Underwater target cooperative positioning method and system based on unmanned aerial vehicle topology control

Abstract

The invention relates to the field of underwater target positioning, and discloses an underwater target cooperative positioning method and system based on unmanned aerial vehicle topology control, and the method comprises the steps: constructing an unmanned aerial vehicle cluster, and achieving the topological reconstruction of an unmanned aerial vehicle cluster structure through a virtual force topology control algorithm; performing preprocessing and time synchronization on the signals received by each unmanned aerial vehicle of the unmanned aerial vehicle cluster so as to calculate an accurate time delay inequality; calculating the specific position coordinates of the underwater target according to the underwater target signals received by the unmanned aerial vehicles and the calculated time delay inequality; compared with a traditional scheme, the unmanned aerial vehicle cluster is dynamically deployed in a self-organized and self-adaptive mode through the virtual force topology so as to ensure stable communication of the cluster structure and expand the sensing coverage range, and the method has the advantages of self-adaptive adjustment and control, good communication robustness and high positioning precision; and underwater collaborative search and accurate positioning of the unmanned system can be realized.

Description

Technical Field

[0001] The present invention relates to the field of underwater target positioning, and in particular to a method and system for collaborative underwater target positioning based on unmanned aerial vehicle topology control. Background Art

[0002] Underwater target positioning is crucial in many fields, including marine reconnaissance, anti-submarine warfare, and environmental monitoring. Electromagnetic wave propagation is limited in water, and sound waves are currently widely used as a medium for underwater information transmission. Traditional positioning systems mostly rely on fixed platforms or underwater vehicles carrying sensor arrays for deployment. These deployments are costly and inflexible, making them difficult to adapt to rapid response and complex terrain environments. The development of drone technology, with its high maneuverability and flexible deployment capabilities, has provided new insights into the construction of air-sea collaborative detection systems. Existing research has attempted to achieve collaborative positioning of underwater targets by using multiple drones equipped with sonar equipment to form distributed arrays. This has initially improved the system's detection capabilities. However, issues such as unstable communication topology, limited positioning accuracy, and a lack of self-organization and dynamic reconfiguration capabilities remain, making it difficult to ensure continuous collaborative perception and high-precision positioning of multiple drone systems without central control. Summary of the Invention

[0003] The purpose of the present invention is to propose a new method that integrates UAV adaptive topology control and collaborative positioning algorithm to improve the stability, flexibility and positioning accuracy of the positioning system in complex environments.

[0004] Specifically, the present invention provides a method for collaborative positioning of underwater targets based on drone topology control, comprising the following steps: S1. Build a UAV cluster and use the virtual force topology control algorithm to achieve the topology reconstruction of the UAV cluster structure; S2. Preprocess and time synchronize the signals received by each drone in the drone cluster and calculate the precise delay difference; S3. Calculate the specific position coordinates of the underwater target based on the underwater target signals and time delay differences received by each UAV.

[0005] An underwater target collaborative positioning system based on UAV topology control, including: Virtual force topology control algorithm module, used to realize the topology reconstruction of the UAV cluster structure; The information preprocessing module is used to preprocess and time synchronize the signals received by each drone in the drone cluster and calculate the precise delay difference; The collaborative perception and positioning algorithm module is used to calculate the specific location coordinates of the underwater target based on the underwater target signals and time delay differences received by each UAV.

[0006] The present invention provides the following beneficial effects: It utilizes a virtual force topology control algorithm to self-organize and adaptively dynamically deploy drone clusters, ensuring stable cluster communication and expanding sensing coverage. Information preprocessing utilizes high-precision GPS time synchronization and signal enhancement technology to effectively enhance underwater acoustic signals, improving the reliability and accuracy of raw data. Collaborative positioning utilizes the TDOA, Chan, and Taylor algorithms to achieve a step-by-step solution from coarse to precise positioning, thereby outputting stable and reliable underwater target position information. This method offers the advantages of adaptive regulation control, robust communication, and high positioning accuracy, enabling collaborative underwater search and precise positioning of unmanned systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 This is a flow chart of the underwater target collaborative positioning method based on UAV topology control of the present invention; Figure 2 Schematic diagram of the drone cluster topology control provided for this application example; Figure 3 Schematic diagram of unmanned cluster collaborative underwater positioning provided for this application example. DETAILED DESCRIPTION

[0008] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0009] Before formally explaining the present invention, the scheme of the present invention is first generally explained for easy understanding.

