Method for positioning multiple moving targets based on azimuth information

Abstract

The invention discloses a method for positioning multiple moving targets based on azimuth information, and belongs to the field of cooperative positioning of unmanned ships. In order to solve the defects of dependence on GNSS, unstable target state estimation and lack of distributed multi-target cooperative sensing ability in the prior art, the technical scheme provided by the invention comprises the following steps: outputting global position, heading angle and speed information of each unmanned ship; determining neighbor state information of each unmanned ship; target azimuth information acquired by each unmanned ship is acquired, and target observation input is generated in combination with own state information and neighbor state information; estimating a position state and a speed state of the target in the global coordinate system based on the target observation input; and adjusting parameters of the observer according to the difference between the estimation state and actual observation so as to converge the error, and outputting a stable target state estimation result. The method is suitable for real-time positioning and tracking operation of multiple moving targets through cooperation of multiple unmanned ships in a GNSS denial or complex environment.

Description

Technical Field

[0001] The present invention belongs to the field of cooperative positioning of unmanned boats, and particularly involves the positioning of multiple moving targets based on orientation information. Background Art

[0002] With the rapid development of unmanned surface vehicle (USV) technology, swarm collaborative operations have become a crucial means of achieving complex mission objectives, such as target tracking, maritime search and rescue, intelligent patrolling, and monitoring. Real-time, high-precision positioning of targets is essential for these applications. Traditional USV positioning methods rely heavily on the Global Navigation Satellite System (GNSS), for example, by integrating GNSS with an inertial navigation system (INS) to achieve both target and USV positioning. However, in nearshore areas, island reefs, or complex electromagnetic environments, GNSS signals are often blocked, interfered with, or denied, severely impacting the stability and accuracy of the positioning system.

[0003] To address GNSS-denied environments, some research has proposed methods that utilize relative measurement information for target positioning, such as target tracking methods based on LiDAR or ultrasonic ranging. These methods can achieve some success in specific scenarios, but they typically place high demands on the target's reflective properties, communication environment, or ranging accuracy, and are difficult to adapt to dynamic, multi-target environments or complex water surfaces.

[0004] In recent years, visual sensors have been widely used in perception and positioning tasks for unmanned systems due to their low cost and rich information. Some researchers have used monocular or binocular vision systems for target detection and orientation estimation, attempting to replace distance measurements with orientation angles for target positioning. For example, some studies have used visual SLAM technology for positioning within known maps, but the effectiveness of SLAM methods is limited in featureless environments such as open water. Other studies have proposed target orientation estimation methods based on single-vessel visual observations, but these methods cannot guarantee stable and continuous observation when faced with multiple moving targets or when targets frequently move out of view.

[0005] Furthermore, current visual positioning methods focus on single-vessel observation and processing, without fully considering the potential for multi-vessel collaboration. While some research has explored collaborative perception mechanisms for swarms, these rely on a global control center or high-bandwidth communication, lacking distributed solutions suitable for scenarios with limited bandwidth, limited viewing angles, or when central nodes are unavailable. This is particularly true when dynamic targets frequently enter and exit the unmanned vessels' field of view, and when there's a lack of unified target awareness among the unmanned vessels. Target positioning performance can be significantly degraded.

[0006] In summary, the existing technologies have the defects of relying on GNSS, limited visual coverage, unstable target state estimation, and lack of distributed multi-target collaborative perception capabilities. Summary of the Invention

[0007] To address the shortcomings of existing technologies, such as reliance on GNSS, limited visual coverage, unstable target state estimation, and lack of distributed multi-target collaborative perception capabilities, the present invention provides the following technical solutions: A method for positioning multiple moving targets based on position information, comprising: The steps of establishing a kinematic model that describes the motion state of the unmanned boat in a two-dimensional plane and outputting the global position, heading angle and velocity information of each unmanned boat; The steps of constructing a communication topology structure of a multi-UAV cluster, determining a neighbor set of each UAV based on a directed graph, and generating neighbor status information; The step of collecting target position information obtained by each unmanned vehicle through a visual sensor and combining the unmanned vehicle's own state information with neighboring state information to generate target observation input; The step of constructing a target state observer based on the target observation input to estimate the position state and velocity state of the target in the global coordinate system; The steps of establishing an observation error system based on the difference between the estimated state and the actual observation, adjusting the observer parameters to make the error converge, and outputting a stable target state estimation result.

