Unmanned aerial vehicle autonomous navigation positioning method in satellite denial environment

Abstract

The invention discloses an unmanned aerial vehicle autonomous navigation and positioning method in a satellite denial environment, and relates to the technical field of unmanned aerial vehicle autonomous navigation and positioning, and the method comprises the following steps: combining a photoelectric pod, a dual-light camera, a frequency analyzer and data entry, collecting multi-dimensional unmanned aerial vehicle data, preprocessing the collected data, setting a radio frequency signal frequency threshold value, and carrying out satellite denial positioning. The method comprises the following steps: estimating the influence degree of radio frequency signal frequency on unmanned aerial vehicle data return, adopting a random forest algorithm, constructing a data return monitoring model, formulating a combined navigation route, combining inertial navigation, visual SLAM and GNSS positioning, and establishing a combined navigation positioning mode. According to the method, a photoelectric pod, a dual-light camera, a frequency analyzer, a data entry technology, a random forest algorithm, an integrated navigation technology and the like are closely combined with a modern information technology, so that the problem that the unmanned aerial vehicle is inaccurate in positioning due to the fact that a single navigation mode is easily interfered under a satellite denial condition is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of autonomous navigation and positioning of unmanned aerial vehicles, and particularly relates to a method for autonomous navigation and positioning of unmanned aerial vehicles in a satellite denial environment. Background Art

[0002] In the current era of rapid technological development, unmanned aerial vehicles (UAVs) are widely used in many fields such as military reconnaissance, civilian mapping, and logistics distribution due to their flexibility and high efficiency. Satellite navigation systems, such as the Global Positioning System, are the key technical supports for UAVs to achieve precise positioning and navigation. However, with the increasingly complex and diverse application scenarios of UAVs, the emergence of satellite denial environments poses severe challenges to the navigation and positioning of UAVs. Traditional UAVs highly rely on the Global Navigation Satellite System. However, in complex terrain areas such as urban canyons and mountains, satellite signals are easily blocked; in strong electromagnetic interference environments, such as military confrontation areas, industrial environments with dense electronic devices, or areas with malicious interference sources, communication channels above 1 GHz are often interfered, resulting in the inability to normally receive satellite signals or significant deviations, which makes it difficult for UAVs to achieve autonomous takeoff and landing, stable flight, and precise positioning in these environments. Machine learning and artificial intelligence technologies have been introduced into the field of autonomous navigation of UAVs. Achieving autonomous navigation and positioning of UAVs in a satellite denial environment is a complex engineering problem, involving the cross-integration of multiple disciplinary fields such as physical measurement, computer vision, pattern recognition, and automatic control. With the continuous progress of related technologies, a method for autonomous navigation and positioning of UAVs in a satellite denial environment has emerged, providing technical support for UAV navigation and positioning; Although the existing technologies have made great progress in the direction of autonomous navigation and positioning of UAVs, there are still some problems to be optimized. Under satellite denial conditions, a single navigation method is vulnerable to interference, resulting in inaccurate positioning of UAVs; when the channel above 1 GHz is interfered, it causes the on-site ground terminal and the remote ground terminal to be unable to normally receive the backhaul data of the UAV and issue feedback control instructions, affecting the remote control process of the UAV. Summary of the Invention

[0003] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for autonomous navigation and positioning of unmanned aerial vehicles in a satellite denial environment, comprising the following steps: Step 1: Combine an optoelectronic pod, a dual-light camera, a frequency analyzer, and data entry to collect multi-dimensional UAV data, providing data support for subsequent steps; Step 2: Preprocess the collected data, set a radio frequency signal frequency threshold, and estimate the influence degree of the radio frequency signal frequency on the UAV data backhaul; Step 3: Use the random forest algorithm to construct a data backhaul monitoring model; Step 4: Develop a route for integrated navigation, combine inertial navigation, visual SLAM, and GNSS positioning to establish an integrated navigation and positioning mode, obtain integrated navigation and positioning information. When GNSS is disturbed, other navigation methods can continue to perform fusion operations and accurately position to improve the robustness and anti-interference ability of the navigation system; Step 5: According to the degree of influence of the radio frequency signal frequency on the data transmission of the UAV, the UAV takes measures to deal with interference and reduce the influence of the radio frequency signal frequency greater than 1 GHz on the data transmission of the UAV; Step 6: Transmit multi-dimensional UAV data and integrated navigation and positioning information; Step 7: The UAV terminal executes the UAV remote control instructions from the remote ground terminal.

