WO2024120187A1

WO2024120187A1 - Method for estimating dynamic target of unmanned aerial vehicle in information rejection environment

Info

Publication number: WO2024120187A1
Application number: PCT/CN2023/133085
Authority: WO
Inventors: 徐升; 朱兵; 侯睿明; 徐蓉蓉; 徐天添; 吴新宇
Original assignee: 中国科学院深圳先进技术研究院
Priority date: 2022-12-05
Filing date: 2023-11-21
Publication date: 2024-06-13
Also published as: CN115790603A

Abstract

A method for estimating a dynamic target of an unmanned aerial vehicle in an information rejection environment. The method comprises: obtaining three-dimensional angle information, the three-dimensional angle information comprising the angle of arrival of an unmanned aerial vehicle to a first anchor point, the angle of arrival of the unmanned aerial vehicle to a second anchor point, and the angle of arrival of the unmanned aerial vehicle to a target; filtering the three-dimensional angle information by using an extended Kalman filter, and obtaining estimated unmanned aerial vehicle state information and corresponding state covariance information, the unmanned aerial vehicle state information being used for characterizing the location and velocity of the unmanned aerial vehicle and the location and velocity of the target; and constructing a loss function by using the unmanned aerial vehicle state information and the state covariance information, so as to optimize the flight path of the unmanned aerial vehicle, implementing observation of the target. According to the described method, tracking the locations of an unmanned aerial vehicle and a target can be implemented by using information of two anchor points for which absolute locations are known.

Description

A method for dynamic target estimation of unmanned aerial vehicles in information denial environment

Technical Field

The present invention relates to the technical field of unmanned aerial vehicles, and more specifically, to a method for estimating dynamic targets of unmanned aerial vehicles in an information denial environment.

Background technique

Target tracking technology is widely used in various applications, such as aerial photography, public safety, and humanitarian search and rescue. Estimating the position and velocity of a target from noisy measurements obtained from different sensors is a widely studied direction. Due to the excellent maneuverability of unmanned aerial vehicles (UAVs), they can be used for target tracking after being equipped with angle of arrival (AOA) sensors. By solving trigonometric geometry problems and filter estimators, the position and velocity of the self and the target can be estimated from noisy AOA measurements. In addition, researchers have proposed different estimation algorithms to estimate the target state from nonlinear AOA measurements. For example, the maximum likelihood estimator (MLE) and pseudo linear estimator (PLE) as classic batch filters are used to estimate the target position and velocity from AOA target measurements with Gaussian noise. Kalman-based methods, which are regarded as recursive filters, are more commonly used in the field of target tracking. For AOA target tracking, the extended Kalman filter (EKF), unscented Kalman filter (UKF), pseudo linear Kalman filter (PLKF) and cubature Kalman filter (CKF) are all used to varying degrees.

Existing studies have shown that the target tracking performance can be significantly improved by optimizing the UAV flight path to make the information collected from the measurements more effective. To improve the target estimation accuracy, a cost function should be designed for UAV path optimization. Common cost functions include the estimated covariance matrix and the Fisher Information Matrix (FIM). Various algorithms can be used to optimize the cost function, such as gradient-based, exhaustive search, and learning-based methods.

In the prior art, the location of the drone is assumed to be accurately obtained using external information, such as the Global Positioning System (GPS). However, the drone may operate in an environment where external signals are missing (or called an information denial environment), such as indoor spaces and interference areas. In these areas, the drone's external signals are missing and its location cannot be obtained. Currently, target positioning without knowing its own location has attracted a lot of interest. For example, in Simultaneous Localization and Mapping (SLAM) In the application, additional information about surrounding anchor points (such as the location of nearby buildings) is added to obtain the absolute target location. In wireless communications, target positioning through multiple anchor points (such as base station positioning) is already relatively mature, but since it is basically performed through the time difference of arrival (TDOA), the number of anchor points required must be greater than or equal to 3 or other prior knowledge must be introduced to uniquely determine the target location.

After analysis, it is found that in the existing technology, whether it is a target estimation scheme based solely on time difference of arrival (TDOA) or angle of arrival (AOA), it can only obtain the distance and angle of the target relative to the drone, but cannot obtain its absolute position.