[0008] Furthermore, in a preferred embodiment, the kinematic model is modeled using an earth-fixed coordinate system and a hull-fixed coordinate system, and the global position and velocity information of each unmanned vehicle is obtained through coordinate transformation.

[0009] Furthermore, in a preferred embodiment, the communication topology is a directed graph structure, each node corresponds to an unmanned boat, and each directed edge represents a neighbor relationship where information can be transferred.

[0010] Furthermore, in a preferred embodiment, each unmanned boat only observes the position information of one moving target at any time, and maps the position information to the earth coordinate system to form an observation input.

[0011] Furthermore, in a preferred embodiment, the target state observer includes two parts: a position observer and a speed observer, which are used to estimate the position state and the speed state of the target respectively.

[0012] Furthermore, in a preferred embodiment, the observation error system constructs a state error matrix by introducing control parameters.

[0013] A device for positioning multiple moving targets based on position information is also provided, comprising: A module is built to describe the kinematic model of the UAV's motion in a two-dimensional plane and output the global position, heading angle, and velocity information of each UAV. Build a communication topology for a multi-UAV cluster, determine the neighbor set of each UAV based on a directed graph, and generate a module for neighbor status information; A module that collects target position information obtained by each unmanned boat through its visual sensor and combines it with the boat's own state information and neighboring state information to generate target observation input; A module that builds a target state observer based on target observation input to estimate the position and velocity states of the target in the global coordinate system; A module that establishes an observation error system based on the difference between the estimated state and the actual observation, adjusts the observer parameters to make the error converge, and outputs a stable target state estimation result.

[0014] A computer storage medium is also provided for storing a computer program, and when the computer program is read by a computer, the computer executes the method.

[0015] A computer is also provided, comprising a processor and a storage medium, wherein when the processor reads a computer program stored in the storage medium, the computer executes the method.

[0016] A computer program product is also provided, which is a computer program that implements the method when the computer program is executed.

[0017] Compared with the prior art, the technical solution provided by the present invention is beneficial in that: By constructing a kinematic model for the unmanned vehicle and introducing both an Earth-fixed coordinate system and a hull-fixed coordinate system, this method accurately describes the vehicle's two-dimensional motion state at the mathematical modeling level, providing a rigorous theoretical foundation for subsequent state estimation and error analysis. Unlike some existing studies that use simplified motion models, this method establishes a more comprehensive coordinate transformation and velocity expression system, which improves the accuracy of target state estimation and the reliability of system modeling.

[0018] By designing a target state observer based on azimuth information, the system achieves real-time estimation of the target's position and velocity in environments where GNSS signals are absent or interfered with, relying solely on target azimuth information obtained by visual sensors and combined with interactive information from neighboring unmanned vehicles. Compared to traditional positioning methods that rely on GNSS positioning or distance measurement, this approach significantly enhances the system's positioning capabilities in complex electromagnetic interference environments, making it particularly suitable for areas where GNSS is unavailable, such as nearshore areas or in complex waters.

[0019] By constructing a communication topology for a swarm of unmanned vehicles and modeling information interactions using a directed graph, the system can collaboratively complete target positioning tasks within a distributed architecture. Unlike existing research that relies on centralized control nodes or global communication links, this solution utilizes local information fusion and distributed observation mechanisms, effectively improving the system's scalability and robustness, adapting to practical applications with limited communication bandwidth or unstable networks.

[0020] To overcome the limited viewing angle of a single visual sensor, a strategy for target perspective switching and coordinated processing of observation data was proposed. By limiting each unmanned vehicle to observing only one target and designing the observation angle relationship with neighboring vehicles, the consistency of target state observation and the system's ability to continuously track targets were enhanced. This approach is more stable than traditional methods that simultaneously observe multiple targets but process mixed information, significantly reducing positioning interruptions caused by targets frequently entering or exiting the field of view.

[0021] By introducing a state observation error system and proving the stability of the observer, we ensure that, under reasonable design parameters, the target position and velocity estimation errors converge globally and asymptotically to zero. This theoretical guarantee mechanism distinguishes itself from existing methods, which often only rely on simulation verification. It provides a mathematical foundation for controllable and verifiable system performance, facilitating performance evaluation and parameter tuning during engineering implementation.

