Unmanned aerial vehicle group cooperative communication and navigation method based on 5G Red Cap module

Abstract

The invention belongs to the technical field of communication, and particularly relates to an unmanned aerial vehicle group cooperative communication and navigation method based on a 5G Red Cap module, and the method comprises the steps: 1, carrying out the high-resolution sampling of a multipath space-time channel between unmanned aerial vehicles in an unmanned aerial vehicle group through a 5G Red Cap module, and constructing a link channel matrix; 2, on the basis of each link channel matrix, aiming at each unmanned aerial vehicle, constructing a joint channel matrix in combination with a neighbor set of the unmanned aerial vehicle, and extracting an optimal beam forming vector; 3, according to the optimal beam forming vector and the link channel matrix, time delay, phase and Doppler frequency shift are extracted, and position information of each unmanned aerial vehicle is estimated; and 4, according to the obtained position information of each unmanned aerial vehicle and the optimal beam forming vector, ensuring collaborative optimization of navigation and communication. According to the invention, interference can be effectively suppressed, energy consumption is reduced, and communication link stability and navigation reliability are ensured.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a collaborative communication and navigation method for a group of unmanned aerial vehicles based on a 5G Red Cap module. Background Art

[0002] With the rapid development and widespread application of drone technology, application scenarios such as drone group collaborative operations, environmental monitoring, logistics transportation, and emergency rescue have received increasing attention. Traditional drone systems mostly rely on a single drone to perform tasks independently and lack effective collaborative communication and navigation mechanisms. However, drone group collaborative operations can achieve resource sharing, information exchange, and task division, thereby greatly improving overall operational efficiency and flexibility. In recent years, with the rise of the fifth-generation mobile communication technology (5G), its low latency, high speed, and large number of connections have provided new technical support for drone group communications, especially the 5G Red Cap (Reduced Capability) module, which has become an ideal choice for many low-cost drones with its low power consumption and low complexity. However, although the existing technology has made certain progress in 5G communication technology and drone navigation and positioning, there are still many problems and shortcomings in practical applications.

[0003] Most of the existing drone group communication systems use traditional wireless communication technologies, such as Wi-Fi, dedicated radio frequency band communications, etc., which have limited transmission rates and anti-interference capabilities, making it difficult to meet the requirements of high-density, multi-node collaborative operations for real-time and stability of data transmission. In addition, since drones encounter multipath effects, signal fading, Doppler frequency shift and other phenomena during flight, traditional communication systems can usually only use omnidirectional antennas or fixed beamforming methods, which makes it difficult to achieve efficient focusing of signal energy and interference suppression in dynamic environments. This causes drone groups to often experience problems such as communication interruption, data loss or increased bit error rate in high-speed movement, formation changes and complex terrain environments, thus affecting the execution of tasks. Summary of the invention

[0004] In view of this, the main purpose of the present invention is to provide a cooperative communication and navigation method for drone swarms based on 5G Red Cap modules, construct a joint channel matrix through high-resolution channel sampling, and then adopt the optimal beamforming technology and multi-dimensional wireless channel characteristics fusion positioning method to achieve high-precision positioning and efficient communication of drone swarms in dynamic, multi-path environments. At the same time, the present invention organically combines flight trajectory planning with dynamic allocation of communication resources, so that drone swarms can adaptively adjust beam direction, transmission power and flight path, effectively suppress interference, reduce energy consumption, and ensure communication link stability and navigation reliability, thereby greatly improving task coordination efficiency and overall system performance, and has broad application prospects.

[0005] The technical solution adopted by the present invention is as follows: A drone group collaborative communication and navigation method based on a 5G Red Cap module, the method comprising: Step 1: Use the 5G Red Cap module to perform high-resolution sampling of the multipath spatiotemporal channels between each drone in the drone group, discretize the spatiotemporal channel data of each link, and construct a link channel matrix; Step 2: Based on the channel matrix of each link, for each UAV, a joint channel matrix is ​​constructed in combination with its neighbor set to extract the optimal beamforming vector; Step 3: According to the optimal beamforming vector and link channel matrix, the delay, phase and Doppler frequency shift are extracted, and the position information of each UAV is estimated by minimizing the residual function of the positioning error in combination with the initial relative position relationship between the UAVs; Step 4: Based on the obtained position information of each UAV and the optimal beamforming vector, predict the effective channel gain of each link, construct an optimization problem, and simultaneously plan the flight trajectory of the UAV and the dynamic communication resource allocation to ensure the coordinated optimization of navigation and communication.

[0006] Furthermore, in step 1, the drones in the drone group With drones The wireless link between the two is affected by multiple paths, each of which has different attenuation, phase, delay, and Doppler shift. The channel impulse response is expressed using the following formula: ; in, Indicates drone With drones The number of valid paths between them, each path corresponds to a signal component; For the The amplitude attenuation coefficient of a path reflects the attenuation degree of the signal when it propagates on the path; For the The delay of a path represents the time it takes for the signal to pass through the path from the transmitter to the receiver. For the The Doppler shift of the path; For the The phase of each path; is an integer subscript index; is the standard deviation of the delay of all paths; is the sampling time of high-resolution sampling; is the delay variable; Is the imaginary number symbol.

[0007] Furthermore, in step 1, for each path, let the sampling time set be And the delayed sampling set is , the spatiotemporal channel data of each link is discretized through the following formula to construct the UAV With drones The link channel matrix between: ; in, and All are integer subscript indices; is the number of sampling moments; is the number of delayed sampling points.

[0008] Furthermore, in step 2, by solving the following generalized eigenvalue problem, based on the channel matrix of each link, for each UAV, a joint channel matrix is ​​constructed in combination with its neighbor set to extract the optimal beamforming vector: ; in, Indicates drone The self-link channel matrix of is the Hermitian conjugation operation of the matrix; is the identity matrix; is the eigenvalue in the generalized eigenvalue problem. When solving the generalized eigenvalue problem, select the eigenvector corresponding to the maximum eigenvalue as the optimal beamforming vector; Indicates drone Neighborhood collection; drone The optimal beamforming vector The following constraints are met: ; Indicates drone The received noise power.

[0009] Furthermore, in step 3, according to the optimal beamforming vector and the link channel matrix, the extracted delay is set to , the phase is , the Doppler shift is , combined with the relative position relationship between drones, the following formula is used to define the residual function that minimizes the positioning error : ; in, For drones The initial position of For drones The initial position of is the Doppler shift variance; is the delay variance; is the phase variance; For drones The velocity vector of For drones The velocity vector of is the transpose operation; For drones The Y-axis coordinate of the initial position; For drones The Y-axis coordinate of the initial position; For drones The X-axis coordinate of the initial position; For drones The X-axis coordinate of the initial position; is the carrier wavelength.

