Unmanned aerial vehicle target positioning method, system and device based on space-time triangular relation, medium and product
By constructing linear constraint equations using the UAV's own orientation measurement data, and solving the overdetermined equations using the least squares method, radial and tangential velocities are calculated, and control commands are generated to achieve UAV target localization and orbiting. This solves the problems of concealment, cost, and dynamic target adaptability of traditional methods, and improves system performance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-22
- Publication Date
- 2026-03-24
AI Technical Summary
Traditional UAV target localization methods rely on active signal ranging, which easily exposes the location. They are costly to hardware, have difficulty handling dynamic and high-speed targets, and the separation of localization and orbit control leads to computational complexity and performance degradation.
Based on the spatiotemporal trigonometric relationship, linear constraint equations are constructed using the UAV's own orientation measurement data. The overdetermined equations are solved using the least squares method to calculate the radial and tangential velocities and generate control commands to achieve target positioning and orbiting.
It avoids active signal exposure, reduces hardware costs, adapts to dynamic targets, solves the problems of dimensional explosion and convergence delay in high-speed target tracking, and improves system integration and adaptability to complex environments.
Smart Images
Figure CN121720474A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of target tracking and navigation, and in particular to a method, system, device, medium and product for UAV target localization based on spatiotemporal triangulation. Background Technology
[0002] Target localization and orbiting are core issues in the field of unmanned systems, widely used in surveillance, reconnaissance, and disaster monitoring. Traditional methods are mostly based on distance measurement or visual odometry, but distance measurement usually requires active signal transmission, which can easily expose the observer's position, and the sensors are expensive and computationally complex. In addition, existing orientation measurement methods often rely on prior knowledge of the target or static assumptions, making it difficult to handle dynamic targets, and prone to dimensionality explosion and convergence delay problems when dealing with high-speed targets.
[0003] Furthermore, traditional methods typically assume the target's location is known and focus on designing control algorithms to allow the observer to orbit the target at a preset distance. However, in practical applications, the target's location is often initially unknown, and its motion state changes dynamically. This requires the observer to simultaneously solve the target localization (estimation problem) and orbit control (control problem), i.e., a dual control problem. For small UAVs, limited payload capacity makes it difficult to carry high-resolution sensors, leading to performance degradation in complex environments. Therefore, there is an urgent need to develop a localization and orbiting method that requires only passive orientation measurement, is computationally efficient, and adaptable to high-speed moving targets. Summary of the Invention
[0004] The purpose of this application is to provide a method, system, device, medium and product for UAV target localization based on spatiotemporal triangulation, which can achieve real-time positioning and orbiting of high-speed moving targets using only the observer's own orientation measurement data and target position estimation.
[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for UAV target localization based on spatiotemporal triangulation, including: Acquire observation data; the observation data includes the observer's position and the target's azimuth angle; the observer is an unmanned aerial vehicle (UAV); Based on the target motion model and geometric relationships, linear constraint equations are constructed on the observation data, forming an overdetermined set of equations; The overdetermined equations are solved using the least squares method to obtain the target state estimate. Calculate the estimated distance from the observer to the target based on the target state estimate; The radial and tangential velocities are calculated based on the estimated distance; The desired velocity is calculated based on the radial velocity and the tangential velocity; Based on the desired velocity, control commands are determined, and based on the control commands, the observer is driven to move around the target to achieve target positioning.
[0006] Secondly, this application provides a UAV target localization system based on spatiotemporal triangulation, comprising: The data acquisition module is used to acquire observation data, which includes the observer's position and the target's azimuth angle; the observer is an unmanned aerial vehicle (UAV). The overdetermined equations generation module is used to construct linear constraint equations from observation data based on the target motion model and geometric relationships, and to form an overdetermined equation system. The solution module is used to solve the overdetermined system of equations using the least squares method to obtain the target state estimate. The distance estimation module is used to calculate the estimated distance from the observer to the target based on the target state estimate. A velocity component calculation module is used to calculate radial velocity and tangential velocity based on the estimated distance; A desired velocity calculation module is used to calculate the desired velocity based on the radial velocity and the tangential velocity; The motion control module is used to determine control commands based on the desired speed, and drive the observer to move around the target based on the control commands to achieve target positioning.
