A UAV swarm control method based on polarization code

Abstract

The present invention discloses a UAV swarm control method based on polarization code, which includes the following steps: a polarization code encoder encodes task data for transmission; the coded signal is transmitted to an adjacent UAV or control station through a wireless channel, and during the coded signal transmission process, a channel estimation and feedback mechanism is used to dynamically adjust the frequency and time slot allocation according to the quality of the channel; the UAV receives the coded signal from other UAVs or control stations and uses a built-in decoder to decode the signal; the error correction mechanism of the polarization code is used to improve the signal recovery quality. The present invention has the following advantages and effects: using the channel estimation and feedback strategy to dynamically adjust the frequency and time slot allocation, and combining the error correction mechanism of the polarization code, not only improves the accuracy of data transmission, but also solves the interference and conflict problems in swarm communication, and provides strong support for the efficient collaborative work of UAV swarms.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) swarm control, and in particular to a UAV swarm control method based on polarization codes. Background Art

[0002] With the rapid development of drone technology, drone swarms have been widely used in many fields, especially in military reconnaissance, environmental monitoring, disaster relief, agricultural spraying and other tasks, showing great potential. The core advantage of drone swarms is the coordinated operations between multiple drones, which can complete complex tasks. However, the stability and efficiency of swarm communication are the key to achieving these tasks, especially in complex and dynamically changing wireless communication environments, signal transmission often faces many challenges.

[0003] Traditional drone swarm communications usually rely on signal transmission technology based on radio frequency bands. Since drone swarms usually need to perform task allocation and data sharing over a large range, strong communication capabilities are required. However, factors such as wireless channel instability, signal interference, signal attenuation, low bandwidth, and multipath effects make signal transmission extremely difficult. Especially in dense environments, signal conflicts may occur between drones, and the communication quality will drop sharply, resulting in an increase in bit error rate, which will affect the accuracy and efficiency of data transmission.

[0004] In order to ensure efficient coordination of drone swarms in complex environments, polarization codes, as a new channel coding technology, have gradually attracted widespread attention. Polarization codes are a coding method based on the principle of channel polarization. They can divide the channel into reliable and unreliable parts, maximize the use of the reliable part for data transmission, and thus maintain a low bit error rate in a low signal-to-noise ratio environment. Compared with traditional LDPC codes and Turbo codes, polarization codes can theoretically reach the Shannon limit and have excellent performance, making them particularly suitable for data transmission in wireless communications.

[0005] However, although polar codes have strong performance advantages in theory, their practical applications still face some challenges. First, in a dynamic environment, the quality of the communication channel of a drone swarm varies greatly, especially in areas with strong signal interference and multipath effects. The fluctuation of signal quality has a direct impact on the performance of polar codes. Second, the computational complexity required in the error correction process is high. In a real-time communication environment, how to achieve low-latency error correction is still a problem that needs to be solved.

[0006] In the existing technology, although some conventional channel coding schemes have been used to improve communication quality, these methods often have problems such as high redundancy, low transmission efficiency, and large decoding delay when dealing with channels with high interference and low signal-to-noise ratio. In addition, the existing swarm communication methods also rely on traditional modulation and demodulation methods, and fail to fully utilize the advantages of polarization codes in variable wireless channel environments. By introducing polarization codes and adaptive feedback mechanisms, the present invention can significantly improve the efficiency of swarm task execution while ensuring communication reliability. Summary of the invention

[0007] The purpose of the present invention is to provide a UAV swarm control method based on polarization codes to solve the problems raised in the background technology.