[0010] Furthermore, in step 3, the residual function is used by the following formula , estimate the position information of each drone: ; in, For the estimated drone location; For the estimated drone The velocity vector.

[0011] Furthermore, in step 4, the drone is predicted With drones The effective link gain between ;in, For drones The optimal beamforming vector of .

[0012] Furthermore, in step 4, the optimization problem is constructed by the following formula, and the flight trajectory of the UAV and the dynamic communication resource allocation are planned at the same time to ensure the coordinated optimization of navigation and communication: ; in, For drones The transmission power; is the target position of the UAV. By solving the optimization problem, we can calculate The minimum corresponding and ; is the L2 norm operation; Interference attenuation coefficient, ranging from 2 to 5.

[0013] Furthermore, drones Transmit power The following constraints are met: ; ; in, is the preset signal-to-noise ratio threshold.

[0014] By adopting the above technical scheme, the present invention produces the following beneficial effects: the present invention adopts a 5G Red Cap module, and through high-resolution sampling and fine discretization of the wireless channel between the drone groups, it realizes the comprehensive capture and accurate modeling of channel characteristics such as multipath propagation, delay, phase and Doppler frequency shift. This precise channel modeling method is not only difficult to achieve in traditional communication technology, but also greatly improves the communication reliability and navigation accuracy of drone groups in complex dynamic environments. Compared with the prior art, the present invention uses advanced joint channel matrix construction and beamforming algorithms to enable each drone to adaptively adjust the signal transmission and reception direction during flight, thereby realizing the focused transmission and interference suppression of signal energy. Such a design effectively solves the problems of signal fading and link instability faced by traditional omnidirectional or fixed beam transmission methods in scenarios of high-speed flight, formation change and severe environmental interference, and provides higher data transmission rates and lower delays for drone group communications. In addition, the present invention combines multidimensional information such as signal propagation delay, phase change and Doppler frequency shift with the actual geometric position and motion state between drones by constructing a residual function based on multiple wireless channel characteristics, thereby realizing the minimization optimization of the relative positioning error of the drone group. After adopting this method, even in the case of GPS signal obstruction or large interference in complex environment, drones can accurately calculate the distance and motion state between each other through the high-precision wireless measurement data obtained by themselves, thereby completing high-precision relative positioning and navigation control. This not only significantly improves the collaborative operation capability of drone formation flight, but also provides a solid data foundation and algorithm support for realizing autonomous navigation, intelligent obstacle avoidance and task collaboration of drones. In terms of communication resource scheduling, the present invention designs a joint optimization scheme that closely combines the flight trajectory planning of drones with dynamic communication resource allocation. By adjusting the target position and transmission power in real time during the flight, each drone can always maintain the best communication link state while ensuring the completion of the task. This scheme not only fully considers the smoothness of the flight trajectory and the continuity of the path, preventing excessive energy consumption caused by sharp turns or frequent adjustments, but also takes into account the maximization of link gain and the minimization of neighboring interference, thereby achieving coordinated optimization of communication and navigation within the group. The results show that this method can significantly reduce the overall energy consumption of the system, improve the stability of the communication link and the reliability of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1A schematic diagram of a method flow of a drone swarm collaborative communication and navigation method based on a 5G Red Cap module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] All features disclosed in this specification, or steps in all methods or processes disclosed, except mutually exclusive features and / or steps, can be combined in any manner.

[0017] Any feature disclosed in this specification (including any additional claims and abstract), unless otherwise stated, may be replaced by other alternative features that are equivalent or have similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

[0018] Example 1: Reference Figure 1 , a drone group collaborative communication and navigation method based on a 5G Red Cap module, the method comprising: Step 1: Use the 5G Red Cap module to perform high-resolution sampling of the multipath spatiotemporal channels between each drone in the drone group, discretize the spatiotemporal channel data of each link, and construct a link channel matrix; The 5G Red Cap module has the ability to efficiently collect channel state information (CSI), and can also reconstruct channel characteristics with high resolution under low power consumption and low complexity. In the communication environment of drone groups, due to the high-speed movement and complex spatial structure of drones, the propagation of signals is inevitably affected by the multipath effect, which causes the received signals to show highly variable characteristics in time, space and frequency domains. Therefore, it is difficult to accurately capture the dynamic evolution characteristics of the channel by relying solely on the traditional single channel modeling method. The application of the 5G Red Cap module provides a channel measurement method that can adapt to the high-speed changing environment of drone groups. Based on advanced channel detection and estimation algorithms, the system can perform fine-grained sampling of multipath propagation characteristics between drones without increasing too much computational burden. In practical applications, the high-resolution sampling of multipath spatiotemporal channels by the 5G Red Cap module mainly relies on its built-in channel detection mechanism, which includes the channel state information acquisition technology based on pilot signals. By sending a specific pilot sequence, the receiving end can parse the channel delay, fading characteristics and spatial direction information. Since the UAV moves in three-dimensional space, its channel model usually involves not only the classic Rayleigh fading or Rice fading effect, but also is affected by complex factors such as dynamic occlusion, Doppler frequency shift and asymmetric path loss. Therefore, the channel estimation method based solely on static cannot meet the needs of high-precision sampling. To this end, the present invention utilizes the high-resolution sampling capability provided by the 5G Red Cap module, continuously samples the channel state in multiple time windows, and combines the adaptive filtering method to improve the accuracy of channel estimation, thereby obtaining more accurate channel state information. On this basis, in order to realize the mathematical modeling of the link channel between UAVs, it is necessary to discretize the sampled data, that is, to numerically approximate the continuous change characteristics of the channel so that it can be expressed in the form of a matrix. Here, the discretization process is not just a simple sampling and storage of the data, but a reasonable dimensionality reduction process of the data is carried out on the premise of ensuring the integrity of the channel information to reduce the computational complexity and improve the stability of subsequent channel processing.