[0004] A further improvement of the technical solution of the present invention lies in that: in the said Step 1, the process of collecting multi-dimensional UAV data by combining an optoelectronic pod, a dual-camera, a frequency analyzer, and data entry includes: The multi-dimensional UAV data includes UAV observation images, radio frequency signal frequencies, and UAV remote control data. Among them, the UAV remote control data includes UAV flight control data, UAV mission payload control data, and UAV power supply data.

[0005] A further improvement of the technical solution of the present invention lies in that: in the said Step 1, the process of collecting UAV observation images and radio frequency signal frequencies by using an optoelectronic pod, a dual-camera, and a frequency analyzer includes: Install the optoelectronic pod and the dual-camera on the UAV according to the installation instructions of the optoelectronic pod and the dual-camera, debug the dual-camera, set the parameters of the dual-camera, calibrate the lens and the image quality, and collect UAV observation images; Select a radio frequency cable to connect the radio frequency signal source to the input interface of the frequency analyzer, set the parameters of the frequency analyzer, turn on the frequency analyzer and wait for the initialization to complete, start the scanning function, the frequency analyzer analyzes the radio frequency signal according to the set parameters, the frequency analyzer obtains the spectrogram of the radio frequency signal, and through this spectrogram, the radio frequency signal frequency is obtained.

[0006] A further improvement of the technical solution of the present invention lies in that: in the said Step 1, the process of collecting UAV remote control data through data entry includes: The user enters UAV remote control instructions from the remote ground terminal. The UAV remote control instructions include UAV flight control instructions, UAV mission payload control instructions, and UAV power supply instructions. The remote ground terminal transmits the UAV remote control instructions to the on-site ground terminal through optical fiber communication, and then wirelessly transmits them to the UAV airborne terminal via the on-site ground terminal to obtain UAV remote control data.

[0007] A further improvement of the technical solution of the present invention lies in: in the second step, preprocessing the collected data, setting a radio frequency signal frequency threshold, and estimating the influence degree of the radio frequency signal frequency on the data transmission of the unmanned aerial vehicle, the process includes: Performing data cleaning on the collected unmanned aerial vehicle observation images, radio frequency signal frequencies, and unmanned aerial vehicle remote control data, and setting the radio frequency signal frequency threshold to 1 GHz according to the radio frequency signal frequency range specified by the wireless communication protocol; When the radio frequency signal frequency is greater than 1 GHz, calculate the free space loss of the radio frequency signal during transmission according to the free space loss formula; according to Shannon's theorem , where C is the data transmission rate of the radio frequency signal, B is the bandwidth, and S / N is the signal-to-noise ratio; According to the application scenario of the unmanned aerial vehicle, set the free space loss weight of the radio frequency signal during transmission to , and the data transmission rate weight of the radio frequency signal to , and calculate the influence degree of the radio frequency signal frequency on the data transmission of the unmanned aerial vehicle by:

[0008] where I is the influence degree of the radio frequency signal frequency on the data transmission of the unmanned aerial vehicle; L is the free space loss of the radio frequency signal during transmission; C is the data transmission rate of the radio frequency signal.

[0009] A further improvement of the technical solution of the present invention lies in: in the third step, using the random forest algorithm to construct a data transmission monitoring model, the process includes: Taking the radio frequency signal frequency and its influence degree on the data transmission of the unmanned aerial vehicle as a data set, and dividing it into a training set and a test set according to a ratio of 7:3, and setting the random forest model parameters; Using the training set data to train the random forest model, taking the radio frequency signal frequency as the input and the influence degree of the radio frequency signal frequency on the data transmission of the unmanned aerial vehicle as the output, learning the non-linear relationship between the radio frequency signal frequency and the influence degree of the radio frequency signal frequency on the data transmission of the unmanned aerial vehicle, and obtaining the trained random forest model; Using the test set data to evaluate the performance of the trained random forest model, and optimizing the random forest model by adjusting the model parameters to obtain the data transmission monitoring model.

