A Distributed UAV Remote Control Method and System for Multi-Source Signal Fusion

Abstract

The present invention discloses a distributed UAV remote control method and system for multi-source signal fusion, which relates to the technical field of UAV control. The method includes: building a multi-source signal positioning link to determine the estimated position of the UAV and the estimated position of the pilot; setting up a dummy pilot device; constructing a distributed control architecture to obtain the data transmission strategies of N nodes; interacting with the estimated position of the UAV to obtain the remote control instruction for the UAV pilot; creating an encrypted anti-jamming communication link to perform node forwarding processing on the remote control instruction for the UAV pilot and remotely control the UAV. The present invention solves the technical problems of insufficient UAV positioning accuracy, weak anti-jamming ability, and poor communication link stability in the prior art, and achieves the technical effect of realizing high-precision positioning and anti-jamming remote control of the UAV in a complex environment through multi-source signal fusion, spoofing interference, distributed adaptive transmission, and encrypted communication technologies.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) control, and particularly to a distributed UAV remote control method and system with multi-source signal fusion. Background Art

[0002] With the rapid development of UAV technology, in terms of positioning, traditional UAVs mostly rely on a single Global Navigation Satellite System (GNSS) for positioning. However, in areas where satellite signals are easily blocked, such as urban canyons and indoors, the GNSS signal weakens or interrupts, resulting in seriously insufficient positioning accuracy of the UAV and being unable to accurately execute tasks. Moreover, a single signal source is extremely vulnerable to interference. An enemy or malicious user can disrupt the normal reception of the positioning signal by transmitting interference signals, leading to the loss of control of the UAV's position. At the communication link level, traditional communication methods have poor stability in the face of a complex electromagnetic environment. Factors such as signal interference and multipath effects often cause communication interruptions and data packet losses, making it impossible for the pilot to timely and accurately convey remote control instructions to the UAV, seriously affecting the safe flight of the UAV. In terms of security risks, the UAV communication link lacks effective encryption measures and is easily cracked, resulting in the tampering and stealing of control instructions, which not only threatens the safety of the UAV itself but may also cause serious safety accidents.

[0003] There are technical problems in the prior art such as insufficient positioning accuracy, weak anti-interference ability, and poor communication link stability of UAVs. Summary of the Invention

[0004] This application provides a distributed UAV remote control method and system with multi-source signal fusion to solve the technical problems of insufficient positioning accuracy, weak anti-interference ability, and poor communication link stability of UAVs in the prior art.

[0005] In view of the above problems, this application provides a distributed UAV remote control method and system with multi-source signal fusion.

[0006] In the first aspect of this application, a distributed UAV remote control method with multi-source signal fusion is provided. The method includes:

[0007] Build a multi-source signal positioning link. The multi-source signal positioning link is configured with a GNSS receiver, an RF receiver, and a visual positioning sensor, and performs fusion processing on the multi-source positioning signal stream obtained by the multi-source signal positioning link to determine the estimated position of the drone and the estimated position of the pilot. Set up a dummy pilot device. The dummy pilot device modulates and emits multi-source spoofing signals according to the estimated position of the pilot. The multi-source spoofing signals are used for interfering with the pilot's position. Build a distributed control architecture. The distributed control architecture includes N transmission control nodes, and adaptively obtains N node data transmission strategies based on the real-time network conditions and interference conditions of the N transmission control nodes. Interact with the estimated position of the drone to obtain a remote control command for the drone pilot. Create an encrypted anti-jamming communication link, and use the N node data transmission strategies through the encrypted anti-jamming communication link to perform node forwarding processing on the remote control command for the drone pilot and remote control the drone.

[0008] In the second aspect of the present application, a distributed drone remote control system with multi-source signal fusion is provided. The system includes:

[0009] A multi-source signal positioning link building module, which is used to build a multi-source signal positioning link. The multi-source signal positioning link is configured with a GNSS receiver, an RF receiver, and a visual positioning sensor, and performs fusion processing on the multi-source positioning signal stream obtained by the multi-source signal positioning link to determine the estimated position of the drone and the estimated position of the pilot. A dummy pilot device setting module, which is used to set up a dummy pilot device. The dummy pilot device modulates and emits multi-source spoofing signals according to the estimated position of the pilot. The multi-source spoofing signals are used for interfering with the pilot's position. A data transmission strategy obtaining module, which is used to build a distributed control architecture. The distributed control architecture includes N transmission control nodes, and adaptively obtains N node data transmission strategies based on the real-time network conditions and interference conditions of the N transmission control nodes. A remote control command obtaining module for the drone pilot, which is used to interact with the estimated position of the drone to obtain a remote control command for the drone pilot. A drone remote control module, which is used to create an encrypted anti-jamming communication link, and use the N node data transmission strategies through the encrypted anti-jamming communication link to perform node forwarding processing on the remote control command for the drone pilot and remote control the drone.

[0010] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0011] Build a multi-source signal positioning link, perform fusion processing on the multi-source positioning signal stream obtained by the multi-source signal positioning link, and determine the estimated position of the unmanned aerial vehicle (UAV) and the estimated position of the drone pilot; set up a dummy drone pilot device to emit multi-source spoofing signals for interfering with the position of the drone pilot; construct a distributed control architecture, and adaptively obtain N node data transmission strategies based on the real-time network conditions and interference situations of the N transmission control nodes; interact with the estimated position of the UAV to obtain a remote control instruction for the drone pilot; create an encrypted anti-jamming communication link, and use the N node data transmission strategies through the encrypted anti-jamming communication link to perform node forwarding processing on the remote control instruction for the drone pilot and remote control the UAV. The technical effect of achieving high-precision positioning and anti-jamming remote control of the UAV in a complex environment is achieved through multi-source signal fusion, spoofing interference, distributed adaptive transmission, and encrypted communication technologies. Brief Description of the Drawings

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0013] Figure 1 Schematic flowchart of a multi-source signal fusion-based distributed UAV remote control method provided by an embodiment of the present application;

[0014] Figure 2 Schematic structural diagram of a multi-source signal fusion-based distributed UAV remote control system provided by an embodiment of the present application.

[0015] Explanation of reference numerals: Multi-source signal positioning link building module 10, dummy drone pilot device setting module 20, data transmission strategy acquisition module 30, remote control instruction acquisition module for the drone pilot 40, UAV remote control module 50. Detailed Embodiments

[0016] The present application provides a multi-source signal fusion-based distributed UAV remote control method and system for solving the technical problems of insufficient UAV positioning accuracy, weak anti-jamming ability, and poor communication link stability in the prior art.

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0018] Example 1, as Figure 1 shown, this application provides a distributed UAV remote control method for multi-source signal fusion. The method includes:

[0019] Step S100: Build a multi-source signal positioning link. The multi-source signal positioning link is configured with a GNSS receiver, an RF receiver, and a vision positioning sensor, and performs fusion processing on the multi-source positioning signal stream obtained by the multi-source signal positioning link to determine the estimated position of the UAV and the estimated position of the pilot.

