Communication methods of dual-channel UAV line-of-sight communication systems

Abstract

This invention relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a communication method for a dual-channel UAV line-of-sight (LAS) communication system. The method includes: acquiring communication quality, acquiring the optimal transmission frequency, acquiring the UAV's mission priority, transmitting video stream data, and performing multipath fading compensation steps. The method sets up high-frequency and low-frequency channels, calculates frequency quality indicators using signal-to-noise ratio (SNR), signal rate, and bit error rate (BER), and dynamically selects the optimal transmission frequency. Communication resources are allocated based on mission priority to ensure priority transmission of critical commands. Innovatively, differential geometry theory is used for multipath fading compensation, mapping the communication signal to a Riemannian manifold space, extracting fading features, and optimizing the processing. High- and low-frequency channel collaborative compensation is achieved, with mutual enhancement through received spectrum analysis and training sequence correction. Adaptive QAM modulation is supported, dynamically adjusting modulation parameters according to manifold curvature to construct a star-shaped multi-node communication network.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a communication method for a dual-channel UAV line-of-sight communication system, applicable to long-distance, highly reliable communication of fixed-wing UAVs. Background Technology

[0002] With the rapid development of drone technology, drones are increasingly being used in military reconnaissance, disaster monitoring, agricultural plant protection, and power line inspection. In these applications, long-distance, highly reliable communication capabilities are crucial for ensuring the safe and efficient execution of drone missions. Traditional drone communication systems typically use a single frequency band for information transmission, such as the common 2.4GHz, 5.8GHz, or 433MHz bands. However, single-band communication faces numerous challenges in practical applications: while high-frequency bands (such as 5.8GHz) offer high bandwidth and can support large data transmissions like video, their transmission distance is short and they are easily blocked by obstacles; low-frequency bands (such as 433MHz), while offering longer transmission distances and stronger penetration capabilities, have limited bandwidth, making it difficult to support high-speed data transmission. Furthermore, during high-speed flight, rapid changes in communication channels, multipath effects, and various interference factors can lead to unstable communication quality, especially in complex terrain and electromagnetic environments, where these problems are more pronounced. In existing technologies, although there are solutions that use dual-band communication, most of them are simple redundancy backups or fixed division of labor, lacking intelligent frequency selection mechanisms and effective channel optimization methods, making it difficult to fully realize the potential of dual channels.

[0003] Traditional methods for addressing multipath fading primarily employ statistical models for analysis and compensation, such as Rayleigh fading models and Rice fading models. While these methods are effective in certain scenarios, they struggle to accurately describe and handle the complex multipath effects in high-speed flight environments, particularly in capturing the nonlinear and time-varying characteristics of multipath environments.

[0004] Therefore, there is an urgent need to develop a UAV communication method that can intelligently utilize the advantages of dual-band communication and effectively address the challenges of multipath fading, so as to improve the range, reliability, and data transmission capabilities of UAV communication. Summary of the Invention

[0005] The purpose of this invention is to provide a communication method for a dual-channel UAV line-of-sight communication system. Through innovative technologies such as intelligent coordination of high-frequency and low-frequency channels and multipath fading compensation based on differential geometry theory, this invention solves the technical challenges of long-distance and highly reliable transmission in existing UAV communication systems.

[0006] This invention proposes a communication method for a dual-channel UAV line-of-sight communication system, comprising:

[0007] Acquiring communication quality includes: setting up high-frequency and low-frequency channels to transmit data to the target UAV and acquiring communication quality indicators; the high-frequency channel is in the 5.8GHz band, and the low-frequency channel is in the 433MHz band; the communication quality indicators include signal-to-noise ratio, signal rate, and bit error rate;

[0008] Obtaining the optimal transmission frequency point includes: determining the frequency point quality of the high-frequency channel and the low-frequency channel based on the communication quality indicators, and determining the optimal transmission frequency point;

[0009] Obtaining the task priority of the UAV includes: determining the task priority type based on the task priority, and selecting the corresponding transmission frequency to transmit control commands based on the task priority type and the optimal transmission frequency.

[0010] Transmitting video stream data; including: acquiring video stream data transmitted by a drone, and transmitting the video stream data to the target drone through the optimal transmission frequency;

[0011] Performing multipath fading compensation includes: acquiring communication signals at multiple times, mapping the communication signals to a differential manifold space, extracting fading features in the differential manifold space, optimizing the communication signals based on the fading features, and generating an optimized communication signal.

[0012] Preferably, the multipath fading compensation specifically includes:

[0013] Obtain the signal-to-noise ratio of communication signals at multiple times;

[0014] The signal-to-noise ratios are sorted, and one signal-to-noise ratio is selected as a reference signal-to-noise ratio;

[0015] Based on the reference signal-to-noise ratio, the average fading depth of the communication signal at multiple times is obtained;

[0016] The signal-to-noise ratio of the communication signal at each time moment is multiplied by the average fading depth to obtain the weight value of the communication signal at each time moment;

[0017] The communication signals at multiple times are superimposed based on the weight values ​​to obtain the superimposed signal;

[0018] Extract the communication baseband signal from the superimposed signal and obtain the signal-to-noise ratio of the communication baseband signal;

[0019] When the signal-to-noise ratio of the communication baseband signal is greater than the signal-to-noise ratio of the communication signal at the plurality of times, it is determined that multipath fading compensation has been completed.

[0020] Preferably, obtaining the optimal transmission frequency specifically includes:

[0021] When the frequency quality index of the low-frequency channel is greater than the first preset threshold and the frequency quality index of the high-frequency channel is available, the high-frequency channel is taken as the best transmission frequency at the current moment.

[0022] When the frequency quality index of the low-frequency channel is less than the first preset threshold and the frequency quality index of the high-frequency channel is available, the low-frequency channel is taken as the best transmission frequency at the current moment.

[0023] When the frequency quality index of the low-frequency channel is unavailable, both the high-frequency channel and the low-frequency channel are taken as the optimal transmission frequency at the current moment.