[0022] It is suitable for real-time positioning and tracking of multiple moving targets by multiple unmanned boats in GNSS-denied or complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic diagram of a method for positioning multiple moving targets based on position information; Figure 2 This is the observation error variation curve of the unmanned boat to multiple moving targets; Figure 3 This is the curve diagram of the observation error change of the unmanned boat 2 to multiple moving targets; Figure 4 This is the observation error variation curve of the unmanned boat 3 to multiple moving targets; Figure 5 This is a curve diagram of the observation error change of the unmanned boat 4 to multiple moving targets. DETAILED DESCRIPTION

[0024] In order to make the advantages and benefits of the technical solution provided by the present invention more clearly reflected, the technical solution provided by the present invention is now further described in detail with reference to the accompanying drawings, specifically: Embodiment 1: This embodiment provides a method for positioning multiple moving targets based on position information, including: establishing a kinematic model describing the motion state of the unmanned surface vehicles in a two-dimensional plane, and outputting global position, heading angle and velocity information of each unmanned surface vehicle; constructing a communication topology structure of the multi-unmanned surface vehicle cluster, determining a neighbor set of each unmanned surface vehicle based on a directed graph, and generating neighbor state information; collecting target bearing information obtained by each unmanned surface vehicle through a visual sensor, and combining the target bearing information with the self state information and the neighbor state information of the unmanned surface vehicle to generate target observation input; constructing a target state observer based on the target observation input, and estimating the position state and velocity state of the target in a global coordinate system; establishing an observation error system based on the difference between the estimated state and the actual observation, adjusting the observer parameters to make the error converge, and outputting a stable target state estimation result.

[0025] In the kinematic model, an earth-fixed coordinate system and a ship-fixed coordinate system are used for modeling, and the global position and velocity information of each unmanned surface vehicle are obtained through coordinate transformation.

[0026] The communication topology structure is a directed graph structure, each node corresponds to an unmanned surface vehicle, and each directed edge represents an information transmissible neighbor relationship.

[0027] Each unmanned surface vehicle only observes the bearing information of one moving target at any time, and maps the bearing information to the earth coordinate system to form the observation input.

[0028] The target state observer includes a position observer and a velocity observer, which are used to estimate the position state and velocity state of the target, respectively.

[0029] The observation error system constructs a state error matrix by introducing control parameters.

[0030] Embodiment two, this embodiment is a further detailed description of the technical solution provided in embodiment one, specifically: A multi-moving target positioning method suitable for GNSS denial environment, mainly aiming at the problem of cooperative positioning of multiple dynamic targets in the task area by a cluster system composed of multiple unmanned surface vehicles. This method fully utilizes the target bearing information obtained by the visual sensor, and combines the inter-boat communication topology and the design of distributed observer to realize real-time estimation of the target position and velocity state. Specifically, the following steps are included: First, a two-dimensional kinematic model of the unmanned surface vehicle is established, which is based on the earth-fixed coordinate system and the ship-fixed coordinate system, and describes the spatial position, heading angle, longitudinal velocity, lateral velocity and bow velocity of each unmanned surface vehicle. Through coordinate transformation method, the local state of each boat is mapped to the global reference coordinate, which lays a mathematical foundation for subsequent state cooperative observation and error fusion.

[0031] Secondly, a communication topology for the swarm system was constructed, using a directed graph to describe the neighbor relationships and information exchange paths between each UAV. In this topology, each node represents an UAV, and edges indicate that the UAV can share state information with its neighbors. Each UAV only needs to exchange state information with its neighbors, eliminating the need for global communication. This reduces system bandwidth requirements and enhances system robustness.

[0032] Next, the visual sensor's azimuth information is collected and observation inputs are established. Because visual sensors have a fixed viewing angle, the system design assumes that each unmanned vehicle can only observe one moving target at any given moment. The target's azimuth is determined by identifying the angle between the target and the unmanned vehicle's bow in the image. Combined with the unmanned vehicle's heading and position information, the target's azimuth data is unified into a global coordinate system. Furthermore, the system limits each target to being observed by at most one unmanned vehicle, ensuring conflicting observation data.

[0033] Based on this, a target state observer based on azimuth information is constructed. This observer consists of two submodules: a target position observer and a target velocity observer. Its inputs are the azimuth angle of the observed target, the position and attitude of the UAV itself, and state information received from neighboring UAVs. By integrating this information, the observer estimates the target's position and velocity in the global coordinate system from the UAV's coordinate system.

