A UWB positioning method and system based on multi-UAV collaboration

Abstract

The present application provides a UWB positioning method and system based on multi-UAV collaboration, the method comprising: initializing the configuration of UAVs equipped with UWB base stations according to preset flight control parameters, establishing communication links between UAVs and generating a node configuration set. An error correction model is constructed using UWB ranging data between known and unknown nodes to solve the positions of unknown nodes and generate a three-dimensional aerial positioning base station coordinate set for all UAV nodes. Time base alignment is performed on all UAVs, and a time difference positioning model is constructed based on the coordinate set. By activating a mobile terminal, it is controlled to broadcast UWB signals to multiple UAVs, the signal propagation duration is obtained and input into the time difference model to calculate its spatial coordinates. The present application solution supports multi-UAV aerial self-organizing network positioning, gets rid of the dependence on fixed anchor points, can be dynamically deployed, quickly networked, and positioned in the air at the rescue site, and can effectively improve the accuracy of positioning and navigation in complex rescue scenarios.

Description

Technical Field

[0001] The present application relates to the field of UAV technology, and in particular to a UWB positioning method and system based on multi-UAV collaboration. Background Art

[0002] Location-based services are one of the key technologies for modern urban comprehensive emergency rescue. With the highly intensive construction of urban infrastructure, urban comprehensive emergency rescue is increasingly facing complex environments such as dense buildings, urban canyons, urban woodlands, super-large complexes, super-high-rise buildings, underground spaces, and indoor and outdoor junctions. How to achieve continuous high-precision positioning indoors and outdoors for comprehensive emergency rescue in complex environments is a technical problem that needs to be solved urgently.

[0003] Under open outdoor observation conditions, China's Beidou Navigation Satellite System (BDS) can provide emergency rescue services with a variety of services, including location information, positioning technology, and short messaging. However, in complex scenarios such as urban canyons, underground spaces, indoor environments, and mountainous forests, rescue workers face problems such as lack of positioning and navigation, and inaccurate disaster situation awareness, as the Beidou Navigation Satellite System (BDS) signals are extremely weak when they reach the ground. This further reduces the efficiency of emergency response and increases the demand for navigation and positioning systems in complex urban and indoor environments. Summary of the Invention

[0004] This application provides a UWB positioning method and system based on multi-UAV collaboration, which is used to solve the problem of inaccurate positioning and navigation in complex rescue scenarios in related technologies.

[0005] In a first aspect, the present application provides a UWB positioning method based on multi-UAV collaboration, and the UWB positioning method based on multi-UAV collaboration includes:

[0006] Initialize the configuration of the UAVs equipped with the UWB positioning base station according to the preset flight control parameters, establish a UWB communication link between the UAVs, and generate a corresponding initialization node configuration set;

[0007] Based on the UWB ranging data between the known UAV nodes and the unknown UAV nodes in the initialization node configuration set, the ranging error correction model is constructed, and the position coordinates of the unknown UAV nodes are calculated to generate a three-dimensional aerial positioning base station coordinate set for all UAV nodes;

[0008] Performing time base alignment on all the UAVs, and performing time difference ranging between the UAVs based on the three-dimensional aerial positioning base station coordinate set to generate a time difference positioning model;

[0009] By activating a mobile terminal equipped with a UWB positioning tag, controlling the mobile terminal to broadcast a UWB positioning signal to N drones; wherein N is an integer greater than or equal to 1;

[0010] Obtain the propagation time of the UWB positioning signal to the N drones, input the propagation time into the time difference positioning model, and determine the position coordinates of the mobile terminal.

[0011] Optionally, in a first implementation of the first aspect of the present application, the ranging error correction model constructed based on the UWB ranging data between the known drone nodes and the unknown drone nodes in the initialization node configuration set, performing position coordinate calculation on the unknown drone nodes, and generating a three-dimensional aerial positioning base station coordinate set for all drone nodes includes:

[0012] Comparing the UWB ranging data between the known UAV node and the unknown UAV node with the actual distance to construct an error ranging set;

[0013] Generate a corrected ranging matrix by performing a semidefinite programming optimization process based on minimizing the sum of squares of ranging residuals on the error ranging set;

[0014] Based on the ranging values ​​between different nodes in the corrected ranging matrix and the spatial coordinates of the known UAV nodes, a joint position derivation is performed on the unknown UAV nodes to generate a three-dimensional position coordinate set of all the unknown UAV nodes;

[0015] By merging the three-dimensional position coordinate set with the known drone node coordinates, a three-dimensional aerial positioning base station coordinate set of all drone nodes is generated.

[0016] Optionally, in a second implementation of the first aspect of the present application, the step of aligning the time reference of all the drones, performing time difference ranging between the drones based on the three-dimensional aerial positioning base station coordinate set, and generating a time difference positioning model includes:

[0017] The target UAV node in the three-dimensional aerial positioning base station coordinate set sends a time synchronization signal to other UAV nodes, and generates a time synchronization dataset of relative delays between all UAVs based on the reception timestamps fed back by the other UAV nodes;

[0018] By performing time drift compensation and frequency offset correction on the time synchronization data set, a unified time table of nodes with unified time base correction is constructed;

[0019] Based on the unified node schedule, the target UAV node sends a ranging signal to the other UAV nodes, and generates a time difference parameter set according to the time difference of the other UAV nodes receiving the ranging signal;

[0020] The time difference parameter set is associated with the corresponding drone node coordinates to construct a time difference positioning model based on the hyperbola equation.

[0021] Optionally, in a third implementation of the first aspect of the present application, after the step of activating a mobile terminal configured with a UWB positioning tag and controlling the mobile terminal to broadcast a UWB positioning signal to N drones, the method further includes:

[0022] Performing UWB bidirectional communication between the mobile terminal and the UAV node according to a preset ranging timing rule, and determining a first sending time of the mobile terminal to send a first positioning request signal;

[0023] Determine a first reception time when the UAV node receives the first positioning request signal, send a first positioning response signal to the mobile terminal after a delay of a preset response interval, and determine a corresponding second sending time;

[0024] Determining a second receiving time when the mobile terminal receives the first positioning response signal, sending a second positioning request signal to the drone node after delaying the preset response interval, and determining a corresponding third sending time and a third receiving time when the drone node receives the second positioning request signal;

[0025] According to all sending times and receiving times, the propagation duration of the positioning response signal between the mobile terminal and the drone node is determined based on a preset timing cancellation operation.

[0026] Optionally, in a fourth implementation of the first aspect of the present application, after the steps of obtaining the propagation time of the UWB positioning signal to the N drones, inputting the propagation time into the time difference positioning model, and determining the position coordinates of the mobile terminal, the method further includes:

[0027] Acquiring state characteristic information of the mobile terminal in a current time period according to the speed data of the mobile terminal, and determining whether the mobile terminal is in a continuously stationary state based on a preset threshold;

[0028] If the mobile terminal is in a non-stationary state, setting a ranging period according to the state characteristic information;

[0029] The timestamps of the positioning request signal and the response signal received within the ranging period are input into the time difference positioning model to determine the location information of the mobile terminal.