[0024] Preferably, the step of selecting the corresponding transmission frequency point and transmitting the control command based on the task priority type and the optimal transmission frequency point specifically includes:

[0025] When the task priority type is the highest priority task, the high-frequency channel or the current best transmission frequency is used as the transmission frequency of the highest priority task, and the control command is transmitted to the target UAV.

[0026] When the task priority type is a sub-high-level task, the frequency point with the higher quality index is used as the transmission frequency point for the sub-high-level task, and the control command is transmitted to the target UAV.

[0027] When the task priority type is normal task, the low-frequency channel or the current best transmission frequency is used as the transmission frequency of normal task, and the control command is transmitted to the target UAV.

[0028] Preferably, mapping the communication signal to the differential manifold space specifically includes:

[0029] Construct a parameter space, where each dimension of the parameter space corresponds to a signal feature at a given time.

[0030] Define a metric tensor and establish the distance concept in the parameter space;

[0031] The parameter space is mapped to a Riemannian manifold through local coordinate transformation, while preserving the topological structure;

[0032] An orthogonal basis is established on the Riemannian manifold for subsequent geometric analysis.

[0033] Preferably, the extraction of fading features in the differential manifold space specifically includes:

[0034] The curvature tensor is calculated on the Riemannian manifold to characterize the degree of local spatial curvature.

[0035] The propagation characteristics of the signal on the Riemannian manifold were calculated using the parallel transmission equation;

[0036] Analyze the principal directions of the curvature tensor to identify the main factors influencing fading;

[0037] A reference plane is established using a reference signal-to-noise ratio, and the geodesic distance from other signal points to the reference plane is calculated.

[0038] The geodesic distance is converted into a fading depth parameter to establish a mapping relationship between geometric and physical quantities.

[0039] Construct a fading depth distribution map to characterize the spatial distribution characteristics of the multipath environment.

[0040] Preferably, the optimization processing of the communication signal based on the fading characteristics specifically includes:

[0041] Calculate the geometric weight of the signal at each time step, which is determined by the product of the signal-to-noise ratio and the average fading depth;

[0042] Calculate the geodesic connecting each signal point on the Riemannian manifold to characterize the optimal signal change path;

[0043] The signal is interpolated piecewise along the geodesic line to maintain phase continuity;

[0044] Construct curvature flow equations on the Riemannian manifold to describe the optimal evolution of the Riemannian manifold over time;

[0045] Set evolutionary boundary conditions to ensure convergence to a physically meaningful solution;

[0046] Controlling the evolution rate and balancing computational efficiency with optimization accuracy;

[0047] The parallel transmission theory ensures the consistency of signal phase during the fusion process.

[0048] As a preferred option, a high- and low-frequency channel coordinated compensation step is also included:

[0049] The high-frequency channel receives the received spectrum of the low-frequency channel;

[0050] Multipath fading information is obtained based on the received spectrum;

[0051] The transmission errors in the low-frequency channel data transmission process are corrected based on the multipath fading information.

[0052] The signal is transmitted from the high-frequency channel to the low-frequency channel;

[0053] A training sequence is added to the low-frequency channel;

[0054] The phase noise of the low-frequency channel transmitted signal is compensated according to the training sequence;

[0055] Construct a joint high- and low-frequency manifold to describe the coupling characteristics of the two channels;

[0056] Analyze the correlation and complementarity of fading characteristics in different frequency bands;

[0057] Establish mapping relationships between frequency bands to achieve complementary enhancement.

[0058] Preferably, an adaptive QAM modulation step is also included:

[0059] Acquire communication data and convert the communication data into multiple signals;

[0060] The multiple signals are modulated with baseband signals, and the baseband signals are spread spectrum.

[0061] Based on the differential manifold analysis results, a channel state reference is provided for high-frequency channel adaptive QAM modulation;

[0062] The signal constellation diagram distribution is automatically adjusted based on multipath characteristics;

[0063] Reduce the modulation order in the high curvature region of the Riemannian manifold;

[0064] Increase the modulation order in the flat region of the Riemannian manifold;

[0065] Establish a two-way information exchange mechanism between the modulation system and the compensation system.

[0066] Preferably, the step of constructing a multi-node communication network is also included:

[0067] Construct a star-shaped network, including a control terminal, relay nodes, slave nodes, and target drones;

[0068] The relay node, slave node, and target UAV are respectively connected to the control terminal;

[0069] Time-series diversity reception is used between any two adjacent nodes;

[0070] The node sends communication signals to the control terminal;

[0071] When the communication signal arrives at the control terminal, there are communication signals at multiple moments within the control terminal;

[0072] QAM modulation is applied to the communication signals at multiple times to obtain the optimal timing sequence at multiple times;

[0073] The communication signal is recovered using the optimal timing sequence of the multiple moments;

[0074] The control unit receives the multi-antenna output signals from the relay node;

[0075] The time delay difference of the output signals of the multiple antennas is compensated and synchronized;

[0076] The compensated signal is superimposed with the received multi-antenna output signal to obtain communication signals at multiple times.

[0077] The present invention has the following beneficial effects:

[0078] 1. By intelligently selecting and coordinating high and low frequency channels, the system balances communication distance and bandwidth requirements, significantly improving the adaptability and reliability of UAV communication systems;

[0079] 2. A channel allocation strategy based on task priority ensures reliable transmission of critical control commands, improving the safety of UAV flight;

[0080] 3. Innovatively, differential geometry theory is applied to multipath fading compensation, and a geometric representation model based on signal manifold is established, which can more accurately capture the nonlinear and time-varying characteristics of multipath environments;

[0081] 4. By using geodesic fusion and curvature flow optimization techniques, optimal synthesis of multipath signals was achieved, effectively improving the signal-to-noise ratio and extending the effective communication distance;

[0082] 5. The cross-frequency band collaborative compensation mechanism for high and low frequency channels fully utilizes the complementary advantages of the two frequency bands, further improving communication reliability;

[0083] 6. Deep integration with adaptive QAM modulation technology enables dynamic matching of channel state and modulation parameters, optimizing spectrum utilization efficiency;

[0084] 7. Supports the construction of multi-node communication networks, providing communication assurance for multi-UAV collaborative missions.