[0010] Please refer to Figure 1 The present invention provides a method for collaborative positioning of underwater targets based on topological control of unmanned aerial vehicles, comprising the following steps: S1. Build a UAV cluster and use the virtual force topology control algorithm to achieve the topology reconstruction of the UAV cluster structure; It should be noted that the virtual force topology control algorithm in step S1 adjusts the virtual force weight according to the minimum and maximum distances between the UAV and its neighbors to ensure that the UAV achieves uniform coverage of the detection area.

[0011] Please refer to Figure 2 , Figure 2 Schematic diagram of the drone cluster topology control provided for this application; Specifically, the virtual force topology control algorithm module starts to work after the initial unmanned cluster is randomly deployed. At this time, all sensor nodes communicate with each other. The virtual force topology control algorithm adjusts the virtual force weight based on the minimum and maximum distances between the drone and its neighbors. When two sensors are placed too close together, they will exert a repulsive force on each other to ensure that the sensors are not too clustered together. If the distance between sensors is too far, they will exert an attractive force on each other, ensuring a globally uniform sensor layout and no communication disconnection.

[0012] The above process can be specifically expressed as follows:

[0013] Among them, the molecular force between the edge nodes of the drone is affected by the node distance The impact of is the repulsion coefficient, is the gravitational coefficient. When the distance between nodes i and j is less than When the distance between nodes i and j is greater than Therefore, the net force of the other nodes acting on the drone node i is:

[0014] The algorithm dynamically adjusts the topology of the drone cluster based on the set distance threshold, which can increase the coverage of sensor collaborative perception. When a link is about to be interrupted, the relevant nodes will recalculate the movement direction based on the virtual force rules and actively approach to repair the link.

[0015] S2. Preprocess and time synchronize the signals received by each drone in the drone cluster and calculate the precise delay difference; It should be noted that the signal received by the drone in step S2 includes: underwater acoustic signal, GPS information and time stamp.

[0016] The preprocessing in step S2 uses a convolutional denoising autoencoder and an adaptive minimum mean square error filtering method to enhance the target signal in a complex underwater acoustic environment.

[0017] Specifically, information preprocessing uses the underwater acoustic sensor carried by the drone to receive the acoustic signals emitted by underwater targets and accurately marks the signal time through the drone GPS timestamp.

[0018] In order to improve the quality of the signal, the convolutional denoising autoencoder and adaptive minimum mean square error filtering method are introduced to enhance the target signal in a complex underwater acoustic environment and reduce the error caused by environmental noise.

[0019] Among them, the CDAE network structure is mainly used to preliminarily remove the interference of environmental noise on the original signal. It has excellent nonlinear modeling capabilities and can effectively remove noise and retain useful information.

[0020] On this basis, the LMS filter updates the filter weights by iterative , to minimize the output error The output of the filter It can be expressed as:

[0021] The filter continuously iteratively updates the adaptive weight formula:

[0022] The filter output error is expressed as:

[0023] On this basis, the hydroacoustic signal, GPS information and timestamp are combined, and these information are packaged and transmitted to the collaborative positioning module to provide high-quality data support for multi-node collaborative target positioning.

[0024] S3. Calculate the specific position coordinates of the underwater target based on the underwater target signals and time delay differences received by each UAV.