Summary of the invention

The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a method for estimating dynamic targets of unmanned aerial vehicles in an information denial environment, the method comprising:

Acquire three-dimensional angle information, where the three-dimensional angle information includes an arrival angle from the drone to the first anchor point, an arrival angle from the drone to the second anchor point, and an arrival angle from the drone to the target;

The three-dimensional angle information is filtered by an extended Kalman filter to obtain estimated drone state information and corresponding state covariance information, wherein the drone state information is used to characterize the position and speed of the drone and the position and speed of the target;

The loss function is constructed using the drone state information and the state covariance information to optimize the flight path of the drone at subsequent moments and achieve observation of the target.

Compared with the prior art, the advantage of the present invention is that it provides a method for autonomous target tracking of drones based on azimuth (angle, which can be obtained by PTZ camera) and anchor points. By introducing anchor points with known absolute positions, the drone can provide the drone with the absolute position information of itself and the target in an environment where external signals (GPS, RTK, etc.) are lost, and track the target, providing a more robust solution for positioning and tracking of the drone itself and the target. The present invention can be applied to the positioning systems of various unmanned aerial vehicles such as multi-rotor drones and fixed-wing drones. It can be used as a redundant means of self-positioning, and can also provide the absolute geographic coordinates of the target according to the azimuth, which improves the positioning speed and the positioning accuracy.

Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

FIG1 is a geometric diagram of target tracking according to an embodiment of the present invention;

FIG2 is a flow chart of a method for estimating a dynamic target of a UAV in an information denial environment according to an embodiment of the present invention;

FIG3 is a flow chart of an extended Kalman filter according to an embodiment of the present invention;

FIG4 is a schematic diagram of constructing a loss function according to an embodiment of the present invention;

FIG. 5 is a process diagram of a path optimization method according to an embodiment of the present invention.

Detailed ways

Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless otherwise specifically stated.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

In all examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

The present invention provides a method for autonomous target positioning of unmanned aerial vehicles in an environment lacking external signals, which mainly includes two parts: target filtering estimation and unmanned aerial vehicle path optimization. The method introduces two stationary markers with known absolute positions, and uses the three-dimensional angle information collected by the angle of arrival (AOA) sensor (including the angle of arrival θ _ub1 between the unmanned aerial vehicle and anchor point 1, the angle of arrival θ _ub2 between the unmanned aerial vehicle and anchor point 2, and the angle of arrival θ _up between the unmanned aerial vehicle and the mobile target) as input. After continuous observation for a period of time, the position and velocity errors of the mobile target and the unmanned aerial vehicle itself can be converged to a lower range, and finally the position and velocity of itself and the target are output. Since the sensor position will affect the filter estimator's observation of the target, the method is introduced after filtering. The path optimization algorithm improves the efficiency and accuracy of target positioning. In addition, for the safety of the drone, different sizes of no-fly zones are set around the target and landmarks for the drone during path planning.

Referring to the target tracking geometry diagram in FIG1 , the positional relationship between the mobile target, the drone, anchor 1 (anchor1) and anchor 2 (anchor2) at time k is shown, where anchor 1 and anchor 2 are markers with known absolute positions. For example, the coordinates of anchor 1 are b ₁ = [x _b1 , y _b1 ] ^T , the coordinates of anchor 2 are b ₂ = [x _b2 , y _b2 ] ^T , and T represents transposition; the coordinates of the mobile target at time k are p _k = [x _pk , y _pk ] ^T , the coordinates are unknown, and the position of the drone at time k is marked as _uk (unknown). k is a discrete time identifier, and the discrete time interval is set to M.

As shown in FIG. 2 , the provided method for estimating dynamic targets of unmanned aerial vehicles in an information denial environment mainly includes: step S110, obtaining AOA sensor measurements; step S120, extending the Kalman filter; step S130, a path optimization algorithm based on gradient descent; step S140, determining whether it is within the no-fly distance range; if the determination is yes, performing path replanning (step S150); and then moving in a specified direction at a fixed speed (step S160).

In the following, the extended Kalman filter and the gradient descent optimization algorithm will be specifically introduced. The filter can use a recursive Kalman filter. Since the sensor measurement and the model are in a nonlinear relationship, nonlinear filtering is ultimately performed based on the extended Kalman filter.