[0020] Specifically, first, a multi-source signal positioning link needs to be built. This link is equipped with a GNSS receiver (GNSS stands for Global Navigation Satellite System, and the GNSS receiver is used to receive satellite signals to obtain information such as position, speed, and time), an RF receiver (RF is radio frequency, and the RF receiver is used to receive radio frequency signals, which can be used for short-distance communication and positioning assistance in the UAV control scenario), and a vision positioning sensor (which determines the position by identifying the visual features of the surrounding environment). After the construction is completed, the multi-source signal positioning link starts to work. It will obtain a multi-source positioning signal stream, which includes a GNSS signal stream, an RF signal stream, and an image signal stream. Next, fusion processing is performed on these different types of signal streams. During the fusion processing, first analyze the characteristics and reliability of each signal stream. For example, the GNSS signal has high accuracy but may be limited in an occluded environment, the RF signal is greatly affected by environmental interference but can provide supplementary information at close range, and the vision positioning sensor can play a unique role in a complex visual environment. Then, comprehensively analyze and process these signals, remove noise and redundant information, and extract key position features. Finally, through the fusion processing, the estimated position of the UAV and the estimated position of the pilot are determined, providing accurate position data support for subsequent UAV control and interfering with the opponent's positioning and other operations.

[0021] Step S200: Set up a dummy pilot device. The dummy pilot device modulates and emits a multi-source spoofing signal according to the estimated position of the pilot, and the multi-source spoofing signal is used to interfere with the pilot's position.

[0022] Specifically, first, an inflatable dummy pilot model is made according to the body size data of the target pilot. To simulate the physiological characteristics of the pilot, heating elements are installed on the model. Inside the model, a multi-source signal transmitter including a GNSS signal transmitter, an RF signal transmitter, and a visual deception signal transmitter is installed. At the same time, the drone remote control device is arranged on the model so that the target pilot can remotely communicate and control the drone through it. After the above components are installed, the inflatable dummy pilot model, the heating elements, the multi-source signal transmitter, the drone remote control device, and the power supply system are integrated and built to obtain the dummy pilot device. When the estimated position of the pilot is obtained, the dummy pilot device maps this position to its own target coordinate system to obtain the target pilot position information. According to the pre-set signal deception target effect, the deception signal modulation parameters are set, and the target pilot position information is modulated to generate multi-source deception signals such as GNSS deception signals, RF deception signals, and visual deception signals. Once the positioning behavior of the opponent is detected, the multi-source signal transmitter in the dummy pilot device emits these multi-source deception signals to interfere with the opponent's positioning of the real position of the pilot and ensure the security of the pilot position information.

[0023] Step S300: Construct a distributed control architecture. The distributed control architecture includes N transmission control nodes, and N node data transmission strategies are adaptively obtained based on the real-time network conditions and interference conditions of the N transmission control nodes.

[0024] Specifically, first, according to the distribution area, terrain characteristics, and communication coverage density requirements of the drone communication area, the number N of required transmission control nodes is accurately calculated and determined. Subsequently, based on this number N, a detailed node deployment analysis of the drone communication area is carried out to determine the specific positions of the N transmission control nodes, and communication parameter configuration and network topology identification are respectively carried out on each node to successfully construct a distributed control architecture. To obtain the N node data transmission strategies, first, data transmission rules including bandwidth allocation, transmission rate adjustment, routing selection, communication frequency band switching, and retransmission mechanism are set. Based on these rules, historical communication data is mined and transmission strategies are optimized to construct a communication data transmission strategy space. Finally, the real-time network conditions and interference conditions of the N transmission control nodes are used as constraint parameters, and strategy adaptive matching is carried out within the strategy space to obtain the N node data transmission strategies applicable to each transmission control node, ensuring efficient and stable data transmission in different network environments.

[0025] Step S400: Interact the estimated position of the drone to obtain the remote control instruction of the drone pilot.

[0026] Specifically, after obtaining the estimated position of the UAV through the pre - processing of multi - source signal positioning link fusion, interactive operations are carried out. The pilot of the UAV interacts with the estimated position data of the UAV fed back by the positioning system through the operation interface. For example, on the operation interface, the pilot can intuitively see the approximate position information of the UAV, and at the same time, this interface is also connected to the control system of the UAV. According to the mission requirements and the actual position of the UAV, the pilot operates on the operation interface, and these operation information will be recognized and converted into remote control instructions for the UAV pilot, such as instructions to control the UAV to take off, land, change flight direction and altitude, etc. These instructions are processed such as being packed and encoded to meet the requirements of communication transmission, preparing for subsequent transmission and control of the UAV through the encrypted anti - interference communication link.

[0027] Step S500: Create an encrypted anti - interference communication link, and forward the remote control instructions of the UAV pilot through the encrypted anti - interference communication link using the N - node data transmission strategy and remotely control the UAV.

[0028] Specifically, according to the data communication security objectives, a quantum key distribution channel and an encryption algorithm channel are configured. These two channels undertake different but complementary security functions. The quantum key distribution channel uses the principles of quantum mechanics to achieve secure key distribution, providing a basis for communication encryption; the encryption algorithm channel uses encryption algorithms to encrypt data. Then, these two channels are set in parallel in the link to create an encrypted anti - interference communication link. At this time, the obtained remote control instructions of the UAV pilot and the N - node data transmission strategy come into play, and the control instructions are transmitted through the encrypted anti - interference communication link. During the transmission process, according to the N - node data transmission strategy, these instructions are sequentially forwarded among N transmission control nodes. Each node optimizes the instructions according to its own strategies, such as bandwidth allocation, transmission rate adjustment and other rules, and then forwards them to the next node. Finally, after a series of node forwarding, the control instructions are accurately transmitted to the UAV, realizing the remote control of the UAV and ensuring that the UAV stably and safely executes the control instructions of the pilot in a complex communication environment.

[0029] In a possible implementation manner, step S100 further includes:

[0030] Step S110: Collect and obtain a multi - source positioning signal stream through the multi - source signal positioning link, and the multi - source positioning signal stream includes a GNSS signal stream, an RF signal stream and an image signal stream.

[0031] Step S120: Analyze the noise characteristics of the multi - source positioning signal stream to obtain the noise characteristics of the multi - source positioning data, and determine the denoising threshold of the multi - source positioning data according to the noise characteristics of the multi - source positioning data.

[0032] Step S130: Identify and filter preprocess the noise data of the multi-source positioning signal stream according to the multi-source positioning data denoising threshold, so as to obtain a standard multi-source positioning signal stream.

[0033] Step S140: Extract and fuse features and estimate positions of the standard multi-source positioning signal to determine the estimated position of the UAV and the estimated position of the operator.

[0034] Specifically, the multi-source signal positioning link starts data acquisition work. The GNSS receiver, RF receiver, and visual positioning sensor equipped in the link each perform their own functions and start the signal acquisition process simultaneously. The GNSS receiver receives the signals transmitted by satellites, analyzes and forms a GNSS signal stream from them. These signals carry position information based on the global satellite positioning system and provide basic geographic coordinate data for determining the positions of the UAV and the operator. The RF receiver is responsible for collecting radio frequency signals of specific frequencies and then generating an RF signal stream. Relying on the characteristics of radio frequency signals propagating in space, it can achieve relatively accurate positioning and communication assistance within a certain range. The visual positioning sensor uses the principle of optical imaging to take real-time pictures of the surrounding environment and convert the taken images into an image signal stream. The image signal stream contains rich environmental visual features, such as landmarks, buildings, etc., which helps to further accurately locate. These three signal streams obtain positioning information from different dimensions and ways and jointly constitute the multi-source positioning signal stream, providing a comprehensive and diverse data source for accurately determining the positions of the UAV and the operator subsequently.

