the first process noise.

5. The method according to claim 4, characterized in that Determining a first Kalman gain according to the first covariance matrix includes: Acquire a first measurement noise of the drone at a current moment, where the first measurement noise is determined by a measurement noise of the first acceleration information of the inertial measurement unit; A first Kalman gain is determined according to the first measurement noise and the first covariance matrix.

6. The method according to claim 1, characterized in that Determining the target heading angle of the UAV at a next moment by performing data fusion processing on the first speed information, the target gyroscope zero bias value, the first acceleration information, and the first angular rate information, includes: Determine a second nominal state of the UAV at the current moment according to the target gyroscope zero bias value, the first angular velocity information, and the first velocity information; the second nominal state is the angular velocity information and velocity information of the UAV under an ideal state; Determine a second error state of the drone at a current moment, and determine a second covariance matrix of the second error state, where the second error state is a zero matrix with six rows and one column; Acquire a second measurement noise of the drone at a current moment, and determine a second Kalman gain according to the second measurement noise and the second covariance matrix; the second measurement noise is determined by the measurement noise of the first velocity information of the GNSS single antenna; determining a second error state according to the second Kalman gain and the first speed information; The second nominal state is updated according to the second error state to determine the target heading angle of the UAV at the next moment.

7. The method according to claim 6, characterized in that Determining a second nominal state of the UAV at a current moment according to the target gyroscope zero bias value, the first angular rate information, and the first speed information includes: Determine second angular rate information according to the target gyroscope zero bias value and the first angular rate information; Determine second speed information of the drone according to the first speed information and the first acceleration information; the second speed information is three-dimensional information; A second nominal state of the drone at the current moment is constructed according to the second angular rate information and the second speed information.

8. The method according to claim 7, characterized in that Determining a second covariance matrix of the second error state includes: Acquire a second process noise of the drone at a current moment, where the second process noise is determined by a gyroscope measurement noise and a measurement noise of the first acceleration information of the inertial measurement unit; A second state transfer matrix of the second error state is determined, and a second covariance matrix of the second error state is determined according to the second state transfer matrix and the second process noise.

9. The method according to claim 1, characterized in that: The total dimension of the data processed by the data fusion is seven dimensions.

10. A UAV heading determination device based on a GNSS single antenna, characterized in that: The drone is equipped with a GNSS single antenna and an inertial measurement unit, the device comprising: A first data fusion module is used to obtain first acceleration information and first angular velocity information of the inertial measurement unit at a current moment, and determine a target gyroscope zero bias value of the UAV by performing data fusion processing on the first acceleration information and the first angular velocity information; The second data fusion module is used to obtain the first speed information of the GNSS single antenna at the current moment, and determine the target heading angle of the UAV at the next moment by performing data fusion processing on the first speed information, the target gyroscope zero bias value, the first acceleration information and the first angular rate information, so as to adjust the heading of the UAV according to the target heading angle; the first speed information is the speed in a preset reference coordinate system.

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