Referring to the process of extended Kalman filtering shown in FIG3 , the process mainly includes the following steps:

Step S210, predicting the prior state and covariance according to the state transition model and the initial state.
X _k|k-1 = FX _k-1|k-1 + m _k (1)
P _k|k-1 =FX _k-1|k-1 F ^T +Q _k (2)

Wherein, X _k|k-1 is the prior estimate based on the state X _k-1|k-1 at time k-1; F is the state transfer matrix; P _k|k-1 is the covariance matrix of the state X _k|k-1 , which is used to characterize the uncertainty of the state X _k|k-1 ; m _k is the process noise, which is used to quantify the system error that has not been fully considered. For example, m _k is an independent zero-mean additive white Gaussian noise, that is, m _k ~N(0,Q _k ), and Q _k is the covariance matrix of the system error m _k ;

The state matrix X consists of the position and velocity of the drone itself and the position and velocity of the target, for example, in, represents the derivative (speed) of _xuk with respect to time. Specifically, _xuk is the position of the drone on the x-axis. is the velocity component of the drone on the x-axis, _yuk is the position of the drone on the y-axis, is the velocity component of the drone on the y-axis, x _pk is the position of the target on the x-axis, is the velocity component of the target on the x-axis, y _pk is the position of the target on the y-axis, is the velocity component of the target on the y-axis.

The state X _k-1|k- 1 at time K-1 is transferred to the prior state X _k|k-1 through the state transfer matrix F, where the state transfer is assumed to be a constant speed model. Therefore, the state transfer matrix is Where _Fi is That is, P _k+1 =P _k +V _k M.

Step S220, calculating the Jacobian matrix according to the current state and measurement.
H _k = Jacobian(X _k|k-1 ,Z _k ) (2)

Wherein, Z _k is the three-dimensional angle information observed by the AOA sensor at time k, that is, Z _k = [θ _b1k , θ _b2k , θ _pk ] ^T , H _k is the measurement matrix at time k, which is a Jacobi matrix with 3 rows and 8 columns, expressed as:

Among them, _dub1 =|| _uk - _b1 || ₂ , _dub2 =||uk _- _b2 || ₂ , _dup =||uk _- _pk || ₂ .

Step S230, calculating the estimated state residual according to the measurement model.

in, is the residual, which represents the error between measurement and estimation; function h(·) is a nonlinear measurement function, which transforms data from state space to measurement space, which is specifically expanded as follows:

Wherein, n _k represents the sensor measurement noise, for example, is an independent zero-mean additive Gaussian white noise, that is, n _k ～N(0,R _k ).

Step S240, calculating the Kalman gain.

Where _Rk is the measurement noise, which is used to characterize the noise of the sensor measurement data. For example, It can be obtained from the specification sheet of the sensor; _Sk is an intermediate variable for simplified writing; _Kk is the Kalman gain at time k.

Step S250, updating the posterior estimate and covariance.

P _k|k =(IK _k H _k )P _k|k-1 (9)

Among them, X _k|k is the posterior estimation state; P _k|k is the posterior covariance matrix; I is the 8*8 identity matrix.

For the path optimization algorithm, it takes the current state as input and predicts the direction of the drone that can effectively reduce the loss function (or cost function) through gradient descent, so that the drone can obtain the best observation path when moving in this direction.

FIG4 is a schematic diagram of constructing a loss function, which takes the current state X _k|k and a small displacement vector δ as input, passes it to a copy of the original filter, completes a one-step prediction, and obtains the state covariance matrix obtained by moving with a small displacement δ, and finally outputs the trace of the covariance matrix.

Among them, δ is a small displacement vector; X _k+1|k,δ is the state of state X _k+1|k after a small displacement.

Then, H _k+1 is calculated according to X _k+1|k,δ .

calculate:

Calculation: P _k+1|k+1 =(IK _k+1 H _k+1 )P _k+1|k (15)

Calculation: J(X` _k+1|k+1 )=tr(P _k+1|k+1 ) (16)

Among them, tr(·) is the matrix trace; J(·) is the cost value after a small shift.

In one embodiment, assuming that the drone moves in a 2D plane, the movement of the drone has two degrees of freedom, so it can be decomposed into two components along the x-axis and the y-axis, which can be divided into positive and negative directions. After combination, four movable directions [(d,0), (-d,0), (0,d), (0,-d)] are obtained. By moving in the above four directions, the cost value of moving in each direction is obtained, and thus the measurement of the degree of observation quality of moving in each direction is obtained.