[0019] Step 2: Based on the channel matrix of each link, for each UAV, a joint channel matrix is ​​constructed in combination with its neighbor set to extract the optimal beamforming vector; In this step, each drone will construct a joint channel matrix based on the link channel matrix obtained in the previous step, combined with its neighbor set, so as to comprehensively consider the characteristics of multiple links, rather than optimizing only a single link. The advantage of this method is that the traditional single-link optimization method often cannot make full use of the synergy of neighboring drones, which easily leads to beam conflicts, signal interference or inefficient power allocation. In the design of the present invention, by constructing a joint channel matrix, the beamforming strategy of each drone can not only be optimized for its own optimal communication direction, but also avoid signal interference with neighboring drones, thereby improving the communication efficiency and synergy of the entire drone group. In the process of constructing the joint channel matrix, the system will comprehensively consider the channel state information between all drones, including the channel gain, phase offset, delay information of each link, and the direction angle of the neighboring drone, so as to establish a complete spatial channel feature characterization. In this process, the low power consumption and low complexity characteristics of the 5G Red Cap module make real-time calculation possible, ensuring that even in high-speed flight or large-scale drone cluster environments, channel estimation and matrix operations can still be completed with low communication delays. Based on the constructed joint channel matrix, each drone needs to extract the optimal beamforming vector, and the core goal of this process is to find the best antenna weight configuration so that the signal energy is maximized in the desired direction and minimized in the interference direction. The intelligent beam management capability of the 5G Red Cap module plays a key role in this process. The beam training and feedback mechanism in 5GNR (NewRadio) enables the drone to dynamically adjust its beam pointing and beam shape to adapt to the changing channel environment. In the dynamic formation process of the drone group, beamforming needs to be able to respond quickly to environmental changes. Therefore, the present invention combines a beam search algorithm based on machine learning to predict the optimal beam direction at future moments by analyzing historical channel data and real-time measurement data, thereby reducing the computational overhead caused by relying on exhaustive search in traditional methods and improving the real-time performance of beam adjustment. In addition, due to the low power consumption characteristics of the Red Cap module, these calculations can be efficiently performed on the embedded platform of the drone without significantly increasing the energy consumption of the system.

[0020] In order to further optimize the beamforming process, the present invention adopts a beam adaptation mechanism based on a feedback closed loop, that is, after the beamforming vector is preliminarily calculated, each drone will make subtle adjustments to the beam direction according to the received signal quality indicators (such as signal strength, bit error rate, signal to noise ratio, etc.), thereby further optimizing the communication quality. This adaptive mechanism can effectively cope with channel changes in complex environments. For example, in urban environments, the reflection and diffraction effects of buildings will cause the channel characteristics to change continuously, and when flying at high altitudes, wind speed and attitude changes will also affect the accuracy of beam pointing. Through closed-loop feedback adjustment, the accuracy and robustness of beamforming can be improved without increasing too much computational complexity. In addition, since drone swarms often need to switch between multiple frequency bands and multiple antenna modes, the optimization of beamforming also needs to combine multi-carrier and multi-antenna information to ensure that the communication performance of different frequency bands can be maintained in the optimal state. In the final stage of this step, all drones will share their optimal beamforming vectors so that the entire drone swarm forms a consistent communication topology, thereby ensuring that any node in the group can efficiently exchange data. This method not only improves the overall communication capability of the drone swarm, but also enhances navigation accuracy, because based on accurate channel modeling and beamforming, each drone can obtain a more stable signal, thereby improving the accuracy of the subsequent positioning algorithm. Compared with the traditional fixed beam configuration scheme, the beamforming optimization method of the present invention can more flexibly adapt to different mission requirements. For example, when performing target tracking tasks, the beam direction can be adjusted to enhance signal coverage of the target area, and when performing formation flight tasks, the beamforming can be optimized to reduce interference between drones. This efficient beamforming optimization scheme enables the present invention to maintain superior communication performance and navigation accuracy in various complex environments.

[0021] Step 3: According to the optimal beamforming vector and link channel matrix, the delay, phase and Doppler frequency shift are extracted, and the position information of each UAV is estimated by minimizing the residual function of the positioning error in combination with the initial relative position relationship between the UAVs; After beamforming is completed, each drone will use the RF signal processing capability of the Red Cap module to perform a detailed analysis of the communication channel between it and the neighboring drones and extract the delay characteristics in the multipath environment. The delay information reflects the propagation time experienced by the wireless signal from the transmitting drone to the receiving drone. Since the millimeter wave and ultra-wideband technology used in 5G communication have extremely high time resolution, it is possible to obtain sub-nanosecond delay information through precise time measurement, thereby calculating the relative distance between drones. In addition, in a high-speed motion environment, the relative motion of the drone will cause the frequency offset of the wireless signal, namely the Doppler shift, which is directly related to the relative speed of the drone and can therefore be used as an important parameter for estimating the dynamic displacement of the drone. In the 5G Red Cap module, with the advanced frequency estimation algorithm, it is possible to maintain extremely high frequency deviation measurement accuracy under low signal-to-noise ratio conditions, which enables drones in dynamic environments to accurately perceive their relative motion state. In addition to delay and Doppler shift, the phase information of the signal is also fully utilized. The extraction of phase information needs to be combined with the coherent reception characteristics of the 5G Red Cap module. By analyzing the cumulative changes in the signal phase, the accuracy of distance measurement can be further improved. The key challenge of phase measurement is that the multipath effect may cause unstable changes in phase. Therefore, in the present invention, the channel characteristics after beamforming optimization are used to maximize the energy of the main path signal, so that the stability of phase measurement is greatly improved. In addition, by modeling the time series of phase information, phase mutations caused by instantaneous interference or channel fading can be effectively filtered out, thereby obtaining a more stable phase measurement result.

[0022] After acquiring the delay, phase and Doppler shift information, the system will combine the initial relative position relationship of the drone to construct a residual function of the positioning error, and the goal of the residual function is to minimize the error between the measured data and the actual geometric position. Since the propagation of wireless signals is affected by environmental factors, such as reflection, diffraction, occlusion, etc., a single measurement value may have deviations, so the method of minimizing the error is used to fuse multiple measurement data, which can effectively improve the stability of positioning. Specifically, the method of the present invention uses an optimization solution algorithm to adjust the relative position estimate of the drone in the process of continuous iteration so that it gradually converges to the true position state. In the process of optimizing the solution, the low power consumption characteristics of the 5G RedCap module are particularly considered, and a lightweight computing framework is used so that the algorithm can run efficiently on the embedded computing unit of the drone without additional reliance on high-performance ground computing resources. Through the above method, the present invention can not only realize high-precision relative positioning of drone groups in an environment where GPS signals are limited, but also can update the position of the drone in real time in a dynamic environment, thereby improving the navigation accuracy of the entire drone group. Compared with the traditional GPS / IMU fusion positioning method, the method of the present invention has stronger robustness in dynamic environments and can provide more stable location information in complex environments. In addition, since this method relies on the channel information extraction capability of the 5G Red Cap module, it can make full use of the existing 5G infrastructure without the need to add additional specialized positioning hardware, making this solution more economical and feasible in actual deployment.

[0023] Step 4: Based on the obtained position information of each UAV and the optimal beamforming vector, predict the effective channel gain of each link, construct an optimization problem, and simultaneously plan the flight trajectory of the UAV and the dynamic communication resource allocation to ensure the coordinated optimization of navigation and communication.