[0030] Optionally, in a fifth implementation of the first aspect of the present application, after the step of merging the three-dimensional position coordinate set with the known drone node coordinates to generate a three-dimensional aerial positioning base station coordinate set of all drone nodes, the step further includes:

[0031] Acquire real-time positioning error data and signal coverage quality indicators of the UAV nodes, and determine a set of UAV nodes that are out of the network based on the real-time positioning error data and the quality indicators;

[0032] Determine the spatial coordinates corresponding to the set of drone nodes and the position distribution of adjacent nodes, and determine the priority area for redeployment and the target flight path by performing spatial topological analysis on the spatial coordinates and the position distribution of adjacent nodes;

[0033] Sending a flight control instruction to the unmanned aerial vehicle node that has left the network according to the priority area and the target flight path, and controlling the unmanned aerial vehicle node that has left the network to fly to a designated location along the planned path;

[0034] After the unmanned aerial vehicle node that has left the network completes its flight to the designated location, the three-dimensional space coordinates of the unmanned aerial vehicle node that has left the network are recalculated using the ranging error correction model.

[0035] Optionally, in a sixth implementation of the first aspect of the present application, after the step of performing joint position derivation on the unknown drone nodes based on the ranging values ​​between different nodes in the corrected ranging matrix and the spatial coordinates of the known drone nodes to generate a set of three-dimensional position coordinates of all the unknown drone nodes, the step further includes:

[0036] Performing an initial position coordinate calculation on the target unknown UAV node according to the ranging error correction model to generate a corresponding first positioning result; wherein the target unknown UAV node is one of all unknown UAV nodes;

[0037] Controlling the target unknown UAV node to fly a preset distance along a set direction, and determining a corresponding theoretical displacement vector based on the flight control information of the target unknown UAV node;

[0038] After completing the preset distance flight, obtaining the ranging information of the target unknown UAV node and the adjacent known UAV nodes, and performing a second position coordinate solution in combination with the ranging error correction model to generate a corresponding second positioning result;

[0039] A positioning change vector is calculated based on the first positioning result and the second positioning result, and the positioning change vector is compared with the theoretical displacement vector to generate a dynamic verification result of the positioning state of the target unknown UAV node.

[0040] A second aspect of the present application provides a UWB positioning system based on multi-UAV collaboration, the UWB positioning system based on multi-UAV collaboration comprising:

[0041] A construction module is used to initialize the configuration of the UAVs configured with the UWB positioning base station according to preset flight control parameters, establish a UWB communication link between the UAVs, and generate a corresponding initialization node configuration set;

[0042] A generation module is configured to construct a ranging error correction model based on UWB ranging data between known UAV nodes and unknown UAV nodes in the initialization node configuration set, calculate the position coordinates of the unknown UAV nodes, and generate a three-dimensional aerial positioning base station coordinate set for all UAV nodes;

[0043] A positioning module, configured to perform time base alignment on all the UAVs, and perform time difference ranging between the UAVs based on the three-dimensional aerial positioning base station coordinate set to generate a time difference positioning model;

[0044] A control module is configured to activate a mobile terminal equipped with a UWB positioning tag and control the mobile terminal to broadcast a UWB positioning signal to N drones;

[0045] The determination module is used to obtain the propagation time of the UWB positioning signal to the N drones, input the propagation time into the time difference positioning model, and determine the position coordinates of the mobile terminal.

[0046] A third aspect of an embodiment of the present application provides an electronic device, including a memory and a processor, wherein the processor is used to execute a computer program stored on the memory. When the processor executes the computer program, it implements the steps of the UWB positioning method based on multi-UAV collaboration provided in the first aspect of the embodiment of the present application.

[0047] The fourth aspect of the embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the UWB positioning method based on multi-UAV collaboration provided in the first aspect of the embodiment of the present application are implemented.

[0048] In summary, according to the UWB positioning method and system based on multi-UAV collaboration provided by the solution of the present application, the UAVs configured with UWB positioning base stations are initialized and configured according to preset flight control parameters, a UWB communication link between the UAVs is constructed, and a corresponding initialization node configuration set is generated; a ranging error correction model is constructed based on the UWB ranging data between known UAV nodes and unknown UAV nodes in the initialization node configuration set, the position coordinates of the unknown UAV nodes are solved, and a three-dimensional aerial positioning base station coordinate set of all UAV nodes is generated; time reference alignment is performed on all the UAVs, and time difference ranging is performed between the UAVs based on the three-dimensional aerial positioning base station coordinate set to generate a time difference positioning model; by activating a mobile terminal configured with a UWB positioning tag, the mobile terminal is controlled to broadcast a UWB positioning signal to N UAVs; the propagation time of the UWB positioning signal to the N UAVs is obtained, the propagation time is input into the time difference positioning model, and the position coordinates of the mobile terminal are determined. By building an aerial positioning network with multiple drones equipped with UWB positioning base stations, there is no need to rely on fixed anchor points. It can be dynamically deployed, quickly networked, and positioned in the air at the rescue site, which can effectively improve the accuracy of positioning and navigation in complex rescue scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of the flow of a UWB positioning method based on multi-UAV collaboration provided in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of signal propagation for bilateral two-way ranging is provided for an embodiment of the present application;

[0051] Figure 3 A schematic diagram of the program modules of a UWB positioning system based on multi-UAV collaboration provided in an embodiment of the present application;

[0052] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0054] In order to solve the problem of inaccurate positioning and navigation in complex rescue scenarios in related technologies, the embodiment of the present application provides a UWB positioning method based on multi-UAV collaboration, such as Figure 1 The flowchart of the UWB positioning method based on multi-UAV collaboration provided in this embodiment includes the following steps:

[0055] Step 110: Initialize the configuration of the UAVs configured with the UWB positioning base station according to the preset flight control parameters, establish a UWB communication link between the UAVs, and generate a corresponding initialization node configuration set.

[0056] Specifically, the system obtains each drone's flight mission number, initial pose parameters, flight altitude range, and device identification information for its onboard UWB (Ultra Wide Band) module. Based on this information, it performs state activation on each drone. By activating the UWB communication module's communication channel and broadcasting node initialization signaling, the self-organizing identity registration process is completed. Furthermore, each drone is marked as a "known drone node" or "unknown drone node" based on its relative position and number priority, and this status is recorded in the initialization configuration table. During the UWB link establishment process, multiple rounds of handshake protocols (such as TWR (Two-Way Ranging) or TDOA (Time Difference of Arrival)) are executed within a specified time window to synchronize communication delays and determine whether a valid direct link exists between nodes. Finally, the number information, role flags, initial position status, and communication channel validity of all drones are integrated to generate an initialization node configuration set for subsequent ranging and positioning.

[0057] Step 120: Based on the UWB ranging data between the known UAV nodes and the unknown UAV nodes in the initialization node configuration set, the ranging error correction model is constructed to solve the position coordinates of the unknown UAV nodes and generate a three-dimensional aerial positioning base station coordinate set for all UAV nodes.

[0058] Specifically, based on the UWB ranging data between known and unknown drone nodes in the initialization node configuration set, a ranging error correction model is constructed, and the position of the unknown node is estimated. By collecting the UWB ranging values ​​between known and unknown nodes and comparing them with the actual ranging, a ranging error set is constructed. The error residuals are minimized using a semidefinite programming method to generate a corrected ranging matrix. Subsequently, the ranging information in the correction matrix is ​​jointly optimized and derived in combination with the three-dimensional coordinates of the known nodes to estimate the three-dimensional position coordinates of the unknown nodes. Finally, by merging the coordinate information of the unknown and known nodes, a complete three-dimensional aerial positioning base station coordinate set is constructed to ensure the consistency of the spatial coordinates of all nodes in the network.

