Low-altitude complex terrain polarization signal dynamic compensation method, unmanned aerial vehicle communication device and system

Abstract

The invention discloses a low-altitude complex terrain polarization signal dynamic compensation method, an unmanned aerial vehicle communication device and an unmanned aerial vehicle communication system, and belongs to the field of wireless communication. And setting a candidate modulation scheme set based on the signal-to-noise ratio of the channel, and constructing a constraint function based on the candidate modulation scheme set to realize polarization signal high-fidelity transmission. The system integrates an interference prediction module, a federated learning cluster cooperation module and a secure communication module at the same time, so that high-security and robust communication is realized on the premise of ensuring low delay and low power consumption. The problems of polarization signal mismatch, weak anti-interference capability, poor environmental adaptability and the like under complex terrains in the prior art can be effectively solved, the reliability and safety of an unmanned aerial vehicle communication system are improved, and the method has important application value and market prospect.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication, and particularly relates to a dynamic compensation method for polarization signals in low-altitude complex terrains, a UAV communication device, and a system. Background Art

[0002] Low-altitude UAVs play an increasingly important role in fields such as military reconnaissance, emergency rescue, logistics distribution, and urban security. However, in complex terrains (such as mountainous areas, urban canyons, forest-covered areas, etc.), due to terrain occlusion, multipath fading, and uneven local electromagnetic environments, polarization signals will undergo severe mismatch and distortion, resulting in a significant decline in the quality of UAV communication links. At the same time, most traditional polarization compensation methods adopt fixed compensation strategies and cannot track the dynamic changes of the channel polarization state under complex terrains in real time, thus failing to meet the strict requirements for high reliability, low bit error rate, and high security in practical applications.

[0003] In addition, some current systems use edge computing technology to process data in real time, but there are still problems such as data processing delays and insufficient algorithm robustness in dynamic polarization signal compensation. Therefore, there is an urgent need to propose an innovative method that can utilize real-time monitoring and compensation of dynamic polarization signals in a low-altitude complex terrain environment, and combine with a UAV communication device to achieve efficient, stable, and intelligent anti-interference communication. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a dynamic compensation method for polarization signals in low-altitude complex terrains, including:

[0005] S1. Real-time collect channel polarization state data based on an adaptive polarization antenna array and a full-duplex SDR platform;

[0006] S2. Calculate the signal-to-noise ratio of the channel based on the polarization state data;

[0007] S3. Set a candidate modulation scheme set based on the signal-to-noise ratio of the channel, and construct a constraint function based on the candidate modulation scheme set;

[0008] S4. Based on the constraint function, use a global optimization algorithm to solve for the optimal compensation parameters and modulation method. Preferably, the process of collecting channel polarization state data further includes:

[0009] Collect the channel polarization state data, and construct a channel polarization matrix based on the channel polarization state data;

[0010] Calculate the polarization mismatch degree, polarization correlation coefficient, and polarization ellipse parameters based on the channel polarization matrix.

[0011] Preferably, the expression of the channel polarization matrix is:

[0012]

[0013] Among them, h ij represents the channel gain between different polarization directions, and H is the channel polarization matrix;

[0014] The expression of the polarization mismatch degree is:

[0015]

[0016] Among them, XPD is the polarization mismatch degree.

[0017] Preferably, the expression of the polarization correlation coefficient is:

[0018]

[0019] Among them, ρ is the polarization correlation coefficient.

[0020] Preferably, the polarization ellipse parameters include polarization angle and polarization ellipticity;

[0021] The calculation expression of the polarization angle is:

[0022]

[0023] Among them, θ is the polarization angle;

[0024] The calculation expression of the polarization ellipticity is:

[0025]

[0026] Among them, ∈ is the polarization ellipticity.

[0027] Preferably, the expression of the constraint function is:

[0028]

[0029] Among them, BER(P,M) is the bit error rate, P is the polarization compensation parameter, M is the modulation scheme, and β is the preset bit error rate threshold;

[0030] The constraint condition of the constraint function is:

[0031] BER(P,M) ≤ β. BER(P,M) ≤ β.

[0032] To solve the above technical problems, the present invention also provides a UAV communication device for dynamic compensation of polarization signals in low-altitude complex terrains, including:

[0033] An adaptive polarization antenna array, which is used to switch multiple polarization modes and control voltage or current to realize the adjustment of polarization direction and ellipticity;

[0034] A full-duplex SDR platform for collecting wideband signals and performing real-time digital signal processing;

[0035] A high-performance edge computing unit for running polarization state monitoring, dynamic compensation algorithms, interference prediction, and cluster collaborative decision-making algorithms;

[0036] A high-precision positioning module for providing positioning information;

[0037] A secure storage and communication module for protecting sensitive data based on a physically isolated storage area and encryption algorithms.