[0024] In the first stage of this step, the system uses the drone position information and optimal beamforming vector obtained in the previous step to predict the channel gain of each link. Channel gain is an important indicator for measuring the quality of wireless communication, which determines the signal reception strength, signal-to-noise ratio and data transmission rate. Since the flight state of the drone is constantly changing, the channel gain also changes dynamically, so it is necessary to predict the channel gain at future times through the wireless propagation model combined with historical channel measurement data. In the implementation of the present invention, the 5G Red Cap module provides high-precision channel state information, including path loss, multipath distribution, shadow fading and other characteristics, so that the channel gain prediction can be dynamically updated based on real measurement data, rather than relying solely on the static calculation of the theoretical model. Through the machine learning algorithm, the system can analyze the movement mode and channel change trend of the drone, predict the channel gain of each link in a short time, and thus provide accurate data support for subsequent optimization decisions. Based on the channel gain prediction results, the system constructs an optimization problem with the goal of maximizing communication quality and minimizing energy consumption, while taking into account the mission requirements and environmental constraints of the drone. The core variables of the optimization problem include the flight trajectory of the drone and the allocation strategy of communication resources. The flight trajectory optimization aims to ensure that the drone can always maintain good communication conditions during the mission execution and minimize power consumption. For example, when the drone swarm performs a cruising mission, the system will give priority to planning trajectories that can reduce frequent channel switching to reduce the risk of communication interruption caused by channel hopping. When performing target tracking tasks, it will give priority to paths that can ensure stable link channel gain to ensure high-quality video or sensor data transmission. In addition, considering the energy-constrained characteristics of drones, the optimization problem also needs to balance the relationship between communication quality and energy consumption to ensure that the drone will not consume power quickly due to frequent path adjustments. Therefore, the optimization goal not only focuses on maximizing communication performance, but also takes into account the endurance of the drone.

[0025] Another key link in this step is dynamic communication resource allocation. Since the drone swarm shares limited wireless spectrum resources, and the communication requirements and channel states of different drones are different, it is necessary to ensure that each drone can obtain sufficient bandwidth and power support through intelligent resource management strategies. With the support of the 5G Red Cap module, the system can perceive the communication needs of the drone in real time and dynamically adjust the resource allocation plan. For example, when the communication load is light, the system can reduce the transmission power to reduce energy consumption, and during the peak period of communication demand, it can intelligently adjust the beamforming strategy to improve the spatial multiplexing efficiency and ensure that multiple drones can communicate effectively in the same frequency band. In addition, by jointly optimizing the trajectory and resource allocation of the drone, the system can avoid the degradation of communication quality due to channel interference or resource conflicts. For example, when the communication interference in a certain area is strong, the system can adjust the flight path of the drone to avoid the interference source, thereby ensuring the stability of data transmission. Finally, the entire optimization process forms a closed loop, that is, in the process of the drone constantly updating its position, the system will adjust the channel gain prediction, optimization calculation and trajectory planning scheme in real time, so that the drone swarm can adapt to complex environments and achieve efficient communication and navigation collaborative optimization.

[0026] Example 2: In step 1, the drones in the drone group With drones The wireless link between the two is affected by multiple paths, each of which has different attenuation, phase, delay, and Doppler shift. The channel impulse response is expressed using the following formula: ; in, Indicates drone With drones The number of valid paths between them, each path corresponds to a signal component; For the The amplitude attenuation coefficient of a path reflects the attenuation degree of the signal when it propagates on the path; For the The delay of a path represents the time it takes for the signal to pass through the path from the transmitter to the receiver. For the The Doppler shift of the path; For the The phase of each path; is an integer subscript index; is the standard deviation of the delay of all paths; is the sampling time of high-resolution sampling; is the delay variable; Is the imaginary number symbol.

[0027] Specifically, in this model, the wireless channel between drones is affected by The signal strength of each path is given by Weighting is performed to characterize the attenuation degree of different paths. Since wireless signals encounter various obstacles when propagating in the air, such as buildings, trees or other drones, these obstacles will cause the signal to propagate through multiple reflection or scattering paths. Therefore, the attenuation on each path is different, affecting the synthesis of the final signal. Compared with the traditional free space propagation model, the channel modeling method of the present invention takes into account the multipath effect in complex environments, making the channel estimation more accurate. In addition, during the flight of the drone, its relative position is constantly changing, and the phase of the signal propagation path will also shift. Therefore, the phase shift of each path is given by The impact of phase shift is particularly significant when the drone is flying at high speed, because the signal propagation path will be affected by the drone's posture and relative motion, resulting in phase changes. In order to compensate for this change, the present invention uses the high-precision coherent detection capability of the 5G Red Cap module to monitor and adjust the beam direction in real time, ensuring that the signal is combined within the appropriate phase range, thereby reducing the signal loss caused by phase mismatch.

[0028] In terms of latency, the channel propagation time between drones is composed of the latency of each path Characterization, and because the propagation distances of different paths are different, the signals do not arrive synchronously at the receiving end. In order to deal with this problem, the present invention introduces Gaussian distribution weights in the delay model to characterize the statistical characteristics of the path delay. In this way, the system can effectively filter out short-term mutation delay errors and improve the time synchronization accuracy of the signal. At the same time, the ultra-wideband characteristics of the 5G Red Cap module can provide extremely high time resolution, so that the accuracy of delay estimation reaches the nanosecond level, thereby improving the relative positioning accuracy of the drone swarm. In terms of Doppler shift, since drones usually fly at a higher speed, the relative motion will cause the frequency offset of the wireless signal. The Doppler shift of each path is given by The Doppler effect will affect the communication quality of the drone group, especially when the drones are maneuvering at high speed or changing formations. The rapid change of the channel state will lead to an increase in frequency offset, thereby affecting the stability of data transmission. The present invention adopts high-precision frequency tracking technology based on the 5G Red Cap module, so that the system can accurately estimate the Doppler frequency shift under low signal-to-noise ratio conditions and adjust the carrier frequency in real time to ensure the stability of the communication link.

[0029] Example 3: In step 1, for each path, let the sampling time set be And the delayed sampling set is , the spatiotemporal channel data of each link is discretized through the following formula to construct the UAV With drones The link channel matrix between: ; in, and All are integer subscript indices; is the number of sampling moments; is the number of delayed sampling points.