[0010] A further improvement of the technical solution of the present invention lies in: in the fourth step, formulating a combined navigation route, combining inertial navigation, visual SLAM, and GNSS positioning, establishing a combined navigation positioning mode, and obtaining combined navigation positioning information, the process includes: Before the UAV performs a flight observation mission, the on-site ground terminal pre-loads terrain data and transmits it to the UAV airborne terminal through wireless communication. Landmarks are arranged in the mission area. The UAV uses an optoelectronic pod and a dual-light camera to photograph the feature points in the mission area, obtains the ground information of the mission area, and assigns initial positioning information to the mission area through a GNSS and inertial navigation combined navigation system. The ground information of the mission area and its initial positioning information are pre-installed on the UAV airborne computer; through the GNSS positioning information, the position and attitude information determined by the UAV at the take-off point, and the self-alignment operation of the inertial navigation system, the attitude information of the initial position in the combined navigation mode is obtained; After the UAV takes off, it performs navigation calculation through the inertial navigation system and outputs 6-degree-of-freedom information in real time. The 6-degree-of-freedom information includes longitude, latitude, altitude, heading angle, roll angle, and pitch angle. The six-degree-of-freedom information is transmitted to the UAV flight control system to guide the UAV's autonomous navigation. The UAV combines the optoelectronic pod and the dual-light camera to scan the mission area. When the UAV identifies a ground feature point, the inertial navigation system is used to assign the feature point to the mission area. The feature point is the real-time position information of the mission area. By analyzing the difference between the real-time position information obtained by the inertial navigation system at adjacent moments, the error of the inertial navigation system is analyzed and corrected; Compare the mission area information obtained by the UAV, which includes the ground information, initial positioning information, initial position and attitude information, and real-time position information of the mission area, with the pre-loaded terrain data to identify the ground feature point information and obtain the positioning result of the inertial navigation system; Match and calculate the scanning data received by the navigation positioning mode from the optoelectronic pod and the dual-light camera with visual SLAM to obtain the visual SLAM positioning result, and combine the visual SLAM positioning result with the inertial navigation system positioning result to obtain the combined navigation positioning information.

[0011] A further improvement of the technical solution of the present invention lies in: in step five, according to the influence degree of the radio frequency signal frequency on the UAV data backhaul, the process of the UAV taking measures to cope with interference includes: When the radio frequency signal frequency is greater than 1 GHz and the degree of influence of the radio frequency signal frequency on the UAV data transmission is less than 30%, the radio frequency signal frequency has a slight influence on the UAV data transmission. The UAV starts the data retransmission mechanism, groups and marks the interfering data, and sends a request retransmission instruction to the on-site ground terminal; when the degree of influence of the radio frequency signal frequency on the UAV data transmission is between 30% and 60%, the radio frequency signal frequency has a moderate influence on the UAV data transmission. The UAV switches the omnidirectional antenna to a directional antenna and concentrates the signal in a specific direction for transmission and reception; when the degree of influence of the radio frequency signal frequency on the UAV data transmission is higher than 60%, the radio frequency signal frequency has a severe influence on the UAV data transmission. The UAV re-plans the flight path to avoid the interference area.

[0012] A further improvement of the technical solution of the present invention lies in that: in the sixth step, the process of transmitting the multi-dimensional UAV data and the combined navigation and positioning information includes: After the UAV adjusts the radio frequency signal frequency to below 1 GHz, it wirelessly interconnects the UAV on-board wireless communication terminal with the ground wireless communication terminal, accesses optical fiber communication between the ground wireless communication terminal and the remote ground station, and the remote ground station deploys a data receiving system and a remote control system; The UAV on-board wireless communication terminal wirelessly transmits the transmitted data to the ground wireless communication terminal. The transmitted data includes the UAV observation image, the UAV remote control data, and the combined navigation and positioning information. The ground wireless communication terminal receives the transmitted data and transmits the transmitted data to the remote ground station through optical fiber communication.