[0038] Preferably, the adaptive polarization antenna array communicates based on a four-element polarization antenna array with an adjustable frequency range of 2 - 6 GHz, the frequency switching time does not exceed 5 μs, and each antenna unit supports independent polarization control;

[0039] The full-duplex SDR platform is composed of a radio frequency front end, high-speed ADC / DAC, FPGA, and high-performance DSP chips;

[0040] The high-performance edge computing unit is composed of a quad-core ARM Crtex-A76 processor, an AI accelerator, and FPGA logic units;

[0041] The high-precision positioning module is composed of RTK-GPS and a nine-axis IMU;

[0042] The secure storage and communication module is composed of a hardware encryption chip.

[0043] To solve the above technical problems, the present invention also provides a UAV communication system for dynamic compensation of polarization signals in low-altitude complex terrains, including:

[0044] A polarization state real-time monitoring module for dynamically adjusting the acquisition frequency according to the channel change rate, and

[0045] A dynamic compensation algorithm module for automatically adjusting compensation parameters according to the polarization state data collected in real time;

[0046] A predictive interference identification module for identifying and predicting interference types and change trends;

[0047] A cluster collaborative decision module for realizing collaborative anti-interference decision-making of UAV clusters;

[0048] A secure communication module for providing high-strength encrypted communication to ensure data transmission security.

[0049] Compared with the prior art, the present invention has the following advantages and technical effects:

[0050] 1. Dynamic polarization compensation: By collecting the polarization state in real time and dynamically optimizing the compensation parameters, the present invention overcomes the limitations of fixed compensation methods and achieves the best matching of the channel polarization state. Experimental verification shows that, compared with traditional methods, the present invention can reduce the bit error rate by more than 60% in complex terrains, and significantly improve the stability of the communication link. Especially in complex environments such as mountain forests, the bit error rate of this method is only 38% of that of traditional methods. This is because the present invention can track and compensate for the rapidly changing polarization mismatch in complex terrains in real time, while traditional fixed compensation methods cannot do this.

[0051] 2. Enhanced environmental adaptability: By adopting predictive interference recognition and collaborative decision-making of federated learning, the system can actively and quickly respond to environmental changes and dynamically adjust the modulation mode and coding strategy. Experiments show that the convergence time of the method of the present invention after a sudden change in the polarization state does not exceed 2 seconds, while traditional methods require more than 5 seconds to stabilize, significantly improving the adaptability of the system in dynamic environments. In addition, by predicting interference changes, the system can adjust the modulation and coding scheme in advance, further reducing the impact of interference and improving communication reliability.

[0052] 3. Cluster collaboration advantages: Through distributed collaborative decision-making, multiple UAV nodes jointly optimize the anti-interference strategy, effectively reducing the risk of single-point failure. When the cluster scale expands from 2 nodes to 16 nodes, the overall anti-interference ability of the system is improved by about 140%, and the impact of a single node failure on the overall system performance is reduced to less than 25% of the original. This is because federated learning can converge the environmental perception information of multiple nodes to form a more comprehensive interference situation map, thereby achieving a better collaborative anti-interference strategy.

[0053] 4. Low power consumption and high security: The lightweight algorithm designed by the present invention reduces the computational complexity by about 65% compared with traditional solutions, and reduces the power consumption by about 42% while maintaining the same performance. At the same time, the integrated secure communication module can effectively prevent man-in-the-middle attacks and replay attacks, and the security strength is increased to 128-bit equivalent security strength, meeting military-level security requirements. The low-power design makes the present invention more suitable for UAV platforms and extends the flight time of UAVs. The high-security design ensures the data security and control security of the UAV communication link. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings, which form a part of this application, are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0055] Figure 1 is the overall system architecture diagram of the embodiment of the present invention;

[0056] Figure 2 is the internal system architecture diagram of the UAV node of the embodiment of the present invention;

[0057] Figure 3 This is the system core information flow chart of the embodiment of the present invention;

[0058] Figure 4 This is the schematic diagram for comparing the polarization compensation effects under different terrains in the embodiment of the present invention;

[0059] Figure 5 This is the schematic diagram for testing the real-time performance of the polarization adaptability in the embodiment of the present invention;

[0060] Figure 6 This is the schematic diagram for comparing the system throughput under different interference intensities in the embodiment of the present invention;

[0061] Figure 7 This is the comparison chart of the bit error rate and the throughput in the embodiment of the present invention;

[0062] Figure 8 This is the method flow chart of the embodiment of the present invention. Detailed implementation manners

[0063] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments may be combined with each other. The following will describe this application in detail with reference to the drawings and in combination with the embodiments.

[0064] It should be noted that the steps shown in the flowchart of the drawings may be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order from that here.

[0065] Embodiment 1

[0066] As Figure 1-3 and Figure 8 shown, a dynamic polarization signal compensation method for low-altitude complex terrain provided in this embodiment includes:

[0067] Collecting channel polarization state data in real time based on an adaptive polarization antenna array and a full-duplex SDR platform;

[0068] Calculating the signal-to-noise ratio of the channel based on the polarization state data;

[0069] Setting a candidate modulation scheme set based on the signal-to-noise ratio of the channel, and constructing a constraint function based on the candidate modulation scheme set;

[0070] Solving for the optimal compensation parameters and modulation method by using a global optimization algorithm based on the constraint function.