[0085] Overall, the dual-channel UAV line-of-sight communication method provided by this invention represents a significant breakthrough in both theoretical innovation and engineering practicality, offering a novel technical path for long-distance, highly reliable communication of UAVs. Attached Figure Description

[0086] Figure 1 This is a flowchart of the communication method of the dual-channel UAV line-of-sight communication system of the present invention;

[0087] Figure 2 This is a flowchart of the high and low frequency channel frequency point quality judgment process of the present invention;

[0088] Figure 3 This is a flowchart of the channel allocation strategy based on task priority in this invention;

[0089] Figure 4 This is a flowchart of the multipath fading compensation method based on differential manifolds of the present invention. Detailed Implementation

[0090] Please refer to the attached document. Figure 1-4 The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art should understand that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0091] Example 1

[0092] like Figure 1 As shown, the present invention provides a dual-channel UAV line-of-sight communication method, comprising the following steps:

[0093] In a preferred embodiment of the present invention, a high-frequency channel and a low-frequency channel are first set up to transmit data to the target UAV and obtain communication quality indicators. Specifically, the high-frequency channel is in the 5.8 GHz band, and the low-frequency channel is in the 433 MHz band. The selection of these two frequency bands is based on the actual needs of UAV communication: the 5.8 GHz band has a higher bandwidth, suitable for transmitting large amounts of information such as video; the 433 MHz band has a longer transmission distance and stronger penetration capability, suitable for transmitting critical information such as control commands.

[0094] Communication quality metrics include signal-to-noise ratio (SNR), signal rate, and bit error rate (BER). In practical applications, communication quality metrics can be calculated using the following formula:

[0095] ,

[0096] in: Frequency quality index, representing the overall quality score of the communication link, with a value range of 0-100; The signal-to-noise ratio (SNR) is the currently measured value, representing the ratio of signal power to noise power, expressed in dB. As a reference signal-to-noise ratio, and as a normalization baseline value, it is usually taken as 10dB; The current signal rate represents the data transmission speed, measured in Mbps. The maximum signal rate represents the maximum data transmission speed supported by the system. High-frequency channels typically use 48Mbps, while low-frequency channels typically use 2Mbps. The current bit error rate represents the ratio of received erroneous bits to total transmitted bits, and is dimensionless. The maximum acceptable bit error rate represents the upper limit of the bit error rate that the system can tolerate, and is usually taken as... ; , , These are weighting coefficients used to adjust the importance of various indicators, and they satisfy... Adjusted according to actual application needs, typical value is , , .

[0097] This calculation method comprehensively considers three key indicators: signal-to-noise ratio, signal rate, and bit error rate, and can fully reflect the quality status of the communication link.

[0098] like Figure 2 As shown, based on communication quality indicators, the frequency quality of high-frequency and low-frequency channels is judged to determine the optimal transmission frequency. The specific judgment strategy is as follows:

[0099] When the frequency quality index of the acquired low-frequency channel is greater than a first preset threshold and the frequency quality index of the acquired high-frequency channel is available, the high-frequency channel is taken as the optimal transmission frequency at the current moment. In a preferred embodiment, the first preset threshold is set to 80, which is an empirical value derived from the analysis of a large amount of experimental data and can achieve a good balance in most application scenarios. "Available" is defined as the frequency quality index being greater than a third preset threshold (usually set to 30).

[0100] When the frequency quality index of the acquired low-frequency channel is less than a first preset threshold and the frequency quality index of the acquired high-frequency channel is available, the low-frequency channel is selected as the optimal transmission frequency for the current moment. Although the quality of the low-frequency channel is not very high at this time, its inherent transmission distance advantage, coupled with the availability of the high-frequency channel, ensures basic communication reliability by prioritizing the low-frequency channel.

[0101] When the acquired low-frequency channel's frequency quality index is unavailable, both the high-frequency and low-frequency channels are used as the optimal transmission frequencies for the current moment. "Unavailable" typically means that the frequency quality index is below a second preset threshold (usually set to 20). In this case, a dual-channel simultaneous transmission strategy is adopted to maximize communication reliability.

[0102] This dynamic frequency selection mechanism can automatically select the best transmission channel based on the real-time communication environment, giving full play to the respective advantages of high and low frequency channels.

[0103] like Figure 3 As shown, the present invention determines the task priority type based on the task priority of the UAV, and selects the corresponding transmission frequency to transmit control commands based on the task priority type and the optimal transmission frequency.

[0104] In a preferred embodiment of the present invention, the task priority types include highest-level tasks, second-highest-level tasks, and ordinary tasks. Highest-level tasks typically involve the safety control of the UAV, such as emergency obstacle avoidance and return-to-home commands; second-highest-level tasks include flight path adjustment and attitude control; and ordinary tasks include non-critical operations such as data acquisition and environmental monitoring.

[0105] The specific channel selection strategy is as follows:

[0106] When the task priority type is highest priority, the high-frequency channel or the best transmission frequency at the current moment will be used as the transmission frequency for the highest priority task, and control commands will be transmitted to the target UAV. The high-frequency channel is preferred due to its higher data transmission rate, ensuring the timely delivery of critical commands.

[0107] When the task priority type is secondary-high priority, the frequency with the highest quality index is used as the transmission frequency for the secondary-high priority task, and control commands are transmitted to the target UAV. This strategy can provide sufficient communication resources for the secondary-high priority task while ensuring communication quality.

[0108] When the task priority type is normal task, the low-frequency channel or the best transmission frequency at the current moment will be used as the transmission frequency for normal task, and control commands will be transmitted to the target UAV. For normal tasks, prioritizing the use of the low-frequency channel can save high-frequency channel resources, while taking advantage of the long-distance transmission capability of the low-frequency channel.