[0008] The above technical objectives of the present invention are achieved through the following technical solutions: A UAV swarm control method based on polarization code includes the following steps: Step S1: a polarization code encoder is configured in each UAV, and the polarization code encoder encodes the mission data for transmission; the polarization code encoder uses polarization coding technology to convert the mission data into a coded signal by generating a matrix, so as to ensure that the signal has a strong anti-interference ability during the transmission process; Step S2: The coded signal is transmitted to the adjacent UAV or control station through the wireless channel. During the coded signal transmission process, the channel estimation and feedback mechanism is used to dynamically adjust the frequency and time slot allocation according to the quality of the channel. Step S3: The drone receives coded signals from other drones or control stations and uses a built-in decoder to decode the signals. The decoding module uses the maximum likelihood estimation algorithm and the Bayesian method to continuously optimize the decoding path through the back-propagation mechanism to improve the accuracy of signal recovery. Step S4: In the transmission process of the coded signal, the occurrence of bit errors is inevitable. In order to ensure the accuracy of decoding, the bit error correction mechanism of the polar code is adopted to improve the signal recovery quality.

[0009] By adopting the above technical solution, Through polarization code coding technology, the mission data is converted into coded signals, which can effectively improve the anti-interference ability of wireless signals; by encoding the mission data through the generation matrix, the signal can withstand the influence of signal attenuation and multipath effects during transmission, while ensuring transmission efficiency, which can effectively compress redundant data and reduce communication delays, reduce bandwidth occupancy, and improve communication efficiency; through channel estimation and feedback mechanism, the channel quality can be monitored in real time and the communication parameters can be dynamically adjusted to ensure stable communication performance in complex environments; the error correction mechanism of polarization code is used to effectively correct possible errors during signal transmission, improve the accurate recovery capability of mission data, and ensure that data transmission in drone swarm collaborative tasks will not be interfered with and fail.

[0010] Further configuration is that, in step S1: Before the polar code encoder encodes the mission data for transmission, it is necessary to obtain the mission data, pre-process the obtained mission data, and initialize the polar code encoder; Ways to obtain mission data include but are not limited to mission instructions issued by the control station, local sensor data of the drone, and shared information within the swarm; among them, Mission instructions include but are not limited to the target's location, speed, flight instructions, path planning information, and environmental change warnings; Drone local sensor data includes but is not limited to GPS positioning, IMU data, visual sensor information, and radar detection data; Information shared within the swarm includes status information from other drones, including but not limited to mission coordination data, obstacle avoidance data, and target tracking information; The preprocessing of the acquired task data includes: De-redundancy processing of task data to eliminate duplicate data, reduce bandwidth usage and improve transmission efficiency; Perform lossless or lossy compression on key data in mission data to reduce data volume and increase transmission rate; According to the urgency of the task, the data is prioritized so that the key data in the task data is encoded and transmitted first; Use filtering technology to remove noise and errors in task data and improve the quality of coded input data; The process of initializing the polar code encoder includes: Self-test and calibration of polar code encoder; During initialization, the channel status of the current environment is obtained in order to set the initial coding parameters; For different mission data and environments, the polar code encoder will load polar code parameters that adapt to different situations to ensure that the encoding strategy can be quickly switched in subsequent communication processes.

[0011] By adopting the above technical solution, the channel state of the current environment is obtained when the polar code encoder is initialized and the encoding strategy is dynamically adjusted, so that the system can adapt to different mission data and environmental conditions, and provide more flexible and efficient communication capabilities; de-redundancy processing, data compression and priority division are performed on the acquired mission data, which helps to reduce the transmission of irrelevant information, reduce bandwidth occupancy, and improve data transmission rate, while ensuring the priority transmission of key information, thereby improving communication efficiency; filtering technology is used to remove noise and errors in the data to ensure the quality of data input into the polar code encoder, thereby improving the accuracy of the encoding result and reducing decoding errors caused by noise.