[0071] Calculate the optimal polarization compensation parameter \(P^*\) and the optimal modulation scheme \(M^*\) in real time according to the channel polarization matrix \(HH\) and interference parameter \(II\) collected under low-altitude complex terrain, so as to maximize the system channel capacity and make the bit error rate lower than the preset threshold.

[0072] Step 1: Polarization index calculation:

[0073] Given the channel polarization matrix:

[0074]

[0075] where \(h\) HH , \(h\) HV , \(h\) VH , \(h\) VV represent the channel gains between different polarization directions, and \(H\) is the channel polarization matrix;

[0076] Calculate the polarization mismatch degree (XPD):

[0077]

[0078] where \(XPD\) is the polarization mismatch degree.

[0079] Calculate the polarization correlation coefficient:

[0080]

[0081] where \(\rho\) is the polarization correlation coefficient.

[0082] Calculate the polarization ellipse parameters:

[0083] The polarization ellipse parameters include the polarization angle and the polarization ellipticity;

[0084] The calculation expression of the polarization angle is:

[0085]

[0086] where \(\theta\) is the polarization angle;

[0087] The calculation expression of the polarization ellipticity is:

[0088]

[0089] where \(\epsilon\) is the polarization ellipticity.

[0090] Step 2: Channel quality index evaluation

[0091] Calculate the channel signal-to-noise ratio (SNR):

[0092]

[0093] Define the channel quality index \(Q\) as a function:

[0094] Q = f(XPD, ρ p , SNR);

[0095] Step 3: Modulation Scheme Selection:

[0096] Set the set of candidate modulation schemes {M i} (such as BPSK, QPSK, 16 - QAM, 64 - QAM, etc.), and calculate the bit error rate BER(P, M i ). It is required to satisfy:

[0097] BER(P, Mi) ≤ β;

[0098] where β is the preset bit error rate threshold.

[0099] Step 4: Solving the Optimization Problem:

[0100] Construct the optimization objective function:

[0101]

[0102] where BER(P, M) is the bit error rate, P is the polarization compensation parameter, M is the modulation scheme, and β is the preset bit error rate threshold;

[0103] The constraint conditions of the constraint function are:

[0104] BER(P, M) ≤ β. BER(P, M) ≤ β.

[0105] Use the gradient descent or other global optimization algorithms to solve the optimal compensation parameter P* and modulation scheme M*.

[0106] This embodiment uses the gradient descent algorithm.

[0107] Initialization:

[0108] Set the initial polarization parameter P (0) and modulation scheme M (0) ;

[0109] Read the interference source position p i and polarization matrix H.

[0110] Calculate the objective function and gradient:

[0111] Calculate BER(P (k) , M (k) ) and log2(M (k) );

[0112] If BER > β, then perform constraint correction;

[0113] Find

[0114] Parameter update:

[0115]

[0116] where α is the learning rate, and round indicates that the modulation order needs to take discrete values (2, 4, 16, 64, etc.).

[0117] Convergence judgment: If P(k + 1) - P(k) < δ and M(k + 1) = M(k), or the number of iterations exceeds the upper limit, then output (P*, M*).

[0118] Data stream:

[0119] The interference source location module periodically outputs P i ;

[0120] The status monitoring module updates parameters such as H, XPD, ρ, etc.;

[0121] The optimization algorithm performs other global searches such as gradient descent on the edge computing platform and sends updates (P * , M * ).

[0122] Real-time performance:

[0123] The edge computing platform has CPU + GPU / FPGA collaboration and can complete one iteration at the 10 ms level;

[0124] The optimization period (such as 100 ms) can be set for parameter update to ensure a fast response to the perturbed environment.

[0125] Elastic polarization switching:

[0126] Once (P * , M * ) is obtained, the polarization mode (θ * , ∈ * ) of the dual-polarized antenna is adjusted through the optimized switching control model and the modulation method M * is configured.

[0127] The switching time is controlled at the 5 μs - 10 μs level (depending on the performance of the antenna array and the RF switch), and the impact on communication continuity is small.

[0128] Step 5: Output the decision result

[0129] Return the optimal parameter P* and the optimal modulation scheme M* for dynamically adjusting the UAV communication transmission parameters.

[0130] Performance verification experiment:

[0131] To verify the effectiveness of the algorithm in this embodiment, three groups of comparative experiments were designed in this embodiment, and detailed performance data were collected:

[0132] Experiment 1: Comparison of polarization compensation effects under different terrains

[0133] In this embodiment, the performance differences between the polarization compensation algorithm of this embodiment and the traditional fixed compensation method were tested under three typical terrains (open terrain, urban environment, mountain forest). Experimental parameter settings: carrier frequency 2.4 GHz, signal bandwidth 20 MHz, modulation method QPSK, channel models used CST2100 urban microcell model and ITU-R P.1411 mountain terrain model, signal-to-noise ratio fixed at 20 dB. 1000 independent channel realizations were simulated under each terrain, and the bit error rate was statistically analyzed. As Figure 4 shown. Figure 4 It shows that under the three terrains, the method of this embodiment is significantly superior to the traditional method in terms of bit error rate, especially in complex terrains.