[0059] In an optional implementation of this embodiment, a ranging error correction model is constructed based on UWB ranging data between known drone nodes and unknown drone nodes in the initialization node configuration set, and the position coordinates of unknown drone nodes are solved to generate a three-dimensional aerial positioning base station coordinate set of all drone nodes, including: comparing the UWB ranging data between known drone nodes and unknown drone nodes with the actual distance to construct an error ranging set; generating a corrected ranging matrix by performing semidefinite programming optimization processing based on minimization of the sum of squares of ranging residuals on the error ranging set; performing joint position derivation on the unknown drone nodes based on the ranging values ​​between different nodes in the corrected ranging matrix and the spatial coordinates of the known drone nodes to generate a three-dimensional position coordinate set of all unknown drone nodes; and generating a three-dimensional aerial positioning base station coordinate set of all drone nodes by merging the three-dimensional position coordinate set with the coordinates of the known drone nodes.

[0060] Specifically, in this embodiment, spatial resolution is performed on a swarm of aerial drones equipped with UWB ranging modules. The initial positions of some drone nodes are known, calibrated using high-precision global navigation satellite systems or pre-deployed locations, becoming reference nodes (i.e., known drone nodes). The remaining drone nodes, which are not equipped with high-precision positioning modules or whose initial positions are unknown, are considered to be located (i.e., unknown drone nodes). To achieve high-precision spatial position inversion, the system must first obtain UWB ranging data between unknown and known nodes and compare it with the actual geometric distance calculated from spatial coordinates. UWB ranging data is acquired by ultra-wideband radio ranging modules using two-way time-of-flight or time-difference-of-arrival (TDOA) methods. However, due to interference factors such as non-line-of-sight propagation, antenna delay, and temperature and humidity variations, the actual measured UWB distances often contain nonlinear errors. Therefore, the comparison results can quantify the ranging deviation between each pair of nodes, thereby constructing an error ranging set, which represents the distribution of the system's measurement error within the spatial topology. To suppress the propagation of ranging errors in the subsequent position derivation process, an optimization process based on minimizing the sum of squared residuals is performed on the set of error ranging measurements to construct a corrected ranging matrix that satisfies nonnegativity constraints. To achieve this goal, the semidefinite programming (SDP) method is introduced. This method is a convex optimization technique suitable for solving optimization problems with matrix inequality constraints. Specifically, a loss function is defined, and the squared residuals between the measured distances and the ideal geometric distances are used as the optimization objective. A system of optimization equations for the distance variables between nodes is constructed, and the SDP algorithm is used to find an optimal set of ranging values ​​that minimizes the overall error. This method effectively maintains global optimality in large-scale networks and is robust to outliers in ranging. For example, if a node is obscured, resulting in large deviations in single-point ranging, the SDP algorithm can suppress this deviation based on global network consistency. The corrected ranging matrix obtained after optimization is input as a constraint into the position solution module, and the three-dimensional position coordinates of all unknown nodes are solved through joint derivation. In this process, the corrected distance values ​​between multiple known drone nodes and an unknown node are first selected, and a nonlinear system of equations is constructed using the three-dimensional Euclidean distance formula. This system of equations is then solved using least squares iteration or the Gauss-Newton algorithm to obtain the coordinate estimate of the unknown node in three-dimensional space. Due to the redundancy of ranging between multiple nodes, joint solution can effectively avoid position drift caused by distortion of single observation data. For example, when there is corrected distance data between an unknown node and three non-coplanar known nodes, its three-dimensional coordinates can be determined by the three-sphere intersection method. If five or more known nodes are used simultaneously, the positioning stability and accuracy can be further improved.After obtaining the three-dimensional position coordinates of all unknown drone nodes, they are merged with the previously known node coordinates to construct a complete set of three-dimensional aerial positioning base station coordinates for the drones. This coordinate set serves as the spatial foundation for the entire UWB time-of-day positioning system and will be used for key tasks such as subsequent mobile terminal signal resolution, network dynamic adjustment, and time-of-day ranging model construction. For example, when the system receives a positioning request signal from a ground terminal, it can directly reference this three-dimensional base station set for time-of-day analysis and multi-point positioning solutions. Therefore, this processing flow not only enhances the consistency of the system's spatial model but also provides a high-confidence geographic reference for collaborative perception and distributed computing in dynamic networks.

[0061] Step 130: align all drones with respect to time base, and perform time difference ranging between drones based on the three-dimensional aerial positioning base station coordinate set to generate a time difference positioning model.

[0062] Specifically, in this embodiment, all drone nodes are time-aligned, and time-difference ranging (TDO) is performed between them based on a set of aerial positioning base station coordinates. By designating a node as a synchronization reference node, a time synchronization signal is transmitted to all other nodes, and each receiving node records the local reception timestamp to construct a time-synchronized dataset. This dataset undergoes drift compensation and frequency offset correction to unify the time reference of all nodes. Subsequently, the ranging signal broadcast by the target node is used to calculate the signal propagation time difference according to a unified time table, generating a set of time difference parameters. This set is then associated with the spatial coordinates of each node and input into a time-difference positioning model based on the hyperbola principle.

[0063] In an optional implementation of this embodiment, all drones are time-base aligned, and time difference ranging is performed between the drones based on the three-dimensional aerial positioning base station coordinate set to generate a time difference positioning model. The steps include: sending a time synchronization signal from the target drone node to other drone nodes in the three-dimensional aerial positioning base station coordinate set, and generating a time synchronization data set of relative delays between all drones based on the reception timestamps fed back by the other drone nodes; constructing a node unified time table with unified time base correction by performing time drift compensation and frequency offset correction on the time synchronization data set; sending a ranging signal from the target drone node to other drone nodes based on the node unified time table, and generating a time difference parameter set based on the time difference of other drone nodes receiving the ranging signal; and associating the time difference parameter set with the corresponding drone node coordinates to construct a time difference positioning model based on a hyperbolic equation.