[0013] A further improvement of the technical solution of the present invention lies in that: in the seventh step, the process of the UAV executing the UAV remote control instruction from the remote ground terminal includes: The UAV remote control instruction includes a UAV flight control instruction, a UAV mission payload control instruction, and a UAV power supply instruction; The UAV on-board wireless communication terminal receives the UAV flight control instruction. The UAV terminal controls the flight altitude, flight speed, flight direction, takeoff and landing of the UAV according to the UAV flight control instruction; The UAV on-board wireless communication terminal receives the UAV mission payload control instruction. The UAV terminal controls the control gimbal angle, the control optical camera focal length, and the remote photographing of the UAV according to the UAV mission payload control instruction; The UAV on-board wireless communication terminal receives the UAV power supply instruction. The UAV terminal remotely controls the power supply of the UAV mission payload and the power supply of the combined navigation equipment according to the UAV power supply instruction.

[0014] The beneficial effects of the present invention are as follows: For an autonomous navigation and positioning method of an unmanned aerial vehicle (UAV) in a satellite denial environment in the present invention, compared with traditional autonomous navigation and positioning methods of UAVs in a satellite denial environment, the electro-optical pod, dual-vision camera, frequency analyzer, data entry technology, random forest algorithm, integrated navigation technology, etc. in the method of the present invention are closely combined with modern information technology. By accurately capturing UAV observation image data through the electro-optical pod and dual-vision camera, accurately obtaining radio frequency signal frequency data using the frequency analyzer, and collecting UAV remote control data through data entry, the acquisition of multi-dimensional UAV data is achieved. During the data processing process, the influence degree of the radio frequency signal frequency on UAV data transmission is estimated, and a data transmission monitoring model is constructed through the random forest algorithm to effectively monitor the data transmission process, solving the problem that when the channel above 1 GHz is interfered, the on-site ground terminal and the remote ground terminal cannot normally receive the UAV's transmitted data and issue feedback control instructions, affecting the remote control process of the UAV. The integrated navigation technology combining inertial navigation, visual SLAM, and GNSS positioning is adopted to solve the problems of being easily interfered and inaccurate positioning of a single navigation method in a satellite denial environment, ensuring that the method in the present invention can refine the dynamic monitoring standard of UAV autonomous navigation and positioning in a satellite denial environment within a more accurate range, making the monitored data more accurate indicators under the same conditions. The research and application of this method significantly enhance the intelligence level of the UAV during the autonomous navigation and positioning process in a satellite denial environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0016] Figure 1 It is a flowchart of an autonomous navigation and positioning method of an unmanned aerial vehicle in a satellite denial environment according to the present invention; Figure 2 It is a schematic diagram of the integrated navigation technology route in the present invention; Figure 3 It is a schematic diagram of the data transmission technology route in the present invention; Figure 4 It is a schematic diagram of the power supply and distribution technology route of the unmanned aerial vehicle in the present invention; Figure 5 It is a schematic diagram of the remote control technology route of the unmanned aerial vehicle in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] As Figure 1 shown, the present invention provides an autonomous navigation and positioning method for an unmanned aerial vehicle (UAV) in a satellite denial environment, which consists of the following steps: Step 1: Combine an optoelectronic pod, a dual-camera, a frequency analyzer, and data entry to collect multi-dimensional UAV data, providing data support for subsequent steps; Step 2: Preprocess the collected data, set the radio frequency (RF) signal frequency threshold, and estimate the influence degree of the RF signal frequency on the UAV data transmission; Step 3: Use the random forest algorithm to construct a data transmission monitoring model; Step 4: Develop a combined navigation route, combine inertial navigation, visual simultaneous localization and mapping (SLAM), and global navigation satellite system (GNSS) positioning to establish a combined navigation and positioning mode, obtain combined navigation and positioning information. When GNSS is disturbed, other navigation methods can continue to perform fusion operations and accurately position, so as to improve the robustness and anti-interference ability of the navigation system; Step 5: According to the influence degree of the RF signal frequency on the UAV data transmission, the UAV takes measures to cope with interference and reduce the influence of the RF signal frequency greater than 1 GHz on the UAV data transmission; Step 6: Transmit the multi-dimensional UAV data and combined navigation and positioning information; Step 7: The UAV executes the UAV remote control instructions from the remote ground terminal.