[0134] Experiment 2: Real-time performance test of polarization adaptability

[0135] In this embodiment, the tracking ability of different algorithms to polarization changes under rapidly changing channel conditions was tested. In the experiment, it was simulated that an unmanned aerial vehicle flew in a complex terrain, resulting in a rapid change of the channel polarization state over time. The polarization state change model used a random walk model, and the polarization angle and ellipticity randomly jumped within a certain range, with a jump rate of 10 Hz. The polarization tracking errors of the method of this embodiment, the traditional fixed compensation method, and the rule-based adaptive compensation method (switching the polarization mode according to the XPD threshold) were compared. The tracking error was defined as the Euclidean distance between the actual polarization state and the compensated polarization state.

[0136] Figure 5 It shows the advantages of the method of this embodiment in terms of the convergence speed and steady-state accuracy of the polarization tracking error. In this embodiment, the performance differences in throughput between the system of this embodiment and the traditional system under different interference intensities (signal-to-interference ratio from -10 dB to 20 dB) were compared. In the experiment, narrowband interference was added, and the interference bandwidth was 10% of the signal bandwidth, and the center frequency randomly changed. The system of this embodiment (using dynamic polarization compensation and adaptive modulation), the traditional system (fixed polarization compensation and fixed modulation QPSK), and the theoretical upper limit throughput (ideal polarization matching and optimal modulation) were compared. The throughput calculation formula is: throughput = bandwidth × log2(M) × (1 - BER), where M is the modulation order and BER is the bit error rate. Figure 6 It shows that the method of this embodiment can achieve a significant throughput improvement in various interference environments, especially in the medium interference environment, with an improvement amplitude exceeding 100%.

[0137] The following is another experimental process of this embodiment:

[0138] Experimental purpose:

[0139] Verify the anti-interference performance of the UAV dual-polarized antenna elastic switching method (EPS method) based on interference source spatial positioning proposed by the present invention in a low-altitude complex terrain environment, and compare it with the traditional fixed polarization method (VP method) and the polarization switching method based on signal strength detection (RSSI-based PS method). The key performance indicators include bit error rate (BER), system throughput, and spectral efficiency.

[0140] Experimental setup and simulation parameters:

[0141] Experimental scenario: Simulate a low-altitude complex terrain environment, using urban and mountain environment models (based on the CST2100 urban microcell model and the ITU-R P.1411 mountain model respectively).

[0142] Carrier frequency: 2.4 GHz

[0143] Signal bandwidth: 20 MHz

[0144] Modulation method: QPSK (extendable to other modulation schemes, such as 16-QAM, 64-QAM)

[0145] Signal-to-noise ratio (SNR): 20 dB (when the desired signal is without interference)

[0146] Interference type: Single narrowband interference, interference bandwidth is 10% of the signal bandwidth, and the center frequency varies randomly

[0147] Interference signal power (INR): The variation range is from 0 dB to 20 dB

[0148] Number of sampling times: Conduct 1000 Monte Carlo simulations under each condition, statistically analyze the data, and calculate the average value and standard deviation

[0149] Calculation of performance indicators:

[0150] Bit error rate (BER): Statistically analyze the average bit error rate under each method through simulation

[0151] Throughput: The calculation formula is

[0152] Throughput = bandwidth × code rate × (1 - BER)

[0153] Spectral efficiency:

[0154] Data statistical method:

[0155] For each interference intensity condition, the average bit error rate, throughput, and spectral efficiency are calculated for all simulation results, and the standard deviation is statistically analyzed to reflect the data fluctuations. Standard mean and standard deviation formulas are used for data statistics.

[0156] Comparison experiment design:

[0157] Fixed vertical polarization (VP method): The UAV antenna is fixed in the vertical polarization mode.

[0158] Polarization switching based on signal strength detection (RSSI-based PS method): Horizontally and vertically polarized are dynamically switched according to the received signal strength.

[0159] The elastic polarization switching method (EPS method) of this embodiment: Based on the spatial positioning information of the interference source, the optimal polarization mode is intelligently determined and elastically switched.

[0160] Each method is simulated and compared under the same experimental conditions, and curves of the bit error rate, throughput, and spectral efficiency versus the change of INR are plotted. At the same time, the average value and standard deviation of each index are marked on the graph.

[0161] Experimental code description:

[0162] Three methods are defined in the code: fixed vertical polarization (VP), polarization switching based on RSSI detection (RSSI), and elastic polarization switching (EPS) of the present invention.

[0163] Each method performs 1000 Monte Carlo simulations under different interference signal power ratios (INR), and the average bit error rate and standard deviation are statistically analyzed, and the throughput is calculated accordingly.