[0012] Further configuration is that step S2 is specifically: Step S21: To ensure efficient communication, the drone or control station will first evaluate the current channel quality through channel estimation, which includes at least signal-to-noise ratio, bit error rate, and bandwidth utilization parameters; based on real-time channel evaluation, the drone or control station will select the channel with the best performance for data transmission; Step S22: Based on real-time channel evaluation, if the quality of the current channel is poor, it can automatically switch to a channel with better signal quality; the goal of channel selection is to minimize communication delay, maximize data throughput, and maintain stability of signal transmission; Step S23: After the channel is selected, the UAV or control station performs frequency scheduling according to the selected channel; based on the dynamic frequency allocation algorithm, the UAV or control station can adaptively select a suitable frequency for signal transmission; when the channel interference is low, the frequency can be selected to a higher value to increase the data transmission rate; when the interference is high, the frequency will be automatically lowered to reduce the impact of the interference; Step S24: During the signal transmission process, the UAV or the control station updates the channel quality through a real-time feedback mechanism; the channel quality will change with time and environment, therefore, through the feedback mechanism, the transmitting UAV, the receiving UAV and the control station can realize the communication of the signal quality feedback results, so that the transmitting UAV and the control station can adjust the signal transmission strategy according to the signal quality feedback results; Step S25: Time slot allocation is performed through the time division multiple access mechanism to ensure that multiple drones can effectively use the same channel for communication, avoiding channel conflicts and data collisions caused by multiple drones sending and receiving data at the same time.

[0013] By adopting the above technical solution, through real-time evaluation of the current channel quality, the channel with the best performance can be automatically selected for data transmission. This mechanism can minimize communication delays and maximize data throughput, thereby improving the communication performance of drone swarms in complex environments. By dynamically adjusting the frequency and time slot allocation according to the channel quality, channel conflicts and data collisions between different drones are effectively avoided, the collaborative efficiency between swarms is improved, and the stability of data exchange between multiple drones is ensured. Through the feedback mechanism, the system can update the channel quality in real time and adjust the signal sending strategy according to the changes in the channel, thereby ensuring the communication stability and reliability of drone swarms in different environments.

[0014] It is further configured that the following steps are further performed after the step S25: Step S26, through adaptive channel scheduling, avoids interference during signal transmission and maximizes the stability and efficiency of signal transmission, which includes the following sub-steps: Step S261: The UAV continuously evaluates the current channel quality and stores the channel status information; Step S262: According to historical data and current channel status, an adaptive channel scheduling algorithm is used to select the most suitable data transmission path; Step S263: When the channel quality decreases, the transmission power and bandwidth allocation of the UAV are automatically adjusted to improve data transmission efficiency.

[0015] By adopting the above technical solutions, the current channel quality is continuously evaluated, and the most suitable data transmission path is selected in combination with historical data and real-time channel status, thereby maximizing the efficiency and stability of signal transmission; when the channel quality decreases, the drone's transmission power and bandwidth allocation are automatically adjusted to adapt to environmental changes, avoid the impact of signal interference on communication effects, and ensure the stability of the data transmission process; by adaptively adjusting the transmission power and bandwidth, the optimal data transmission rate can be achieved under different channel conditions, ensuring the real-time and accuracy of mission data.

[0016] Further configuration is that the step S3 is specifically: Step S31, preliminarily decoding the received coded signal by using a maximum likelihood estimation algorithm; Step S32: Based on the result obtained by the maximum likelihood estimation algorithm, the decoding path is further optimized by using the Bayesian method, the posterior probability of each possible bit sequence is calculated, and the bit sequence with the maximum posterior probability is selected as the final decoding result.

[0017] By adopting the above technical solution, preliminary decoding is performed through maximum likelihood estimation, and then the decoding path is optimized using the Bayesian method. This can effectively improve the accuracy of decoding, correct errors that may occur during the preliminary decoding process, and ensure the accurate recovery of task data. The combination of maximum likelihood estimation and the Bayesian method can more comprehensively analyze the received signal and improve the reliability of decoding by considering historical data and current signal status.

[0018] It is further configured that the following steps are further performed after the step S32: Step S33: Use the back propagation mechanism to gradually adjust the path during the decoding process to minimize the decoding error. Each back propagation will calculate the error between the current path and the ideal path, and adjust the decoding strategy according to the error.

[0019] By adopting the above technical solution, through the back-propagation mechanism, the decoding path can be gradually adjusted to minimize the error between the current path and the ideal path, ultimately achieving accurate decoding and ensuring the quality of task data recovery. Each back-propagation will adjust the decoding strategy according to the current decoding error, which can optimize the decoding path in real time and improve the adaptability in complex channel environments.