[0019] In Step 1, the process of combining an optoelectronic pod, a dual-camera, a frequency analyzer, and data entry to collect multi-dimensional UAV data includes: The multi-dimensional UAV data includes UAV observation images, RF signal frequencies, and UAV remote control data. Among them, the UAV remote control data includes UAV flight control data, UAV mission payload control data, and UAV power supply data.

[0020] In Step 1, the process of using an optoelectronic pod, a dual-camera, and a frequency analyzer to collect UAV observation images and RF signal frequencies includes: Install the optoelectronic pod and the dual-camera on the UAV according to the installation instructions of the optoelectronic pod and the dual-camera, debug the dual-camera, set the dual-camera parameters, calibrate the lens and image quality, and collect UAV observation images; Select a radio frequency cable to connect the radio frequency signal source to the input interface of the frequency analyzer. Set the parameters of the frequency analyzer, turn on the frequency analyzer and wait for the initialization to complete. Start the scanning function. The frequency analyzer analyzes the radio frequency signal according to the set parameters, obtains the spectrogram of the radio frequency signal, and obtains the radio frequency signal frequency through this spectrogram.

[0021] In step one, the process of collecting the remote control data of the unmanned aerial vehicle through data entry includes: The user enters the remote control instructions for the unmanned aerial vehicle from the remote ground end. The remote control instructions for the unmanned aerial vehicle include the flight control instructions for the unmanned aerial vehicle, the mission payload control instructions for the unmanned aerial vehicle, and the power supply instructions for the unmanned aerial vehicle. The remote ground end transmits the remote control instructions for the unmanned aerial vehicle to the on-site ground end through optical fiber communication, and then wirelessly transmits them to the airborne end of the unmanned aerial vehicle to obtain the remote control data of the unmanned aerial vehicle.

[0022] In step two, the process of preprocessing the collected data and setting the radio frequency signal frequency threshold and estimating the influence degree of the radio frequency signal frequency on the data transmission back of the unmanned aerial vehicle includes: Perform data cleaning on the collected observation images of the unmanned aerial vehicle, radio frequency signal frequencies, and remote control data of the unmanned aerial vehicle. According to the radio frequency signal frequency range specified by the wireless communication protocol, set the radio frequency signal frequency threshold to 1 GHz; When the radio frequency signal frequency is greater than 1 GHz, calculate the free space loss of the radio frequency signal during transmission according to the free space loss formula; according to Shannon's theorem , where C is the data transmission rate of the radio frequency signal, B is the bandwidth, and S / N is the signal-to-noise ratio; According to the application scenario of the unmanned aerial vehicle, set the free space loss weight of the radio frequency signal during transmission as , and the data transmission rate weight of the radio frequency signal as , and calculate the influence degree of the radio frequency signal frequency on the data transmission back of the unmanned aerial vehicle:

[0023] where I is the influence degree of the radio frequency signal frequency on the data transmission back of the unmanned aerial vehicle; L is the free space loss of the radio frequency signal during transmission; C is the data transmission rate of the radio frequency signal.

[0024] In step three, the process of constructing a data transmission back monitoring model using the random forest algorithm includes: Take the radio frequency signal frequency and its influence degree on the data transmission back of the unmanned aerial vehicle as the data set, and divide it into a training set and a test set according to a ratio of 7:3, and set the parameters of the random forest model; Train a random forest model using the training set data, with the radio frequency signal frequency as the input and the degree of influence of the radio frequency signal frequency on the UAV data transmission as the output, learn the non-linear relationship between the radio frequency signal frequency and the degree of influence of the radio frequency signal frequency on the UAV data transmission, and obtain a trained random forest model; Use the test set data to evaluate the performance of the trained random forest model, optimize the random forest model by adjusting the model parameters, and obtain a data transmission monitoring model.

