A method for eliminating errors of precise synchronization of unmanned aerial vehicle cluster network time

Abstract

The application relates to the technical field of unmanned aerial vehicle cluster network communication, in particular to a method for eliminating errors of an unmanned aerial vehicle cluster network time accurate synchronization, which comprises the following steps: electing a master node in an unmanned aerial vehicle cluster, and establishing stable communication links between the master node and each slave node; based on the assumption that two-way transmission time delays are the same, constructing a time interaction model between the master node and each slave node; through back-and-forth timing interaction, realizing the transmission of time parameters between the master node and the slave nodes, and obtaining time stamp parameters required by a two-way comparison method; adopting Kalman filtering to optimize a time difference, eliminate random noise errors, output a final time correction amount, and complete the time accurate synchronization of the unmanned aerial vehicle cluster network nodes. The method can solve the problem that in the prior art, in a poor satellite signal or dynamic cluster scene, effective error elimination is difficult to realize, and the synchronization accuracy is greatly reduced.

Description

Technical Field

[0001] The embodiments of this application relate to the field of unmanned aerial vehicle (UAV) swarm network communication technology, and in particular to a method for precise time synchronization of UAV swarm networks to eliminate errors. Background Technology

[0002] In UAV swarm collaborative tasks, time synchronization errors mainly originate from three categories: propagation delay, systematic errors, and random noise. These errors can lead to time reference deviations between swarm nodes, resulting in problems such as channel collisions, data fusion errors, and task scheduling disorder. For example, in swarm target tracking tasks, if the time deviation between nodes exceeds 1 microsecond (μs), it will increase the target position calculation error and affect tracking accuracy. In TDMA channel access, a time deviation exceeding 10% of the time slot duration will cause signal collisions between nodes, reducing communication efficiency.

[0003] Currently, the research and development focus of UAV swarm time synchronization technology has shifted from "achieving synchronization" to "precise synchronization." The core objective is to eliminate the impact of various errors on synchronization accuracy and achieve microsecond-level or even nanosecond-level time synchronization. Existing precise time synchronization technologies are mostly based on a hybrid scheme of GNSS and auxiliary correction. In scenarios with good satellite signal, an initial high-precision time can be obtained through GNSS, and then a simple filtering algorithm is used to eliminate a small amount of random noise, achieving relatively high-precision synchronization. However, in scenarios with poor satellite signal or dynamic swarming, the impact of various errors increases significantly, and existing technologies struggle to effectively eliminate these errors, leading to a substantial decrease in synchronization accuracy. Summary of the Invention

[0004] In view of this, embodiments of this application propose a method for precise time synchronization of unmanned aerial vehicle (UAV) swarm networks to eliminate errors, aiming to solve the problem that existing technologies are unable to achieve effective error elimination in scenarios with poor satellite signals or dynamic swarms, resulting in a significant decrease in synchronization accuracy.

[0005] To achieve the above objectives, embodiments of this application propose a method for precise time synchronization in a drone swarm network to eliminate errors, the method comprising the following steps: Elect a master node within the drone swarm and establish stable communication links between the master node and each slave node; Based on the assumption that the bidirectional transmission delay is the same, a time interaction model is constructed between the master node and each slave node; the time interaction model is used to derive the propagation delay and the time difference between the master and slave nodes in order to eliminate the propagation delay error. By using round-trip timing interaction, time parameters can be transferred between master and slave nodes, and the timestamp parameters required for bidirectional comparison can be obtained. Kalman filtering is used to optimize the time difference, eliminate random noise errors, and output the final time correction value to achieve accurate time synchronization of UAV swarm network nodes.

[0006] To achieve the above objectives, embodiments of this application also propose a precise time synchronization system for UAV swarm networks to eliminate errors, the system comprising: The master node election and link establishment module is used to elect a master node in the drone cluster and establish a stable communication link between the master node and each slave node. The model building module is used to construct a time interaction model between the master node and each slave node based on the assumption that the bidirectional transmission delay is the same. The time interaction model is used to derive the propagation delay and the time difference between the master and slave nodes to eliminate propagation delay errors. The parameter acquisition module is used to transfer time parameters between master and slave nodes through round-trip timing interaction, and to obtain the timestamp parameters required for the bidirectional comparison method. The correction output module is used to optimize the time difference using Kalman filtering, eliminate random noise errors, and output the final time correction value to achieve accurate time synchronization of UAV swarm network nodes.