[0035] For the multi-source positioning signal stream, use a spectrum analysis tool to perform spectrum analysis on the GNSS signal stream, RF signal stream, and image signal stream respectively to obtain the energy distribution of each signal stream in different frequency bands, so as to identify the frequency characteristics of the noise. By statistically analyzing the fluctuation of the signal amplitude over a period of time, study the amplitude characteristics of the noise, and then summarize the noise characteristics of the multi-source positioning data. For example, if it is found that there is strong interference noise in a specific frequency range of the GNSS signal and its amplitude shows periodic changes, this characteristic can be recorded. When determining the denoising threshold, based on the signal-to-noise ratio requirement and noise distribution of the signal, use an adaptive threshold algorithm for calculation. This algorithm dynamically adjusts the threshold according to the local characteristics of the signal. For example, for areas with large fluctuations in noise amplitude, appropriately increase the threshold to effectively remove the noise; for areas where the signal is relatively stable, lower the threshold to avoid misdeleting useful signals, and finally determine the denoising threshold applicable to the multi-source positioning data.

[0036] Noise data identification and filtering preprocessing are carried out on the multi-source positioning signal stream including GNSS signal stream, RF signal stream and image signal stream. For the GNSS signal stream, by using the method of threshold comparison, the signal amplitude is compared with the denoising threshold, and the part of the amplitude exceeding the threshold is determined as noise data; for the RF signal stream, noise is also identified based on the threshold, and band-pass filtering technology is used to filter out the signal components within the noise frequency range; when processing the image signal stream, the gray value of each pixel is compared with the threshold to identify noise pixels, and algorithms such as median filtering are used to process the noise pixels and replace them with reasonable values of surrounding pixels. By separately carrying out noise data identification and filtering processing on these three signal streams, interference information is removed, the signal quality is improved, and finally a standard multi-source positioning signal stream is integrated, providing a stable and reliable signal basis for determining the positions of the drone and the operator later.

[0037] For the obtained standard multi-source positioning signal, according to the respective characteristics and advantages of the GNSS signal, RF signal and image signal, feature extraction is carried out separately. Basic positioning features such as longitude, latitude and speed are extracted from the GNSS signal; features related to distance and direction such as signal strength and angle of arrival are obtained from the RF signal; environmental features such as landmarks and feature points are extracted from the image signal by using image recognition algorithms. Then, the weighted fusion algorithm is used to assign weights to different types of features according to the credibility and accuracy of the signal sources, and the extracted multi-source features are fused to form a comprehensive positioning feature set. Finally, with the help of the position estimation model based on Kalman filtering, combined with the previously obtained comprehensive positioning feature set, through continuous iterative calculation, considering the dynamic changes of the signal and the influence of noise, the estimated positions of the drone and the operator are finally accurately determined, providing accurate position data support for the subsequent remote control of the drone.

[0038] In a possible implementation manner, step S140 further includes:

[0039] Step S141: Determine the multi-source signal feature extraction method and the multi-source signal position feature type according to the data characteristic information of the standard multi-source positioning signal.

[0040] Step S142: Perform associated feature extraction on the standard multi-source positioning signal according to the multi-source signal feature extraction method and the multi-source signal position feature type to obtain a multi-source signal associated feature set.

[0041] Step S143: Set the dynamic allocation rule of the signal source weight according to the signal source credibility and the signal source accuracy.

[0042] Step S144: Based on the dynamic allocation rule of the signal source weight, perform fusion processing and position estimation on the multi-source signal associated feature set to determine the estimated positions of the drone and the operator.

[0043] Specifically, for the standard multi-source positioning signals obtained after denoising preprocessing, their data characteristic information is deeply analyzed. GNSS signals are characterized by providing high-precision absolute positioning data globally. Based on this, a satellite positioning solution algorithm is used as its feature extraction method, and the type of multi-source signal position feature extracted is accurate longitude and latitude coordinates, so as to obtain the approximate positions of the UAV and the operator in the global coordinate system. RF signals have advantages in short-range positioning and signal propagation characteristics. Data such as signal strength, arrival angle, and propagation time can reflect the relative position relationship with surrounding signal sources. Therefore, signal strength detection, angle measurement, and time difference calculation algorithms are used as feature extraction methods, and the corresponding position feature types are relative distance, azimuth angle, etc. Image signals contain rich environmental visual information. Feature extraction is carried out through algorithms such as feature point detection and template matching in image recognition technology. For example, unique landmarks and significant feature points in the image are extracted, which constitute the position feature types for environmental matching and precise positioning. By synthesizing the data characteristics of these different signals, the appropriate multi-source signal feature extraction methods and multi-source signal position feature types are respectively determined, laying a foundation for accurately extracting position-related feature information subsequently.

[0044] Associate feature extraction is performed on the standard multi-source positioning signals. For GNSS signals, accurate longitude and latitude coordinates are obtained as position features through a positioning solution algorithm; for RF signals, information such as signal strength and time difference of arrival is analyzed to obtain the relative position relationship features with surrounding signal sources; when processing image signals, representative landmarks and feature points are found using a feature point detection algorithm to form environmental matching features. The features extracted from different signal sources are associated and integrated to obtain a multi-source signal association feature set, which contains rich position-related information.

[0045] Since there are differences in the credibility and accuracy of different signal sources, in order to more accurately fuse these features, according to the signal source credibility and signal source accuracy, a dynamic allocation rule for signal source weights is set. GNSS signals have high accuracy and strong stability, and are given a higher weight; RF signals have advantages in short-range positioning but are greatly affected by the environment, and their weights are dynamically adjusted according to the actual environmental conditions; image signals rely on environmental features and their credibility increases in an environment with rich features, and the weight is correspondingly increased. Through this dynamic allocation rule, it is ensured that the roles of each signal source can be reasonably allocated in different environments.

[0046] Fuse the multi-source signal correlation feature set. Calculate the features of different signal sources by weighting according to the weights, so that the signal features with high precision and strong credibility play a more important role in the fusion result. After the fusion process, use the position estimation model for position estimation. This model combines the fused features, considers the dynamic changes of the signals and the influence of environmental factors, and finally determines the estimated position of the UAV and the estimated position of the pilot, providing key position data support for the remote control of the UAV and ensuring the accuracy of UAV control.

[0047] In a possible implementation manner, step S144 further includes:

[0048] Step S1441: Perform weight distribution calculation on the multi-source signal correlation feature set based on the signal source weight dynamic distribution rule to obtain signal source weight factor information.

[0049] Step S1442: Perform weighted fusion processing on the multi-source signal correlation feature set according to the signal source weight factor information to obtain a positioning signal fusion feature set.

[0050] Step S1443: Classify and identify the positioning signal fusion feature set to obtain a UAV positioning fusion feature set and a pilot positioning fusion feature set.

[0051] Step S1444: Initialize the Kalman filter, and use the Kalman filter to perform iterative position estimation on the UAV positioning fusion feature set and the pilot positioning fusion feature set to determine the estimated position of the UAV and the estimated position of the pilot.