[0025] As Figure 2 shown, in step four, the process of formulating the route of integrated navigation, combining inertial navigation, visual SLAM, and GNSS positioning, and establishing an integrated navigation positioning mode to obtain integrated navigation positioning information includes: Before the UAV executes the flight observation mission, the on-site ground terminal pre-loads the terrain data and transmits it wirelessly to the UAV airborne terminal. Landmarks are deployed in the mission area. The UAV uses an optoelectronic pod and a dual-light camera to take pictures of the feature points in the mission area, obtains the ground information of the mission area, and gives the initial positioning information to this mission area through the GNSS and inertial navigation integrated navigation system. Pre-install the ground information and its initial positioning information of the mission area on the UAV airborne computer; through the GNSS positioning information, the position and attitude information determined by the UAV at the take-off point, and the self-alignment operation of the inertial navigation system, obtain the attitude information of the initial position in the integrated navigation mode; After the UAV takes off, perform navigation solution through the inertial navigation system and output 6-degree-of-freedom information in real time. The 6-degree-of-freedom information includes longitude, latitude, altitude, heading angle, roll angle, and pitch angle. Transmit the six-degree-of-freedom information to the UAV flight control system to guide the UAV's autonomous navigation. The UAV combines the optoelectronic pod and the dual-light camera to scan the mission area. When the UAV identifies a ground feature point, use the inertial navigation system to give the feature point to this mission area. The feature point is the real-time position information of the mission area. Analyze and correct the error of the inertial navigation system by analyzing the difference between the real-time position information obtained by the inertial navigation system at adjacent moments; Compare the mission area information obtained by the UAV, which includes the ground information, initial positioning information, initial position and attitude information, and real-time position information of the mission area, with the pre-loaded terrain data, identify the ground feature point information, and obtain the positioning result of the inertial navigation system; Match and calculate the scanning data received by the navigation positioning mode from the optoelectronic pod and the dual-light camera with visual SLAM to obtain the visual SLAM positioning result, and combine the visual SLAM positioning result with the inertial navigation system positioning result to obtain the integrated navigation positioning information.

[0026] In Step 5, according to the influence degree of the radio frequency signal frequency on the UAV data transmission, the process of the UAV taking measures to cope with interference includes: When the radio frequency signal frequency is greater than 1 GHz, when the influence degree of the radio frequency signal frequency on the UAV data transmission is less than 30%, the radio frequency signal frequency has a slight influence on the UAV data transmission. The UAV starts the data retransmission mechanism, groups and marks the interference data, and sends a request retransmission instruction to the on-site ground terminal; when the influence degree of the radio frequency signal frequency on the UAV data transmission is between 30% and 60%, the radio frequency signal frequency has a moderate influence on the UAV data transmission. The UAV switches the omnidirectional antenna to a directional antenna, and concentrates the signal in a specific direction for transmission and reception; when the influence degree of the radio frequency signal frequency on the UAV data transmission is higher than 60%, the radio frequency signal frequency has a severe influence on the UAV data transmission. The UAV replans the flight path to avoid the interference area.

[0027] Such as Figure 3 shown, in Step 6, the process of transmitting the multi-dimensional UAV data and the combined navigation and positioning information includes: After the UAV adjusts the radio frequency signal frequency to below 1 GHz, it wirelessly interconnects the UAV on-board wireless communication terminal with the ground wireless communication terminal, accesses optical fiber communication between the ground wireless communication terminal and the remote ground station, and the remote ground station deploys a data receiving system and a remote control system; The UAV on-board wireless communication terminal wirelessly transmits the transmitted data to the ground wireless communication terminal. The transmitted data includes the UAV observation image, the UAV remote control data, and the combined navigation and positioning information. The ground wireless communication terminal receives the transmitted data and transmits the transmitted data to the remote ground station through optical fiber communication.

[0028] Such as Figure 4 and Figure 5 shown, in Step 7, the process of the UAV executing the UAV remote control instruction from the remote ground terminal includes: The UAV remote control instruction includes the UAV flight control instruction, the UAV mission payload control instruction, and the UAV power supply instruction; The UAV on-board wireless communication terminal receives the UAV flight control instruction. The UAV terminal controls the flight altitude, flight speed, flight direction, takeoff and landing of the UAV according to the UAV flight control instruction; The UAV on-board wireless communication terminal receives the UAV mission payload control instruction. The UAV terminal controls the control gimbal angle, the control optical camera focal length, and the remote photographing of the UAV according to the UAV mission payload control instruction; The UAV on-board wireless communication terminal receives the UAV power supply instruction. The UAV terminal remotely controls the power supply of the UAV mission payload and the power supply of the combined navigation device according to the UAV power supply instruction.