[0020] It is further configured that the error correction mechanism of the polarization code in step S4 includes: By comparing the decoded data with the expected data, possible bit errors that may occur during transmission can be detected; Redundant bits are added to enable error correction at the receiving end; when errors are detected, the redundant bits can be used to recover the lost or erroneous bits, thus ensuring the accuracy of decoding; The receiving drone feeds back the error information to the transmitting drone or other drones or the error correction mechanism of the polar code to update the decoding path and optimize signal recovery.

[0021] By adopting the above technical solution, by comparing the decoded data with the expected data, it is possible to promptly discover and correct bit errors, ensure that the recovered data is consistent with the task instructions, and improve the decoding accuracy; adding redundant bits to perform bit error correction at the receiving end can effectively deal with interference and signal attenuation in the wireless channel, ensuring that accurate data can be recovered even under poor channel conditions; the receiving end feeds back error information to the sending end or other drones, and can dynamically update the decoding path based on these feedbacks, further optimizing the signal recovery process and ensuring that collaborative tasks between drone swarms can proceed smoothly.

[0022] In summary, the present invention has the following beneficial effects: By utilizing channel estimation and feedback strategies, dynamically adjusting the allocation of frequency and time slots, and combining the error correction mechanism of polarization codes, not only the accuracy of data transmission is improved, but also the interference and conflict problems in swarm communications are solved, providing strong support for the efficient collaborative work of drone swarms. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart; Figure 2 This is a performance comparison table of Example 1; Figure 3 This is a performance comparison table of Example 2; Figure 4 The following is a performance comparison table of Examples 1 and 2. DETAILED DESCRIPTION

[0024] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0025] As attached Figure 1 As shown; This embodiment discloses a UAV swarm control method based on polarization code, comprising the following steps: Step S1: A polarization code encoder is configured in each UAV, and the polarization code encoder encodes the mission data for transmission; the polarization code encoder uses polarization coding technology to convert the mission data into a coded signal by generating a matrix, so as to ensure that the signal has a strong anti-interference ability during the transmission process; Step S2: The coded signal is transmitted to the adjacent UAV or control station through the wireless channel. During the coded signal transmission process, the channel estimation and feedback mechanism is used to dynamically adjust the frequency and time slot allocation according to the quality of the channel. Step S3: The drone receives coded signals from other drones or control stations and uses a built-in decoder to decode the signals. The decoding module uses the maximum likelihood estimation algorithm and the Bayesian method to continuously optimize the decoding path through the back-propagation mechanism to improve the accuracy of signal recovery. Step S4: In the transmission process of the coded signal, the occurrence of bit errors is inevitable. In order to ensure the accuracy of decoding, the bit error correction mechanism of the polar code is adopted to improve the signal recovery quality.

[0026] Among them, in step S1: Before the polar code encoder encodes the mission data for transmission, it is necessary to obtain the mission data, pre-process the obtained mission data, and initialize the polar code encoder; Ways to obtain mission data include but are not limited to mission instructions issued by the control station, local sensor data of the drone, and shared information within the swarm; among them, Mission instructions include but are not limited to the target's location, speed, flight instructions, path planning information, and environmental change warnings; Drone local sensor data includes but is not limited to GPS positioning, IMU data, visual sensor information, and radar detection data; Information shared within the swarm includes status information from other drones, including but not limited to mission coordination data, obstacle avoidance data, and target tracking information; The preprocessing of the acquired task data includes: De-redundancy processing of task data to eliminate duplicate data, reduce bandwidth usage and improve transmission efficiency; Perform lossless or lossy compression on key data in mission data to reduce data volume and increase transmission rate; According to the urgency of the task, the data is prioritized so that the key data in the task data is encoded and transmitted first; Use filtering technology to remove noise and errors in task data and improve the quality of coded input data; The process of initializing the polar code encoder includes: Self-test and calibration of polar code encoder; During initialization, the channel status of the current environment is obtained in order to set the initial coding parameters; For different mission data and environments, the polar code encoder will load polar code parameters that adapt to different situations to ensure that the encoding strategy can be quickly switched in subsequent communication processes.