[0030] Specifically, first set the sampling time set ,in Represents the number of sampling moments, each moment Corresponding to a high-precision channel measurement. Since the UAV flies at a high speed, its channel characteristics may change dramatically in a short period of time. Therefore, the present invention adopts an adaptive time sampling strategy to dynamically adjust the sampling interval according to the channel change rate, so that the sampling density is increased when the channel changes rapidly, and the sampling frequency is reduced when the channel is relatively stable, thereby taking into account both computational efficiency and data accuracy. Compared with the traditional fixed sampling interval method, this adaptive sampling strategy can effectively reduce redundant data while ensuring that key channel features are not lost. At the same time, in order to accurately capture the delay characteristics of the channel, a delayed sampling set is defined ,in Represents the number of delayed sampling points. Each delayed point Corresponding to a discrete delay value. In a multipath propagation environment, the propagation time of wireless signals on different paths is different. Therefore, the signal will experience different delay distributions at the receiving end. In order to accurately characterize these propagation characteristics, the present invention uses the high-resolution delay measurement capability of the 5G Red Cap module to construct a sampling set covering all possible channel delays and calculate the channel impulse response at discrete time points. In this way, sufficient resolution can be obtained in the delay domain to improve the accuracy of subsequent beamforming and navigation estimation. Based on the above-mentioned spatiotemporal sampling strategy, the drone With drones The channel information between them is organized into a two-dimensional matrix , where each element Represents the sampling time and delay The channel response value under . Each row of the matrix corresponds to a different time sampling, and each column corresponds to a different delay sampling, thus forming a complete space-time channel representation. This matrix form of channel modeling method has multiple advantages. First, it can provide high-dimensional channel information, so that the system can simultaneously analyze the time evolution characteristics and delay characteristics, and improve the accuracy of channel estimation; second, the matrix structure helps to utilize modern signal processing technology, such as deep learning-based channel prediction methods, which can improve the modeling accuracy of drone channels through matrix feature learning; finally, the matrix can be directly used for subsequent optimal beamforming calculations, so that the system can dynamically adjust the beam pointing according to the current channel state, thereby improving communication quality and navigation accuracy.

[0031] Embodiment 4: In step 2, by solving the following generalized eigenvalue problem, based on the channel matrices of each link, for each drone, a joint channel matrix is ​​constructed in combination with its neighbor set to extract the optimal beamforming vector: ; in, Indicates drone The self-link channel matrix of is the Hermitian conjugation operation of the matrix; is the identity matrix; is the eigenvalue in the generalized eigenvalue problem. When solving the generalized eigenvalue problem, select the eigenvector corresponding to the maximum eigenvalue as the optimal beamforming vector; Indicates drone Neighborhood collection; drone The optimal beamforming vector The following constraints are met: ; Indicates drone The received noise power.

[0032] Specifically, during the drone group communication process, each drone Need to ensure its own communication quality while minimizing the impact on neighboring drones Therefore, it is necessary to construct an optimization problem that can balance gain and interference. To this end, the system first obtains the self-link channel matrix of each drone , the matrix represents the drone The propagation characteristics of the wireless channel after the signal is sent. At the same time, since the wireless communication of the drone group is usually multipath propagation, the channel matrix includes factors such as multipath effect, phase offset and fading. When calculating the beamforming vector, in addition to paying attention to its own channel matrix In addition, it is necessary to combine all neighboring drones The link information of the UAV is used to construct the joint channel matrix, so that the final beamforming strategy can both enhance its own signal and reduce the interference to other UAVs. In order to achieve this optimization goal, the system needs to solve the generalized eigenvalue problem, where the goal is to maximize the beamforming vector Channel gain under action, while suppressing neighboring drones In the optimization expression, the left-hand side term Representative drone The channel gain of the UAV is , while the right-hand side contains the channel matrix of the neighboring UAV The sum of .in, Representative drone The received noise power, and the identity matrix It is used to ensure numerical stability and avoid ill-conditioned matrix problems when solving eigenvalues. In this optimization framework, by solving the generalized eigenvalue decomposition, the system can find the optimal beamforming vector , which corresponds to the maximum eigenvalue, making the drone The signal gain is maximized while suppressing interference from neighboring drones.

[0033] The core advantage of this method is that, through joint channel matrix modeling, the beamforming optimization not only considers the channel characteristics of a single drone, but also combines the synergy of the drone group, so it can significantly improve the communication efficiency of the entire network. Compared with the traditional beamforming method, the method of the present invention uses the solution of the generalized eigenvalue problem to make the calculation of the beamforming vector more robust, even in a complex channel environment, the beam direction can be adaptively adjusted, thereby ensuring that the drone group always maintains the best communication quality during the flight. In addition, since the 5G Red Cap module has low-power computing power, the optimization method can run efficiently on the embedded computing platform of the drone without introducing too much computing overhead, thereby ensuring the real-time communication optimization needs of the drone group. In the dynamic environment of the drone, the optimal beamforming vector needs to be continuously updated over time, so the system needs to regularly recalculate the generalized eigenvalue and adjust the beam direction. In the method of the present invention, an intelligent prediction mechanism is adopted so that the beam optimization can predict channel changes in advance and make pre-adjustments according to the motion trajectory of the drone. This prediction method combines historical channel data with real-time measurement data, so that beamforming can be adjusted before the channel changes dramatically, thereby reducing the fluctuation of signal quality and improving communication stability. In addition, in order to further reduce the computational complexity, the present invention utilizes the matrix sparsity analysis method, so that in the process of solving the generalized eigenvalue, only the main influencing factors need to be paid attention to, while the secondary channel components are ignored, thereby speeding up the calculation speed and improving the real-time performance. Another key technical point is that the optimization method can adapt to different task requirements. For example, in a formation flight mission, the system can optimize the beam direction so that the communication link between all drones remains stable, and in an environmental monitoring mission, the beamforming strategy can be adjusted so that the drone can establish an optimal communication link with the ground station. In addition, in a high-density drone formation, the traditional fixed beamforming method is prone to signal interference problems, and the method of the present invention optimizes the joint channel matrix so that the beamforming of each drone can be adaptively adjusted, thereby reducing signal conflicts and improving the overall network capacity. In addition, the optimization method can also effectively reduce the power consumption of the drone. Since the optimal beamforming vector can maximize the channel gain, the drone can use a lower transmission power under the same signal quality requirements, thereby reducing energy consumption and extending the flight time. This technical feature makes the method of the present invention particularly suitable for long-flight drone missions, such as large-scale regional monitoring, air logistics networks, and remote disaster relief applications. Compared with the traditional full-power transmission solution, the method of the present invention can optimize the power control strategy while ensuring the communication quality, so that the energy utilization efficiency of the entire drone group is significantly improved.

[0034] Example 5: In step 3, according to the optimal beamforming vector and the link channel matrix, the extracted delay is set to , the phase is , the Doppler shift is , combined with the relative position relationship between drones, the following formula is used to define the residual function that minimizes the positioning error : ; in, For drones The initial position of For drones The initial position of is the Doppler shift variance; is the delay variance; is the phase variance; For drones The velocity vector of For drones The velocity vector of is the transpose operation; For drones The Y-axis coordinate of the initial position; For drones The Y-axis coordinate of the initial position; For drones The X-axis coordinate of the initial position; For drones The X-axis coordinate of the initial position; is the carrier wavelength.