FIG5 is a schematic diagram of the path optimization process, in which one branch is used to calculate the cost value of the UAV flying along the x-axis, and the other branch is used to calculate the cost value of the UAV flying along the y-axis, which specifically includes the following steps:

The cost value J _xp of moving along the positive direction of the x-axis is calculated using δ = [d, 0] as a parameter, where d is a smaller scalar, indicating the step size;

Calculate the cost J _xn of moving along the negative x-axis direction using δ = [-d, 0] as a parameter;

Calculate the total cost of the drone flying along the x-axis

Calculate the cost J _yp of moving along the positive direction of the y-axis using δ = [0, d] as a parameter;

Calculate the cost J _yn of moving along the negative direction of the y-axis using δ = [0, -d] as a parameter;

Calculate the total cost of the drone flying along the y-axis

Calculate the total cost: J = [J _x ,J _y ];

Calculate the optimized direction at time k, expressed as:

Among them, ||.|| ₂ is the L2 regularization, and v represents the constant flight speed of the drone.

After completing the path planning, the closer the drone is to the marker or target, the more accurate the information it obtains. This phenomenon will cause the drone to keep getting closer to the marker or target until they overlap and collide. Therefore, after completing the path planning, it is necessary to set a no-fly zone based on the position of the drone and the marker or target to prevent the drone from flying too close and causing danger.

In summary, the present invention introduces markers (one or more) to cooperate with the AOA sensor to complete the acquisition of the drone's own coordinates, and introduces markers (one or more) to cooperate with the AOA sensor to complete the acquisition of the target coordinates. In addition, the gradient descent of the cost function is completed by random perturbation to optimize the flight path of the drone and obtain the best observation of the moving target. The present invention only requires, for example, a PTZ camera to measure the azimuth between the drone and the anchor point and the target. The absolute position information of the drone and the target can be obtained without any other information. At least two anchor point information with known absolute positions can be used to complete the position tracking of the drone and the target. It has been verified that the present invention improves the efficiency and accuracy of target tracking based on AOA.

The present invention may be a system, a method and/or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing/processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and/or edge servers. The network adapter card or network interface in each computing/processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device.

The computer program instructions for performing the operations of the present invention may be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, Python, etc., and conventional procedural programming languages such as "C" language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to access the user's computer). In some embodiments, the electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can execute computer-readable program instructions by utilizing the state information of the computer-readable program instructions to implement various aspects of the present invention.

Various aspects of the present invention are described herein with reference to the flow charts and/or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each box of the flow chart and/or block diagram and the combination of each box in the flow chart and/or block diagram can be implemented by computer-readable program instructions.

These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions/actions specified in one or more boxes in the flowchart and/or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and/or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions/actions specified in one or more boxes in the flowchart and/or block diagram.

Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions/actions specified in one or more boxes in the flowchart and/or block diagram.

The flowcharts and block diagrams in the accompanying drawings show possible architectures, functions, and operations of systems, methods, and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a portion of an instruction, and the module, program segment, or a portion of an instruction contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the boxes may also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes may actually be executed substantially in parallel, and they may sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and/or flowchart, and combinations of boxes in the block diagram and/or flowchart, may be It can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or it can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.

Embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the marketplace, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

A method for estimating a dynamic target of an unmanned aerial vehicle in an information denial environment comprises the following steps:

Acquire three-dimensional angle information, where the three-dimensional angle information includes an arrival angle from the drone to the first anchor point, an arrival angle from the drone to the second anchor point, and an arrival angle from the drone to the target;

The three-dimensional angle information is filtered by an extended Kalman filter to obtain estimated drone state information and corresponding state covariance information, wherein the drone state information is used to characterize the position and speed of the drone and the position and speed of the target;

The UAV state information and the state covariance information are used to construct a loss function to optimize the flight path of the UAV and achieve observation of the target.
The method according to claim 1 is characterized in that, for the three-dimensional angle information, filtering is performed using an extended Kalman filter to obtain estimated drone state information and corresponding state covariance information, including:

Predict the drone’s prior state and covariance, expressed as:
X k|k-1 =FX k-1|k-1 +m k
P k|k-1 =FX k-1|k-1 F T +Q k

Among them, X k|k-1 is the prior estimate based on the state X k-1|k-1 at time k-1, P k|k-1 is the covariance matrix of the state X k|k-1 , m k is the process noise, Q k is the covariance matrix of m k , x uk is the position of the drone on the x-axis, is the velocity component of the drone on the x-axis, yuk is the position of the drone on the y-axis, is the velocity component of the drone on the y-axis, x pk is the position of the target on the x-axis, is the velocity component of the target on the x-axis, y pk is the position of the target on the y-axis, is the velocity component of the target on the y-axis, and F is the state transfer matrix;

Compute the Jacobian matrix, expressed as:
H k = Jacobian(X k|k-1 ,Z k )

Among them, Zk is the three-dimensional angle information observed at time k, and Hk is the Jacobian matrix with 3 rows and 8 columns

Calculate the estimated state residual, expressed as:

in, is the residual, h(X k|k-1 ) is expressed as:

Calculate the Kalman gain, expressed as:

Where R k is the measurement noise, S k is the intermediate variable, and K k is the Kalman gain at time k;

Update the posterior estimate and covariance as the estimated drone state information and the corresponding state covariance information, expressed as:

P k|k =(IK k H k )P k|k-1

Wherein, Xk |k is the posterior estimated state, Pk |k is the posterior covariance matrix, I is the 8*8 identity matrix, xb1 is the x-axis coordinate of the first anchor point, yb1 is the y-axis coordinate of the first anchor point, xb2 is the x-axis coordinate of the second anchor point, yb2 is the y-axis coordinate of the second anchor point, xpk is the x-axis coordinate of the target at time k, ypk is the y-axis coordinate of the target at time k, dub1 =|| uk - b1 || 2 , dub2 = || uk-b2|| 2 , dup =|| uk- pk || 2 , b1 =[ xb1 , yb1 ] T , b2 =[ xb2 , yb2 ] T , pk =[xpk, ypk ] T , uk =[ xuk , yuk ] T , and nk is the measurement noise of the sensor.
The method according to claim 2, characterized in that the loss function is constructed according to the following steps:

The state after calculating the state X k+1|k with a small displacement δ is expressed as:
X k+1|k,δ =X k+1|k +δ;

Calculate H k+1 according to X k+1|k,δ ;

calculate

calculate

Calculate P k+1|k+1 =(IK k+1 H k+1 )P k+1|k ;

Calculate J(X` k+1|k+1 )=tr(P k+1|k+1 );

Among them, tr(·) is the matrix trace, and J(·) is the cost value after a small shift.
The method according to claim 3, characterized in that constructing a loss function based on the drone state information and the state covariance information to optimize the flight path of the drone comprises:

Calculate the cost J xp of the drone moving along the positive direction of the x-axis using δ = [d, 0] as a parameter;

Calculate the cost J xn of the drone moving along the negative x-axis using δ = [-d, 0] as a parameter;

Calculate the total cost of the drone along the x-axis

Calculate the cost J yp of the drone moving along the positive direction of the y axis using δ = [0, d] as a parameter;

Calculate the cost J yn of the drone moving along the negative direction of the y axis using δ = [0, -d] as a parameter;

Calculate the total cost of the drone along the y-axis

Calculate the total cost J = [J x , J y ];

The calculated total cost value is used to obtain the direction of the optimized k-th moment, expressed as:

Among them, ||.|| 2 is L2 regularization, and d represents the step size.
The method according to claim 1 is characterized in that the three-dimensional angle information is obtained by measuring an angle of arrival sensor.
The method according to claim 1 is characterized in that it also includes: setting a no-fly zone around the target and landmark during the process of optimizing the flight path of the drone.
The method according to claim 1 is characterized in that the drone is an unmanned aerial vehicle, including a multi-rotor drone or a fixed-wing drone.
The method according to claim 1 is characterized in that it also includes: determining the absolute geographic coordinates of the target based on the arrival angle of the drone to the target.
A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 8.
A computer device comprises a memory and a processor, wherein a computer program that can be run on the processor is stored in the memory, and wherein the processor implements the steps of any one of the methods of claims 1 to 8 when executing the computer program.