[0052] Specifically, according to the previously set signal source weight dynamic distribution rule, perform detailed weight distribution calculation on the multi-source signal correlation feature set. This rule is formulated according to the credibility and accuracy of GNSS, RF, and image signal sources. For example, due to its high positioning accuracy and global coverage, the GNSS signal is given a higher basic weight; the RF signal has advantages in short-range positioning and communication, and its weight will be dynamically adjusted according to factors such as signal interference degree and transmission stability in the specific environment; the image signal has high positioning accuracy in specific scenarios with rich environmental features, and its weight will also be dynamically set according to the richness and distinguishability of environmental features in the scenario. Distribute these weights to each feature in the multi-source signal correlation feature set, so as to obtain signal source weight factor information, which accurately quantifies the importance of each signal source feature in the subsequent fusion calculation.

[0053] According to the obtained signal source weight factor information, perform weighted fusion processing on the multi-source signal correlation feature set. Multiply the features corresponding to the GNSS signal, such as longitude and latitude coordinates, by their respective weight factors; for features such as the signal strength and relative distance of the RF signal, as well as environmental matching features such as landmarks and feature points of the image signal, also multiply them by their respective corresponding weight factors. Then sum up these weighted features, so that the dominant features of different signal sources are reasonably reflected in the fusion process, and finally obtain the positioning signal fusion feature set. This fusion feature set combines the advantages of multiple signals and more comprehensively and accurately reflects the position-related information of the drone and the pilot.

[0054] In order to distinguish and separately process the position information of the drone and the pilot, classify and label the positioning signal fusion feature set. According to the correlation between the signal features and the drone and the pilot, screen out the features in the positioning signal fusion feature set that are closely related to the drone's position to form the drone positioning fusion feature set; similarly, classify the features related to the pilot's position into the pilot positioning fusion feature set. For example, features related to the flight attitude of the drone are classified into the drone positioning fusion feature set, while features related to the pilot's operation position are included in the pilot positioning fusion feature set. Such classification and labeling facilitate subsequent precise positioning.

[0055] In order to accurately determine the positions of the drone and the pilot, first initialize the Kalman filter. During the initialization process, set the initial state of the filter, including the initial estimated values of state variables such as the positions and speeds of the drone and the pilot, and at the same time determine the covariance matrix to measure the uncertainty of the estimated values. Then, sequentially input the previously obtained drone positioning fusion feature set and the pilot positioning fusion feature set into the Kalman filter. The filter uses its own prediction and update mechanisms to iteratively process the input feature set. In the prediction stage, predict the current state based on the estimated state at the previous moment and the system model; in the update stage, combine the currently measured positioning fusion feature set to correct the prediction result. Through continuous iteration, the Kalman filter can effectively integrate the position information brought by multi-source signals and at the same time suppress the noise in the measurement process. As the number of iterations increases, the estimation result becomes more and more accurate, and finally the high-precision estimated positions of the drone and the pilot are determined, providing key data support for realizing precise remote control of the drone.

[0056] In a possible implementation manner, step S200 further includes:

[0057] Step S210: Simulate and produce an inflatable dummy pilot model according to the body size data of the target pilot, and install heating elements on the inflatable dummy pilot model to simulate the physiological characteristics of the pilot through the heating elements.

[0058] Step S220: Install a multi-source signal transmitter inside the inflatable dummy pilot model. The multi-source signal transmitter includes a GNSS signal transmitter, an RF signal transmitter, and a visual deception signal transmitter.

[0059] Step S230: Deploy the drone remote control device on the inflatable dummy pilot model. The target pilot uses the drone remote control device to remotely communicate and control the drone.

[0060] Step S240: Integrate and build the inflatable dummy pilot model, the heating element, the multi-source signal transmitter, the drone remote control device, and the power supply system to obtain the dummy pilot device.

[0061] Specifically, first collect accurate body size data of the target pilot. Key information such as height, weight, limb proportions, and body shape contours is within the scope of collection. Based on these data, select suitable inflatable materials and simulate and produce the inflatable dummy pilot model to maximize the restoration of the target pilot's body posture in terms of appearance. To make the dummy pilot model more realistic and conform to the physiological characteristics of a real pilot, install a heating element on the model. Through the circuit design of the heating element and the setting of the temperature control system, simulate the normal heat dissipation of the pilot's body and the heat dissipation changes of the human body at different ambient temperatures, so that the dummy pilot model has deception not only in appearance but also in physiological heat characteristics, thus interfering with the opponent's detection of the real position of the pilot using thermal sensing and other means.

[0062] After the inflatable dummy pilot model is made and the heating element is installed, start the key installation work of the multi-source signal transmitter inside the model. To effectively interfere with the position of the pilot, the multi-source signal transmitter covers a GNSS signal transmitter, an RF signal transmitter, and a visual deception signal transmitter. Place the GNSS signal transmitter at a suitable position inside the model to ensure its stable operation. This transmitter will emit false GNSS signals that mimic the characteristics of real satellite positioning signals, causing the enemy's GNSS positioning equipment to receive incorrect position information, thereby misleading the opponent's judgment of the pilot's position. At the same time, the RF signal transmitter is also accurately installed. It will emit forged RF signals and use the characteristics of RF signals propagating in space to interfere with the enemy's positioning and monitoring systems based on RF signals. In addition, the visual deception signal transmitter is also installed inside the model. Its function is to emit specific optical signals or simulate image signals of real scenes to interfere with the enemy's visual positioning and monitoring equipment, making it difficult for the enemy to determine the real position of the pilot at the visual level. By reasonably installing these three different types of signal transmitters inside the inflatable dummy pilot model, an all-round signal interference system is constructed, laying a solid foundation for subsequent interference with the opponent's positioning of the pilot's position.

[0063] After the inflatable dummy flyer model has been installed with multi-source signal transmitters, the UAV remote control device is deployed on the inflatable dummy flyer model. The selection of this position should not only ensure that the remote control device can be firmly attached to the model, but also ensure the convenience and comfort of the target flyer during operation. The target flyer stands beside the inflatable dummy flyer model and sends various control commands to the UAV by operating the remote control device, such as takeoff, landing, changing flight direction and altitude commands. These commands are transmitted in a specific wireless communication manner, which is the same as the communication mechanism for a flyer to control a UAV under normal circumstances, realizing remote communication control of the UAV. At the same time, it cooperates with other parts of the dummy flyer device to enhance the overall deception and interfere with the opponent's judgment of the flyer's true position.

[0064] Integrate and build the inflatable dummy flyer model, heating element, multi-source signal transmitter, UAV remote control device and power supply system. First, take the inflatable dummy flyer model installed with the heating element as the basic carrier, and fix the components with GNSS signal transmitter, RF signal transmitter and visual deception signal transmitter installed inside to ensure its stability inside the model and that its working performance will not be affected by factors such as vibration. Then, install the UAV remote control device at a position on the inflatable dummy flyer model that is convenient for the target flyer to operate, connect the relevant lines to ensure the stability of signal transmission. Subsequently, connect to the power supply system and route the wires, connecting the power supply lines to the heating element, multi-source signal transmitter and UAV remote control device respectively to provide stable power supply for these devices so that they can operate normally. During the integration and building process, it is also necessary to debug and calibrate each component to ensure that all parts work together. Finally, complete the integration and building, and successfully set up the dummy flyer device, which can play a key role in the subsequent task of interfering with the flyer's position positioning.

[0065] In a possible implementation manner, step S200 further includes:

[0066] Step S250: Obtain the target coordinate system of the dummy flyer device, map and convert the estimated flyer position to the target coordinate system to obtain the target flyer position information.