[0164] Finally, two charts are plotted. The left side is the bit error rate comparison chart, and the right side is the throughput comparison chart. Error bars are included in the charts to show the data fluctuations.

[0165] The experimental results are described as Figure 7 shown:

[0166] Figure 7 It shows that in the case of strong interference (INR>10dB), the average bit error rate of the EPS method is significantly lower than that of the VP and RSSI methods, and the throughput is higher;

[0167] For example, when INR = 15dB, the average BER of the EPS method is less than 0.06, while that of the VP method may reach 0.08, and the throughput is increased by more than 100%.

[0168] The error bars (standard deviation) are small, indicating that the data is stable, verifying the robustness and real-time advantages of this method.

[0169] This embodiment has the following technical effects: 1. Dynamic polarization compensation: By collecting the polarization state in real time and dynamically optimizing the compensation parameters, this embodiment overcomes the limitations of the fixed compensation method and achieves the best matching of the channel polarization state. Experimental verification shows that compared with the traditional method, this embodiment can reduce the bit error rate by more than 60% under complex terrain, and the stability of the communication link is significantly improved. Especially in complex environments such as mountain forests, the bit error rate of this method is only 38% of the traditional method. This is because this embodiment can track and compensate the rapidly changing polarization mismatch under complex terrain in real time, while the traditional fixed compensation method cannot do this.

[0170] 2. Enhanced environmental adaptability: By using predictive interference recognition and federated learning for collaborative decision-making, the system can actively and quickly respond to environmental changes and dynamically adjust the modulation method and coding strategy. Experiments show that the convergence time of the method in this embodiment does not exceed 2 seconds after the polarization state mutation, while the traditional method takes more than 5 seconds to stabilize, significantly improving the adaptability of the system in a dynamic environment. In addition, by predicting interference changes, the system can adjust the modulation and coding scheme in advance to further reduce the impact of interference and improve communication reliability.

[0171] 3. Cluster collaboration advantage: Through distributed collaborative decision-making, multiple drone nodes jointly optimize the anti-interference strategy, effectively reducing the risk of single-point failure. When the cluster size expands from 2 nodes to 16 nodes, the overall anti-interference ability of the system is increased by about 140%, and the impact of a single node failure on the overall system performance is reduced to less than 25% of the original. This is because federated learning can converge the environmental perception information of multiple nodes to form a more comprehensive interference situation map, thereby achieving a better collaborative anti-interference strategy.

[0172] 4. Low power consumption and high security: The lightweight algorithm designed in this embodiment reduces the computational complexity by about 65% compared with the traditional scheme, and the power consumption is reduced by about 42% while maintaining the same performance. At the same time, the integrated secure communication module can effectively prevent man-in-the-middle attacks and replay attacks, and the security strength is increased to 128-bit equivalent security strength, meeting military-level security requirements. The low-power design makes this embodiment more suitable for the drone platform and extends the flight time of the drone. The high-security design ensures the data security and control security of the drone communication link.

[0173] Embodiment 2

[0174] As Figure 1-3 shown, this embodiment provides a drone communication device for dynamic compensation of polarization signals in low-altitude complex terrain, including:

[0175] Adaptive Polarization Antenna Array: A four-element polarization antenna array with an adjustable frequency range of 2 - 6 GHz is adopted to achieve fast switching among multiple polarization modes (linear, circular polarization, elliptical polarization), and the frequency switching time does not exceed 5 μs. Each antenna unit supports independent polarization control, and the polarization direction and ellipticity are adjusted by controlling voltage or current. The antenna array adopts a compact design with dimensions not larger than 10 cm x 10 cm x 5 cm and a weight not exceeding 200 g, meeting the requirements of UAV payloads.

[0176] Full-duplex SDR Platform: It supports signal acquisition and real-time digital signal processing with a 100 MHz bandwidth, a sampling rate as high as 200 MSPS, and a dynamic range not less than 80 dB. The SDR platform integrates a radio frequency front end, high-speed ADC / DAC, FPGA, and high-performance DSP chips, supports software-defined radio functions, and can flexibly configure communication protocols such as modulation and demodulation, channel coding, etc. The platform adopts a full-duplex design, supports simultaneous data transmission and reception, and meets the requirements of real-time communication.

[0177] High-performance Edge Computing Unit: It integrates a quad-core ARM Cortex-A76 processor (main frequency 2.2 GHz), a dedicated AI accelerator (supporting a computing power of 8 TPS), and an FPGA logic unit (equivalent to 1 million logic gates), with the power consumption controlled within 10 W. The edge computing unit is responsible for running core software modules such as polarization state monitoring, dynamic compensation algorithms, interference prediction, and cluster collaborative decision-making. The AI accelerator is used to accelerate the inference calculation of deep learning models, and the FPGA logic unit is used to achieve high-speed data processing and hardware acceleration.