[0064] Specifically, in this embodiment, to achieve high-precision three-dimensional positioning based on UWB, a unified time base is first established between drones to prevent the propagation of time errors caused by clock drift and frequency deviation. Based on a known set of three-dimensional aerial positioning base station coordinates, a target drone node with stable communication and low position estimation error is first selected as the master reference node for time synchronization. This node then sequentially transmits a time synchronization signal to all other drone nodes in the network. The signal carries the timestamp of the current reference node's local clock. Upon receiving this synchronization signal, the other receiving nodes immediately record the receiving timestamp of their own local clocks and return this reception time information to the target node via a feedback channel. The reference node compares the transmission time with the reception time returned in the feedback. Based on the actual distance between nodes in three-dimensional space and the signal propagation speed, it calculates and generates the relative delay between each pair of nodes, thereby forming a time synchronization dataset. This dataset, which includes the time difference information between all communicating pairs, reflects the relative clock offset within the entire drone network and forms the basis for establishing a unified time system. Subsequently, the time-synchronized dataset is subjected to precise time drift compensation and frequency offset correction. Time drift is caused by slight differences in the crystal oscillator frequencies used by different nodes, resulting in a linear shift in their internal clocks over time. Frequency offset refers to the random offset caused by insufficient clock frequency stability within a node. By introducing multi-period timestamp series data and modeling the inter-node time offset trajectory using linear regression or Kalman filtering, the drift coefficient and frequency offset value for each node can be estimated. Based on this, an affine transformation is applied to the local timestamps of all nodes to construct a unified node timetable under a unified time base correction. This timetable maps the local times of all nodes to the same reference time axis, forming a standardized time reference and providing a consistent basis for subsequent signal arrival time difference calculations. For example, if a drone's clock drifts by 2 microseconds per second, uncorrected within a 10-second operating cycle will result in a systematic error of 20 microseconds. The aforementioned drift compensation mechanism effectively mitigates this error. In the unified time domain after time synchronization, the target drone node broadcasts a ranging signal to the remaining nodes. After receiving the ranging signal, each receiving node adjusts the receiving timestamp using a unified timetable to determine the actual time difference between the target node's signal propagating to each node. The time difference between different nodes receiving the same signal is then compared with the transmission time to generate a set of time difference parameters with the target node as a reference. Since all node times are unified, the time difference can be directly considered a function of the signal propagation path distance. This parameter set maps the isochronous surface information of each receiving node relative to the target node. This time difference parameter set is then correlated with the coordinates of the three-dimensional aerial positioning base station. By constructing a time difference positioning model based on the hyperbolic equation, the position of unknown target points in space can be accurately determined.The principle of hyperbolic positioning is based on the TDOA method. Its core idea is to treat a base station pair consisting of any two nodes as the focus of the hyperbola, and the unknown target point is located on the hyperbola that satisfies a specific propagation time difference between the two nodes. By introducing multiple base station pairs to form multiple hyperbolic equations and numerically solving their intersections, the unique solution for the target position in three-dimensional space can be inferred. For example, when the TDOA information between the target node and three non-collinear base station pairs is known, the target position can be uniquely determined by combining the three-dimensional coordinate data. Therefore, this positioning model, which fuses time domain parameters with spatial coordinates, is a key supporting mechanism for achieving non-cooperative three-dimensional positioning of mobile terminals. It also provides a highly reliable spatial information foundation for tasks such as drone collaborative navigation, aerial monitoring, and post-disaster search and rescue.

[0065] Step 140: Activate the mobile terminal equipped with the UWB positioning tag and control the mobile terminal to broadcast the UWB positioning signal to N drones.

[0066] Specifically, N is an integer greater than or equal to 1. In this embodiment, when a mobile terminal equipped with a UWB positioning tag is activated and a UWB positioning signal is broadcast to N drones, the UWB chip on the mobile terminal is first triggered by a control instruction to enter an active transmission state, and a pulse positioning signal is emitted in multiple time slices according to the set broadcast period and frequency. The broadcast signal can adopt a standard TWR or TDOA format, and the content includes a unique device identifier, a transmission timestamp, a frame number, and a synchronization check code. At this time, multiple drone nodes deployed in the air receive UWB signals by monitoring their own bound channels, and sample the first detected signal, record the reception time, and attach the current local unified time reference. Each drone node performs a frame structure check on the received signal, and if the check passes, the signal reception event is reported to the collaborative positioning calculation module. By aggregating the signal propagation timestamps received by all nodes, a complete time data set can be provided for the subsequent mobile terminal position inversion based on the time difference positioning model.

[0067] Step 150: Obtain the propagation time of the UWB positioning signal to the N drones, input the propagation time into the time difference positioning model, and determine the position coordinates of the mobile terminal.

[0068] Specifically, in this embodiment, after obtaining the propagation time of the UWB signal to N drones, the collected propagation time difference is input into the constructed time difference positioning model to solve the three-dimensional coordinates. First, based on the reception time recorded by each node under a unified time reference, the propagation time difference between each pair of nodes and the target terminal is calculated. If the TDOA positioning mode is used, the reception time difference of other nodes relative to a certain node is calculated with reference to that node. Subsequently, combining the three-dimensional aerial coordinates of all drones, a set of multiple hyperbolic equations for the target terminal's position is constructed, and the propagation time difference parameters are mapped as constraints in this set of equations. This set of equations is numerically solved using the least squares method, constrained optimization, or iterative solution algorithm to obtain the coordinate values ​​of the terminal in three-dimensional space. This process can also introduce prior information such as the maximum terminal motion speed and the positioning stability window to enhance the robustness of the solution, ultimately obtaining the terminal's spatial position estimate at the current moment.

[0069] In an optional implementation of this embodiment, after the step of activating a mobile terminal configured with a UWB positioning tag and controlling the mobile terminal to broadcast a UWB positioning signal to N drones, the method further includes: performing UWB two-way communication between the mobile terminal and the drone node according to a preset ranging timing rule, and determining a first sending time when the mobile terminal sends a first positioning request signal; determining a first receiving time when the drone node receives the first positioning request signal, sending a first positioning response signal to the mobile terminal after a delay of a preset response interval, and determining a corresponding second sending time; determining a second receiving time when the mobile terminal receives the first positioning response signal, sending a second positioning request signal to the drone node after a delay of the preset response interval, and determining a corresponding third sending time and a third receiving time when the drone node receives the second positioning request signal; and determining the propagation time of the positioning response signal between the mobile terminal and the drone node based on a preset timing cancellation operation according to all sending times and receiving times.

[0070] Specifically, in a multi-UAV collaborative UWB positioning system, to achieve accurate distance measurement between mobile terminals and UAV nodes, a UWB two-way communication mechanism based on preset ranging timing rules is required. This mechanism, centered on Time-of-Flight Two-Way Ranging (TWR), avoids ranging errors caused by incomplete clock synchronization between nodes. First, according to a pre-defined ranging control protocol, a mobile terminal proactively sends a first positioning request signal to a UAV node within its coverage area. This request signal embeds a timestamp recorded by the mobile terminal's internal high-precision clock. This timestamp serves as the first transmission time, used for subsequent propagation time calculations. This time represents the time anchor point when the ranging signal begins to be transmitted from the terminal. It is unique and irreversible, and serves as the starting point for subsequent propagation time calculations. Inaccurate recording will result in overall ranging errors. Upon receiving the first positioning request signal, the UAV node immediately records its own local reception timestamp as the first reception time. After waiting for a preset fixed time interval (i.e., the response interval), it proactively sends a first positioning response signal to the mobile terminal. The time this response signal is sent is the second transmission time. This time point is recorded to establish a time reference point in the positioning return path. The response interval is set primarily to mitigate the uncertainty of device processing delays. Its value is precisely set during the system design phase and remains consistent between both communicating parties. After receiving the first positioning response signal, the mobile terminal again records the reception time in its local clock as the second reception time. To establish a cancellation path, after waiting for the same response interval as the previous one, the mobile terminal sends a second positioning request signal to the same drone node, recording the transmission timestamp of this request as the third transmission time. Upon receiving this signal, the drone node also records the reception time, generating the third reception time. This completes a round-trip, bidirectional, sequential communication link between the mobile terminal and the drone node, containing six key timestamps corresponding to the time information in the two rounds of transmission and reception interactions. To remove the influence of signal propagation time rather than device processing delay from this time information, a specific timing cancellation operation is required. This cancellation operation is based on a two-way ranging algorithm. Its core principle is to use the time information difference along the bidirectional path to offset the asymmetric processing delays of the devices at both ends, while preserving the pure signal propagation time. like Figure 2 The figure shows the signal propagation diagram of bilateral two-way ranging, where Corresponding to the first sending moment, Corresponding to the first receiving moment, Corresponding to the second sending time, Corresponding to the second receiving moment, Corresponding to the third sending time, Corresponding to the third receiving time, the propagation time between the mobile terminal and the drone node can be obtained based on the first sending time and the first receiving time. , Indicates the length of the response interval in the first round, Indicates the response interval of the second round, Indicates the first round of positioning reception and the propagation time of the response signal, It represents the propagation time of the second round of positioning reception and response signal. The corresponding propagation time calculation formula can be expressed as:

[0071] ,

[0072] It should be noted that there will be an overlapping part after adding the first round of propagation time and the second round of propagation time. Therefore, four propagation times should be counted in practice, and finally the average value is obtained to determine the propagation time of the positioning response signal between the mobile terminal and the drone node.

[0073] In an optional implementation of this embodiment, state characteristic information of the mobile terminal in the current time period is obtained based on the speed data of the mobile terminal, and whether the mobile terminal is in a continuously stationary state is determined based on a preset threshold; if the mobile terminal is in a non-stationary state, a ranging period is set according to the state characteristic information; and the timestamps of the positioning request signal and the response signal received during the ranging period are input into a time difference positioning model to determine the position information of the mobile terminal.

[0074] Specifically, in this embodiment, to achieve intelligent perception of the mobile terminal's state and dynamic ranging cycle control, continuous velocity data must first be acquired from the mobile terminal's integrated inertial measurement unit (IMU) or velocity sensor module. Velocity data can include time-varying physical quantities such as linear velocity and angular velocity. By analyzing the amplitude changes in velocity within a specific time window, state characteristics representing the current motion trend are extracted. When the mobile terminal is stationary or nearly stationary, the amplitude of its velocity changes will remain extremely low. If this amplitude remains below a system-defined stationary threshold, such as 0.05 m / s, for several consecutive time periods, the terminal is determined to be in a continuously stationary state. It is understood that after powering on, the mobile terminal first enters the INIT state and completes initialization, then enters the ACCESS state. During this phase, if network access is successful within one minute, the system proceeds to the RANGING state, executes the positioning and ranging process, and activates the ranging module CM, ultimately completing a ranging cycle. If network access is unsuccessful, the system disables Bluetooth advertising and enters deep sleep mode, awaiting awakening by significant movement. The significant motion wake-up mechanism uses low-power sensors to detect whether the terminal transitions from a stationary state to a moving state, for example, by detecting a sudden change in acceleration. When this condition is met, the main control unit (MCU) wakes up. After the MCU boots up, it restarts Bluetooth advertising and scanning and starts the SMD motion detection timer. This timer also configures the number of network access attempts and the timer period to ensure that the system completes network re-entry and positioning preparation within a specified time window. When the mobile terminal is in an active state after being awakened by motion, the system sets the ranging period based on real-time status information. If the current status indicates low-speed movement, such as 0.2-0.5 m / s, a longer ranging period can be set to reduce energy consumption, such as performing ranging every 5 seconds. If the status indicates high-speed movement, such as exceeding 1 m / s, the ranging period should be shortened to less than 1 second to maintain the timeliness and spatial continuity of location information. During the set ranging cycle, the system continuously records various timestamp information generated during UWB communication between the mobile terminal and multiple drone nodes, including the sending time of the positioning request signal, the receiving time of the drone node, the round-trip time of the positioning response, etc. These timestamps are passed as input data to the time difference positioning model, and the spatial coordinates of the mobile terminal are solved using the hyperbolic equation group.

[0075] In an optional implementation of this embodiment, real-time positioning error data and signal coverage quality indicators of drone nodes are obtained, and a set of drone nodes that have left the network is determined based on the real-time positioning error data and quality indicators; the spatial coordinates corresponding to the set of drone nodes and the position distribution of adjacent nodes are determined, and the priority areas for redeployment and the target flight path are determined by performing spatial topological analysis on the spatial coordinates and the position distribution of adjacent nodes; based on the priority areas and the target flight path, flight control instructions are sent to the drone nodes that have left the network to control the drone nodes that have left the network to fly to the designated location along the planned path; after the drone nodes that have left the network complete the flight to the designated location, the three-dimensional spatial coordinates of the drone nodes that have left the network are recalculated through the ranging error correction model.

[0076] Specifically, in this embodiment, during the multi-UAV network deployment process, it is necessary to continuously obtain real-time positioning error data and signal coverage quality indicators for each UAV node during flight. Real-time positioning error data refers to the positioning deviation value calculated by comparing with a known reference point or a correction model, reflecting the accuracy of the current node positioning. The signal coverage quality indicator is generally composed of wireless communication parameters such as received signal strength indicator, signal-to-noise ratio, and link packet loss rate, and is used to measure the communication capabilities between the UAV and other nodes in the network. When a node's positioning error exceeds a preset tolerance range, or its signal quality continuously falls below a set threshold, such as RSSI below -90 dBm, it indicates that the node has deviated from the optimal network structure. After filtering out all nodes that meet these conditions, a set of UAV nodes that have deviated from the network is formed. For each node in this set, its current three-dimensional spatial coordinates and the position distribution of its nearest neighboring nodes are obtained to construct a complete local spatial network structure. The position distribution of neighboring nodes can be determined using UWB ranging data or the relative position of broadcast beacons, combined with historical flight trajectories and ranging matrix information. Based on this spatial data, spatial topology analysis methods are used. These methods include constructing a node connectivity graph based on an adjacency matrix, calculating the area of ​​coverage holes, and analyzing node density gradients. Ultimately, these methods identify priority coverage gaps that require filling and generate corresponding priority redeployment areas. Simultaneously, based on inter-node connectivity, a flight obstacle avoidance model, and coverage redundancy assessment results, a target flight path is calculated. This path avoids existing densely covered areas as much as possible, balancing the dual objectives of minimizing energy consumption and maximizing mission efficiency. For example, if the connection between two nodes is broken and one is located at the edge of a coverage gap, a path planning algorithm such as A* or Dijkstra can be used to guide the disconnected node along the shortest path to the center of the gap, thus correcting the topological gap. Once the priority areas and flight paths are determined, the flight control system sends flight control commands to the disconnected UAV node, directing it to adjust its spatial position according to the planned path. This process typically involves adjusting the UAV's navigation parameters, including pitch angle, heading angle, and flight speed, to ensure smooth flight within the designated airspace and avoid other flying nodes, ensuring safe and accurate redeployment. Control commands can be sent via UWB, Wi-Fi, or 4G links via relay nodes or a ground control center. During flight, the flight path can be corrected in real time to address unexpected airspace obstacles or environmental interference. Once a node that has left the network reaches the target area and stabilizes, the positioning error correction process must be re-executed. By invoking a previously constructed ranging error correction model, the residual sum of squares of the ranging data between the node and surrounding nodes is minimized, eliminating ranging errors caused by channel interference, non-line-of-sight propagation, or attitude deflection. Ultimately, high-precision three-dimensional spatial coordinates are calculated.For example, by comparing its ranging data with three neighboring nodes with known locations and applying the hyperbolic surface fitting method, its coordinates are relocated to the optimal geometric center of the network topology, thereby ensuring the positioning consistency and coverage integrity of the entire networking system.