[0067] Step S260: Set the deception signal modulation parameters according to the preset target effect of signal deception.

[0068] Step S270: Modulate the target flyer position information based on the deception signal modulation parameters to generate multi-source deception signals, where the multi-source deception signals include GNSS deception signals, RF deception signals and visual deception signals.

[0069] Step S280: When a positioning behavior of the opponent is detected, emit the multi-source deception signals through the multi-source signal transmitter in the dummy flyer device.

[0070] Specifically, the target coordinate system of the dummy drone pilot device is defined. This coordinate system is the spatial reference for subsequent signal processing and is set based on the installation environment and interference strategy of the dummy drone pilot device. After determining the target coordinate system, the estimated position of the drone pilot obtained through multi-source signal fusion before is mapped and transformed from the original coordinate system to the target coordinate system using a coordinate transformation algorithm, thereby obtaining the target drone pilot position information. This information is the key basic data for generating spoofing signals subsequently.

[0071] To achieve the purpose of interfering with the opponent's positioning system, the spoofing signal modulation parameters are set specifically according to the pre-set spoofing target effect of the signal. If it is desired to cause a deviation in a specific direction and distance for the opponent's GNSS positioning device, the structure and characteristics of the GNSS signal are studied in depth. By adjusting parameters such as the carrier frequency, code phase, and signal strength of the signal, the difference between the spoofing signal and the real signal is changed, thereby misleading the opponent's positioning calculation. For RF spoofing signals, according to the operating frequency band and sensitivity of the opponent's RF positioning device, parameters such as the transmission frequency, power, and modulation method of the spoofing signal are set, so that the opponent's RF receiver receives false signals and misjudges the position of the drone pilot. For the setting of modulation parameters of visual spoofing signals, it is necessary to combine the principle of object recognition in the visual positioning system. For example, parameters such as the brightness, contrast, and edge features of the image are adjusted to generate an image that is similar to the real scene but has incorrect position information, interfering with the opponent's visual positioning device's recognition of the position of the drone pilot, thereby interfering with different types of positioning means of the opponent in all aspects and achieving the expected spoofing effect.

[0072] To generate multi-source spoofing signals including GNSS spoofing signals, RF spoofing signals, and visual spoofing signals, a variety of specific means are adopted. For GNSS spoofing signals, a GNSS signal generation device is used to simulate the characteristics of the carrier wave, pseudo-code, etc. of the real satellite signal according to the spoofing signal modulation parameters. By adjusting the frequency, phase, and amplitude of the signal, the generated signal is made to be consistent with the real GNSS signal in format but carry false position information, such as modifying the longitude and latitude data in the signal, to mislead the opponent's GNSS positioning device. For RF spoofing signals, a radio frequency signal generator is used to adjust the transmission frequency, power, and modulation method according to the modulation parameters. Using direct digital frequency synthesis technology, a spoofing signal that precisely matches the target frequency band is generated and transmitted within the receiving range of the opponent's RF positioning device, interfering with its judgment of the position of the drone pilot and making it receive false information such as signal strength and angle of arrival. In terms of generating visual spoofing signals, according to the modulation parameters, the image containing the target drone pilot position is processed. By changing attributes such as the brightness, contrast, and color of the image, as well as adding or modifying elements such as feature points and landmarks in the image, an image signal that is similar to the real scene but has incorrect drone pilot position information is simulated, interfering with the opponent's visual positioning system.

[0073] The dummy pilot device is always in a monitoring state. The equipped monitoring system continuously scans the surrounding environment and analyzes various electromagnetic signals using signal detection technology. Once the monitoring system identifies a signal that conforms to the opponent's positioning behavior characteristics, it immediately triggers the dummy pilot device. The multi-source signal transmitter integrated inside the dummy pilot device is a key component for implementing interference. This transmitter includes a GNSS signal transmitter, an RF signal transmitter, and a visual deception signal transmitter. When triggered, the GNSS signal transmitter emits GNSS spoofing signals that are extremely similar to real GNSS signals but carry false location information according to pre-set spoofing parameters, interfering with the opponent's satellite positioning system. The RF signal transmitter emits RF spoofing signals at a specific frequency and power, disrupting the opponent's positioning device based on RF signals. At the same time, the visual deception signal transmitter emits pre-generated visual deception signals, interfering with the opponent's visual positioning means. These multi-source spoofing signals act simultaneously from different dimensions, causing the opponent's positioning device to receive incorrect information, thereby misleading the opponent's judgment of the pilot's position and effectively protecting the true position of the pilot from being exposed.

[0074] In a possible implementation manner, step S300 further includes:

[0075] Step S310: Determine the number of transmission control nodes N according to the distribution area and terrain distribution of the UAV communication area, as well as the communication coverage density requirement.

[0076] Step S320: Perform node deployment analysis on the UAV communication area based on the number of transmission control nodes N to obtain N transmission control nodes.

[0077] Step S330: Configure communication parameters and network topology identification on the N transmission control nodes respectively to construct the distributed control architecture.

[0078] Specifically, when constructing a distributed control architecture for UAV remote control, the precise distribution area and detailed terrain data of the UAV communication area are obtained with the help of Geographic Information System (GIS) technology, including the location and height information of geographical elements such as mountains, buildings, and water areas. At the same time, combined with the communication coverage density requirements, the signal strength, data transmission rate, and signal stability standards required for each area are determined. For areas with a large distribution area, the required node base is initially estimated. Then, according to the terrain distribution, if there are areas where signals are easily blocked, such as mountainous areas, signal propagation models, such as ray-tracing-based models, are used to analyze the attenuation and occlusion of signals under different terrains, and the number of nodes is appropriately increased in areas with severe signal attenuation or easy occlusion. For the communication coverage density requirements, by calculating factors such as the number of users and the frequency of UAV flight missions in each area, the communication load of the area is determined. According to the communication load, using the genetic algorithm, on the premise of meeting the communication quality requirements, the distribution of transmission control nodes is optimized, and finally the number N of transmission control nodes is determined.

[0079] Analyze the geographical characteristics of the communication area, including terrain features, building distribution, etc. If there are complex terrains such as mountains and canyons in the communication area, which will have effects such as blocking and reflection on signal propagation, then transmission control nodes need to be reasonably arranged near these terrains to ensure that the signal can effectively cover the surrounding area; if there is a dense building area where the signal is easily blocked and interfered, the node density needs to be appropriately increased in this area to ensure signal stability. At the same time, the usage scenarios and user distribution in the communication area also need to be considered. For example, in crowded activity places, the demand for UAV communication is relatively large. In order to meet the requirements of many users using UAVs for communication at the same time, transmission control nodes need to be centrally deployed in these areas to improve the strength and quality of signal coverage; while in remote areas with few people, the number of node deployments can be appropriately reduced to reduce construction costs on the premise of meeting basic communication needs. Finally, the accurate deployment positions of N transmission control nodes are obtained, thus effectively building a distributed control architecture and providing stable and efficient support for subsequent UAV communication.