[0178] High-precision Positioning Module: It integrates RTK-GPS and a nine-axis IMU to achieve centimeter-level precise positioning of the UAV, provides position information for the compensation algorithm, and supports inertial navigation in case of GNSS signal interruption. The positioning module outputs information such as position, speed, and attitude, with an update frequency as high as 100 Hz. In case of GNSS signal loss, the IMU can provide short-term high-precision inertial navigation to ensure the continuity of positioning information.

[0179] Secure Storage and Communication Module: It uses a physically isolated storage area and high-strength encryption algorithms to protect sensitive data and achieve high-strength encrypted communication. The secure storage module adopts a hardware encryption chip and anti-tampering design to prevent data leakage and malicious attacks. The secure communication module supports national encryption algorithms such as SM2 / SM3 / SM4 and the international standard encryption algorithm AES-256 to ensure the confidentiality and integrity of data transmission.

[0180] The system consists of a low-altitude UAV cluster, a ground control station (GCS), and a wireless communication channel. Each UAV node is equipped with the communication device of this embodiment to achieve environmental monitoring, polarization state estimation, dynamic compensation, and collaborative decision-making. The ground control station is responsible for mission planning, command and control, data processing, and security management. Data interaction between the UAV cluster and the ground control station is carried out through the wireless communication channel.

[0181] The internal of the UAV node consists of an adaptive polarization antenna, an SDR platform, a high-performance edge computing unit, and each functional module. Each module is connected through an internal high-speed bus (such as PCIe, AXI) to collaboratively complete signal acquisition, processing, compensation, and communication tasks.

[0182] Embodiment III

[0183] This embodiment provides a UAV communication system for dynamic compensation of polarization signals in low-altitude complex terrains, including:

[0184] Polarization state real-time monitoring module: The acquisition frequency is adjustable from 100Hz to 1kHz and is dynamically adjusted according to the channel change rate. When the channel changes violently, the acquisition frequency is increased, and vice versa, to balance real-time performance and power consumption.

[0185] Polarization parameter estimation accuracy: XPD error < 0.5dB, phase error < 2°. An estimation algorithm based on the minimum mean square error (MMSE) is adopted, combined with Kalman filtering for time-domain smoothing to improve the estimation accuracy and robustness.

[0186] Processing delay: < 5ms, meeting the real-time compensation requirement. The module adopts an optimized algorithm and hardware acceleration to reduce the processing delay.

[0187] Dynamic compensation algorithm module:

[0188] Compensation parameter update rate: Up to 200Hz, matching the acquisition frequency of the polarization state monitoring module.

[0189] Compensation accuracy: Polarization angle error < 3°, polarization ellipticity error < 5%. An iterative optimization algorithm is adopted to continuously adjust the compensation parameters to approach the optimal compensation effect.

[0190] Algorithm complexity: 0(n·lgn), ensuring real-time performance. Efficient algorithms such as the fast Fourier transform (FFT) are adopted to reduce the computational complexity.

[0191] Predictive interference identification module:

[0192] Interference type identification: Supports 10 typical interferences such as narrowband interference, frequency sweep interference, and pulse interference. A deep learning-based interference classification model is adopted, and a convolutional neural network (CNN) is used to extract interference features to achieve high-precision identification.

[0193] Including but not limited to:

[0194] Spectral characteristics of the interference signal, such as the center frequency, bandwidth, power spectral density, etc.; through spectral analysis, accurately identify the distribution of the interference signal in the frequency domain and distinguish the spectral characteristics of different types of interference.

[0195] Time-domain waveform characteristics of the interference signal, such as pulse width, pulse repetition frequency, modulation method, etc.; analyze the variation law of the interference signal in the time domain and distinguish interference types with different time-domain characteristics, such as pulse interference and continuous-wave interference.

[0196] Polarization characteristics of the interference signal, such as polarization ratio, polarization angle, etc.; use polarization information to distinguish co-polarization interference and cross-polarization interference and improve the recognition ability for specific polarization interference.

[0197] Spatial characteristics of the interference signal, such as Angle of Arrival (AOA), etc.; through array signal processing technology, estimate the arrival direction of the interference signal, assist in locating the interference source, and distinguish interference signals from different directions.

[0198] High-precision recognition: Based on a deep learning-based interference classification model, use a convolutional neural network (CNN) to extract interference features and achieve high-precision recognition. The specific structure of the convolutional neural network (CNN) model is as follows:

[0199] Input layer: Receive preprocessed interference signal data, such as time-domain sampling data or frequency-domain spectral data. The input data dimension can be adjusted according to the actual application scenario and the dimension of the interference features. For example, a fixed-length time-domain sampling sequence or a two-dimensional spectrogram can be input.

[0200] Convolutional Layers: Stack multiple convolutional layers to automatically extract deep features in the interference signal.

[0201] The first convolutional layer: Adopt a smaller convolutional kernel size (such as 3x3 or 5x5) to extract local time-domain or frequency-domain features, such as edges, textures, etc.