[0029] As described above, it is only the specific implementation manner of the present application. However, the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claimed rights.

Claims

1. A method for autonomous navigation and positioning of unmanned aerial vehicles in a satellite denial environment, characterized by: The following steps are involved: Step 1: Collect multi-dimensional drone data by combining optoelectronic pod, dual-light camera, frequency analyzer and data entry; Step 2: Pre-process the collected data, set the RF signal frequency threshold, and estimate the impact of the RF signal frequency on the drone data return; Step 3: Use the random forest algorithm to build a data feedback monitoring model; Step 4: Develop a combined navigation route, combine inertial navigation, visual SLAM and GNSS positioning, establish a combined navigation positioning mode, and obtain combined navigation positioning information; Step 5: Based on the impact of the RF signal frequency on the drone data return, the drone takes measures to deal with the interference; Step 6: Transmit the multi-dimensional UAV data and combined navigation positioning information back; Step 7: The drone executes the drone remote control command from the remote ground terminal.

2. The method for autonomous navigation and positioning of unmanned aerial vehicles in a satellite-denied environment according to claim 1, characterized in that: In step 1, the process of collecting multi-dimensional drone data by combining an optoelectronic pod, a dual-light camera, a frequency analyzer and data entry includes: The multi-dimensional UAV data includes UAV observation images, radio frequency signal frequencies and UAV remote control data, wherein the UAV remote control data includes UAV flight control data, UAV mission load control data and UAV power supply data.

3. The method for autonomous navigation and positioning of unmanned aerial vehicles in a satellite-denied environment according to claim 2, characterized in that: In step 1, the process of collecting the drone observation image and the radio frequency signal frequency using the optoelectronic pod, the dual-light camera and the frequency analyzer includes: According to the installation instructions of the optoelectronic pod and the bi-optical camera, install the optoelectronic pod and the bi-optical camera on the UAV, debug the bi-optical camera, set the bi-optical camera parameters, calibrate the lens and image quality, and collect the observation images of the UAV; Select an RF cable to connect the RF signal source to the input interface of the frequency analyzer, set the parameters of the frequency analyzer, turn on the frequency analyzer and wait for initialization to complete, start the scanning function, and the frequency analyzer analyzes the RF signal according to the set parameters. The frequency analyzer obtains the spectrum diagram of the RF signal, and obtains the RF signal frequency through the spectrum diagram.

4. The method for autonomous navigation and positioning of unmanned aerial vehicles in a satellite-denied environment according to claim 3, characterized in that: In step 1, the process of collecting drone remote control data through data entry includes: The user enters the UAV remote control instructions from the remote ground terminal data. The UAV remote control instructions include UAV flight control instructions, UAV mission payload control instructions and UAV power supply instructions. The remote ground terminal transmits the UAV remote control instructions to the on-site ground terminal through optical fiber communication, and then wirelessly transmits them to the UAV onboard terminal via the on-site ground terminal to obtain the UAV remote control data.

5. The method for autonomous navigation and positioning of unmanned aerial vehicles in a satellite-denied environment according to claim 4, characterized in that: In step 2, the process of preprocessing the collected data, setting the RF signal frequency threshold, and estimating the impact of the RF signal frequency on the drone data return includes: Data cleaning is performed on the collected drone observation images, RF signal frequencies, and drone remote control data. According to the RF signal frequency range specified in the wireless communication protocol, the RF signal frequency threshold is set to 1 GHZ; When the RF signal frequency is greater than 1 GHZ, the free loss of the RF signal during transmission is calculated according to the free loss formula; according to Shannon's theorem , where C is the data transmission rate of the RF signal, B is the bandwidth, and S / N is the signal-to-noise ratio; According to the application scenario of drones, the free loss weight of RF signals during transmission is set as , the data transmission rate weight of the RF signal is , by calculating the impact of RF signal frequency on drone data return: Among them, I is the influence of RF signal frequency on drone data transmission; L is the free loss of RF signal during transmission; C is the data transmission rate of RF signal.