[0025] Please refer to Figure 3 , Figure 3 Schematic diagram of unmanned swarm collaborative underwater positioning provided for this application example; It should be noted that step S3 is specifically as follows: S31, calculate the time difference of the underwater acoustic signal received by each UAV node according to the TDOA algorithm; S32. Combine the time difference and use the fusion strategy of Chan algorithm to establish a linear least squares model to solve the initial position of the target; In three-dimensional space, the TDOA positioning algorithm is studied based on the Chan algorithm. Assuming that N array elements are deployed, the coordinates of the underwater target position are ( ), the array element position coordinates are ( ), is the corresponding array element number, For the The distance from each array element to the underwater target, taking the first array element as the reference point, the distance difference between the other array elements and array element 1 can be expressed as:

[0026] definition , simplified to matrix form for solution:

[0027] Generally, only three delay differences are needed to solve the target position. When the number of array elements is greater than four, redundant data information will be generated. In this case, the weighted least squares method is used to effectively utilize the redundant data. At this time, the initial equations are converted into linear equations, and the initial position is solved using the weighted least squares method. is an unknown vector, where , establish the following linear equation:

[0028] in , Assumptions The elements in are independent of each other, so we can get The weighted least squares result of :

[0029] in is the error vector The covariance matrix of , for TDOA the covariance matrix of the measurements, , , can be Solve it out.

[0030] exist In, when and When they are independent of each other, The weighted least squares method is used to estimate the initial coordinates of the target. However, if the covariance matrix is ​​directly used to approximate the covariance matrix of the error vector, additional errors may be generated. Therefore, in order to improve the positioning accuracy, the relationship between the auxiliary variables and the target can be used for optimization. First, the estimated position is calculated in a noisy environment. The covariance matrix of .

[0031]

[0032] set up ,but And its covariance matrix is:

[0033]

[0034] Through the relationship between various parameters, a linear equation system is established to solve:

[0035] in, , , The expression of the target position can be obtained by calculation:

[0036] S33. This initial position is used as the initial input for iterative optimization. A local nonlinear least squares optimization function is constructed based on the Taylor algorithm. The target position estimate is continuously corrected by recursive iteration until the current iterative error is less than the preset threshold. The specific position coordinates of the underwater target are then output.

[0037] Estimate the initial target position based on Chan algorithm , Taylor expansion is performed based on this position, Convert to The least squares solution is:

[0038] In the next recursion, define:

[0039] Repeat the recursion until If it is less than the set threshold, then the solution is This is a more accurate target calculation position.

[0040] An underwater target collaborative positioning system based on UAV topology control, comprising: Virtual force topology control algorithm module, used to realize the topology reconstruction of the UAV cluster structure; The information preprocessing module is used to preprocess and time synchronize the signals received by each drone in the drone cluster and calculate the precise delay difference; The collaborative perception and positioning algorithm module is used to calculate the specific location coordinates of the underwater target based on the underwater target signals and time delay differences received by each UAV.

[0041] It should be noted that the virtual force topology control algorithm module adjusts the virtual force weight according to the minimum and maximum distances between the UAV and its neighbors to ensure that the UAV achieves uniform coverage of the detection area.

[0042] It should be noted that the signals received by the drone in the information preprocessing module include: underwater acoustic signals, GPS information and timestamps.

[0043] It should be noted that the preprocessing in the information preprocessing module uses a convolutional denoising autoencoder and an adaptive minimum mean square error filtering method to enhance the target signal in a complex underwater acoustic environment.

[0044] It should be noted that the processing process of the collaborative perception and positioning algorithm module is specifically as follows: according to the TDOA algorithm, the time difference of the underwater acoustic signal received by each UAV node is calculated; the time difference is combined, and the linear least squares model is established using the fusion strategy of the Chan algorithm to solve the initial position of the target; this initial position is used as the initial input for iterative optimization, and a local nonlinear least squares optimization function is constructed based on the Taylor algorithm. The target position estimate is continuously corrected by recursive iteration until the current iteration error is less than the preset threshold, and the specific position coordinates of the underwater target are output.