[0080] For the N transmission control nodes with determined positions, communication parameter configuration and network topology identification are carried out one by one to construct a distributed control architecture. First is the communication parameter configuration. According to the communication requirements of the unmanned aerial vehicle (UAV) and the environment where each node is located, parameters such as transmission power, communication frequency, and bandwidth are set. For example, for nodes in areas with strong signal interference, the transmission power is appropriately increased to ensure stable signal transmission; according to the service requirements and frequency band resources of different nodes, the communication frequency is reasonably allocated to avoid frequency band conflicts. At the same time, an appropriate bandwidth is set to ensure the efficiency of data transmission and meet the timely transmission of UAV remote control commands. After completing the communication parameter configuration, network topology identification is carried out for the N transmission control nodes. This includes assigning a unique network address to each node and clarifying the connection relationship and data transmission path between nodes. Through network topology identification, each node can clearly know the other nodes connected to it, as well as the transmission direction and priority of data in the network. For example, a tree-shaped, mesh-shaped or hybrid network topology structure is adopted to orderly connect each node, making the entire distributed control architecture form an organic whole. Through such communication parameter configuration and network topology identification, a stable and efficient distributed control architecture is finally constructed, providing strong support for UAV remote control.

[0081] In a possible implementation manner, step S300 further includes:

[0082] Step S340: Set data transmission rules, where the data transmission rules include bandwidth allocation, transmission rate adjustment, routing selection, communication frequency band switching, and retransmission mechanism.

[0083] Step S350: Based on the data transmission rules, conduct historical communication data mining and transmission strategy optimization, and construct a communication data transmission strategy space.

[0084] Step S360: Take the real-time network conditions and interference situations of the N transmission control nodes as constraint parameters, and perform strategy adaptive matching within the communication data transmission strategy space to obtain the data transmission strategies of the N nodes.

[0085] Specifically, according to the requirements of drone remote control for data transmission, detailed data transmission rules are formulated. In terms of bandwidth allocation, based on the data volume requirements of different tasks, the available bandwidth resources of each transmission control node are divided to ensure that important instructions and key data can obtain sufficient bandwidth for transmission first. The transmission rate adjustment changes the data transmission rate dynamically according to the network load and signal quality, increasing the rate when the network condition is good to speed up data transmission, and decreasing the rate when there is more interference or network congestion to ensure the accuracy of data transmission. In terms of route selection, considering the connection quality, distance between nodes and the priority of data transmission comprehensively, the optimal data transmission path is selected to avoid delays or losses of data during transmission. The communication frequency band switching is used to switch between different frequency bands. When a certain frequency band is interfered, it quickly switches to other available frequency bands to maintain stable communication. The retransmission mechanism stipulates how to retransmit data when data transmission fails, including the number of retransmissions, the interval time, etc., to ensure the reliable transmission of data.

[0086] Collect and integrate historical communication data, and store it classified according to different dimensions such as network conditions (such as network latency, packet loss rate), interference conditions (interference signal strength, frequency), and data transmission rules (bandwidth allocation, transmission rate adjustment, route selection, communication frequency band switching, retransmission mechanism). Then, for each combination of data transmission rules, traverse the historical data and extract the transmission performance indicators under different network and interference conditions, such as data transmission success rate, average transmission delay, throughput, etc. Using these performance indicators, evaluate each transmission strategy through a scoring mechanism. The higher the score, the better the transmission effect of the strategy under the corresponding conditions. Then, set a screening threshold to filter out those strategies with scores lower than the threshold and retain the strategies with better performance. Finally, organize and summarize these selected strategies according to different rule combinations and applicable scenarios to construct a communication data transmission strategy space, providing rich options for selecting appropriate transmission strategies according to real-time network conditions and interference situations in the future.

[0087] Real-time monitor and data collection are carried out on the real-time network conditions and interference situations of N transmission control nodes. Using network monitoring tools, such as network probes, signal strength detection devices, etc., obtain network condition data such as the network bandwidth occupancy rate, signal transmission delay, packet loss rate, etc. of each node, as well as interference situation data such as the frequency, intensity, and interference source direction of interference signals. Input these real-time data as constraint parameters into the communication data transmission strategy space. The communication data transmission strategy space is pre-constructed and contains a variety of different transmission strategies, each of which is optimized for different network conditions and interference situations. When performing strategy adaptive matching, a genetic algorithm is adopted, regarding different transmission strategies as individuals in the genetic algorithm, and screening out better strategies by calculating the matching degree (fitness) of each individual with the current real-time network conditions and interference situations. For example, for nodes with high network latency, preferentially select those strategies with data caching and asynchronous transmission mechanisms; for nodes affected by interference in a specific frequency band, select strategies that can automatically switch to other available frequency bands. After multiple rounds of selection, crossover, and mutation operations, find the transmission strategy that best suits the current situation of each node from the communication data transmission strategy space, and finally obtain the data transmission strategies of N nodes respectively to ensure that the UAV remote control instructions can be transmitted efficiently and stably through these nodes.

[0088] In a possible implementation manner, step S500 further includes:

[0089] Step S510: Configure a quantum key distribution channel and an encryption algorithm channel according to the data communication security objective.

[0090] Step S520: Set the quantum key distribution channel and the encryption algorithm channel in parallel in the link to create the encrypted anti-interference communication link.

[0091] Specifically, analyze the security requirements of the UAV remote control data communication based on the data communication security objectives. For example, ensure that the instruction transmission is not stolen or tampered with, and maintain the authenticity of the identities of both communication parties. For these requirements, select the appropriate quantum key distribution technology, such as the decoy state-based quantum key distribution scheme, which can effectively improve the security of key distribution in long-distance communication. When configuring the quantum key distribution channel, set parameters such as the transmission power and wavelength of the quantum signal to ensure that the quantum key can be transmitted stably and securely. At the same time, deploy the quantum key receiving device and calibrate and debug it so that it can accurately receive and process the quantum signal. For the encryption algorithm channel, select the appropriate encryption algorithm according to the sensitivity of the data and the requirements of processing efficiency. If high-efficiency symmetric encryption is pursued, the AES algorithm is adopted; if the characteristics of asymmetric encryption are required, the RSA algorithm is an optional solution. During the process of configuring the encryption algorithm channel, set parameters such as the length of the encryption key and the encryption mode to balance the encryption strength and the consumption of computing resources. For example, for the critical UAV control instructions, select a longer key length to enhance the encryption security; while for some general status feedback data, select a shorter key length to improve the encryption and decryption speed on the premise of ensuring security. By completing the configuration of the quantum key distribution channel and the encryption algorithm channel in such a rigorous manner, it lays the foundation for building an encrypted anti-jamming communication link subsequently.

[0092] Perform link parallel setting on the configured quantum key distribution channel and encryption algorithm channel to create an encrypted anti-jamming communication link. At the hardware level, connect the quantum key distribution device and the encryption algorithm processing device through communication lines and interface devices. Ensure that the quantum key distribution channel and the encryption algorithm channel are physically independent and can work together at the same time. For example, use high-speed transmission media such as optical fibers to connect the quantum key distribution device and the encryption algorithm device respectively to ensure the rapid transmission of data between the two channels. At the software level, develop the corresponding control programs and protocols to enable the two channels to operate in coordination. When there is data to be transmitted, the quantum key distribution channel first generates and distributes the encryption key. These keys are transmitted to the receiving end through a secure quantum channel, and the encryption algorithm channel at the receiving end uses the received keys to encrypt the data. The encryption algorithm channel at the sending end encrypts the data according to the same keys and then transmits the encrypted data through the communication link. At the receiving end, the quantum key distribution channel first receives the keys, and then the encryption algorithm channel uses the keys to decrypt the received encrypted data. Through this link parallel setting method combining hardware and software, give full play to the high security of the quantum key distribution channel and the high-efficiency encryption characteristics of the encryption algorithm channel, effectively resist external interference and attacks, and thus create an encrypted anti-jamming communication link that meets the requirements of UAV remote control, ensuring the security, accuracy and integrity of the UAV pilot's remote control instructions during transmission.