[0007] To achieve the above objectives, embodiments of this application also propose an electronic device, including a processor and a memory, wherein the memory stores instructions executable by the processor, and the processor is configured to execute the instructions such that the electronic device can implement the above-described method for precise time synchronization of a drone swarm network to eliminate errors.

[0008] To achieve the above objectives, embodiments of this application also propose a computer-readable storage medium storing a computer program that, when executed by a processor, enables a precise time synchronization method for eliminating errors in a drone swarm network as described above.

[0009] This application proposes a method for precise time synchronization in a drone swarm network to eliminate errors. The method involves electing a master node within the drone swarm and establishing stable communication links between the master node and each slave node. Based on the assumption of identical bidirectional transmission delay, a time interaction model is constructed between the master node and each slave node. This model is used to derive the propagation delay and the time difference between the master and slave nodes to eliminate propagation delay errors. Round-trip timing interaction enables the transfer of time parameters between the master and slave nodes and obtains the timestamp parameters required for bidirectional comparison. Kalman filtering is used to optimize the time difference, eliminate random noise errors, and output the final time correction value. This solution achieves precise time synchronization of UAV swarm network nodes. It employs multi-dimensional error elimination to achieve microsecond-level synchronization accuracy. Specifically, it addresses the core error sources in UAV swarm time synchronization by deriving propagation delay and master-slave time difference through bidirectional comparison, completely eliminating propagation delay errors during signal transmission. Kalman filtering continuously optimizes the time difference, effectively suppressing random noise interference. Furthermore, the master-slave node election mechanism prioritizes nodes with high clock accuracy, reducing inherent system errors at the source. Therefore, this solution achieves precise synchronization through autonomous interaction and algorithm optimization among master and slave nodes within the swarm, without relying on satellite signals. It maintains stable synchronization accuracy even under satellite obstruction, satellite signal interference, or dynamic swarm scenarios, solving the problem of existing technologies struggling to effectively eliminate errors in poor satellite signal or dynamic swarm scenarios, leading to a significant decrease in synchronization accuracy.

[0010] Optionally, a master node is elected within the UAV swarm, and a stable communication link is established between the master node and each slave node. This includes: employing a joint election mechanism to determine the node with the smallest node identifier and a clock deviation less than or equal to the deviation threshold within the UAV swarm as the master node, while simultaneously designating all nodes in the UAV swarm other than the master node as slave nodes; wherein the joint election mechanism includes node identifier priority and clock precision; the master node sends a communication establishment request message to all slave nodes; wherein the communication establishment request message includes the master node identifier, communication frequency band, and time slot allocation scheme for duration; the slave nodes receive the communication establishment request message and send feedback confirmation information to the master node; after the master node receives the feedback confirmation information, the stable communication link between the master node and each slave node is established.

[0011] Optionally, the deviation threshold includes 50 microseconds / hour; the communication frequency band includes 2.4 GHz to 5.8 GHz; and the duration of the time slot allocation scheme includes 8 microseconds.

[0012] Optionally, based on the bidirectional comparison method, a time interaction model between the master node and each slave node is constructed, including: defining master and slave node time parameters; wherein, the master and slave node time parameters include: the local timestamp of the slave node sending the query pulse, the local timestamp of the node receiving the query pulse, the local timestamp of the master node sending the response pulse, and the local timestamp of the slave node receiving the response pulse; based on the assumption that the bidirectional transmission delay is the same and the master and slave node time parameters, a time parameter relationship equation is established; based on the time parameter relationship equation, the propagation delay and the master and slave node time difference are derived, and the propagation delay error is eliminated by the time difference.

[0013] Optionally, time parameters are transmitted between master and slave nodes through round-trip timing interaction, and the timestamp parameters required for the bidirectional comparison method are obtained. This includes: sending round-trip timing query pulses from the slave node to the master node at preset time intervals and recording the local timestamp of the query pulse sent by the slave node; estimating the arrival timestamp of the query pulse using a timestamp extraction algorithm using the master node, and sending back a response pulse within a preset delay; wherein the response pulse includes the local timestamp of the query pulse sent by the slave node, the local timestamp of the query pulse received by the node, and the current system timestamp of the master node; and receiving the response pulse using the slave node and extracting the local timestamp of the query pulse sent by the slave node and the local timestamp of the query pulse received by the node.

[0014] Optionally, the method provided in the embodiments of this application further includes: if the slave node fails to receive a response pulse, then re-initiating the query after a preset time.