[0007] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described UAV target localization method based on spatiotemporal triangulation.
[0008] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described UAV target localization method based on spatiotemporal triangulation.
[0009] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned UAV target localization method based on spatiotemporal triangulation.
[0010] According to the specific embodiments provided in this application, this application has the following technical effects: (1) Avoid the risk of active signal exposure and improve concealment: The observation data only includes the position of the UAV and the target azimuth angle. There is no need to actively transmit the ranging signal, which directly solves the problem of the observer's position being exposed due to the active signal transmission in traditional distance measurement methods. It is especially suitable for scenarios with high concealment requirements such as surveillance and reconnaissance. (2) Reduce hardware costs and computational load to adapt to small UAVs: This application abandons high-cost ranging sensors and complex distance calculation logic, and can achieve target positioning by only azimuth measurement and linear constraint equation construction; at the same time, it adopts the least squares method to solve the overdetermined equation system, which has low algorithm complexity and high computational efficiency, and effectively adapts to the hardware characteristics of small UAVs with limited load capacity and insufficient computing resources. (3) Breaking through the limitations of prior knowledge and static assumptions of the target, adapting to dynamic targets: This application constructs linear constraint equations based on the target motion model, without relying on prior conditions of known target position, and without requiring the target to remain stationary; by solving the target state estimate and further calculating the radial velocity and tangential velocity, the target motion state can be tracked in real time, solving the shortcomings of traditional methods in handling dynamic targets; (4) Solving the problem of dimensional explosion and convergence delay in high-speed target tracking: Traditional methods are prone to dimensional explosion when facing high-speed targets. The essence is that the localization and control links are not coupled, which leads to the dimensionality of the state variables increasing with the number of observations. This application constructs linear constraints through geometric relationships, decoupling the dual problems of target localization and orbit control into an ordered process of "state estimation-velocity calculation-control command generation". The linear solution characteristics of the least squares method can quickly converge to obtain the target state estimate, avoiding the convergence delay caused by complex nonlinear iteration, and realizing stable tracking of high-speed targets. (5) Integrated target positioning and orbit control, simplifying system architecture: This application forms a closed loop from observation data acquisition to control command output: the positioning task is completed by estimating the target state, the expected speed is calculated based on the estimated distance and speed parameters and control commands are generated to drive the UAV to directly achieve orbiting motion without the need for additional positioning and control connection modules, which solves the problem of separation of positioning and control in traditional methods and improves system integration and real-time response. (6) Improve adaptability and robustness in complex environments: Traditional methods rely on high-resolution sensors, which are susceptible to interference in complex environments, leading to performance degradation. This application is based on passive orientation measurement and geometric modeling, which has low requirements for sensor accuracy. Moreover, the least squares solution of the overdetermined equations has the ability to resist observation noise interference. Even if there are a small number of errors in the observation data obtained in complex environments, it can still output reliable target state estimates, ensuring positioning and orbiting effects. Attached Figure Description
[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0012] Figure 1 A flowchart illustrating a UAV target localization method based on spatiotemporal triangulation provided in an embodiment of this application; Figure 2 A diagram showing the comparison of target location estimation errors; Figure 3 This is a schematic diagram of the target orbiting and tracking process; where (a)-(f) are schematic diagrams of the target orbiting and tracking process at different times; Figure 4 A schematic diagram illustrating the target location estimation error; Figure 5 This is a diagram illustrating the speed estimation error. Detailed Implementation
[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0014] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0015] In one exemplary embodiment, such as Figure 1 As shown, a method for UAV target localization based on spatiotemporal triangulation is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is described using a server as an example, and includes the following steps S1 to S7. Wherein: S1: Acquire observation data; the observation data includes the observer's position and the target's azimuth angle; the observer is a drone.