[0077] In an optional implementation of this embodiment, the target unknown UAV node is initially positionally calculated based on a ranging error correction model to generate a corresponding first positioning result; the target unknown UAV node is controlled to fly a preset distance in a set direction, and a corresponding theoretical displacement vector is determined based on the flight control information of the target unknown UAV node; after completing the preset distance flight, the ranging information of the target unknown UAV node and the adjacent known UAV nodes is obtained, and a second position coordinate solution is performed in combination with the ranging error correction model to generate a corresponding second positioning result; the positioning change vector is calculated based on the first positioning result and the second positioning result, and the positioning change vector is compared with the theoretical displacement vector to generate a dynamic verification result of the positioning state of the target unknown UAV node.

[0078] Specifically, the target unknown UAV node is one of all unknown UAV nodes. During the high-precision dynamic positioning verification of the target unknown UAV node, its initial position coordinates are calculated based on the constructed ranging error correction model. This process uses ultra-wideband ranging data between the target node and its neighboring known UAV nodes as input. A residual minimization optimization process based on semidefinite programming is performed within the ranging error correction model to output a preliminary positioning point, i.e., the first positioning result. This positioning result is the estimated position of the unknown node in three-dimensional space at the current moment. Although error-corrected, its accuracy requires further verification due to the potential for interference reflections and multipath effects in the initial environment. Subsequently, the control system issues navigation commands to the target unknown UAV node, guiding it to fly a fixed, preset distance in a set direction, for example, due east at a distance of 5 meters. During the flight, flight control parameters are recorded in real time to obtain data such as its attitude information, velocity, and heading angle. The theoretical displacement vector is calculated based on the flight duration. This vector represents the expected displacement of the node in space under the desired state, with a defined direction and magnitude, and serves as a reference for subsequent positioning comparisons. After a node completes its flight along the set path and stabilizes, it collects distance measurement information from multiple nearby known drone nodes. This data is then fed into the same error correction model for a second position coordinate solution, generating a second positioning result. This result provides an accurate estimate of the node's new position, reflecting its spatial coordinates after the flight. The two positioning results form a pair of three-dimensional coordinate points with a temporal relationship and an actual distance difference in space. By performing a difference operation on the first and second positioning results, a positioning change vector is calculated. This vector represents the actual displacement of the node between the two ranging measurements and reflects the true impact of the external positioning system. This positioning change vector is then compared with the theoretical displacement vector previously calculated based on flight control information, including directional consistency judgment and module length deviation analysis. If there is a high degree of consistency between the two in terms of direction angle and displacement amplitude, the positioning state can be determined to be stable and reliable, indicating that the ranging error correction model has effectively calibrated non-ideal factors in the current environment; if there is a significant difference, such as a theoretical displacement of 5 meters but an actual change vector of only 2 meters or a directional offset exceeding a threshold of 15 degrees, it indicates that the node is affected by a combination of environmental interference, flight control system errors, or positioning errors during flight, and it is necessary to perform dynamic verification and correction processing on the positioning state or replan its path and ranging strategy. Through this dynamic verification mechanism, not only can the accuracy of the current positioning model in the actual environment be evaluated, but real-time feedback and positioning strategy adjustments can also be achieved for the target node in different states, ensuring that the spatial accuracy and system stability of drone networking in complex airspace environments continue to meet high-reliability application requirements.

[0079] According to the UWB positioning method based on multi-UAV collaboration provided by the present application, UAVs equipped with UWB positioning base stations are initialized and configured according to preset flight control parameters, UWB communication links are established between the UAVs, and a corresponding initialization node configuration set is generated. A ranging error correction model is constructed based on UWB ranging data between known and unknown UAV nodes in the initialization node configuration set, and the position coordinates of unknown UAV nodes are calculated to generate a three-dimensional aerial positioning base station coordinate set for all UAV nodes. Time base alignment is performed on all UAVs, and time difference ranging is performed between UAVs based on the three-dimensional aerial positioning base station coordinate set to generate a time difference positioning model. By activating a mobile terminal equipped with a UWB positioning tag, the mobile terminal is controlled to broadcast UWB positioning signals to N UAVs. The propagation time of the UWB positioning signal to the N UAVs is obtained, and the propagation time is input into the time difference positioning model to determine the position coordinates of the mobile terminal. By building an aerial positioning network with multiple UAVs equipped with UWB positioning base stations, it is possible to dynamically deploy, quickly network, and perform aerial positioning at the rescue site without relying on fixed anchor points, effectively improving the accuracy of positioning and navigation in complex rescue scenarios.

[0080] Figure 3 The embodiment of the present application provides a UWB positioning system based on multi-UAV collaboration, which can be used to implement the UWB positioning method based on multi-UAV collaboration in the aforementioned embodiment. Figure 3 As shown in the figure, the UWB positioning system based on multi-UAV collaboration mainly includes:

[0081] Construction module 10 is used to initialize the configuration of the UAVs configured with the UWB positioning base station according to the preset flight control parameters, establish the UWB communication link between the UAVs, and generate the corresponding initialization node configuration set;

[0082] A generation module 20 is configured to construct a ranging error correction model based on the UWB ranging data between known UAV nodes and unknown UAV nodes in the initialization node configuration set, calculate the position coordinates of the unknown UAV nodes, and generate a set of three-dimensional aerial positioning base station coordinates for all UAV nodes;

[0083] The positioning module 30 is used to align the time base of all UAVs and perform time difference ranging between UAVs based on the three-dimensional aerial positioning base station coordinate set to generate a time difference positioning model;

[0084] The control module 40 is configured to activate a mobile terminal equipped with a UWB positioning tag and control the mobile terminal to broadcast a UWB positioning signal to N drones;

[0085] The determination module 50 is used to obtain the propagation time of the UWB positioning signal to N drones, input the propagation time into the time difference positioning model, and determine the position coordinates of the mobile terminal.

[0086] In an optional implementation of this embodiment, the generation module is specifically used to: compare the UWB ranging data between known drone nodes and unknown drone nodes with the actual distance to construct an error ranging set; generate a corrected ranging matrix by performing semidefinite programming optimization processing on the error ranging set based on minimization of the sum of squares of ranging residuals; perform joint position derivation on the unknown drone nodes based on the ranging values ​​between different nodes in the corrected ranging matrix and the spatial coordinates of the known drone nodes to generate a three-dimensional position coordinate set of all unknown drone nodes; and generate a three-dimensional aerial positioning base station coordinate set of all drone nodes by merging the three-dimensional position coordinate set with the coordinates of the known drone nodes.

[0087] In an optional implementation of this embodiment, the positioning module is specifically used to: send a time synchronization signal from the target drone node to other drone nodes in the three-dimensional aerial positioning base station coordinate set, and generate a time synchronization data set of relative delays between all drones based on the reception timestamps fed back by other drone nodes; construct a node unified time table with a unified time base correction by performing time drift compensation and frequency offset correction on the time synchronization data set; based on the node unified time table, send a ranging signal to other drone nodes through the target drone node, and generate a time difference parameter set based on the time difference of other drone nodes receiving the ranging signal; associate the time difference parameter set with the corresponding drone node coordinates to construct a time difference positioning model based on the hyperbolic equation.