[0035] Specifically, in the residual function In the definition of , the first term represents the ranging error based on the propagation time of the wireless signal. Since the propagation speed of the wireless signal is constant, the delay Can be used to calculate drones and drones The distance between ,in is the speed of light. However, the actual measured signal propagation time is affected by multipath effects, channel fading and noise, so the calculated distance is different from the actual physical distance. There may be deviations. In order to optimize this ranging error, the variance normalization method is used, that is, the delay variance As a weight, the ranging errors under different channel conditions can be reasonably balanced, thereby reducing the impact of extreme errors. This optimization process ensures that measurements with smaller ranging errors contribute more to the final positioning result, while measurements with larger noise have lower weights, thereby improving the overall positioning accuracy. The second term of the residual function represents the angular error based on phase measurement. Since the 5G Red Cap module has high-precision phase estimation capabilities, it can measure the UAV and drones The relative phase shift between According to the geometric relationship, the direction of the connection between the two drones can be calculated by the coordinate relationship, that is, , indicating that from arrive However, in actual measurement, the phase information will be affected by channel phase noise, environmental interference and equipment errors, so the error term needs to be calculated through the phase variance Normalization is performed so that the phase information with higher measurement accuracy has a greater impact on the positioning result, while the phase measurement with more serious interference has a lower weight. This optimization strategy can effectively reduce the impact of phase measurement error on the final positioning result and improve positioning robustness. The third term of the residual function is based on Doppler frequency shift measurement and is used to estimate the relative speed between drones. Since the drone is in flight, its velocity vector and Directly affects the frequency deviation of the signal. According to the Doppler effect, the frequency shift It is proportional to the projection of the relative speed of the drones on the direction of their connection. Specifically, The theoretical Doppler shift value based on the position and velocity information was calculated and compared with the actual measured value. Compare and calculate the square of the error. Since Doppler frequency shift measurement is also affected by noise, the frequency shift variance is used Normalization is performed so that the path with smaller Doppler measurement error has a higher weight, while the measurement with larger error has less impact. This method can use Doppler information to optimize the motion estimation of the UAV and improve the positioning accuracy in dynamic environments. Residual function It combines three independent but complementary wireless channel information of time delay, phase and Doppler frequency shift, and optimizes the positioning error by variance normalization, so that the final drone position estimation is more accurate and stable. Compared with the traditional GPS positioning method, the method of the present invention can provide high-precision relative positioning in an environment where the GPS signal is limited or severely interfered. At the same time, combined with the real-time channel estimation capability of the 5G Red Cap module, the system can continuously optimize the position estimation of the drone in a dynamic environment and improve the collaborative navigation capability of the drone group. In addition, since this method uses a variety of wireless channel characteristics for comprehensive optimization, compared with a single measurement method, its positioning accuracy is higher and its anti-interference ability is stronger, which is particularly suitable for autonomous flight, formation control and mission execution of drone groups in complex environments.

[0036] Example 6: In step 3, the residual function is used by the following formula: , estimate the position information of each drone: ; in, For the estimated drone location; For the estimated drone The velocity vector.

[0037] Specifically, the wireless signal features extracted from the optimal beamforming vector and link channel matrix are combined with the actually measured channel parameters and the geometry and motion model between drones to construct an error evaluation index. Here, the residual function The error term based on delay is used to estimate the distance between drones by measuring the propagation delay of wireless signals and compare it with the error term based on the speed of light. The distance obtained by multiplying the measured delay is compared; secondly, the phase-based error term uses the geometric relationship between the UAVs to The function calculates the theoretical phase angle and compares it with the actual measured phase offset. Finally, the Doppler frequency shift error term uses the speed information of the drone and its relative position to calculate the theoretical Doppler frequency shift and compare it with the actual measured frequency shift value. Each term is calculated by the corresponding variance parameter (such as the delay variance , phase variance and Doppler shift variance ) is normalized so that each error term can automatically adjust its weight according to the measurement accuracy during the optimization process, avoiding a single noise source or extreme measurement error from having too much impact on the overall positioning result. The residual function constructed in this way can not only fully reflect the intrinsic connection between the wireless channel measurement data and the physical position and speed, but also effectively suppress the random errors caused by factors such as multipath effects, signal fading, and environmental interference. When drones are performing missions, due to high-speed flight and environmental changes, traditional GPS or inertial navigation systems often find it difficult to maintain continuous and stable positioning accuracy. This method uses the high-resolution channel sampling and beamforming technology of the 5G Red Cap module to obtain more detailed delay, phase, and Doppler information, thereby providing a solid data foundation for positioning optimization.

[0038] In the actual solution process, the system inputs the real-time collected wireless signal characteristics into the residual function In the process, numerical optimization algorithms (such as gradient descent, Newton method or extended Kalman filter, etc.) are used to search for the optimal solution in the state space of the UAV, that is, to minimize the error function. and The key to this step is to construct a smooth and globally optimal objective function to ensure that the multivariable nonlinear optimization problem can quickly converge to the global optimal solution. With velocity vector It represents the drone state that best conforms to the measurement data and physical motion laws under the current wireless channel environment, so that the entire positioning system can adapt to complex and changing environments and provide real-time and accurate position references in the collaborative communication and navigation process of drone swarms. It is worth noting that this optimization positioning method based on residual function is not only applicable to the positioning problem of a single drone, but more importantly, it can utilize the collaborative information between drone swarms, that is, through the relative measurement data between neighboring drones, to form a distributed positioning network, further improving the overall robustness and positioning accuracy of the system. The low latency and high bandwidth characteristics of the 5G Red Cap module ensure that these calculations can be completed in real time on the drone embedded processing unit without affecting the real-time performance of the flight mission. At the same time, through multi-source information fusion, this method can effectively resist the risk of single measurement data failure or extreme noise interference, so that drone swarms can still maintain stable and continuous positioning services in complex urban environments, indoors or in scenes where GPS signals are blocked.

[0039] Example 7: In step 4, predicting drones With drones The effective link gain between ;in, For drones The optimal beamforming vector of .