[0093] Embodiment 2. Based on the same inventive concept as the method for remotely controlling a distributed unmanned aerial vehicle by fusing multi-source signals in the foregoing embodiment, as Figure 2 shown, the present application provides a system for remotely controlling a distributed unmanned aerial vehicle by fusing multi-source signals. The system in the embodiments of the present application and the method embodiments are based on the same inventive concept. Among them, the system includes:

[0094] A multi-source signal positioning link building module 10 for building a multi-source signal positioning link. The multi-source signal positioning link is configured with a GNSS receiver, an RF receiver, and a visual positioning sensor, and performs fusion processing on the multi-source positioning signal stream obtained by the multi-source signal positioning link to determine the estimated position of the unmanned aerial vehicle and the estimated position of the pilot.

[0095] A dummy pilot device setting module 20 for setting a dummy pilot device. The dummy pilot device modulates and emits a multi-source spoofing signal according to the estimated position of the pilot, and the multi-source spoofing signal is used for interfering with the position of the pilot.

[0096] A data transmission strategy acquisition module 30 for constructing a distributed control architecture. The distributed control architecture includes N transmission control nodes, and adaptively obtains N node data transmission strategies based on the real-time network conditions and interference conditions of the N transmission control nodes.

[0097] A pilot remote control instruction acquisition module 40 for interacting with the estimated position of the unmanned aerial vehicle to obtain a pilot remote control instruction for the unmanned aerial vehicle.

[0098] An unmanned aerial vehicle remote control module 50 for creating an encrypted anti-jamming communication link, and performing node forwarding processing and remote control of the unmanned aerial vehicle on the pilot remote control instruction for the unmanned aerial vehicle by using the N node data transmission strategies through the encrypted anti-jamming communication link.

[0099] Furthermore, the system is also used to implement the following functions:

[0100] Collect and obtain a multi-source positioning signal stream through the multi-source signal positioning link. The multi-source positioning signal stream includes a GNSS signal stream, an RF signal stream, and an image signal stream; perform noise characteristic analysis on the multi-source positioning signal stream to obtain the noise characteristics of the multi-source positioning data, and determine the denoising threshold of the multi-source positioning data according to the noise characteristics of the multi-source positioning data; perform noise data recognition and filtering preprocessing on the multi-source positioning signal stream according to the denoising threshold of the multi-source positioning data to obtain a standard multi-source positioning signal stream; perform feature extraction fusion and position estimation on the standard multi-source positioning signal to determine the estimated position of the unmanned aerial vehicle and the estimated position of the pilot.

[0101] Furthermore, the system is also used to implement the following functions:

[0102] Determine the multi-source signal feature extraction method and the multi-source signal position feature type according to the data characteristic information of the standard multi-source positioning signal; perform associated feature extraction on the standard multi-source positioning signal according to the multi-source signal feature extraction method and the multi-source signal position feature type to obtain a multi-source signal associated feature set; set a dynamic signal source weight allocation rule according to the signal source credibility and the signal source accuracy; perform fusion processing and position estimation on the multi-source signal associated feature set based on the dynamic signal source weight allocation rule to determine the estimated position of the unmanned aerial vehicle and the estimated position of the drone operator.

[0103] Further, the system is also used to implement the following functions:

[0104] Perform a weight allocation calculation on the multi-source signal associated feature set based on the dynamic signal source weight allocation rule to obtain signal source weight factor information; perform weighted fusion processing on the multi-source signal associated feature set according to the signal source weight factor information to obtain a positioning signal fusion feature set; perform classification and identification on the positioning signal fusion feature set to obtain an unmanned aerial vehicle positioning fusion feature set and a drone operator positioning fusion feature set; initialize a Kalman filter, and use the Kalman filter to perform iterative position estimation on the unmanned aerial vehicle positioning fusion feature set and the drone operator positioning fusion feature set to determine the estimated position of the unmanned aerial vehicle and the estimated position of the drone operator.

[0105] Further, the system is also used to implement the following functions:

[0106] Simulate and produce an inflatable dummy drone operator model according to the body size data of the target drone operator, install heating elements on the inflatable dummy drone operator model, and simulate the physiological characteristics of the drone operator through the heating elements; install a multi-source signal transmitter inside the inflatable dummy drone operator model, and the multi-source signal transmitter includes a GNSS signal transmitter, an RF signal transmitter, and a visual deception signal transmitter; deploy the unmanned aerial vehicle remote control device on the inflatable dummy drone operator model, and the target drone operator remotely communicates and controls the unmanned aerial vehicle through the unmanned aerial vehicle remote control device; integrate and build the inflatable dummy drone operator model, the heating elements, the multi-source signal transmitter, the unmanned aerial vehicle remote control device, and the power supply system, and set to obtain the dummy drone operator device.

[0107] Further, the system is also used to implement the following functions:

[0108] Obtain the target coordinate system of the dummy drone pilot device, map and convert the estimated position of the drone pilot to the target coordinate system to obtain the target drone pilot position information; set the spoofing signal modulation parameters according to the preset target effect of signal spoofing; perform spoofing signal modulation on the target drone pilot position information based on the spoofing signal modulation parameters to generate multi-source spoofing signals, where the multi-source spoofing signals include GNSS spoofing signals, RF spoofing signals, and visual spoofing signals; when a competitor's positioning behavior is detected, emit the multi-source spoofing signals through the multi-source signal transmitter in the dummy drone pilot device.

[0109] Further, the system is also used to implement the following functions:

[0110] Determine the number of transmission control nodes N according to the distribution area and terrain distribution of the UAV communication area, as well as the communication coverage density requirement; perform node deployment analysis on the UAV communication area based on the number of transmission control nodes N to obtain N transmission control nodes; perform communication parameter configuration and network topology identification on the N transmission control nodes respectively to construct the distributed control architecture.

[0111] Further, the system is also used to implement the following functions:

[0112] Set data transmission rules, where the data transmission rules include bandwidth allocation, transmission rate adjustment, routing selection, communication frequency band switching, and retransmission mechanism; perform historical communication data mining and transmission strategy optimization based on the data transmission rules to construct a communication data transmission strategy space; use the real-time network status and interference conditions of the N transmission control nodes as constraint parameters to perform strategy adaptive matching within the communication data transmission strategy space to obtain the data transmission strategies of the N nodes.

[0113] Further, the system is also used to implement the following functions:

[0114] Configure a quantum key distribution channel and an encryption algorithm channel according to the data communication security objective; set the quantum key distribution channel and the encryption algorithm channel in parallel for the link to create the encrypted anti-jamming communication link.