[0015] Optionally, Kalman filtering is used to optimize the time difference, eliminate random noise errors, and output the final time correction value to achieve accurate time synchronization of UAV swarm network nodes, including: Establish the state equation and the observation equation; the state equation and the observation equation are expressed as follows: ; ; in, express The state vector at time t is a vector containing time deviation and frequency deviation. Represents the state transition matrix; The filtering period is the filter update period; The noise is the process noise, which follows a mean of 0 and a variance of . Gaussian distribution; express The observation equation for a given time, i.e., the observation time deviation; For the observation matrix, ; To observe the noise, it follows a mean of 0 and a variance of . Gaussian distribution; Calculate the prior state estimates and the prior covariance matrix; where, the prior state estimates... and prior covariance matrix It is expressed as follows: ; ; Calculate the Kalman gain and update the posterior state estimate and posterior covariance matrix based on the Kalman gain; where, Kalman gain... The expression is as follows: ; Updated posterior state estimate It is expressed as follows: ; Updated posterior covariance matrix It is expressed as follows: ; By using the time difference in the posterior state estimate from the slave node to correct the local time, the state equation and observation equation are repeatedly executed to achieve continuous optimization, so that the synchronization error reaches the preset standard, thereby completing the precise time synchronization of the UAV swarm network nodes. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies of this application will be briefly introduced below. Obviously, the following drawings are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. The drawings described herein are only used to explain this application and are not intended to limit this application.

[0017] Figure 1 This is a flowchart of a method for precise time synchronization in a drone swarm network to eliminate errors, provided in one embodiment of this application; Figure 2 This is a flowchart of another method for precise time synchronization of a drone swarm network to eliminate errors, provided in one embodiment of this application; Figure 3 This is a schematic diagram of a precise time synchronization system for drone swarm networks used to eliminate errors, provided in another embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. Those skilled in the art will understand that many technical details have been presented in the embodiments of this application to facilitate better understanding. However, the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of this application. The following embodiments can be combined with and referenced by each other without contradiction.

[0019] In UAV swarm collaborative tasks, time synchronization errors mainly originate from three categories: propagation delay, systematic errors, and random noise. These errors can lead to time reference deviations between swarm nodes, resulting in problems such as channel collisions, data fusion errors, and task scheduling disorder. For example, in swarm target tracking tasks, if the time deviation between nodes exceeds 1 microsecond (μs), it will increase the target position calculation error and affect tracking accuracy. In TDMA channel access, a time deviation exceeding 10% of the time slot duration will cause signal collisions between nodes, reducing communication efficiency.

[0020] Currently, the research and development focus of UAV swarm time synchronization technology has shifted from "achieving synchronization" to "precise synchronization." The core objective is to eliminate the impact of various errors on synchronization accuracy and achieve microsecond-level or even nanosecond-level time synchronization. Existing precise time synchronization technologies are mostly based on a hybrid scheme of GNSS and auxiliary correction. In scenarios with good satellite signal, an initial high-precision time can be obtained through GNSS, and then a simple filtering algorithm is used to eliminate a small amount of random noise, achieving relatively high-precision synchronization. However, in scenarios with poor satellite signal or dynamic swarming, the impact of various errors increases significantly, and existing technologies struggle to effectively eliminate these errors, leading to a substantial decrease in synchronization accuracy.

[0021] In view of this, embodiments of this application propose a method for precise time synchronization of unmanned aerial vehicle (UAV) swarm networks to eliminate errors, aiming to solve the problem that existing technologies are unable to achieve effective error elimination in scenarios with poor satellite signals or dynamic swarms, resulting in a significant decrease in synchronization accuracy.

[0022] One embodiment of this application proposes a method for precise time synchronization in a drone swarm network to eliminate errors, applied to an electronic device, wherein the electronic device can be a terminal or a server. This embodiment and the following embodiments will use a server as an example for illustration. The implementation details of the method for precise time synchronization in a drone swarm network to eliminate errors proposed in this embodiment are described below. The following implementation details are provided for ease of understanding and are not essential for implementing this solution.

[0023] For example, the following description uses a scenario of time synchronization of a drone swarm for target tracking tasks as an example to illustrate the method provided by the embodiments of this application.