[0109] This task-priority-based channel allocation strategy ensures the reliable transmission of critical commands and improves the safety and effectiveness of UAV control.

[0110] After completing the transmission of control commands, the present invention acquires the video stream data transmitted by the UAV and transmits the video stream data to the target UAV through the optimal transmission frequency.

[0111] In practical applications, video stream data typically has a large data volume and requires high transmission bandwidth. Therefore, when communication conditions are good, video data should be transmitted via high-frequency channels first; when communication conditions are poor or long-distance transmission is required, critical video information can be transmitted via low-frequency channels by reducing the resolution or frame rate.

[0112] Furthermore, this invention can dynamically adjust video encoding parameters and transmission strategies based on the importance of the video content. For example, higher encoding quality can be used for key areas containing target recognition results, while lower encoding quality can be used for background areas to save bandwidth resources.

[0113] like Figure 4 As shown, one innovation of this invention is a multipath fading compensation method based on differential geometry theory. This method includes: acquiring communication signals at multiple time points, mapping the communication signals to a differential manifold space, extracting fading features in the differential manifold space, optimizing the communication signals based on the fading features, and generating an optimized communication signal.

[0114] In a preferred embodiment of the invention, communication signals are collected at n time points, where n is an integer greater than 2, with a recommended value of 5-7. The interval between the collection time points is typically set to 1 / 3 of the channel coherence time to ensure that dynamic changes in the channel are captured. For fixed-wing UAVs in the 5.8 GHz band, the coherence time is approximately 10-20 ms, therefore a sampling interval of 3-7 ms is more appropriate.

[0115] For each sampling point, complete signal waveform characteristics are recorded, including amplitude, phase, frequency, and timestamp information. The antenna position is kept relatively stable during sampling to reduce additional fading caused by position changes.

[0116] This invention innovatively maps the acquired communication signals onto a differential manifold space to construct a geometric representation model of the signals. The specific implementation includes the following steps:

[0117] Construct a parameter space, where each dimension corresponds to a signal feature at a given time. For example, for signals acquired at n times, an n-dimensional parameter space can be constructed, where each dimension represents the signal feature (such as amplitude, phase, etc.) at the corresponding time.

[0118] Define a metric tensor to establish the concept of distance in the parameter space. Metric Tensor It can be represented as:

[0119] ,

[0120] in: The components of the metric tensor are represented by the metric in the i-th and j-th coordinate directions in the parameter space, and are dimensionless. The kth component of the signal eigenvector may be a physical quantity such as amplitude or phase. , These are coordinates in the parameter space, representing parameters at different times or with different characteristics; The dimension of the signal feature vector represents the number of features; Representation of features For parameters The partial derivatives of the parameter characterize the degree to which changes in the parameter affect the feature.

[0121] The parameter space is mapped to a Riemannian manifold through local coordinate transformation, preserving the topological structure. The coordinate transformation can be expressed as:

[0122] ,

[0123] in: These are the transformed coordinates, representing the position in the new coordinate system; This is a coordinate transformation function, representing the mapping relationship from the original coordinates to the new coordinates; These are the original coordinates, representing the position in the original coordinate system.

[0124] Establishing orthogonal bases on Riemannian manifolds provides a foundation for subsequent geometric analysis. Orthogonal bases can be generated through the Gram-Schmidt orthogonalization process, ensuring that the basis vectors are mutually orthogonal, facilitating subsequent geometric calculations.

[0125] This signal characterization method based on differential manifolds can transform discrete time-domain signals into continuous geometric structures, providing a mathematical basis for the extraction and analysis of fading features.

[0126] On the constructed Riemannian manifold, this invention extracts the features of multipath fading through geometric analysis, with the specific steps as follows:

[0127] The curvature tensor is computed on a Riemannian manifold to characterize the degree of local spatial curvature. Riemannian curvature tensor It can be represented as:

[0128] ,

[0129] in: is a component of the Riemann curvature tensor, representing the degree of curvature of the manifold in different directions, and is dimensionless; , These are components of the metric tensor, describing distance relationships on the manifold; The terms represent the second-order partial derivatives of the metric tensor with respect to the coordinates, reflecting the rate of change of the metric tensor; The symbol for Christopher is:

[0130] ,

[0131] in: denoted by Christopher notation, it describes the rate of change of a curve transported parallel to the manifold, and is dimensionless. The inverse matrix elements of the metric tensor satisfy the following condition: ,in The Kronecker function; The terms "etc" represent the partial derivatives of the metric tensor with respect to the coordinates.

[0132] The propagation characteristics of a signal on a Riemannian manifold are calculated using the parallel transmission equation:

[0133] ,

[0134] in: Let be the components of the vector field, representing vectors on the manifold; It represents the covariant derivative of a vector field along a curve, describing the rate of change of the vector along the curve; The ordinary derivative of a vector field; The tangent vector to the curve describes the direction of the curve; The curve parameter can be understood as time.

[0135] Analyze the principal directions of the curvature tensor to identify the main factors influencing fading. This can be achieved through eigenvalue decomposition of the Riemann curvature tensor.

[0136] ,

[0137] in: For the simplified Riemann curvature tensor, for the... The contraction, that is ; The eigenvalue represents the magnitude of the principal curvature; Let Kronecker function be used when If it is true, it equals 1; otherwise, it equals 0.

[0138] A reference plane is established using a reference signal-to-noise ratio, and the geodesic distances from other signal points to the reference plane are calculated. These geodesic distances can be obtained by solving the geodesic equations.

[0139] ,

[0140] in: Points on geodesics have coordinates on the manifold; Christopher symbol; and These are the tangent vector components of the geodesic; These are the acceleration components of the geodesic; These are the parameters of the geodesic.

[0141] Convert geodesic distance into fading depth parameters to establish a mapping relationship between geometric and physical quantities:

[0142] ,

[0143] in: This is the fading depth parameter, representing the degree of signal fading, and the unit can be dB; For geodesic distance, denoted as the shortest distance between two points on a manifold, it is dimensionless; This is a scaling factor used to convert geometric distance into physical fading. It is typically calibrated based on actual measurement data, with a typical value of 0.1-0.5 dB / unit distance.