[0027] Among them, in step S1: The polar code encoder uses polar coding technology to convert task data into coded signals by generating a matrix based on the following formula: ; Where u is the input task data bit sequence, c is the input task data bit sequence u through the generator matrix G N The output result after encoding, G N Generate a matrix, mod2 means to perform a modulo 2 operation on the output result to adapt to binary communication; Generate a matrix G N The calculation formula is: ; Among them, B N is the bit reverse order matrix, F ⊗n is the polarization transformation matrix.

[0028] Among them, step S2 is specifically: Step S21: To ensure efficient communication, the drone or control station will first evaluate the current channel quality through channel estimation, which includes at least signal-to-noise ratio, bit error rate, and bandwidth utilization parameters; based on real-time channel evaluation, the drone or control station will select the channel with the best performance for data transmission; For example, the pilot signal can be used for estimation. The system obtains the channel status information by receiving and analyzing the pilot signal. The channel quality can be evaluated using the following formula: ; Among them, H(t) is the channel estimation value at the current moment, Y p (t) is the received pilot signal, X p (t) is the known pilot signal sent; Step S22: Based on real-time channel evaluation, if the quality of the current channel is poor, it can automatically switch to a channel with better signal quality; the goal of channel selection is to minimize communication delay, maximize data throughput, and maintain stability of signal transmission; Step S23: After the channel is selected, the UAV or control station performs frequency scheduling according to the selected channel; based on the dynamic frequency allocation algorithm, the UAV or control station can adaptively select a suitable frequency for signal transmission; when the channel interference is low, the frequency can be selected to a higher value to increase the data transmission rate; when the interference is high, the frequency will be automatically lowered to reduce the impact of the interference; Step S24: During the signal transmission process, the UAV or the control station updates the channel quality through a real-time feedback mechanism; the channel quality will change with time and environment, therefore, through the feedback mechanism, the transmitting UAV, the receiving UAV and the control station can realize the communication of the signal quality feedback results, so that the transmitting UAV and the control station can adjust the signal transmission strategy according to the signal quality feedback results; Signal quality feedback includes the strength of the received signal, bit error rate, delay, and channel attenuation information. This information enables the drone and control station to understand the current channel status in a timely manner and adjust the transmission strategy based on this information. For example, in a low-quality channel, the transmitter can choose to add redundant information to enhance anti-interference capabilities, or choose to adjust the transmission power to improve reception quality; Based on the received feedback information, the system automatically adjusts the channel selection, transmission power, coding rate and redundancy parameters. For example, when the channel quality is poor, the system will automatically increase data redundancy and use stronger error correction codes to ensure data reliability; conversely, when the channel is good, it will reduce redundancy to increase the transmission rate.

[0029] Step S25: Time slot allocation is performed through the time division multiple access mechanism to ensure that multiple drones can effectively use the same channel for communication, avoiding channel conflicts and data collisions caused by multiple drones sending and receiving data at the same time.

[0030] Specifically, appropriate time slots are allocated based on each drone's mission priority, communication requirements, and current location. When the drone's mission is more urgent, shorter time slots are allocated first to ensure the timeliness of data transmission; when the drone has fewer missions, the system will adjust the time slot length to improve bandwidth utilization; The communication time slots of all drones must be synchronized to avoid conflicts between multiple drones in the same time slot. The synchronization mechanism is carried out through a wireless clock synchronization protocol to ensure that the time between drones is consistent, thereby achieving accurate allocation of time slots.

[0031] More specifically, time slot allocation can adopt a binary optimization scheduling strategy: T i =B available / N active； Among them, T i is the available time slot of the i-th UAV, B available is the current available bandwidth, N active is the number of currently active communication drones; During the time slot optimization process, the time slot length of the drone can be continuously adjusted to ensure the fairness and efficiency of communication.