[0024] In the implementation scenario, the drone swarm collaboratively tracks the target, which includes 5 multi-rotor drones, model DJIMatrice 300 RTK, with swarm nodes identified as Node1~Node5. The drone swarm is in a suburban urban scenario, where satellite signal obstruction exists in some areas (GNSS positioning accuracy drops to the meter level). The swarm nodes move dynamically (flight speed ≤15m / s, distance between nodes 50~200 meters). The time synchronization error of the swarm nodes is ≤1 microsecond, ensuring that the target position fusion calculation accuracy is ≤0.5 meters.

[0025] The parameter configurations of the embodiments of this application are as follows: Table 1

[0026] The specific process of the precise time synchronization method for UAV swarm networks proposed in this embodiment for eliminating errors can be described as follows: Figure 1 As shown, it includes: Step 101: Elect a master node within the drone cluster and establish stable communication links between the master node and each slave node.

[0027] Understandably, step 101 involves establishing a high-precision time reference source, i.e., the master node, within the dynamic drone swarm, and constructing an ordered, low-collision communication framework. This step corresponds to... Figure 2 Master-slave communication is established.

[0028] In one possible embodiment, step 101 includes: using a joint election mechanism to determine the node with the smallest node identifier and clock deviation less than or equal to the deviation threshold within the UAV cluster as the master node, and simultaneously determining all nodes in the UAV cluster other than the master node as slave nodes; using the master node to send communication establishment request information to all slave nodes; using the slave nodes to receive the communication establishment request information and send feedback confirmation information to the master node; after the master node receives the feedback confirmation information, establishing a stable communication link between the master node and each slave node.

[0029] The joint election mechanism includes node identifier priority and clock precision; the communication establishment request information includes the master node identifier, communication frequency band, and time slot allocation scheme for duration.

[0030] For example, the deviation threshold includes 50 microseconds / hour; the communication frequency band includes 2.4 GHz to 5.8 GHz; and the duration of the time slot allocation scheme includes 8 microseconds.

[0031] Specifically, the election of the master node can employ a joint election mechanism combining node identifier priority and clock precision. After the cluster starts, each node reports its local clock drift rate (i.e., clock skew). The node with the smallest node identifier (such as a pre-assigned ID number) is selected first, but its clock skew must not exceed a preset skew threshold (e.g., 50 microseconds / hour). If the skew of the node with the smallest identifier exceeds the threshold, the next node with the smallest identifier is considered in turn. This mechanism selects the most stable clock from the source as the master node to reduce the inherent error of the system.

[0032] After being elected as the master node, it broadcasts a communication establishment request to all slave nodes. This request message includes the master node identifier, the specified communication frequency band (e.g., 2.4 GHz to 5.8 GHz), and a crucial time slot allocation scheme. The time slot allocation scheme assigns each slave node a specific, non-overlapping communication time window (e.g., 8 microseconds in length), thereby avoiding channel conflicts caused by multiple slave nodes simultaneously sending data at the MAC layer, laying the foundation for stable communication. Upon receiving the request, each slave node sends an acknowledgment message to the master node within its allocated time slot. Once the master node receives all acknowledgments, it declares the stable communication link established.

[0033] For example, after the master node election starts the cluster, each node autonomously reports its local clock deviation (detected through the built-in clock module): Node1 (30 microseconds / hour), Node2 (45 microseconds / hour), Node3 (60 microseconds / hour), Node4 (40 microseconds / hour), and Node5 (55 microseconds / hour). Based on the "node identifier priority + clock precision" mechanism, Node1 (with the smallest identifier and a clock deviation ≤ 50 microseconds / hour) is elected as the master node, and the rest are slave nodes (Node2~Node5).

[0034] The master node sends a communication request. The master node Node1 broadcasts the communication establishment request to all slave nodes. The message content includes: master node identifier (Node1), communication frequency band (2.4 GHz), and time slot allocation scheme (each slave node has a communication time slot of 8 microseconds, and the time slot order is: Node2→Node3→Node4→Node5).

[0035] After receiving requests sequentially, slave nodes Node2 through Node5 send back confirmation messages (including slave node identifier and clock status) within their respective allocated time slots. Once the master node Node1 has received all confirmation messages, a stable master-slave communication link is established, with a measured packet loss rate below 0.1%.

[0036] Step 102: Based on the assumption that the bidirectional transmission delay is the same, construct a time interaction model between the master node and each slave node.

[0037] Among them, the time interaction model is used to derive the propagation delay and the time difference between master and slave nodes in order to eliminate propagation delay errors.