[0016] S2: Based on the target motion model and geometric relationships, construct linear constraint equations for the observation data and form an overdetermined set of equations.
[0017] S3: Solve the overdetermined system of equations using the least squares method to obtain the target state estimate.
[0018] S4: Calculate the estimated distance from the observer to the target based on the target state estimate.
[0019] S5: Calculate the radial velocity and tangential velocity based on the estimated distance.
[0020] S6: Calculate the desired velocity based on the radial velocity and the tangential velocity.
[0021] S7: Determine control commands based on the desired velocity, and drive the observer to move around the target based on the control commands to achieve target positioning.
[0022] By implementing steps S1 to S78 above, real-time positioning and orbiting of a high-speed moving target can be achieved using only the observer's own orientation measurement data and target position estimation.
[0023] In one specific embodiment, the process further includes problem modeling and hypothesis making before performing step S1.
[0024] Set the target in time The position is The observer's position is The observer only knows its own position (relative to the initial frame) and the target's azimuth. (That is, the angle of the target relative to the observer). The target motion model is a second-order integrator (uniform acceleration model), and the state vector includes the estimated position of the target. ,speed and acceleration : .
[0025] The target motion model is as follows: in, n Representing discrete moments, For time intervals.
[0026] The observer motion model is a single integrator: ,in To control the input. The goal is to estimate. The design control law makes the observer move at a radius Orbit the target.
[0027] To handle high-speed targets, the velocity assumption is relaxed, and an adaptive learning rate is introduced. The target velocity is assumed to be bounded, but without a slow constraint; instead, it is adjusted through the estimator gain.
[0028] In a specific embodiment, step S1 specifically includes: In time Azimuth observations are conducted, and the observation data includes the observer's position and the target's azimuth. The observer's position is represented as... The target azimuth angle is calculated using the arctangent function: At each time step, azimuth angle measurements are performed, and the measurement data is added to the data buffer. The buffer uses a sliding window mechanism to maintain a fixed length of historical data. When the data volume exceeds the maximum value, the oldest data point is automatically discarded to ensure data freshness and validity.
[0029] In one specific embodiment, step S2 specifically includes: S21: Establish the geometric relationship of the observation data based on the principles of trigonometry.
[0030] The equations for the geometric relationships are established based on the principles of trigonometry: .
[0031] S22: Establish historical relationship equations of target position based on target motion model.
[0032] Maintain a sliding window buffer to store the most recent Each observation data point is analyzed. For each observation data point, the time interval is calculated. ,in, Indicates the time when new observation data is added. Indicates the number stored in the time window k The moment of each observation data point Indicates the first k An index of observation data. Based on the target motion model, an equation relating the historical position of the target is established: .
[0033] in, This represents the sliding window derived from the uniformly accelerated motion model. The target location at any given time.
[0034] S23: Construct linear constraint equations based on the geometric relationship and the historical relationship equation of the target position, and form an overdetermined system of equations.
[0035] Substituting the geometric relationships into the historical relationship equation for the target location, we obtain the linear constraint equation: Rearrange the equation into standard linear form: The specific expression for the coefficient matrix is as follows: When the azimuth angle is close to To avoid numerical singularity, a special handling method is adopted: the coefficient matrix is set as follows: The bias term is .
[0036] Overdetermined system of equations: .
[0037] in It is a coefficient matrix, where each row corresponds to the coefficients of the linear constraint equation at a given time step. A total of eight time steps were used to obtain the observation data. There are 8 lines in total, encoding the geometric relationships and the target motion model (when the azimuth angle is...). When, the row vector is Each row vector has 6 columns, corresponding to 6 data points. It is the target state vector to be estimated, defined as ; It is a constant term vector, composed of observer positions (e.g.) ).