[0144] Construct a fading depth distribution map to characterize the spatial distribution characteristics of the multipath environment. This can be achieved by mapping the fading depth parameters to the original signal space.

[0145] This differential geometry-based analysis method allows for a deeper understanding of the essential characteristics of multipath fading from a geometric perspective, providing a theoretical foundation for subsequent signal optimization.

[0146] This invention, based on extracted fading features, employs geodesic fusion and curvature flow optimization methods to process communication signals, generating optimized communication signals. The specific steps are as follows:

[0147] Calculate the geometric weight of the signal at each time step, which is determined by the product of the signal-to-noise ratio and the average fading depth:

[0148] ,

[0149] in: Let be the geometric weight of the signal at time i, representing the importance of the signal at that time in the synthesis process, and it is dimensionless; Let be the signal-to-noise ratio of the signal at time i, in dB; The average fading depth of the signal at time i, which can be expressed in dB; The normalization coefficients are used to ensure that the sum of the weights is 1. The calculation method is as follows: .

[0150] Calculate the geodesics connecting the signal points on the Riemannian manifold to characterize the optimal signal variation path. The solution to the geodesic equation can be expressed as:

[0151] ,

[0152] in: Points on geodesics have coordinates on the manifold. The starting point represents the initial position; The initial velocity represents the initial direction. The initial acceleration is determined by Christopher's symbol; This represents a higher-order term of the third order or above, which can be ignored for small t values.

[0153] ,

[0154] in: The symbol for Christopher at the starting point; and This represents the initial velocity component. The signal is interpolated piecewise along the geodesic to maintain phase continuity. The interpolation formula can be expressed as:

[0155] ,

[0156] in: The interpolated signal represents the signal obtained from the parameter. The signal value at that location; For the first The signal at each moment is a known discrete signal value; For interpolation basis functions, functions that can maintain phase continuity are usually chosen, such as cubic spline functions; The number of moments.

[0157] Construct the curvature flow equation on the Riemannian manifold to describe the optimal evolution of the Riemannian manifold over time. The curvature flow equation can be expressed as:

[0158] ,

[0159] in: To measure the tensor, which describes the distance relationships on the manifold; Let Ricci curvature tensor be a contraction of Riemann curvature tensor; This represents the rate of change of a metric tensor over time. These are evolutionary parameters, which can be understood as time.

[0160] Set evolutionary boundary conditions to ensure convergence to a physically meaningful solution. Typical boundary conditions include keeping the total volume of the manifold constant or keeping the curvature properties of a specific region constant.

[0161] Controlling the evolution rate to balance computational efficiency and optimization accuracy. Evolution step size. The choice is usually related to the curvature value:

[0162] ,

[0163] in: The evolution step size represents the time interval between each iteration; These are control parameters used to control the stability of the evolution; typical values ​​are 0.01-0.1. Let be the absolute value of the maximum component of the Ricci curvature tensor, and represent the maximum curvature of the manifold. The parallel transmission theory ensures the consistency of signal phase during the fusion process. Phase correction matrix. It can be represented as:

[0164] ,

[0165] in: This is a phase correction matrix used to adjust the signal phase. This represents the path from the reference point to the target point; For Christopher's symbol; For the infinitesimal elements of the path; This represents the matrix exponential function.

[0166] This signal optimization method based on differential geometry can effectively improve signal quality and mitigate the negative impact of multipath fading while maintaining key signal characteristics.

[0167] Example 2

[0168] In another embodiment of the invention, such as Figure 4 As shown, the specific implementation steps for multipath fading compensation are as follows:

[0169] Obtain the signal-to-noise ratio of communication signals at multiple times;

[0170] Sort the signal-to-noise ratios and select one as the reference signal-to-noise ratio;

[0171] Based on the reference signal-to-noise ratio, the average fading depth of the communication signal at multiple times is obtained;

[0172] The signal-to-noise ratio of the communication signal at each time moment is multiplied by the average fading depth to obtain the weight value of the communication signal at each time moment;

[0173] The superimposed signal is obtained by superimposing the communication signals at multiple times based on the weight values.

[0174] Extract the communication baseband signal from the superimposed signals and obtain the signal-to-noise ratio of the communication baseband signal;

[0175] When the signal-to-noise ratio (SNR) of the baseband communication signal is greater than the SNR of the communication signal at multiple times, multipath fading compensation is considered complete.

[0176] In practical applications, the selected reference signal-to-noise ratio (SNR) is usually the median or weighted average of the SNRs at multiple time points. This reduces the impact of extreme values ​​on the system. The average fading depth can be calculated using the following formula:

[0177] ,

[0178] in: The average fading depth represents the average degree of signal fading and is dimensionless. For reference signal-to-noise ratio, the median or weighted average of the signal-to-noise ratio at multiple times is usually taken, in dB. Let be the signal-to-noise ratio at time i, in dB; Number of moments; This represents the absolute value of the relative deviation between the signal-to-noise ratio and the reference signal-to-noise ratio at time i.

[0179] The formula for calculating the weight value is:

[0180] ,

[0181] in: Let be the weight value of the communication signal at time i, representing the importance of the signal at that time in the superposition process, which is dimensionless; Let be the signal-to-noise ratio at time i, in dB; The average fading depth is dimensionless. This is the sum of the products of the signal-to-noise ratio and the average fading depth at all times, used for normalization.

[0182] The formula for superimposing communication signals at multiple times based on weight values ​​is as follows:

[0183] ,

[0184] in: The superimposed signal represents the synthesized signal. Let be the weight value of the communication signal at time i, which is dimensionless; Let be the communication signal at time i. This indicates a weighted summation of the signals at all times.