[0088] In an optional implementation of this embodiment, the determination module is also used to: perform UWB two-way communication between the mobile terminal and the drone node according to a preset ranging timing rule, and determine the first sending time when the mobile terminal sends the first positioning request signal; determine the first receiving time when the drone node receives the first positioning request signal, send the first positioning response signal to the mobile terminal after delaying the preset response interval, and determine the corresponding second sending time; determine the second receiving time when the mobile terminal receives the first positioning response signal, send the second positioning request signal to the drone node after delaying the preset response interval, and determine the corresponding third sending time and the third receiving time when the drone node receives the second positioning request signal; determine the propagation time of the positioning response signal between the mobile terminal and the drone node based on the preset timing cancellation operation according to all sending times and receiving times.

[0089] In an optional implementation of this embodiment, the positioning module is further used to: obtain state characteristic information of the mobile terminal in the current time period based on the speed data of the mobile terminal, and determine whether the mobile terminal is in a continuously stationary state based on a preset threshold; if the mobile terminal is in a non-stationary state, set a ranging period based on the state characteristic information; input the timestamps of the positioning request signal and the response signal received during the ranging period into the time difference positioning model to determine the location information of the mobile terminal.

[0090] In an optional implementation of this embodiment, the positioning module is also used to: obtain real-time positioning error data and signal coverage quality indicators of the drone nodes, and determine the set of drone nodes that have left the network based on the real-time positioning error data and quality indicators; determine the spatial coordinates corresponding to the set of drone nodes and the position distribution of adjacent nodes, and determine the priority area for redeployment and the target flight path by performing spatial topology analysis on the spatial coordinates and the position distribution of adjacent nodes; send flight control instructions to the drone nodes that have left the network based on the priority area and the target flight path, and control the drone nodes that have left the network to fly to the designated position along the planned path; after the drone nodes that have left the network complete the flight to the designated position, recalculate the three-dimensional spatial coordinates of the drone nodes that have left the network through the ranging error correction model.

[0091] In an optional implementation of this embodiment, the generation module is also used to: perform an initial position coordinate solution on the target unknown UAV node according to the ranging error correction model to generate a corresponding first positioning result; control the target unknown UAV node to fly a preset distance along a set direction, and determine the corresponding theoretical displacement vector according to the flight control information of the target unknown UAV node; after completing the preset distance flight, obtain the ranging information of the target unknown UAV node and the adjacent known UAV nodes, and perform a second position coordinate solution in combination with the ranging error correction model to generate a corresponding second positioning result; calculate the positioning change vector according to the first positioning result and the second positioning result, and compare the positioning change vector with the theoretical displacement vector to generate a dynamic verification result of the positioning state of the target unknown UAV node.

[0092] According to the UWB positioning system based on multi-UAV collaboration provided by the present application, UAVs equipped with UWB positioning base stations are initialized and configured according to preset flight control parameters, UWB communication links are established between the UAVs, and a corresponding initialization node configuration set is generated. A ranging error correction model is constructed based on UWB ranging data between known and unknown UAV nodes in the initialization node configuration set, and the position coordinates of unknown UAV nodes are calculated to generate a three-dimensional aerial positioning base station coordinate set for all UAV nodes. Time base alignment is performed on all UAVs, and time difference ranging is performed between UAVs based on the three-dimensional aerial positioning base station coordinate set to generate a time difference positioning model. By activating a mobile terminal equipped with a UWB positioning tag, the mobile terminal is controlled to broadcast UWB positioning signals to N UAVs. The propagation time of the UWB positioning signal to the N UAVs is obtained, and the propagation time is input into the time difference positioning model to determine the position coordinates of the mobile terminal. By building an aerial positioning network with multiple UAVs equipped with UWB positioning base stations, there is no need to rely on fixed anchor points. It can be dynamically deployed, quickly networked, and positioned in the air at the rescue site, effectively improving the accuracy of positioning and navigation in complex rescue scenarios.

[0093] Figure 4 An electronic device provided in an embodiment of the present application. This electronic device can be used to implement the UWB positioning method based on multi-UAV collaboration in the aforementioned embodiment, mainly including:

[0094] Memory 401, processor 402, and computer program 403 stored on memory 401 and executable on processor 402. Memory 401 and processor 402 are communicatively connected. When processor 402 executes computer program 403, the UWB positioning method based on multi-UAV collaboration described in the aforementioned embodiment is implemented. The number of processors may be one or more.

[0095] The memory 401 can be a high-speed random access memory (RAM) memory or a non-volatile memory such as a disk drive. The memory 401 is used to store executable program code, and the processor 402 interacts with the memory 401 .

[0096] Furthermore, the embodiment of the present application also provides a computer-readable storage medium, which can be provided in the electronic device in the above embodiments. The computer-readable storage medium can be the above Figure 4 Memory in the illustrated embodiment.

[0097] The computer-readable storage medium stores a computer program that, when executed by a processor, implements the UWB positioning method based on multi-UAV collaboration described in the aforementioned embodiment. Furthermore, the computer-readable storage medium may be a USB flash drive, a mobile hard drive, a read-only memory (ROM), RAM, a magnetic disk, or an optical disk, among other media capable of storing program code.

[0098] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0099] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0100] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A UWB positioning method based on multi-UAV collaboration, characterized in that: include: Initialize the configuration of the UAVs equipped with the UWB positioning base station according to the preset flight control parameters, establish a UWB communication link between the UAVs, and generate a corresponding initialization node configuration set; Based on the UWB ranging data between the known UAV nodes and the unknown UAV nodes in the initialization node configuration set, the ranging error correction model is constructed, the position coordinates of the unknown UAV nodes are solved, and a three-dimensional aerial positioning base station coordinate set of all UAV nodes is generated, including: comparing the UWB ranging data between the known UAV nodes and the unknown UAV nodes with the actual distance to construct an error ranging set; generating a corrected ranging matrix by performing a semidefinite programming optimization process based on minimization of the sum of squares of ranging residuals on the error ranging set; performing a joint position derivation on the unknown UAV nodes according to the ranging values ​​between different nodes in the corrected ranging matrix and the spatial coordinates of the known UAV nodes to generate a three-dimensional position coordinate set of all the unknown UAV nodes; generating a three-dimensional aerial positioning base station coordinate set of all UAV nodes by merging the three-dimensional position coordinate set with the known UAV node coordinates; Performing time base alignment on all the UAVs, and performing time difference ranging between the UAVs based on the three-dimensional aerial positioning base station coordinate set to generate a time difference positioning model; By activating a mobile terminal equipped with a UWB positioning tag, controlling the mobile terminal to broadcast a UWB positioning signal to N drones; wherein N is an integer greater than or equal to 1; Obtaining a propagation time of the UWB positioning signal to the N drones, inputting the propagation time into the time difference positioning model, and determining the position coordinates of the mobile terminal; Acquiring state characteristic information of the mobile terminal in a current time period according to the speed data of the mobile terminal, and determining whether the mobile terminal is in a continuously stationary state based on a preset threshold; If the mobile terminal is in a non-stationary state, setting a ranging period according to the state characteristic information; Inputting the timestamps of the positioning request signal and the response signal received during the ranging period into the time difference positioning model to determine the location information of the mobile terminal; After the step of generating a three-dimensional aerial positioning base station coordinate set of all drone nodes by merging the three-dimensional position coordinate set with the known drone node coordinates, the method further includes: Acquire real-time positioning error data and signal coverage quality indicators of the UAV nodes, and determine a set of UAV nodes that are out of the network based on the real-time positioning error data and the quality indicators; Determine the spatial coordinates corresponding to the set of drone nodes and the position distribution of adjacent nodes, and determine the priority area for redeployment and the target flight path by performing spatial topological analysis on the spatial coordinates and the position distribution of adjacent nodes; Sending a flight control instruction to the unmanned aerial vehicle node that has left the network according to the priority area and the target flight path, and controlling the unmanned aerial vehicle node that has left the network to fly to a designated location along the planned path; After the unmanned aerial vehicle node that has left the network completes its flight to the designated location, the three-dimensional space coordinates of the unmanned aerial vehicle node that has left the network are recalculated using the ranging error correction model.