[0038] In a specific embodiment, step S3 specifically includes: The overdetermined equations are solved using the least squares method to obtain the target state estimate: The state vector is defined as: . Indicates the estimated target location, i.e. The x-axis represents the estimated target state value. The vertical axis represents the estimated target state value; Indicates the estimated target speed; This represents the estimated target acceleration. This step utilizes mathematical optimization methods to fully leverage observational data, thereby improving the accuracy and robustness of target state estimation.
[0039] In a specific embodiment, steps S4-S6 specifically include: Expected speed Decomposed into radial velocity and tangential velocity : in, It is the radial unit vector of the azimuth angle. This is the azimuth tangential unit vector.
[0040] Radial velocity is used to control the distance to the target: in, To estimate the distance, For the desired orbital radius, This is the proportional gain.
[0041] Tangential velocity ensures orbital motion: This design ensures that the total speed does not exceed the maximum tracking speed. At the same time, it achieves a stable orbital trajectory.
[0042] The specific calculation process includes: Relative position estimation: , ; Calculate the azimuth angle: ; Estimated distance: .
[0043] In one specific embodiment, step S7 specifically includes: Acceleration control quantity (i.e., control command) is calculated using a proportional control law: in, For acceleration gain, The current velocity of the observer.
[0044] The system records state information at each time step, including the target's true state, the observer's state, and the estimated state. Performance evaluation is conducted by comparing the estimated state with the true state, with key metrics including position estimation error, velocity estimation error, and orbital stability.
[0045] This application also includes simulation results and analysis.
[0046] Target position error comparison chart (e.g.) Figure 2 As shown in the figure, the superiority of the method in this application is highlighted through comparative experiments. Figure 2 The position estimation method used in this application (solid blue line) was compared with the position error of existing projection-based estimation methods (dashed orange line) throughout the simulation. The results show that during the 300-second simulation, the error of the method in this application remained at a low level and changed smoothly; while the traditional method showed huge errors with drastic fluctuations, often exceeding 100 meters in peak value. This comparison demonstrates that the method in this application significantly improves estimation accuracy and stability compared to traditional methods.
[0047] Target tracking snapshots (such as) Figure 3 (a)-(f) in the figure) are snapshots of the target tracking status within 100 seconds of the simulation time. Figure 3 The image clearly shows the movement trajectories of the observer (blue square) and the target (red dot), as well as a comparison between the estimated trajectory (green line) and the actual trajectory (red line) of the target position as described in this application. At this point, the estimated target position (green asterisk) is very close to the actual position. This snapshot visually demonstrates that, using the method described in this application, the observer can effectively approach the target and achieve high-precision real-time positioning.
[0048] Estimation error analysis diagram (e.g.) Figures 4-5 The figure shows how the estimation error of the target position and velocity changes over time. Figure 4The position estimation error curve shows that the position estimation error of the method in this application does not diverge after the initial transient, but stabilizes within a certain range (about 0-15 meters) and fluctuates periodically. This indicates that the method in this application has excellent convergence and stability and can achieve continuous and reliable tracking of the target position. Figure 5 The velocity estimation error curves also exhibit similar stable characteristics, which together verify the effectiveness of the proposed method in estimating the entire target motion state (position and velocity).
[0049] Based on the same inventive concept, this application also provides a system for implementing the above-mentioned UAV target localization method based on spatiotemporal triangulation. The solution provided by this system is similar to the implementation described in the above method. Therefore, the specific limitations of one or more UAV target localization system embodiments based on spatiotemporal triangulation provided below can be found in the above-described limitations of the UAV target localization method based on spatiotemporal triangulation, and will not be repeated here.
[0050] In one exemplary embodiment, a UAV target localization system based on spatiotemporal triangulation is provided, comprising the following modules.
[0051] The data acquisition module is used to acquire observation data, which includes the observer's position and the target's azimuth angle; the observer is a drone.
[0052] The overdetermined equations generation module is used to construct linear constraint equations from observation data based on the target motion model and geometric relationships, and to form an overdetermined equations system.
[0053] The solution module is used to solve the overdetermined system of equations using the least squares method to obtain the target state estimate.