[0185] Extracting the communication baseband signal from the superimposed signals can be achieved through bandpass filtering and demodulation. The condition for determining whether compensation is complete is:

[0186] ,

[0187] in: Signal-to-noise ratio of the superimposed signals, in dB; This represents the maximum signal-to-noise ratio at all times, expressed in dB. This indicates a greater than relationship, used to determine whether the signal-to-noise ratio (SNR) of the superimposed signal exceeds the SNR of all the original signals.

[0188] This weighted superposition method based on signal-to-noise ratio and fading depth is a simplified implementation of the differential geometry optimization method and is suitable for scenarios with limited computing resources.

[0189] Example 3

[0190] In yet another embodiment of the present invention, the high- and low-frequency channel coordinated compensation step includes:

[0191] Receive the received spectrum of the low-frequency channel through the high-frequency channel;

[0192] Multipath fading information is obtained from the received spectrum;

[0193] Correcting transmission errors in low-frequency channel data transmission based on multipath fading information;

[0194] Transmit signals from the high-frequency channel to the low-frequency channel;

[0195] Add training sequences to the low-frequency channels;

[0196] The phase noise of the low-frequency channel transmitted signal is compensated based on the training sequence;

[0197] Construct a joint high- and low-frequency manifold to describe the coupling characteristics of the two channels;

[0198] Analyze the correlation and complementarity of fading characteristics in different frequency bands;

[0199] Establish mapping relationships between frequency bands to achieve complementary enhancement.

[0200] This high- and low-frequency channel collaborative compensation mechanism fully utilizes the complementary advantages of the two frequency bands, further improving communication reliability. Specifically, the high-frequency channel (5.8GHz) has higher bandwidth and resolution, enabling it to accurately capture channel characteristics; the low-frequency channel (433MHz) has stronger penetration capability and stability, making it suitable as a basic communication guarantee.

[0201] In this embodiment, after the high-frequency channel receives the received spectrum from the low-frequency channel, multipath fading information is extracted through spectrum analysis. Multipath fading information can be represented by the power delay spectrum (PDP):

[0202] ,

[0203] in: This is the power delay spectrum, representing the signal power distribution at different delay times, with units of W / s; The power of the i-th path is expressed in W. Let be the delay of the i-th path, in seconds; The number of multipaths represents the total number of propagation paths. Let be the Dirac function, which has an infinite value when the parameter is 0, and a value of 0 otherwise, and its integral is 1.

[0204] Based on the extracted multipath fading information, a channel estimation model can be constructed to correct transmission errors in low-frequency channels. The channel estimation model can be expressed as:

[0205] ,

[0206] in: Channel frequency response describes the effect of the channel on signals of different frequencies; it is dimensionless. Let be the amplitude of the i-th path, representing the signal strength of that path, which is dimensionless; Let be the delay of the i-th path, in seconds; Frequency, in Hz; The imaginary unit satisfies ; This represents a complex exponential function that describes the phase delay.

[0207] Transmitting a signal from the high-frequency channel to the low-frequency channel and adding a training sequence to the low-frequency channel can help accurately estimate phase noise. The training sequence is usually selected from sequences with good autocorrelation characteristics, such as the Zadoff-Chu sequence or the PN sequence.

[0208] Phase noise compensation can be achieved through the following steps:

[0209] 1. Estimating phase noise using training sequences:

[0210] ,

[0211] in: Phase noise represents random fluctuations in the phase of a signal, measured in radians. To receive the signal, it includes amplitude and phase information; The training sequence is known, and the reference signal is used. Represents the phase angle, taking the argument of a complex number, with the unit being radians.

[0212] 2. Compensate for phase noise:

[0213] ,

[0214] in: The received signal after compensation; The original received signal; This is a compensation factor used to cancel phase noise. The joint high- and low-frequency manifold can be constructed using tensor product space:

[0215] ,

[0216] in: This is a joint manifold, representing the combined characteristics of the high and low frequency channels; This is a high-frequency channel manifold that describes the characteristics of high-frequency signals. This is a low-frequency channel manifold, describing the characteristics of low-frequency signals. The tensor product is a mathematical operation that combines two manifolds.

[0217] On the joint manifold, the correlation and complementarity of fading characteristics in different frequency bands can be analyzed, and mapping relationships between frequency bands can be established to achieve complementary enhancement. This joint analysis method can fully tap the synergistic advantages of high- and low-frequency channels and improve overall communication quality.

[0218] Example 4

[0219] In yet another embodiment of the present invention, the adaptive QAM modulation step includes:

[0220] Acquire communication data and convert it into multiple signals;

[0221] Multi-channel signals are modulated using baseband signals, and the baseband signals are then spread.

[0222] Based on the results of differential manifold analysis, a channel state reference is provided for high-frequency channel adaptive QAM modulation;

[0223] The signal constellation diagram distribution is automatically adjusted based on multipath characteristics;

[0224] Reduce the modulation order in the high curvature region of the Riemannian manifold;

[0225] Increase the modulation order in the flat region of the Riemannian manifold;

[0226] Establish a two-way information exchange mechanism between the modulation system and the compensation system.

[0227] Adaptive QAM modulation is another innovation of this invention. It is deeply integrated with the multipath fading compensation method based on differential geometry, realizing dynamic matching between channel state and modulation parameters.

[0228] In practical implementation, the communication data is first converted into four signals (in the preferred embodiment), and then modulated into baseband signals by a QAM modulator. The constellation diagram of the QAM modulation can be dynamically adjusted according to the channel state, ranging from QPSK (4QAM) to 256QAM.

[0229] Spreading the baseband signal can enhance its anti-interference capability. The spreading factor is usually chosen between 4 and 16, depending on the communication environment and bandwidth requirements.

[0230] A key innovation of this invention is providing a channel state reference for adaptive QAM modulation based on differential manifold analysis results. Specifically, based on the curvature distribution of the Riemannian manifold, "flat regions" and "high curvature regions" of the channel can be identified. Flat regions correspond to the stable parts of the channel state and are suitable for using higher-order modulation (such as 64QAM, 256QAM); high-curvature regions correspond to the rapidly changing parts of the channel state and should use lower-order modulation (such as QPSK, 16QAM).