[0038] Understandably, this step aims to establish a mathematical model to extract the critical propagation delay and master-slave time difference from the measurements, thereby eliminating errors introduced by signal transmission in the air. This step 102 corresponds to... Figure 2 The two-way comparison error is eliminated.

[0039] In one possible embodiment, step 102 includes: defining master-slave node time parameters; establishing a time parameter relationship equation based on the assumption that bidirectional transmission delay is the same and the master-slave node time parameters; deriving the propagation delay and master-slave node time difference based on the time parameter relationship equation, and eliminating the propagation delay error through the time difference.

[0040] The master-slave node time parameters include: the local timestamp of the query pulse sent by the slave node, the local timestamp of the query pulse received by the node, the local timestamp of the response pulse sent by the master node, and the local timestamp of the response pulse received by the slave node.

[0041] First, define four key timestamps involved in a complete "request-response" interaction: the local timestamp when the node sends the query pulse. The master node receives the local timestamp of the query pulse. Local timestamp of the master node sending the response pulse The local timestamp of the response pulse received from the node. .

[0042] Based on the reasonable assumption that the wireless signal propagation paths from "slave node to master node" and "master node to slave node" are symmetrical, i.e., the bidirectional transmission delay is the same, the following relationship equation can be established: ; in, This represents the propagation delay, which is the signal transmission time between nodes and is positively correlated with the distance between nodes.

[0043] Master-slave node time difference The calculation formula is as follows: ; The obtained master-slave node time difference This refers to the pure master-slave clock difference after eliminating propagation delay errors. The slave node adjusts its local clock to compensate for the time difference between the master and slave nodes. This allows for alignment with the master node on the time base.

[0044] Step 103: Through round-trip timing interaction, the time parameters between the master and slave nodes are transferred, and the timestamp parameters required for the bidirectional comparison method are obtained.

[0045] Understandably, this step implements the specific interaction protocol of the mathematical model in the above steps, responsible for collecting these four timestamps with high precision. This step 103 corresponds to... Figure 2 Timely interaction between the two sides.

[0046] In one possible embodiment, step 103 includes: sending round-trip timed query pulses from the slave node to the master node at preset time intervals, and recording the local timestamp of the query pulse sent by the slave node; using the master node, estimating the arrival timestamp of the query pulse through a timestamp extraction algorithm, and sending back a response pulse within a preset delay; using the slave node, receiving the response pulse, and extracting the local timestamp of the query pulse sent by the slave node and the local timestamp of the query pulse received by the node.

[0047] The response pulse includes the local timestamp of the query pulse sent by the slave node, the local timestamp of the query pulse received by the node, and the current system timestamp of the master node. Based on a stable communication link, each slave node sends a query pulse for round-trip timing to the master node within its allocated time slot at a fixed period (e.g., 30 microseconds to 100 microseconds, 50 microseconds in this embodiment), and accurately records its local timestamp at the moment of transmission. .

[0048] After receiving the query pulse, the master node estimates the pulse's arrival time using a high-precision timestamp extraction algorithm (such as pulse rising edge detection) and records it as... Subsequently, the master node constructs and sends an acknowledgment pulse within an extremely short delay (≤8 microseconds, 5 microseconds in this example). This acknowledgment pulse carries three key parameters: those sent from the node... The master node just recorded and the current system time of the master node (which can be used as...) Reference or direct use ).

[0049] After receiving the response pulse from the node, record the local timestamp of arrival. And extract from pulse message parsing and This completes one interaction, obtaining all the parameters needed to calculate the time difference from the node. , , , To ensure reliability, if a slave node does not receive a response within the expected time, it will re-initiate the query after a short delay (e.g., 10 microseconds).

[0050] Example of an implementation: From node Node2 in A query pulse is sent at 100000μs. Master node Node1 sends a query pulse at 100000μs. Received at =100002μs, and in A response pulse is emitted at =100005μs (containing... =100000, =100002). Node2 in A response was received at 100007 μs.

[0051] Substitute into the formula to calculate: propagation delay Time difference This indicates that the clocks of Node2 and Node1 were synchronized during this measurement.

[0052] In one possible embodiment, the method provided by the embodiments of this application further includes: if the slave node fails to receive a response pulse, then re-initiating the query after a preset time period.

[0053] For example, if a slave node fails to receive the data (e.g., Node4 fails to receive the data due to momentary interference), the query will be re-initiated after a preset time (e.g., 10 microseconds) to ensure a 100% success rate in obtaining the parameters.