[0032] Among them, after step S25, there are further steps: Step S26, through adaptive channel scheduling, avoids interference during signal transmission and maximizes the stability and efficiency of signal transmission, which includes the following sub-steps: Step S261: The UAV continuously evaluates the current channel quality and stores the channel status information; Step S262: According to historical data and current channel status, an adaptive channel scheduling algorithm is used to select the most suitable data transmission path; Step S263: When the channel quality decreases, the transmission power and bandwidth allocation of the UAV are automatically adjusted to improve data transmission efficiency; wherein, In step S261, the adaptive channel scheduling algorithm is specifically: ; Among them, S opt is the optimal channel, S i is the candidate channel set, R i is the data rate of channel i, P i is the transmission power of channel i.

[0033] Among them, step S3 is specifically: Step S31: Perform preliminary decoding on the received coded signal by using the maximum likelihood estimation algorithm, the formula of which is as follows: ; in, is the decoded bit sequence, u is the bit sequence to be decoded, and P(y|u) is the conditional probability of receiving the signal y given the bit sequence u; Step S32: Based on the result obtained by the maximum likelihood estimation algorithm, the decoding path is further optimized by using the Bayesian method, the posterior probability of each possible bit sequence is calculated, and the bit sequence with the maximum posterior probability is selected as the final decoding result.

[0034] Among them, after step S32, there are further steps: Step S33: Use the back propagation mechanism to gradually adjust the path during the decoding process to minimize the decoding error. Each back propagation will calculate the error between the current path and the ideal path, and adjust the decoding strategy according to the error, as follows: ; Among them, e(t) is the error at the current moment, y(t) is the received signal, It is the signal estimation value obtained through preliminary decoding.

[0035] The error correction mechanism of the polarization code in step S4 includes: By comparing the decoded data with the expected data, possible bit errors that may occur during the transmission process are detected. The bit error rate is used as the standard for bit error detection to check whether the decoding result is consistent with the original data; Redundant bits are added to enable error correction at the receiving end; when errors are detected, the redundant bits can be used to recover the lost or erroneous bits, thus ensuring the accuracy of decoding; The receiving drone feeds back the error information to the transmitting drone or other drones or the error correction mechanism of the polar code to update the decoding path and optimize signal recovery.

[0036] Example 1 Background: Using drones to simulate reconnaissance missions, 30 drones were deployed to form a swarm to perform regional 3D mapping. The mission requires high real-time performance (delay < 50ms), high reliability (bit error rate < 1e-5), and resistance to strong electromagnetic interference (SNR ≥ 15dB); the drones are equipped with GPS, IMU, laser radar and high-definition cameras, and the single-machine sensor data rate reaches 50Mbps.

[0037] Step S1, the control station sends the path planning command (bandwidth usage: 2kbps, highest priority); LiDAR point cloud data (compressed rate: 20Mbps, using JPEG-LS lossless compression, compression rate 60%); IMU data (200Hz sampling, data volume reduced by 30% after redundant filtering).

[0038] Step S2: Channel assessment: Initial channel: 2.4 GHz frequency band, bandwidth 20 MHz, measured SNR = 12 dB (interference exists); After switching to the 5.8 GHz band, the SNR increased to 18 dB and the bandwidth utilization increased from 75% to 92%; Dynamic Frequency Allocation: Select the optimal channel in real time, and the switching response time is less than 5ms; The time slot allocation adopts the time division multiple access mechanism, the time slot width is 10ms, and it supports 30 nodes without conflict communication.

[0039] Step S3: Using the maximum likelihood estimation algorithm, the initial bit error rate is 1e-3. After optimization by the Bayesian method, the bit error rate is reduced to 3e-6 (confidence level 99.7%). The back propagation mechanism optimizes the decoding delay from 15ms to 9ms. Figure 2 shown.

[0040] Step S4: Add a 16-bit CRC check code, and the error detection rate is >99.99%; the receiving end feeds back the error position through ACK / NACK, and the retransmission success rate is 100%; Under strong interference of SNR=10dB, the end-to-end data transmission integrity reaches 99.999%.