[0231] The choice of curvature threshold is usually based on empirical values, for example:

[0232] When curvature At that time, use 256QAM;

[0233] when At that time, 64QAM was used;

[0234] when At that time, 16QAM is used;

[0235] when When using QPSK.

[0236] in, The norm of the Riemann curvature tensor is expressed as:

[0237] ,

[0238] in: Let be the norm of the Riemann curvature tensor, representing a comprehensive measure of the degree of manifold curvature, which is dimensionless; These are the components of the Riemann curvature tensor; This represents the summation of all indicators.

[0239] The bidirectional information exchange mechanism between the modulation and compensation systems ensures their coordinated operation. The modulation system provides signal characteristic information, such as modulation order and constellation distribution, to the compensation system; the compensation system feeds back channel quality assessments, such as curvature distribution and multipath characteristics, to the modulation system. This bidirectional interaction mechanism forms a closed-loop control, enabling real-time optimization of modulation parameters and improving spectrum utilization efficiency.

[0240] Example 5

[0241] In yet another embodiment of the present invention, the steps of constructing a multi-node communication network include:

[0242] Construct a star-shaped network, including a control terminal, relay nodes, slave nodes, and target drones;

[0243] The relay node, slave node, and target UAV are respectively connected to the control terminal;

[0244] Time-series diversity reception is used between any two adjacent nodes;

[0245] The node sends communication signals to the control terminal;

[0246] When the communication signal arrives at the control terminal, there are multiple communication signals at the control terminal at different times;

[0247] QAM modulation is applied to the communication signals at multiple times to obtain the optimal timing sequence at multiple times;

[0248] The optimal timing sequence at multiple points is used to recover the communication signal;

[0249] The control unit receives the multi-antenna output signals from the relay node;

[0250] Compensate for and synchronize the time delay difference of the output signals from multiple antennas;

[0251] The compensated signal is superimposed with the received multi-antenna output signal to obtain communication signals at multiple times.

[0252] This multi-node communication network structure supports collaborative missions involving multiple UAVs, providing communication assurance for complex mission scenarios. In the star network, the control unit acts as the central node, responsible for coordinating communication between nodes; relay nodes are used to extend the communication range; slave nodes can be other UAVs or ground stations; and the target UAV is the ultimate mission executor.

[0253] Timing diversity reception is an important feature of this network. It combines signals received at different times to improve communication reliability. In specific implementation, when a node sends a communication signal to the control end, the control end contains communication signals at four times (S1, S2, S3, S4).

[0254] QAM modulation of the communication signal at each moment can improve spectrum utilization efficiency. Optimal timing is typically determined based on signal quality metrics such as signal-to-noise ratio (SNR) and bit error rate (BER). Recovering the communication signal using optimal timing can minimize channel interference.

[0255] When the control terminal receives multi-antenna output signals from relay nodes, time delays will occur due to spatial differences between the antennas. Compensating for and synchronizing these time delays is a crucial step in ensuring correct signal merging. Time delay compensation can be achieved through the following methods:

[0256] ,

[0257] in: Let be the time delay estimate for the i-th antenna, representing the time difference relative to the reference antenna, in seconds; Let be the received signal of the i-th antenna, which is a function of time; The antenna with the best signal quality is usually selected as the reference antenna. Represents the complex conjugate of the reference signal; This means finding the expression that makes the following expression the largest. value; This represents the integral over time, used to calculate the cross-correlation function of two signals.

[0258] After time delay compensation, the signals from each antenna are combined:

[0259] ,

[0260] in: The merged signal is a time function; The weighting coefficient for the i-th antenna is typically related to signal quality and satisfies the following conditions: ; This is the signal of the i-th antenna after time delay compensation; This refers to the number of antennas. This represents a weighted summation over all antennas.

[0261] This multi-node network structure, combined with time-series diversity reception technology, can significantly improve the robustness and coverage of the communication system, providing reliable communication support for multi-UAV collaborative missions.

[0262] The dual-channel UAV line-of-sight communication method provided by this invention comprehensively improves the distance, reliability, and data transmission capabilities of UAV communication systems through innovative technologies such as intelligent coordination of high and low frequency channels, multipath fading compensation based on differential geometry theory, task priority-driven channel allocation, cross-frequency band collaborative compensation for high and low frequencies, adaptive QAM modulation, and multi-node communication networks. This method represents a significant breakthrough in both theoretical innovation and engineering practicality, providing a novel technical path for long-distance, highly reliable UAV communication, and possesses significant application value and promising prospects for widespread adoption.

[0263] The embodiments described above are merely illustrative of specific implementations of the present invention, and while the descriptions are detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A two-channel unmanned vehicle line-of-sight communication method, characterized by, The method comprises the following steps: acquiring communication quality; including: setting up high-frequency channels and low-frequency channels to transmit data to the target UAV, and acquiring communication quality indicators; the high-frequency channel is a 5.8 GHz frequency band, and the low-frequency channel is a 433 MHz frequency band; the communication quality indicators include signal-to-noise ratio, signal rate and bit error rate; acquiring the best transmission frequency point; including: judging the frequency point quality of the high-frequency channel and the low-frequency channel based on the communication quality indicators, and determining the best transmission frequency point; acquiring the task priority of the UAV; including: determining the task priority type according to the task priority, and selecting the corresponding transmission frequency point to transmit the control instruction based on the task priority type and the best transmission frequency point; transmitting video stream data; including: acquiring the video stream data transmitted by the UAV, and transmitting the video stream data to the target UAV through the best transmission frequency point; performing multipath fading compensation; including: collecting communication signals at multiple time points, mapping the communication signals to a differential manifold space, extracting fading features in the differential manifold space, optimizing the communication signals based on the fading features, and generating optimized communication signals; the communication signals are mapped to the differential manifold space, which specifically comprises: constructing a parameter space, each dimension of the parameter space corresponding to a signal feature at a time point; defining a metric tensor to establish a distance concept in the parameter space; mapping the parameter space to a Riemann manifold through local coordinate transformation, keeping the topological structure unchanged; establishing an orthogonal basis on the Riemann manifold for subsequent geometric analysis; the fading features in the differential manifold space are extracted, which specifically comprises: calculating the curvature tensor on the Riemann manifold to represent the local space bending degree; calculating the propagation characteristics of the signal on the Riemann manifold through parallel transmission equation; analyzing the principal direction of the curvature tensor to identify the main influencing factors of fading; establishing a reference plane with a reference signal-to-noise ratio, and calculating the geodesic distance from other signal points to the reference plane; convert the geodesic distance into a fading depth parameter to establish a mapping relationship between geometric quantities and physical quantities; constructing a fading depth distribution diagram to represent the spatial distribution characteristics of the multipath environment.