[0040] Specifically, drones face complex factors such as multipath effects, signal fading, environmental interference, and Doppler frequency shift caused by relative motion during flight. These factors make it difficult for traditional omnidirectional antennas or fixed beam communication methods to meet the needs of high-precision communication and navigation. The 5G Red Cap module, with its high-resolution spatiotemporal sampling capability and low latency advantages, can measure and estimate the complex state of the wireless channel in real time, and convert the obtained channel matrix into As a fine description of the channel characteristics between UAVs, the beamforming vector and The optimal transmit and receive beam directions are obtained in the early stage through joint channel matrix construction and generalized eigenvalue decomposition and other algorithm optimization. This beamforming technology can maximize the signal gain in the desired direction while suppressing interference from other directions. It is used to ensure that the receiving end can accurately correct the phase information of the signal during the signal synthesis process, so as to achieve the coherent superposition of the signals of each path. Its absolute value operation converts the complex result into the actual signal amplitude, which directly reflects the effective gain of the link. Pre-designed transmit beams and The comprehensive channel characteristics such as multipath, fading and noise contained in the multi-channel are multiplied, and combined with the beamforming at the receiving end After that, the system can obtain a quantitative index reflecting the actual signal transmission quality between drones. , this indicator not only directly reflects the effect of beam directivity on signal enhancement, but also effectively reduces the adverse effects caused by interference and noise. At the same time, this formula also contains important significance for the collaborative operation of drone swarms, because in actual tasks, the communication link status between different drones directly affects the connectivity and data transmission efficiency of the overall network. Using this link gain prediction model, the system can update the link status information between drones in real time during the flight, so as to reasonably schedule communication resources, and dynamically adjust the flight trajectory and beamforming strategy according to the changes in link gain, so that the entire drone swarm can still maintain high-quality, low-latency communication links when facing complex dynamic environments. Especially in scenarios where drones are moving at high speed, formations are changing, or interference is encountered suddenly, the traditional fixed beam mode often cannot quickly adapt to the drastic fluctuations in channel status, and the method of the present invention is to use the channel matrix measured in real time Combined with the finely optimized beamforming vector, adaptive adjustment of the channel gain is achieved. This method not only provides an effective link gain evaluation model mathematically, but also provides a strong technical support for collaborative navigation and communication within a group of drones, enabling the system to maintain stable and reliable communication performance in a highly dynamic and complex interference environment. On the other hand, by leveraging the low power consumption and high processing power advantages of the 5G Red Cap module, the link gain prediction method can run in real time on the drone's embedded processing unit without relying on a large central processing unit, thereby greatly reducing the overall power consumption and latency of the system. Furthermore, by adjusting the link gain Through dynamic monitoring, the system can also predict possible channel fading or interference areas in the future, and then adopt avoidance or channel switching strategies in flight control and mission planning to ensure that the drone swarm always maintains the best communication status when performing monitoring, search and rescue, logistics and transportation tasks.

[0041] Example 8: In step 4, the optimization problem is constructed by the following formula, and the flight trajectory of the drone and the dynamic communication resource allocation are planned at the same time to ensure the coordinated optimization of navigation and communication: ; in, For drones The transmission power; is the target position of the UAV. By solving the optimization problem, we can calculate The minimum corresponding and ; is the L2 norm operation; Interference attenuation coefficient, ranging from 2 to 5.

[0042] Specifically, the first For measuring drones Target location With the current estimated position The difference in distance between the two reflects the degree of deviation of the flight trajectory. This item plays a role in smoothing trajectory adjustments and limiting large position jumps during the optimization process, ensuring that the drone can meet the mission objectives when adjusting the trajectory without increasing flight risks or energy consumption due to frequent or drastic trajectory changes. Especially in complex environments, drones may need to adjust their flight paths due to obstacle avoidance, interference, or mission switching. This item emphasizes the priority of small adjustments in the form of a sum of squares, thereby achieving navigation accuracy while minimizing stability issues caused by path changes. Next, the second item It directly involves the dynamic allocation of communication resources, where Indicates drone The transmission power of For drones With target drone The link gain between is the noise power in the system. This design is intended to balance the relationship between the transmit power and the channel conditions. When it is higher, it means that the channel condition is good and the signal can be fully amplified during transmission. At this time, the denominator is larger and the overall penalty term is smaller, so low-power communication can be achieved; conversely, if the link gain is lower, the denominator decreases and the penalty term increases, forcing the system to increase power consumption. Or adjust the beamforming strategy to improve communication quality. In this way, the optimization problem can dynamically adapt to environmental changes, achieve power saving management while ensuring the stability of the communication link, and reduce the impact of wireless channel uncertainty on communication performance. The focus is on the interference problem and spatial synergy effect within the drone group. In a network composed of multiple drones, the drones will affect each other due to signal overlap and interference. This is achieved by The influence of Norm of Power (where is the path attenuation coefficient, usually between 2 and 5) as the attenuation function to reflect the natural inhibitory effect of distance on interference intensity.

[0043] When two drones are far apart, the interference is naturally small; when the distance is close, the interference effect will increase significantly, causing the penalty term to increase rapidly, prompting the system to adjust the flight trajectory or transmission power to reduce the interference between each other. The first part reiterates the importance of signal gain in the communication link. Only with high link gain can the signal be effectively transmitted. This also implies the reliance on beamforming technology, because efficient beamforming can maximize the signal energy in the desired direction while suppressing unnecessary side lobe interference. Cumulative optimization is performed on a time scale, which means that the system must continuously minimize the objective function throughout the flight process, thereby achieving real-time and dynamic collaborative optimization. The optimization formula in Example 8 not only mathematically unifies the navigation and communication problems of the drone into a comprehensive goal, but also fully utilizes the advantages of the 5G Red Cap module's high-precision channel measurement, beamforming technology, and low-latency data processing in engineering implementation. When a swarm of drones is performing a mission, the fine-tuning of its flight trajectory is directly related to the quality of the communication link, and this formula achieves a dynamic balance between the two by jointly considering position changes, transmission power, and neighboring interference. Especially in complex and changing environments, drones need to constantly adjust their flight routes to avoid obstacles while ensuring stable communication with other drones and ground control stations; and this optimization problem uses a penalty mechanism for various indicators to ensure that drones can both efficiently complete their tasks and reduce energy consumption and interference risks under conditions of limited communication resources. In addition, by adjusting the path attenuation coefficient The system can be flexibly configured for different application scenarios and environmental conditions, so that the method can achieve optimal performance in complex scenarios such as urban environments, mountainous areas or indoors.

[0044] Example 9: Drone Transmit power The following constraints are met: ; ; in, is the preset signal-to-noise ratio threshold.