[0202] Subsequent convolutional layers: As the network depth increases, the convolutional kernel size can gradually increase to extract more abstract and global features. Different numbers and sizes of convolutional kernels can be used to increase the diversity of feature extraction.

[0203] Activation function: Usually connect an activation function, such as ReLU (Rectified Linear Unit) or LeakyReLU, after each convolutional layer to increase the non-linear expression ability of the network.

[0204] Pooling Layers: Insert pooling layers, such as MaxPooling or AveragePooling, between or after convolutional layers to reduce the dimension of the feature map, reduce the computational amount, and enhance the translational invariance of features.

[0205] Fully Connected Layers: After multiple convolutional and pooling layers, connect several fully connected layers to map the extracted features to the category space.

[0206] Hidden Layers: The fully connected layers can contain multiple hidden layers to increase the learning ability of the network.

[0207] Activation Functions: The fully connected layers can also use activation functions such as ReLU or LeakyReLU.

[0208] Output Layer: The last fully connected layer serves as the output layer, using the Softmax activation function to output the probability values corresponding to each interference type. The output dimension corresponds to the number of interference types (e.g., 10 typical interferences).

[0209] Training Method: Use labeled interference sample data for training, and use the backpropagation algorithm and optimizers (such as Adam or SGD) to optimize the network parameters. The loss function can choose the cross-entropy loss function.

[0210] Model Parameters: Model parameters such as the number of convolutional kernels, the size of convolutional kernels, the type and parameters of pooling layers, the number of fully connected layers and nodes can be adjusted and optimized according to the actual application scenario and data characteristics.

[0211] Recognition Accuracy: >92% (under the condition of SINR > -10dB). It can still maintain a high recognition rate under low signal-to-noise ratio conditions.

[0212] Prediction Time Window: Adjustable from 50ms to 500ms, adjusted according to the interference change characteristics and system response speed.

[0213] Recognition Accuracy: >92% (under the condition of SINR > -10dB). It can still maintain a high recognition rate under low signal-to-noise ratio conditions.

[0214] Prediction Time Window: Adjustable from 50ms to 500ms, adjusted according to the interference change characteristics and system response speed.

[0215] Cluster Cooperative Decision-making Module:

[0216] Node Scale: Supports up to 32 UAV nodes to cooperate. Adopting a distributed architecture, it is easy to expand.

[0217] Federated learning iteration period: adjustable from 100 ms to 1 s, dynamically adjusted according to the channel change rate and computing resources. Communication overhead: <10 kbps / node, low communication overhead, suitable for bandwidth-constrained UAV communication networks. Compression coding and sparse update technologies are adopted to reduce communication overhead.

[0218] Secure communication module: Encryption algorithms: Support national cryptographic algorithms such as SM2 / SM3 / SM4 and international standard AES-256. The encryption algorithm can be flexibly configured to meet the requirements of different security levels.

[0219] Key update frequency: configurable, from 10 s to 600 s. Support for periodic key update and event-triggered key update to improve key security.

[0220] Encryption calculation delay: <1 ms, with little impact on real-time communication. Hardware acceleration and optimized algorithms are adopted to achieve low-latency encryption.

[0221] Secure communication module:

[0222] Encryption algorithms: Support national cryptographic algorithms such as SM2 / SM3 / SM4 and international standard AES-256. The encryption algorithm can be flexibly configured to meet the requirements of different security levels.

[0223] Key update frequency: configurable, from 10 s to 600 s. Support for periodic key update and event-triggered key update to improve key security.

[0224] Encryption calculation delay: <1 ms, with little impact on real-time communication. Hardware acceleration and optimized algorithms are adopted to achieve low-latency encryption.

[0225] The communication protocol is:

[0226] Polarization state negotiation protocol: Define the reference signal structure (preamble + training sequence), polarization state feedback format (compressed representation of the polarization matrix H), and compensation parameter negotiation process (three-way handshake mechanism). The reference signal uses a pseudo-random sequence, which has good autocorrelation and cross-correlation. The polarization state feedback format uses differential coding and quantization compression to reduce feedback overhead. This protocol mainly operates between the polarization processing module and the control module. The polarization processing module is responsible for adjusting the polarization state according to the negotiation result, while the control module is responsible for initiating and managing the negotiation process, and storing and transmitting negotiation parameters.

[0227] Interference Information Sharing Protocol: It supports the efficient transmission of interference feature data among nodes and adopts differential coding. This protocol mainly operates between the interference recognition module and the information fusion module (or control module). The interference recognition module is responsible for detecting and extracting interference features, and transmitting the data to the information fusion module (or control module) through this protocol for centralized processing and analysis, so as to conduct global interference situation awareness and collaborative interference suppression. If the system does not have a dedicated information fusion module, the control module is responsible for receiving and processing interference information.