[0041] Example 2 Background: A farmland needs 50 drones to coordinate pesticide spraying, requiring the swarm to share terrain data in real time (accuracy ±0.1m) and dynamically adjust the spraying path. The environment has multipath effects (delay extension 50ns) and tree occlusion (path loss increased by 15dB), and the control command transmission success rate must be guaranteed to be >99.9%.

[0042] Step S1, input the following data: multispectral camera image (10MP / frame, 5Mbps after HEVC compression); real-time terrain elevation data (highest priority, ZigBee broadcast cycle 100ms); remaining drug status information (update cycle 1s, using differential coding to reduce 80% redundancy).

[0043] Step S2, channel selection: The 900MHz frequency band has better penetration, and the path loss is 22dB lower than that of 2.4GHz; The dynamic frequency allocation algorithm reduces the channel conflict probability from 12% to 0.7%.

[0044] Step S3: Decoding delay is reduced from 25ms to 8ms, and memory usage is optimized to 32KB; specific data is as shown in the attached Figure 3 As shown; Step S4: Key instructions are sent through dual channels, and the packet loss rate is reduced from 1.2% to 0.01%. Data retransmission is completed within 50ms after NACK triggering. During the 8-hour continuous operation, there is zero loss of control instructions and 100% consistency of terrain data.

[0045] The effects of Example 1 and Example 2 are compared as shown in the attached figure. Figure 4 As shown, it has the following obvious advantages: maintaining ultra-low bit error rate in complex electromagnetic environments; channel switching and power control improve spectrum efficiency; and extend the flight endurance of the drone.

[0046] This specific embodiment is merely an explanation of the present invention and is not a limitation of the present invention. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed. However, as long as they are within the scope of the claims of the present invention, they are protected by the patent law.

Claims

1. A UAV swarm control method based on polarization code, characterized in that: The following steps are included: Step S1: a polarization code encoder is configured in each UAV, and the polarization code encoder encodes the mission data for transmission; the polarization code encoder uses polarization coding technology to convert the mission data into a coded signal by generating a matrix, so as to ensure that the signal has a strong anti-interference ability during the transmission process; Step S2: The coded signal is transmitted to the adjacent UAV or control station through the wireless channel. During the coded signal transmission process, the channel estimation and feedback mechanism is used to dynamically adjust the frequency and time slot allocation according to the quality of the channel. Step S3: The drone receives coded signals from other drones or control stations and uses a built-in decoder to decode the signals. The decoding module uses the maximum likelihood estimation algorithm and the Bayesian method to continuously optimize the decoding path through the back-propagation mechanism to improve the accuracy of signal recovery. Step S4: In the transmission process of the coded signal, the occurrence of bit errors is inevitable. In order to ensure the accuracy of decoding, the bit error correction mechanism of the polar code is adopted to improve the signal recovery quality.

2. The method for controlling a swarm of unmanned aerial vehicles based on polarization codes according to claim 1, characterized in that: In step S1: Before the polar code encoder encodes the mission data for transmission, it is necessary to obtain the mission data, pre-process the obtained mission data, and initialize the polar code encoder; Ways to obtain mission data include but are not limited to mission instructions issued by the control station, local sensor data of the drone, and shared information within the swarm; among them, Mission instructions include but are not limited to the target's location, speed, flight instructions, path planning information, and environmental change warnings; Drone local sensor data includes but is not limited to GPS positioning, IMU data, visual sensor information, and radar detection data; The information shared within the swarm includes status information from other drones, including but not limited to mission coordination data, obstacle avoidance data, and target tracking information; The preprocessing of the acquired task data includes: De-redundancy processing of task data to eliminate duplicate data, reduce bandwidth usage and improve transmission efficiency; Perform lossless or lossy compression on key data in mission data to reduce data volume and increase transmission rate; According to the urgency of the task, the data is prioritized so that the key data in the task data is encoded and transmitted first; Use filtering technology to remove noise and errors in task data and improve the quality of coded input data; The process of initializing the polar code encoder includes: Self-test and calibration of polar code encoder; During initialization, the channel status of the current environment is obtained in order to set the initial coding parameters; For different mission data and environments, the polar code encoder will load polar code parameters that adapt to different situations to ensure that the encoding strategy can be quickly switched in subsequent communication processes.