[0045] Specifically, the constraint system is implemented in two aspects. The first aspect is to restrict drones. The transmission power must not exceed the upper limit determined by the channel gain and path attenuation characteristics, that is, The denominator here should reflect the actual distance between drones, thereby reflecting the path attenuation effect of wireless signals when propagating in space. The essence of this constraint is that the signal will experience more significant attenuation as the distance increases during transmission. If the drone is at a long distance or the channel state is not good, the power required for its transmission should be strictly limited, so as to ensure transmission efficiency while avoiding excessive interference caused by excessive transmission power, thereby saving energy and maintaining the stability of the entire drone swarm communication network. The second aspect introduces a signal-to-noise ratio (SNR) constraint, which measures the drone’s The relative relationship between the effectiveness of its own signal transmission and the interference from other drones and system noise. Specifically, this part of the constraint constructs a ratio whose numerator is The effective signal gain obtained after beamforming optimization in its own link Estimated change from current position The denominator is the ratio of all other drones to the current drone. The sum of the interference caused by adding the noise power at the receiving end This construction method cleverly takes into account the channel status, spatial position changes, and the impact of neighboring drones on interference, so that the entire ratio must not be lower than a preset SNR threshold. In other words, if the drone The effective signal is weak relative to the interference and noise. This constraint will force the system to readjust its transmit power, beamforming strategy or flight position to ensure that the link quality is always maintained at a safe and reliable level. Such a design is particularly important in practical applications, because drone swarms are usually in a dynamically changing environment when performing tasks, and the channel state, relative position and interference situation may change rapidly. Only by satisfying constraints like the above in real time can the communication link between drones be sufficiently robust, thereby providing stable data support for navigation and task collaboration. Furthermore, based on the high-resolution channel measurement and real-time computing capabilities of the 5G Red Cap module, this power constraint and SNR guarantee can be quickly solved in the drone embedded system, thereby achieving dynamic adjustment without the need for additional hardware support. This method can not only prevent a single drone from causing local interference due to excessive transmit power, but also optimize the allocation of wireless resources at the entire group level, balancing the energy consumption and performance of each drone between communication and navigation.

[0046] Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that these specific embodiments are only illustrative, and those skilled in the art may omit, replace, and change the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, merging the above method steps so as to perform substantially the same functions in substantially the same manner to achieve substantially the same results is within the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. A drone swarm collaborative communication and navigation method based on a 5G Red Cap module, characterized in that: The method comprises: Step 1: Use the 5G Red Cap module to perform high-resolution sampling of the multipath spatiotemporal channels between each drone in the drone group, discretize the spatiotemporal channel data of each link, and construct a link channel matrix; Step 2: Based on the channel matrix of each link, for each UAV, a joint channel matrix is ​​constructed in combination with its neighbor set to extract the optimal beamforming vector; Step 3: According to the optimal beamforming vector and link channel matrix, the delay, phase and Doppler frequency shift are extracted, and the position information of each UAV is estimated by minimizing the residual function of the positioning error in combination with the initial relative position relationship between the UAVs; Step 4: Based on the obtained position information of each UAV and the optimal beamforming vector, predict the effective channel gain of each link, construct an optimization problem, and simultaneously plan the flight trajectory of the UAV and the dynamic communication resource allocation to ensure the coordinated optimization of navigation and communication.

2. The drone group collaborative communication and navigation method based on the 5G Red Cap module as claimed in claim 1, characterized in that: In step 1, the drones in the drone group With drones The wireless link between the two is affected by multiple paths, each of which has different attenuation, phase, delay, and Doppler shift. The channel impulse response is expressed using the following formula: ； in, Indicates drone With drones The number of valid paths between them, each path corresponds to a signal component; For the The amplitude attenuation coefficient of a path reflects the attenuation degree of the signal when it propagates on the path; For the The delay of a path represents the time it takes for the signal to pass through the path from the transmitter to the receiver. For the The Doppler shift of the path; For the The phase of each path; is an integer subscript index; is the standard deviation of the delay of all paths; is the sampling time of high-resolution sampling; is the delay variable; Is the imaginary number symbol.

3. The drone group collaborative communication and navigation method based on the 5G Red Cap module as claimed in claim 2, characterized in that: In step 1, for each path, let the sampling time set be And the delayed sampling set is , the spatiotemporal channel data of each link is discretized through the following formula to construct the UAV With drones The link channel matrix between: ； in, and All are integer subscript indices; is the number of sampling moments; is the number of delayed sampling points.

4. The drone group collaborative communication and navigation method based on the 5G Red Cap module as claimed in claim 3, characterized in that: In step 2, by solving the following generalized eigenvalue problem, based on the channel matrix of each link, for each drone, a joint channel matrix is ​​constructed in combination with its neighbor set to extract the optimal beamforming vector: ； in, Indicates drone The self-link channel matrix of is the Hermitian conjugation operation of the matrix; is the identity matrix; is the eigenvalue in the generalized eigenvalue problem. When solving the generalized eigenvalue problem, select the eigenvector corresponding to the maximum eigenvalue as the optimal beamforming vector; Indicates drone Neighborhood collection; drone The optimal beamforming vector The following constraints are met: ; Indicates drone The received noise power.

5. The drone group collaborative communication and navigation method based on the 5G Red Cap module as claimed in claim 4, characterized in that: In step 3, according to the optimal beamforming vector and link channel matrix, the extracted delay is set to , the phase is , the Doppler shift is , combined with the relative position relationship between drones, the following formula is used to define the residual function that minimizes the positioning error : ； in, For drones The initial position of For drones The initial position of is the Doppler shift variance; is the delay variance; is the phase variance; For drones The velocity vector of For drones The velocity vector of is the transpose operation; For drones The Y-axis coordinate of the initial position; For drones The Y-axis coordinate of the initial position; For drones The X-axis coordinate of the initial position; For drones The X-axis coordinate of the initial position; is the carrier wavelength.

6. The drone group collaborative communication and navigation method based on the 5G Red Cap module as claimed in claim 5, characterized in that: In step 3, the residual function is used by the following formula , estimate the position information of each drone: ； in, For the estimated drone location; For the estimated drone The velocity vector.

7. The drone group cooperative communication and navigation method based on the 5G Red Cap module as claimed in claim 6, characterized in that: In step 4, predict the drone With drones The effective link gain between ;in, For drones The optimal beamforming vector of .

8. The drone group cooperative communication and navigation method based on the 5G Red Cap module as claimed in claim 6, characterized in that: In step 4, the optimization problem is constructed through the following formula, and the flight trajectory of the drone and the dynamic communication resource allocation are planned at the same time to ensure the coordinated optimization of navigation and communication: ; in, For drones The transmission power; is the target position of the UAV. By solving the optimization problem, we can calculate The minimum corresponding and ; is the L2 norm operation; Interference attenuation coefficient, ranging from 2 to 5.

9. The drone group cooperative communication and navigation method based on the 5G Red Cap module as claimed in claim 8, characterized in that: Drones Transmit power The following constraints are met: ； ； in, is the preset signal-to-noise ratio threshold.

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