2. The dual channel unmanned line of sight communication method of claim 1, wherein, The multipath fading compensation specifically comprises: acquiring the signal-to-noise ratio of the communication signals at multiple time points; sorting the signal-to-noise ratios and selecting one as a reference signal-to-noise ratio; based on the reference signal-to-noise ratio, acquiring the average fading depth of the communication signals at multiple time points; multiplying the signal-to-noise ratio of the communication signal at each time point by the average fading depth to obtain the weight value of the communication signal at each time point; based on the weight value, superimposing the communication signals at multiple time points to obtain a superimposed signal; extracting the communication baseband signal from the superimposed signal and acquiring the signal-to-noise ratio of the communication baseband signal; when the signal-to-noise ratio of the communication baseband signal is greater than that of the communication signals at multiple time points, it is determined that the multipath fading compensation is completed.

3. The dual channel unmanned line of sight communication method of claim 1, wherein, the best transmission frequency point is acquired, which specifically comprises: When the acquired frequency point quality index of the low frequency channel is greater than a first preset threshold and the acquired frequency point quality index of the high frequency channel is available, the high frequency channel is taken as the best transmission frequency point at the current moment; When the acquired frequency point quality index of the low frequency channel is less than the first preset threshold and the acquired frequency point quality index of the high frequency channel is available, the low frequency channel is taken as the best transmission frequency point at the current moment; When the acquired frequency point quality index of the low frequency channel is unavailable, the high frequency channel and the low frequency channel are both taken as the best transmission frequency point at the current moment.

4. The dual channel unmanned line of sight communication method of claim 1, wherein, The selecting of the corresponding transmission frequency point for transmitting the control instruction based on the task priority type and the best transmission frequency point specifically comprises: When the task priority type is the highest level task, the high frequency channel or the best transmission frequency point at the current moment is taken as the transmission frequency point of the highest level task, and the control instruction is transmitted to the target unmanned aerial vehicle; When the task priority type is the second highest level task, the transmission frequency point of the second highest level task is the one with the higher frequency point quality index, and the control instruction is transmitted to the target unmanned aerial vehicle; When the task priority type is the ordinary task, the low frequency channel or the best transmission frequency point at the current moment is taken as the transmission frequency point of the ordinary task, and the control instruction is transmitted to the target unmanned aerial vehicle.

5. The dual channel unmanned line of sight communication method of claim 1, wherein, The optimization processing of the communication signal based on the fading feature specifically comprises: calculating the geometric weight of the signal at each moment, the geometric weight being determined by the product of the signal-to-noise ratio and the average fading depth; calculating the geodesic connecting each signal point on the Riemannian manifold to represent the optimal signal change path; segmenting and interpolating the signal along the geodesic to maintain the phase continuity; constructing the curvature flow equation on the Riemannian manifold to describe the optimal evolution of the Riemannian manifold over time; setting the evolution boundary condition to ensure convergence to a physically meaningful solution; controlling the evolution rate to balance the calculation efficiency and the optimization accuracy; ensuring the consistency of the signal phase in the fusion process through the parallel transmission theory.

6. The dual channel unmanned line of sight communication method of claim 1, wherein, It also includes a high-low frequency channel cooperative compensation step: receiving the receiving spectrum of the low frequency channel through the high frequency channel; acquiring multipath fading information based on the receiving spectrum; correcting transmission errors in the low frequency channel data transmission process according to the multipath fading information; transmitting signals to the low frequency channel through the high frequency channel; adding a training sequence in the low frequency channel; compensating for the phase noise of the low frequency channel transmission signal according to the training sequence; constructing a high-low frequency joint manifold to describe the coupling characteristics of the double channels; analyzing the correlation and complementarity of the fading characteristics of different frequency bands; establishing a mapping relationship between the frequency bands to achieve complementary enhancement.

7. The dual channel unmanned line of sight communication method of claim 1, wherein, It also includes an adaptive QAM modulation step: acquiring communication data and converting the communication data into multiple signals; modulating the baseband signals of the multiple signals and spreading the baseband signals; based on the analysis result of the differential manifold, providing a channel state reference for adaptive QAM modulation of the high frequency channel; automatically adjusting the distribution of the signal constellation according to the multipath characteristics; reducing the modulation order in the high curvature region of the Riemannian manifold; increasing the modulation order in the flat region of the Riemannian manifold; A bidirectional information interaction mechanism between the modulation system and the compensation system is established.

8. The dual channel unmanned line of sight communication method of claim 1, wherein, The method further comprises the step of constructing a multi-node communication network: The star network comprises a control terminal, relay nodes, slave nodes and target UAVs; The relay nodes, slave nodes and target UAVs are connected to the control terminal; Any two adjacent nodes adopt time diversity reception; The slave nodes send communication signals to the control terminal; When the communication signals arrive at the control terminal, there are multiple time instants of communication signals in the control terminal; The multiple time instants of communication signals are respectively QAM modulated to obtain optimal timings of the multiple time instants; The communication signals are recovered using the optimal timings of the multiple time instants; The control terminal receives multi-antenna output signals of the relay nodes; The time delay difference of the multi-antenna output signals is compensated and synchronized; The compensated signals and the received multi-antenna output signals are superimposed to obtain the multiple time instants of communication signals.

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