3. The method for controlling a swarm of unmanned aerial vehicles based on polarization codes according to claim 1, characterized in that: The step S2 is specifically as follows: Step S21: To ensure efficient communication, the drone or control station will first evaluate the current channel quality through channel estimation, which includes at least signal-to-noise ratio, bit error rate, and bandwidth utilization parameters; based on real-time channel evaluation, the drone or control station will select the channel with the best performance for data transmission; Step S22: Based on real-time channel evaluation, if the quality of the current channel is poor, it can automatically switch to a channel with better signal quality; the goal of channel selection is to minimize communication delay, maximize data throughput, and maintain stability of signal transmission; Step S23: After the channel is selected, the UAV or control station performs frequency scheduling according to the selected channel; based on the dynamic frequency allocation algorithm, the UAV or control station can adaptively select a suitable frequency for signal transmission; when the channel interference is low, the frequency can be selected to a higher value to increase the data transmission rate; when the interference is high, the frequency will be automatically lowered to reduce the impact of the interference; Step S24: During the signal transmission process, the UAV or the control station updates the channel quality through a real-time feedback mechanism; the channel quality will change with time and environment, therefore, through the feedback mechanism, the transmitting UAV, the receiving UAV and the control station can realize the communication of the signal quality feedback results, so that the transmitting UAV and the control station can adjust the signal transmission strategy according to the signal quality feedback results; Step S25: Time slot allocation is performed through the time division multiple access mechanism to ensure that multiple drones can effectively use the same channel for communication, avoiding channel conflicts and data collisions caused by multiple drones sending and receiving data at the same time.

4. The method for controlling a swarm of unmanned aerial vehicles based on polarization codes according to claim 3, characterized in that: After step S25, the following steps are also included: Step S26, through adaptive channel scheduling, avoids interference during signal transmission and maximizes the stability and efficiency of signal transmission, which includes the following sub-steps: Step S261: The UAV continuously evaluates the current channel quality and stores the channel status information; Step S262: According to historical data and current channel status, an adaptive channel scheduling algorithm is used to select the most suitable data transmission path; Step S263: When the channel quality decreases, the transmission power and bandwidth allocation of the UAV are automatically adjusted to improve data transmission efficiency.

5. The method for controlling a swarm of unmanned aerial vehicles based on polarization codes according to claim 1, characterized in that: The step S3 is specifically as follows: Step S31, preliminarily decoding the received coded signal by using a maximum likelihood estimation algorithm; Step S32: Based on the result obtained by the maximum likelihood estimation algorithm, the decoding path is further optimized by using the Bayesian method, the posterior probability of each possible bit sequence is calculated, and the bit sequence with the maximum posterior probability is selected as the final decoding result.

6. The method for controlling a swarm of unmanned aerial vehicles based on polarization codes according to claim 5, characterized in that: After step S32, the following steps are also included: Step S33: Use the back propagation mechanism to gradually adjust the path during the decoding process to minimize the decoding error. Each back propagation will calculate the error between the current path and the ideal path, and adjust the decoding strategy according to the error.

7. The method for controlling a swarm of unmanned aerial vehicles based on polarization codes according to claim 1, characterized in that: The polar code error correction mechanism in step S4 includes: By comparing the decoded data with the expected data, possible bit errors that may occur during transmission can be detected; Adding redundant bits to perform error correction at the receiving end; When bit errors are detected, redundant bits can be used to recover the lost or erroneous bits, thus ensuring decoding accuracy; The receiving drone feeds back the error information to the transmitting drone or other drones or the error correction mechanism of the polar code to update the decoding path and optimize signal recovery.

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