[0034] Furthermore, a target observation error system is constructed. By defining the error term between the target's estimated and true positions and combining it with the azimuth vector relationship between the unmanned vehicle and the target, an error state dynamic system is established. In this system, the observer gain parameters are adjusted so that the state matrix of the error system becomes a Hurwitz matrix, thus ensuring error convergence. Theoretical derivation shows that as long as the parameter selection conditions are met, the observation error will globally converge asymptotically to zero, ensuring the accuracy and stability of the target state estimation.

[0035] Finally, simulation experiments validated the effectiveness of the proposed method. The simulation scenario involved multiple dynamic targets randomly moving within a two-dimensional mission area. Multiple unmanned aerial vehicles observed the visible targets and formed a collaborative network through neighbor communication. Over multiple rounds of simulation, the unmanned aerial vehicles' estimated positions and velocities of the targets closely matched their true trajectories, and the observation errors gradually converged over time, fully demonstrating the adaptability and robustness of the method in complex real-world environments.

[0036] Implementation Method 3: Combination Figure 1-5 This embodiment further describes the above technical solution in detail through specific examples, specifically: (1) Establish a mathematical model of the unmanned boat's kinematics.

[0037] consider A network system consisting of unmanned boats Then, two related coordinate systems are introduced to describe the arbitrary motion of the unmanned vehicle: the earth-fixed coordinate system and the hull-fixed coordinate system. Both coordinate systems follow the Cartesian right-hand rule. The kinematic mathematical model of the unmanned vehicle is expressed as: Where, Indicates the The location of the unmanned boat, Indicates the The bow direction of the unmanned boat. 、 and Respectively represent the longitudinal, lateral and bow speeds of the unmanned boat.

[0038] Definition The position of the unmanned boat is , the speed is In addition, you can obtain: In the formula Represents the rotation matrix from the ship-fixed coordinate system to the earth-fixed coordinate system. The specific form is: The interactive topological relationship between the unmanned boat swarm systems is represented by a directed graph: , where Represents a node set, Denotes an edge set. Definition Indicates the The neighbor set of the unmanned boat. , represents the adjacency matrix, where , .

[0039] For a multi-objective system, define is the target node set, represents a matrix where if The unmanned boat can obtain the target information, then ,otherwise For the The position of a target in the earth-fixed coordinate system is , the speed state is . No. The unmanned boat pointed to The target vector is . No. The unmanned boat pointed to The unit vector of the target is . No. The goal is The speed of the fixed coordinate system of the unmanned boat hull is .

[0040] (2) Construct a positioning method for multiple moving targets.

[0041] definition Indicates the The initial heading of the unmanned boat, Indicates the unmanned boats The heading at the moment. unmanned boats The error between the heading at the moment and the initial heading is The rotation matrix at the current moment is defined as: definition Considering the limited viewing angle of each UAV's visual sensor, a single UAV cannot simultaneously observe all moving targets within the mission area. To demonstrate the superiority of the proposed target localization method, it is assumed that each UAV can observe the position information of at most one target. The position information of each target can only be observed by at most one UAV.

[0042] Definition The unmanned boat is the first The angle difference between the two unmanned boats is , No. The fixed coordinate system of the unmanned boat hull and the The rotation matrix of the fixed coordinate system of the unmanned boat hull is expressed as Based on the The goal is The position of the fixed coordinate system of the unmanned boat , we can get the The goal is The position of the fixed coordinate system of the unmanned boat is ,in .

[0043] Assume that The goal is The position of the fixed coordinate system of the unmanned boat is unknown, so define and Respectively represent The goal is The estimated values ​​of the position and velocity of the unmanned vehicle in the fixed coordinate system.

[0044] Therefore, the target observer based on orientation information can be obtained as: Where, and represents a positive design parameter.

[0045] The technical effects are: By using visual sensors to obtain the target azimuth, and combining it with the constructed target observer and cluster collaborative information, it is possible to effectively observe and estimate the position and velocity state of the moving target in the hull coordinate system in complex mission environments where GNSS signals fail or are denied, significantly improving the autonomous operation capability of the unmanned boat cluster without satellite navigation support.