[0045] In summary, the present invention has the following beneficial effects: It utilizes a virtual force topology control algorithm to self-organize and adaptively dynamically deploy drone clusters, ensuring stable cluster structure communication and expanding sensing coverage; information preprocessing utilizes high-precision GPS time synchronization and signal enhancement technology to effectively enhance underwater acoustic signals, improving the reliability and accuracy of raw data; collaborative positioning utilizes TDOA, Chan, and Taylor algorithms to achieve a step-by-step solution from coarse positioning to precise positioning, thereby outputting stable and reliable underwater target position information. This method offers the advantages of adaptive regulation control, good communication robustness, and high positioning accuracy, enabling collaborative underwater search and precise positioning of unmanned systems.

[0046] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within 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 collaborative underwater target positioning based on UAV topology control, characterized by: The method comprises the following steps: S1. Build a UAV cluster and use the virtual force topology control algorithm to achieve the topology reconstruction of the UAV cluster structure; S2. Preprocess and time synchronize the signals received by each drone in the drone cluster to facilitate the calculation of accurate delay difference; S3. Calculate the specific position coordinates of the underwater target based on the underwater target signals received by each UAV and the calculated precise time delay difference.

2. The underwater target collaborative positioning method based on UAV topology control according to claim 1, characterized in that: The virtual force topology control algorithm described in step S1 adjusts the virtual force weight according to the minimum and maximum distances between the drone and its neighbors to ensure that the drone achieves uniform coverage of the detection area.

3. The underwater target collaborative positioning method based on UAV topology control according to claim 1, characterized in that: The signals received by the drone in step S2 include: underwater acoustic signals, GPS information and timestamps.

4. The underwater target collaborative positioning method based on UAV topology control according to claim 1, characterized in that: The preprocessing in step S2 uses a convolutional denoising autoencoder and an adaptive minimum mean square error filtering method to enhance the target signal in a complex underwater acoustic environment.

5. The underwater target collaborative positioning method based on UAV topology control according to claim 1, characterized in that: Step S3 is as follows: S31, calculate the time difference of the underwater acoustic signal received by each UAV node according to the TDOA algorithm; S32. Combine the time difference and use the fusion strategy of Chan algorithm to establish a linear least squares model to solve the initial position of the target; S33. This initial position is used as the initial input for iterative optimization. A local nonlinear least squares optimization function is constructed based on the Taylor algorithm. The target position estimate is continuously corrected by recursive iteration until the current iterative error is less than the preset threshold. The specific position coordinates of the underwater target are then output.

6. An underwater target collaborative positioning system based on UAV topology control, characterized by: include: Virtual force topology control algorithm module, used to realize the topology reconstruction of the UAV cluster structure; The information preprocessing module is used to preprocess and time synchronize the signals received by each drone in the drone cluster to facilitate the calculation of accurate delay difference; The collaborative perception and positioning algorithm module is used to calculate the specific position coordinates of the underwater target based on the underwater target signals received by each drone and the calculated precise time delay difference.

7. The underwater target collaborative positioning system based on UAV topology control according to claim 6, characterized in that: The virtual force topology control algorithm module adjusts the virtual force weight according to the minimum and maximum distances between the UAV and its neighbors to ensure that the UAV achieves uniform coverage of the detection area.

8. The underwater target collaborative positioning system based on UAV topology control according to claim 6, characterized in that: The signals received by the drone in the information preprocessing module include: underwater acoustic signals, GPS information and timestamps.

9. The underwater target collaborative positioning system based on UAV topology control according to claim 6, characterized in that: The preprocessing in the information preprocessing module uses a convolutional denoising autoencoder and an adaptive minimum mean square error filtering method to enhance the target signal in a complex underwater acoustic environment.

10. The underwater target collaborative positioning system based on UAV topology control according to claim 6, characterized in that: The specific processing process of the collaborative perception and positioning algorithm module is as follows: the time difference of the underwater acoustic signal received by each UAV node is calculated according to the TDOA algorithm; the time difference is combined and the linear least squares model is established using the fusion strategy of the Chan algorithm to solve the initial position of the target; this initial position is used as the initial input for iterative optimization, and a local nonlinear least squares optimization function is constructed based on the Taylor algorithm. The target position estimate is continuously corrected by recursive iteration until the current iteration error is less than the preset threshold, and the specific position coordinates of the underwater target are output.

Images