[0115] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0116] The above are only the preferred embodiments of the present application, and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0117] This specification and the drawings are merely exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A distributed UAV remote control method based on multi-source signal fusion, characterized in that: The method comprises: Build a multi-source signal positioning link, which is equipped with a GNSS receiver, an RF receiver and a visual positioning sensor, and perform fusion processing on the multi-source positioning signal stream obtained by the multi-source signal positioning link to determine the estimated position of the drone and the estimated position of the pilot; A dummy pilot device is provided, wherein the dummy pilot device modulates and sends a multi-source spoofing signal according to the estimated position of the pilot, and the multi-source spoofing signal is used to interfere with the position of the pilot; Constructing a distributed control architecture, the distributed control architecture comprising N transmission control nodes, and adaptively obtaining N node data transmission strategies based on real-time network conditions and interference conditions of the N transmission control nodes; Interact with the estimated position of the drone to obtain remote control instructions from the drone pilot; Creating an encrypted anti-interference communication link, and using the N-node data transmission strategy to perform node forwarding processing and remote control of the drone pilot's remote control instructions through the encrypted anti-interference communication link; Wherein, the provision of a dummy flying hand device comprises: According to the body shape data of the target pilot, an inflatable model of a dummy pilot is simulated and manufactured, and a heating sheet is installed on the inflatable model of the dummy pilot to simulate the physiological characteristics of the pilot through the heating sheet; A multi-source signal transmitter is installed inside the inflatable model of the dummy pilot, wherein the multi-source signal transmitter includes a GNSS signal transmitter, an RF signal transmitter and a visual deception signal transmitter; The drone remote control device is arranged on the inflatable model of the dummy pilot, and the target pilot performs remote communication control on the drone through the drone remote control device; The inflatable model of the dummy pilot, the heating sheet, the multi-source signal transmitter, the UAV remote control device and the power supply system are integrated and constructed to obtain the dummy pilot device; The dummy pilot device modulates and sends a multi-source deception signal according to the estimated position of the pilot, including: Acquire the target coordinate system of the dummy pilot device, transform the estimated position mapping of the pilot into the target coordinate system, and obtain the target pilot position information; According to the preset target effect of signal deception, set the modulation parameters of the deception signal; Performing spoofing signal modulation on the target pilot's position information based on the spoofing signal modulation parameters to generate a multi-source spoofing signal, wherein the multi-source spoofing signal includes a GNSS spoofing signal, an RF spoofing signal, and a visual spoofing signal; When the positioning behavior of the opponent is monitored, the multi-source deception signal is sent out through the multi-source signal transmitter in the dummy pilot device.

2. A distributed UAV remote control method based on multi-source signal fusion as claimed in claim 1, characterized in that: Determining the estimated position of the drone and the estimated position of the pilot includes: Acquire a multi-source positioning signal stream through the multi-source signal positioning link collection, wherein the multi-source positioning signal stream includes a GNSS signal stream, an RF signal stream and an image signal stream; Performing noise characteristic analysis on the multi-source positioning signal stream to obtain noise characteristics of multi-source positioning data, and determining a multi-source positioning data denoising threshold according to the noise characteristics of the multi-source positioning data; Perform noise data identification and filtering preprocessing on the multi-source positioning signal stream according to the multi-source positioning data denoising threshold to obtain a standard multi-source positioning signal stream; The standard multi-source positioning signal is subjected to feature extraction, fusion and position estimation to determine the estimated position of the UAV and the estimated position of the pilot.

3. A distributed UAV remote control method with multi-source signal fusion as claimed in claim 2, characterized in that: Determining the estimated position of the drone and the estimated position of the pilot includes: Determining a multi-source signal feature extraction method and a multi-source signal position feature type according to the data characteristic information of the standard multi-source positioning signal; Extracting correlation features from the standard multi-source positioning signal according to the multi-source signal feature extraction method and the multi-source signal position feature type to obtain a multi-source signal correlation feature set; According to the signal source credibility and signal source accuracy, set the signal source weight dynamic allocation rules; Based on the signal source weight dynamic allocation rule, the multi-source signal association feature set is fused and position estimated to determine the estimated position of the drone and the estimated position of the pilot.

4. A distributed UAV remote control method with multi-source signal fusion as claimed in claim 3, characterized in that: Determining the estimated position of the drone and the estimated position of the pilot includes: Performing weight allocation calculation on the multi-source signal association feature set based on the signal source weight dynamic allocation rule to obtain signal source weight factor information; Performing weighted fusion processing on the multi-source signal association feature set according to the signal source weight factor information to obtain a positioning signal fusion feature set; Classifying and labeling the positioning signal fusion feature set to obtain a UAV positioning fusion feature set and a pilot positioning fusion feature set; Initialize the Kalman filter, use the Kalman filter to iteratively estimate the position of the UAV positioning fusion feature set and the pilot positioning fusion feature set, and determine the estimated position of the UAV and the estimated position of the pilot.

5. The distributed UAV remote control method of multi-source signal fusion according to claim 1, characterized in that: The construction of a distributed control architecture includes: Determine the number of transmission control nodes N based on the distribution area and terrain distribution of the drone communication area, as well as the communication coverage density requirements; Performing node deployment analysis on the UAV communication area based on the number of transmission control nodes N to obtain N transmission control nodes; Communication parameter configuration and network topology identification are performed on the N transmission control nodes respectively to construct the distributed control architecture.

6. A distributed UAV remote control method with multi-source signal fusion as claimed in claim 1, characterized in that: The adjusting and obtaining the N node data transmission strategies based on the real-time network status and interference conditions of the N transmission control nodes includes: Setting data transmission rules, wherein the data transmission rules include bandwidth allocation, transmission rate adjustment, route selection, communication frequency band switching, and retransmission mechanism; Based on the data transmission rules, historical communication data mining and transmission strategy optimization are performed to construct a communication data transmission strategy space; The real-time network status and interference conditions of the N transmission control nodes are used as constraint parameters, and strategy adaptive matching is performed in the communication data transmission strategy space to obtain the data transmission strategy of the N nodes.

7. A distributed UAV remote control method based on multi-source signal fusion as claimed in claim 1, characterized in that: The step of creating an encrypted anti-interference communication link comprises: Configure quantum key distribution channels and encryption algorithm channels according to data communication security goals; The quantum key distribution channel and the encryption algorithm channel are set up in parallel to create the encrypted anti-interference communication link.

8. A distributed UAV remote control system with multi-source signal fusion, characterized in that: The system is used to implement a distributed UAV remote control method for multi-source signal fusion according to any one of claims 1 to 7, and the system comprises: A multi-source signal positioning link building module is used to build a multi-source signal positioning link. The multi-source signal positioning link is configured with a GNSS receiver, an RF receiver and a visual positioning sensor, and the multi-source positioning signal stream obtained by the multi-source signal positioning link is fused to determine the estimated position of the UAV and the estimated position of the pilot; A dummy pilot device setting module, used to set a dummy pilot device, wherein the dummy pilot device modulates and sends a multi-source deception signal according to the estimated position of the pilot, and the multi-source deception signal is used to interfere with the position of the pilot; A data transmission strategy acquisition module, used to construct a distributed control architecture, the distributed control architecture includes N transmission control nodes, and adaptively obtains N node data transmission strategies based on the real-time network status and interference conditions of the N transmission control nodes; A pilot remote control command acquisition module is used to interact with the estimated position of the drone and obtain the drone pilot remote control command; The UAV remote control module is used to create an encrypted anti-interference communication link, and use the N-node data transmission strategy to perform node forwarding processing and UAV remote control on the UAV pilot's remote control instructions through the encrypted anti-interference communication link.

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