[0046] It can simultaneously locate and track multiple moving targets within its field of view. Its target observer design fully considers the inherent physical limitations of vision sensors, particularly viewing angle limitations. Through algorithmic design, it ensures robustness and practicality in the positioning process, avoiding positioning failures caused by the sensor's limited field of view.

[0047] By establishing a communication topology for a multi-UAV swarm system, information exchange and collaborative observation are achieved within the swarm. Each UAV not only uses its own visual sensors to obtain target position information, but also integrates position or status information transmitted by neighboring UAVs. This distributed collaborative mechanism effectively overcomes the bottleneck of a single UAV's limited field of view, expands the spatial coverage of target positioning, and significantly improves the accuracy and reliability of target position and velocity state estimation. The entire solution completely eliminates dependence on GNSS signals, providing a viable solution for target positioning in complex adversarial or restricted environments.

[0048] Each unmanned boat relies solely on its own visual sensors and communicates with its neighbors within a limited range. Using a built-in position-based target observer, it can achieve a local estimate of the target's state. This architecture reduces dependence on central nodes or global communication networks, improving the system's scalability and survivability. Furthermore, by relying primarily on position information and inter-boat coordination, it reduces the need for high-precision external positioning infrastructure, significantly reducing the complexity and cost of system deployment and operation.

[0049] The above further describes the technical solution provided by the present invention in detail through several specific embodiments in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the several specific embodiments described above are not intended to limit the present invention. Any reasonable modification and improvement of the present invention, combination of embodiments and equivalent replacement based on the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for positioning multiple moving targets based on position information, characterized in that: include: The steps of establishing a kinematic model that describes the motion state of the unmanned boat in a two-dimensional plane and outputting the global position, heading angle and velocity information of each unmanned boat; The steps of constructing a communication topology structure of a multi-UAV cluster, determining a neighbor set of each UAV based on a directed graph, and generating neighbor status information; The step of collecting target position information obtained by each unmanned vehicle through a visual sensor and combining the unmanned vehicle's own state information with neighboring state information to generate target observation input; The step of constructing a target state observer based on the target observation input to estimate the position state and velocity state of the target in the global coordinate system; The steps of establishing an observation error system based on the difference between the estimated state and the actual observation, adjusting the observer parameters to make the error converge, and outputting a stable target state estimation result.

2. A method for positioning multiple moving targets based on position information according to claim 1, characterized in that: The kinematic model is built using the earth-fixed coordinate system and the ship-fixed coordinate system, and the global position and velocity information of each unmanned boat is obtained through coordinate transformation.

3. The method for positioning multiple moving targets based on position information according to claim 1, characterized in that: The communication topology is a directed graph structure, where each node corresponds to an unmanned boat and each directed edge represents a neighbor relationship where information can be transmitted.

4. The method for positioning multiple moving targets based on position information according to claim 1, wherein: Each unmanned boat only observes the position information of one moving target at any time and maps the position information to the earth coordinate system to form the observation input.

5. The method for positioning multiple moving targets based on position information according to claim 1, characterized in that: The target state observer consists of two parts: position observer and velocity observer, which are used to estimate the position state and velocity state of the target respectively.

6. The method for positioning multiple moving targets based on position information according to claim 1, characterized in that: The observation error system constructs the state error matrix by introducing control parameters.

7. A positioning device for multiple moving targets based on position information, characterized in that: include: A module is built to describe the kinematic model of the UAV's motion in a two-dimensional plane and output the global position, heading angle, and velocity information of each UAV. Build a communication topology for a multi-UAV cluster, determine the neighbor set of each UAV based on a directed graph, and generate a module for neighbor status information; A module that collects target position information obtained by each unmanned boat through its visual sensor and combines it with the boat's own state information and neighboring state information to generate target observation input; A module that builds a target state observer based on target observation input to estimate the position and velocity states of the target in the global coordinate system; A module that establishes an observation error system based on the difference between the estimated state and the actual observation, adjusts the observer parameters to make the error converge, and outputs a stable target state estimation result.

8. A computer storage medium for storing a computer program, characterized in that When the computer program is read by a computer, the computer executes the method according to claim 1 .

9. A computer comprising a processor and a storage medium, characterized in that When the processor reads the computer program stored in the storage medium, the computer executes the method according to claim 1 .

10. A computer program product, being a computer program, characterized in that When the computer program is executed, the method according to claim 1 is implemented.

Images