2. The UWB positioning method based on multi-UAV collaboration according to claim 1 is characterized in that: The step of aligning the time base of all the drones, performing time difference ranging between the drones based on the three-dimensional aerial positioning base station coordinate set, and generating a time difference positioning model includes: The target UAV node in the three-dimensional aerial positioning base station coordinate set sends a time synchronization signal to other UAV nodes, and generates a time synchronization dataset of relative delays between all UAVs based on the reception timestamps fed back by the other UAV nodes; By performing time drift compensation and frequency offset correction on the time synchronization data set, a unified time table of nodes with unified time base correction is constructed; Based on the unified node schedule, the target UAV node sends a ranging signal to the other UAV nodes, and generates a time difference parameter set according to the time difference of the other UAV nodes receiving the ranging signal; The time difference parameter set is associated with the corresponding drone node coordinates to construct a time difference positioning model based on the hyperbola equation.

3. The UWB positioning method based on multi-UAV collaboration according to claim 1, characterized in that: After the step of activating the mobile terminal equipped with the UWB positioning tag and controlling the mobile terminal to broadcast the UWB positioning signal to N drones, the method further includes: Performing UWB bidirectional communication between the mobile terminal and the UAV node according to a preset ranging timing rule, and determining a first sending time of the mobile terminal to send a first positioning request signal; Determine a first reception time when the UAV node receives the first positioning request signal, send a first positioning response signal to the mobile terminal after a delay of a preset response interval, and determine a corresponding second sending time; Determining a second receiving time when the mobile terminal receives the first positioning response signal, sending a second positioning request signal to the drone node after delaying the preset response interval, and determining a corresponding third sending time and a third receiving time when the drone node receives the second positioning request signal; According to all sending times and receiving times, the propagation duration of the positioning response signal between the mobile terminal and the drone node is determined based on a preset timing cancellation operation.

4. The UWB positioning method based on multi-UAV collaboration according to claim 1, characterized in that: After the step of performing joint position derivation on the unknown drone nodes based on the ranging values ​​between different nodes in the corrected ranging matrix and the spatial coordinates of the known drone nodes to generate a set of three-dimensional position coordinates of all the unknown drone nodes, the method further includes: Performing an initial position coordinate calculation on the target unknown UAV node according to the ranging error correction model to generate a corresponding first positioning result; wherein the target unknown UAV node is one of all unknown UAV nodes; Controlling the target unknown UAV node to fly a preset distance along a set direction, and determining a corresponding theoretical displacement vector based on the flight control information of the target unknown UAV node; After completing the preset distance flight, obtaining the ranging information of the target unknown UAV node and the adjacent known UAV nodes, and performing a second position coordinate solution in combination with the ranging error correction model to generate a corresponding second positioning result; A positioning change vector is calculated based on the first positioning result and the second positioning result, and the positioning change vector is compared with the theoretical displacement vector to generate a dynamic verification result of the positioning state of the target unknown UAV node.

5. A UWB positioning system based on multi-UAV collaboration, characterized in that: The UWB positioning system based on multi-UAV collaboration includes: A construction module is used to initialize the configuration of the UAVs configured with the UWB positioning base station according to preset flight control parameters, establish a UWB communication link between the UAVs, and generate a corresponding initialization node configuration set; A generation module is used to construct a ranging error correction model based on the UWB ranging data between the known drone nodes and the unknown drone nodes in the initialization node configuration set, solve the position coordinates of the unknown drone nodes, and generate a three-dimensional aerial positioning base station coordinate set of all drone nodes, including: comparing the UWB ranging data between the known drone nodes and the unknown drone nodes with the actual distance to construct an error ranging set; generating a corrected ranging matrix by performing a semidefinite programming optimization process based on minimization of the sum of squares of ranging residuals on the error ranging set; performing a joint position derivation on the unknown drone nodes based on the ranging values ​​between different nodes in the corrected ranging matrix and the spatial coordinates of the known drone nodes to generate a three-dimensional position coordinate set of all the unknown drone nodes; generating a three-dimensional aerial positioning base station coordinate set of all drone nodes by merging the three-dimensional position coordinate set with the coordinates of the known drone nodes; A positioning module, configured to perform time base alignment on all the UAVs, and perform time difference ranging between the UAVs based on the three-dimensional aerial positioning base station coordinate set to generate a time difference positioning model; A control module is configured to activate a mobile terminal equipped with a UWB positioning tag and control the mobile terminal to broadcast a UWB positioning signal to N drones; a determination module, configured to obtain a propagation time of the UWB positioning signal to the N drones, input the propagation time into the time difference positioning model, and determine the position coordinates of the mobile terminal; The positioning module is further configured to obtain state characteristic information of the mobile terminal within a current time period based on the speed data of the mobile terminal, and determine whether the mobile terminal is in a continuously stationary state based on a preset threshold; if the mobile terminal is in a non-stationary state, set a ranging period based on the state characteristic information; and input timestamps of positioning request signals and response signals received within the ranging period into the time difference positioning model to determine the location information of the mobile terminal; The positioning module is also used to obtain the real-time positioning error data and signal coverage quality index of the UAV node, and determine the set of UAV nodes that have left the network based on the real-time positioning error data and the quality index; determine the spatial coordinates corresponding to the set of UAV nodes and the position distribution of adjacent nodes, and determine the priority area for redeployment and the target flight path by performing spatial topology analysis on the spatial coordinates and the position distribution of adjacent nodes; send flight control instructions to the UAV node that has left the network according to the priority area and the target flight path, and control the UAV node that has left the network to fly to the designated position along the planned path; after the UAV node that has left the network completes the flight to the designated position, recalculate the three-dimensional spatial coordinates of the UAV node that has left the network through the ranging error correction model.

6. An electronic device, characterized in that: Comprising a memory and a processor, wherein: The processor is configured to execute a computer program stored in the memory; When the processor executes the computer program, the steps of the UWB positioning method based on multi-UAV collaboration described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the UWB positioning method based on multi-UAV collaboration described in any one of claims 1 to 4 are implemented.

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