[0228] Federated Learning Collaboration Protocol: It stipulates the processes of model training, parameter aggregation, and decision distribution, including node weighting mechanism, differential privacy protection, and abnormal node detection. The node weighting mechanism dynamically adjusts the weights according to the node contribution degree. The differential privacy protection mechanism prevents the leakage of node data privacy during the model training process. The abnormal node detection mechanism identifies malicious nodes and faulty nodes to improve the robustness of the system. This protocol mainly operates between the model training module and the control module. The model training module is responsible for executing the federated learning training tasks, and the control module is responsible for coordinating and managing the entire federated learning process, including parameter aggregation, model distribution, node management, and policy decision-making.

[0229] Secure Communication Protocol: It ensures the security of confidential key distribution, storage, and management, and supports identity authentication, session protection, and integrity verification. The key distribution adopts a key exchange protocol based on the public key infrastructure (PKI). The identity authentication adopts digital certificates and a two-way authentication mechanism. The session protection adopts an encrypted tunnel and a secure transmission protocol (such as TLS / SSL). The integrity verification adopts a message authentication code (MAC) and digital signature technology. This protocol mainly operates between the security module and the control module, as well as among all modules in the system that require secure communication. The security module is responsible for providing various security functions, such as key management, identity authentication, data encryption, and integrity verification. The control module is responsible for configuring and managing security policies and coordinating the secure communication requirements of each module. Other modules in the system that require secure communication, such as the data acquisition module, signal processing module, information fusion module, etc., all need to cooperate with the security module and follow the secure communication protocol for data exchange.

[0230] The above is only a preferred specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by this application should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for dynamic compensation of polarization signals in low-altitude complex terrain, characterized in that: include: S1, real-time acquisition of channel polarization state data based on adaptive polarization antenna array and full-duplex SDR platform; S2. Calculating a signal-to-noise ratio of a channel based on the polarization state data; S3. Setting a set of candidate modulation schemes based on the signal-to-noise ratio of the channel, and constructing a constraint function based on the set of candidate modulation schemes; S4. Based on the constraint function, a global optimization algorithm is used to solve the optimal compensation parameters and modulation method.

2. The method according to claim 1, characterized in that The process of collecting channel polarization state data also includes: collecting the channel polarization state data, and constructing a channel polarization matrix based on the channel polarization state data; The polarization mismatch degree, polarization correlation coefficient and polarization ellipse parameter are calculated based on the channel polarization matrix.

3. The method according to claim 2, characterized in that The expression of the channel polarization matrix is: Among them, h ij represents the channel gain between different polarization directions, and H is the channel polarization matrix; The expression of the polarization mismatch is: Here, XPD is the polarization mismatch.

4. The method according to claim 2, characterized in that: The expression of the polarization correlation coefficient is: Where ρ is the polarization correlation coefficient.

5. The method according to claim 2, characterized in that: The polarization ellipse parameters include polarization angle and polarization ellipse ratio; The calculation expression of the polarization angle is: Where θ is the polarization angle; The calculation expression of the polarization ellipticity is: Where ∈ is the polarization ellipticity.

6. The method according to claim 1, characterized in that The expression of the constraint function is: Wherein, BER(P,M) is the bit error rate, P is the polarization compensation parameter, M is the modulation scheme, and β is the preset bit error rate threshold; The constraint conditions of the constraint function are: BER(P,M)≤β.BER(P,M)≤β.

7. A UAV communication device with a dynamic compensation method for polarization signals in low-altitude complex terrain, characterized in that: include: Adaptive polarization antenna array, used to switch between multiple polarization modes, control voltage or current to adjust polarization direction and ellipticity; Full-duplex SDR platform for acquiring broadband signals and performing real-time digital signal processing; High-performance edge computing unit for running polarization state monitoring, dynamic compensation algorithms, interference prediction, and cluster collaborative decision-making algorithms; High-precision positioning module, used to provide positioning information; Secure storage and communication module, used to protect sensitive data based on physically isolated storage areas and encryption algorithms.

8. The device according to claim 7, characterized in that The adaptive polarization antenna array is based on a four-element polarization antenna array communication with an adjustable frequency range of 2-6GHz, the frequency switching time does not exceed 5μs, and each antenna unit supports independent polarization control; The full-duplex SDR platform is based on RF front-end, high-speed ADC / DAC, FPGA and high-performance DSP chip; The high-performance edge computing unit is based on a quad-core ARMCrtex-A76 processor, an AI accelerator and an FPGA logic unit; The high-precision positioning module is based on RTK-GPS and nine-axis IMU; The secure storage and communication module is constructed based on a hardware encryption chip.

9. A UAV communication system with a dynamic compensation method for polarization signals in low-altitude complex terrain, characterized in that: include: Polarization state real-time monitoring module, used to dynamically adjust the acquisition frequency according to the channel change rate, and A dynamic compensation algorithm module is used to automatically adjust compensation parameters according to polarization state data collected in real time; Predictive interference identification module, used to identify and predict interference types and changing trends; Cluster collaborative decision-making module, used to realize collaborative anti-interference decision-making of drone clusters; The secure communication module is used to provide high-intensity encrypted communication to ensure data transmission security.

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