[0054] The distance estimation module is used to calculate the estimated distance from the observer to the target based on the target state estimate.
[0055] The velocity component calculation module is used to calculate the radial velocity and tangential velocity based on the estimated distance.
[0056] The desired velocity calculation module is used to calculate the desired velocity based on the radial velocity and the tangential velocity.
[0057] The motion control module is used to determine control commands based on the desired speed, and drive the observer to move around the target based on the control commands to achieve target positioning.
[0058] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. The computer device may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device stores data to be processed. The I / O interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with an external terminal via a network connection. When the computer program is executed by the processor, it implements the steps in the above-described method embodiments.
[0059] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0060] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0061] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0062] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0063] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0064] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0065] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for UAV target localization based on spatiotemporal triangulation, characterized in that, include: Acquire observation data; the observation data includes the observer's position and the target's azimuth angle; the observer is an unmanned aerial vehicle (UAV); Based on the target motion model and geometric relationships, linear constraint equations are constructed on the observation data, forming an overdetermined set of equations; The overdetermined equations are solved using the least squares method to obtain the target state estimate. Calculate the estimated distance from the observer to the target based on the target state estimate; The radial and tangential velocities are calculated based on the estimated distance; The desired velocity is calculated based on the radial velocity and the tangential velocity; Based on the desired velocity, control commands are determined, and based on the control commands, the observer is driven to move around the target to achieve target positioning.
2. The UAV target localization method based on spatiotemporal triangulation as described in claim 1, characterized in that, Based on the target motion model and geometric relationships, linear constraint equations are constructed from the observed data, forming an overdetermined system of equations, specifically including: Establish geometric relationships in observation data based on trigonometric principles; Establish historical relationship equations of target position based on target motion model; Based on the geometric relationship and the historical relationship equation of the target position, linear constraint equations are constructed, forming an overdetermined system of equations.
3. The UAV target localization method based on spatiotemporal triangulation as described in claim 1, characterized in that, The formula for calculating the estimated distance from the observer to the target is: in, This is the estimated distance from the observer to the target. For relative position estimation, The target location in the target state estimate. The ordinate represents the estimated target state value. Let t be the observer's position at time t.
4. The UAV target localization method based on spatiotemporal triangulation as described in claim 3, characterized in that, The formulas for calculating the radial velocity and the tangential velocity are as follows: in, Radial velocity, For the desired orbital radius, For proportional gain, For tangential velocity, This represents the maximum tracking speed.
5. The UAV target localization method based on spatiotemporal triangulation as described in claim 4, characterized in that, The formula for calculating the desired speed is: in, For the desired speed, It is the radial unit vector of the azimuth angle. This is the azimuth tangential unit vector.
6. The UAV target localization method based on spatiotemporal triangulation as described in claim 5, characterized in that, The expression for the control command is: in, For control commands, For acceleration gain, The current velocity of the observer.
7. A UAV target localization system based on spatiotemporal triangulation, characterized in that, include: The data acquisition module is used to acquire observation data, which includes the observer's position and the target's azimuth angle; the observer is an unmanned aerial vehicle (UAV). The overdetermined equations generation module is used to construct linear constraint equations from observation data based on the target motion model and geometric relationships, and to form an overdetermined equation system. The solution module is used to solve the overdetermined system of equations using the least squares method to obtain the target state estimate. The distance estimation module is used to calculate the estimated distance from the observer to the target based on the target state estimate. A velocity component calculation module is used to calculate radial velocity and tangential velocity based on the estimated distance; A desired velocity calculation module is used to calculate the desired velocity based on the radial velocity and the tangential velocity; The motion control module is used to determine control commands based on the desired speed, and drive the observer to move around the target based on the control commands to achieve target positioning.
8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the UAV target localization method based on spatiotemporal triangulation as described in any one of claims 1-6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the UAV target localization method based on spatiotemporal triangulation as described in any one of claims 1-6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the UAV target localization method based on spatiotemporal triangulation as described in any one of claims 1-6.