[0054] Step 104: Use Kalman filtering to optimize the time difference, eliminate random noise errors, output the final time correction amount, and complete the precise time synchronization of the UAV cluster network nodes.

[0055] Understandably, this step addresses the random observation noise and clock frequency drift in the original time difference sequence calculated in the previous steps, outputting a smoother and more accurate time correction through optimal estimation. Step 104 corresponds to... Figure 2 Kalman filter optimization in [the context of the text].

[0056] The Kalman filter defines the state of the system as a two-dimensional vector: ; in, for Time deviation at any moment for Time-frequency deviation; In one possible embodiment, step 104 includes: establishing state equations and observation equations; wherein the state equations and observation equations are expressed as follows: ; ; in, express The state vector at time t is a vector containing time deviation and frequency deviation. Represents the state transition matrix; The filtering period is the filter update period; The noise is the process noise, which follows a mean of 0 and a variance of . Gaussian distribution; express The observation equation for a given time, i.e., the observation time deviation; For the observation matrix, ; To observe the noise, it follows a mean of 0 and a variance of . Gaussian distribution; The process noise is represented as follows: ; Calculate the prior state estimates and the prior covariance matrix; where, the prior state estimates... and prior covariance matrix It is expressed as follows: ; ; Calculate the Kalman gain and update the posterior state estimate and posterior covariance matrix based on the Kalman gain; where, Kalman gain... The expression is as follows: ; Updated posterior state estimate It is expressed as follows: ; Updated posterior covariance matrix It is expressed as follows: ; By using the time difference in the posterior state estimate from the slave node to correct the local time, the state equation and observation equation are repeatedly executed to achieve continuous optimization, so that the synchronization error reaches the preset standard, thereby completing the precise time synchronization of the UAV swarm network nodes.

[0057] For example, the local clock is eventually corrected by the node using the time deviation component from the optimal state estimate. This process iterates every 10 microseconds, enabling real-time tracking and filtering of noise in the clock deviation, dynamically adapting to delay changes caused by node movement, and ensuring that the time synchronization accuracy of the entire UAV swarm network reaches and stabilizes at the microsecond level.

[0058] By sequentially and cyclically executing the above steps, it is possible to achieve full-process error elimination, from establishing a synchronization architecture, eliminating fixed propagation delays, collecting high-precision data, to filtering out random noise, ultimately achieving high-precision and robust time synchronization of drone swarms in complex scenarios.

[0059] This application proposes a method for precise time synchronization in a drone swarm network to eliminate errors. The method involves electing a master node within the drone swarm and establishing stable communication links between the master node and each slave node. Based on the assumption of identical bidirectional transmission delay, a time interaction model is constructed between the master node and each slave node. This model is used to derive the propagation delay and the time difference between the master and slave nodes to eliminate propagation delay errors. Round-trip timing interaction enables the transfer of time parameters between the master and slave nodes and obtains the timestamp parameters required for bidirectional comparison. Kalman filtering is used to optimize the time difference, eliminate random noise errors, and output the final time correction value. This solution achieves precise time synchronization of UAV swarm network nodes. It employs multi-dimensional error elimination to achieve microsecond-level synchronization accuracy. Specifically, it addresses the core error sources in UAV swarm time synchronization by deriving propagation delay and master-slave time difference through bidirectional comparison, completely eliminating propagation delay errors during signal transmission. Kalman filtering continuously optimizes the time difference, effectively suppressing random noise interference. Furthermore, the master-slave node election mechanism prioritizes nodes with high clock accuracy, reducing inherent system errors at the source. Therefore, this solution achieves precise synchronization through autonomous interaction and algorithm optimization among master and slave nodes within the swarm, without relying on satellite signals. It maintains stable synchronization accuracy even under satellite obstruction, satellite signal interference, or dynamic swarm scenarios, solving the problem of existing technologies struggling to effectively eliminate errors in poor satellite signal or dynamic swarm scenarios, leading to a significant decrease in synchronization accuracy.

[0060] It is also understandable that the communication link is stable and the collision rate is low. During the master-slave node establishment phase, a time slot allocation scheme is used to clearly define the communication time window for each node, avoiding channel conflicts caused by simultaneous transmission from multiple nodes. Simultaneously, a query pulse retransmission mechanism is designed to ensure the reliability of timestamp parameter transmission, improving the stability and efficiency of cluster network communication. Real-time dynamic optimization adapts to dynamic changes in the cluster. Continuous iterative optimization of the Kalman filter (repeatedly executing state prediction and correction processes) can track the dynamic changes in node time deviations in real time, adapting to dynamic scenarios such as node position movement and link delay fluctuations during UAV cluster mission execution, ensuring continuous and stable synchronization accuracy.

[0061] The steps described above are for clarity only. In implementation, they can be combined into one step, or some steps can be broken down into multiple steps, as long as they involve the same logical relationship, they are all within the scope of protection of this application. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, without changing the core design of the algorithm and process, are also within the scope of protection of this application.

[0062] Another embodiment of this application proposes a precise time synchronization system for UAV swarm networks to eliminate errors. The details of this precise time synchronization system for UAV swarm networks are described below. The following implementation details are provided for ease of understanding and are not essential for implementing this example. Figure 3 This is a schematic diagram of the structure of a precise time synchronization system for UAV swarm networks proposed in this embodiment for eliminating errors, including: The master node election and link establishment module 210 is used to elect a master node in the UAV cluster and establish a stable communication link between the master node and each slave node. The model building module 220 is used to construct a time interaction model between the master node and each slave node based on the assumption that the bidirectional transmission delay is the same; wherein, the time interaction model is used to derive the propagation delay and the time difference between the master and slave nodes in order to eliminate the propagation delay error. The parameter acquisition module 230 is used to transfer time parameters between master and slave nodes through round-trip timing interaction, and to obtain the timestamp parameters required for the bidirectional comparison method. The correction output module 240 is used to optimize the time difference using Kalman filtering, eliminate random noise errors, and output the final time correction value to achieve accurate time synchronization of UAV cluster network nodes.

[0063] It is not difficult to see that this embodiment is a system embodiment corresponding to the above method embodiments, and this embodiment can be implemented in conjunction with the above method embodiments. The relevant technical details and technical effects mentioned in the above method embodiments are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above method embodiments.

[0064] It is worth mentioning that all modules and units involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units do not exist in this embodiment.

[0065] Another embodiment of this application provides an electronic device, such as Figure 4 As shown, it includes a processor 31 and a memory 32. The memory 32 stores instructions that the processor 31 can execute. When the processor 31 is configured to execute the instructions, the electronic device can implement a method for precise time synchronization of a drone swarm network for eliminating errors, as described in the above method embodiment.

[0066] The memory and processor are connected via a bus, which includes any number of interconnecting buses and bridges, connecting various circuits of one or more processors and the memory. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0067] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0068] Another embodiment of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, enables a method for precise time synchronization of a drone swarm network for eliminating errors, as described in the above method embodiments.

[0069] That is, those skilled in the art will understand that all or part of the steps in the above method embodiments can be implemented by a program instructing related hardware. The program is stored in a storage medium and includes several instructions to cause a device (such as a microcontroller, chip, etc.) or processor to execute all or part of the steps of the method described in the method embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0070] Those skilled in the art will understand that the above embodiments are specific implementations of this application, and in practical applications, various changes can be made in form and detail without departing from the spirit and scope of this application. For those skilled in the art, several improvements and modifications can be made without departing from the principles of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A method for precise time synchronization in a drone swarm network to eliminate errors, characterized in that, The method includes: Elect a master node within the drone swarm and establish stable communication links between the master node and each slave node; Based on the assumption that the bidirectional transmission delay is the same, a time interaction model is constructed between the master node and each slave node; the time interaction model is used to derive the propagation delay and the time difference between the master and slave nodes in order to eliminate the propagation delay error. By using round-trip timing interaction, time parameters can be transferred between master and slave nodes, and the timestamp parameters required for bidirectional comparison can be obtained. Kalman filtering is used to optimize the time difference, eliminate random noise errors, and output the final time correction value to achieve accurate time synchronization of UAV swarm network nodes.

2. The method according to claim 1, characterized in that, The process of electing a master node within the drone swarm and establishing stable communication links between the master node and each slave node includes: A joint election mechanism is adopted to determine the master node in the drone cluster with the smallest node identifier and a clock deviation less than or equal to the deviation threshold, while all other nodes in the drone cluster except the master node are determined as slave nodes; the joint election mechanism includes node identifier priority and clock precision. The master node sends a communication establishment request to all slave nodes; the communication establishment request includes the master node identifier, communication frequency band, and time slot allocation scheme for duration. The slave node receives communication establishment request information and sends feedback confirmation information to the master node. After the master node receives the feedback confirmation information, it completes the establishment of a stable communication link between the master node and each slave node.

3. The method according to claim 2, characterized in that, The deviation threshold includes 50 microseconds / hour; the communication frequency band includes 2.4 GHz to 5.8 GHz; and the duration of the time slot allocation scheme includes 8 microseconds.

4. The method according to claim 1, characterized in that, The method based on bidirectional comparison constructs a time interaction model between the master node and each slave node, including: Define the master and slave node time parameters; wherein, the master and slave node time parameters include: the local timestamp of the slave node sending the query pulse, the local timestamp of the node receiving the query pulse, the local timestamp of the master node sending the response pulse, and the local timestamp of the slave node receiving the response pulse; Based on the assumption that bidirectional transmission delay is the same and the time parameters of master and slave nodes, a time parameter relationship equation is established. Based on the time parameter relationship equation, the propagation delay and the time difference between master and slave nodes are derived, and the propagation delay error is eliminated by the time difference.

5. The method according to claim 1, characterized in that, The process of transmitting time parameters between master and slave nodes and obtaining the timestamp parameters required for the bidirectional comparison method through round-trip timing interaction includes: According to a preset time interval, the slave node sends round-trip timed query pulses to the master node, and records the local timestamp of the query pulse sent by the slave node; Using the master node, the arrival time of the query pulse is estimated through a timestamp extraction algorithm, and a response pulse is sent back within a preset delay; wherein, the response pulse includes the local timestamp of the query pulse sent by the slave node, the local timestamp of the query pulse received by the node, and the current system timestamp of the master node; Using the slave node, receive the response pulse and extract the local timestamp of the slave node sending the query pulse and the local timestamp of the node receiving the query pulse.

6. The method according to claim 5, characterized in that, The method further includes: If the node fails to receive a response pulse, the query will be re-initiated after a preset time.

7. The method according to claim 5, characterized in that, The process of using Kalman filtering to optimize the time difference, eliminate random noise errors, and output the final time correction value to achieve precise time synchronization of UAV swarm network nodes includes: Establish the state equation and the observation equation; the state equation and the observation equation are expressed as follows: ； ； in, express The state vector at time t is a vector containing time deviation and frequency deviation. Represents the state transition matrix; The filtering period is the filter update period; The noise is the process noise, which follows a mean of 0 and a variance of . Gaussian distribution; express The observation equation for a given time, i.e., the observation time deviation; For the observation matrix, ; To observe the noise, it follows a mean of 0 and a variance of . Gaussian distribution; Calculate the prior state estimates and the prior covariance matrix; where, the prior state estimates... and prior covariance matrix It is expressed as follows: ； ； Calculate the Kalman gain and update the posterior state estimate and posterior covariance matrix based on the Kalman gain; where, Kalman gain... The expression is as follows: ； Updated posterior state estimate It is expressed as follows: ； Updated posterior covariance matrix It is expressed as follows: ； By using the time difference in the posterior state estimate from the slave node to correct the local time, the state equation and observation equation are repeatedly executed to achieve continuous optimization, so that the synchronization error reaches the preset standard, thereby completing the precise time synchronization of the UAV swarm network nodes.

8. A precise time synchronization system for unmanned aerial vehicle (UAV) swarm networks to eliminate errors, characterized in that, The system includes: The master node election and link establishment module is used to elect a master node in the drone cluster and establish a stable communication link between the master node and each slave node. The model building module is used to construct a time interaction model between the master node and each slave node based on the assumption that the bidirectional transmission delay is the same. The time interaction model is used to derive the propagation delay and the time difference between the master and slave nodes to eliminate propagation delay errors. The parameter acquisition module is used to transfer time parameters between master and slave nodes through round-trip timing interaction, and to obtain the timestamp parameters required for the bidirectional comparison method. The correction output module is used to optimize the time difference using Kalman filtering, eliminate random noise errors, and output the final time correction value to achieve accurate time synchronization of UAV swarm network nodes.

9. An electronic device, characterized in that, include: The processor and memory, wherein the memory stores instructions that the processor can execute, and the processor is configured to, when executing the instructions, enable the electronic device to implement a method for precise time synchronization of a drone swarm network for eliminating errors, as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it can implement a method for precise time synchronization of a drone swarm network for eliminating errors, as described in any one of claims 1 to 7.

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