6. The method for autonomous navigation and positioning of unmanned aerial vehicles in a satellite-denied environment according to claim 5, characterized in that: In step 3, the process of using the random forest algorithm to build a data feedback monitoring model includes: The RF signal frequency and its impact on the drone data transmission are used as the data set, and divided into a training set and a test set in a ratio of 7:3, and the random forest model parameters are set; Use the training set data to train the random forest model, take the RF signal frequency as input, take the influence of the RF signal frequency on the drone data return as output, learn the nonlinear relationship between the RF signal frequency and the influence of the RF signal frequency on the drone data return, and obtain the trained random forest model; Use the test set data to evaluate the performance of the trained random forest model, optimize the random forest model by adjusting the model parameters, and obtain data to feedback the monitoring model.

7. The method for autonomous navigation and positioning of unmanned aerial vehicles in a satellite-denied environment according to claim 6, characterized in that: In the step 4, a combined navigation route is formulated, and a combined navigation positioning mode is established by combining inertial navigation, visual SLAM and GNSS positioning. The process of obtaining combined navigation positioning information includes: Before the UAV performs a flight observation mission, the on-site ground terminal preloads terrain data and transmits it to the UAV airborne terminal through wireless communication. Landmarks are laid out in the mission area. The UAV uses an optoelectronic pod and a dual-light camera to photograph the characteristic points of the mission area and obtain ground information of the mission area. The initial positioning information is given to the mission area through the GNSS and inertial navigation combined navigation system. The ground information of the mission area and its initial positioning information are preloaded on the UAV airborne computer; the attitude information of the initial position in the combined navigation mode is obtained through the GNSS positioning information, the position and attitude information of the UAV determined at the take-off point, and the self-alignment operation of the inertial navigation system; After the drone takes off, it uses the inertial navigation system to perform navigation calculations and output 6-degree-of-freedom information in real time. The 6-degree-of-freedom information includes longitude, latitude, altitude, heading angle, roll angle, and pitch angle. The 6-degree-of-freedom information is transmitted to the drone's flight control system to guide the drone's autonomous navigation. The drone combines the optoelectronic pod and the dual-light camera to scan the mission area. When the drone identifies a ground feature point, it uses the inertial navigation system to assign a feature point to the mission area. The feature point is the real-time position information of the mission area. By analyzing the difference between the real-time position information obtained by the inertial navigation system at adjacent moments, the error of the inertial navigation system is analyzed and corrected. The mission area information obtained by the UAV, including ground information, initial positioning information, initial position attitude information and real-time position information of the mission area, is compared with the pre-loaded terrain data to identify ground feature point information and obtain the positioning result of the inertial navigation system; The scanning data received by the optoelectronic pod and the dual-light camera in the navigation and positioning mode are matched and calculated with the visual SLAM to obtain the visual SLAM positioning result, and the visual SLAM positioning result is combined with the positioning result of the inertial navigation system to obtain the combined navigation and positioning information.

8. The method for autonomous navigation and positioning of unmanned aerial vehicles in a satellite-denied environment according to claim 7, characterized in that: In step 5, according to the degree of influence of the RF signal frequency on the drone data return, the process in which the drone takes measures to deal with the interference includes: When the RF signal frequency is greater than 1GHZ, when the impact of the RF signal frequency on the drone data return is less than 30%, the RF signal frequency has a slight impact on the drone data return. The drone starts the data retransmission mechanism, groups and marks the interference data, and sends a request for retransmission to the ground end on site; when the impact of the RF signal frequency on the drone data return is between 30% and 60%, the RF signal frequency has a moderate impact on the drone data return. The drone switches the omnidirectional antenna to a directional antenna and concentrates the signal in a specific direction for transmission and reception; when the impact of the RF signal frequency on the drone data return is higher than 60%, the RF signal frequency has a severe impact on the drone data return, and the drone replans the flight path to avoid the interference area.

9. The method for autonomous navigation and positioning of unmanned aerial vehicles in a satellite-denied environment according to claim 8, characterized in that: In step 6, the process of transmitting the multi-dimensional drone data and the combined navigation positioning information back includes: After the UAV adjusts the RF signal frequency to below 1GHZ, the UAV airborne wireless communication terminal is wirelessly connected with the ground wireless communication terminal, and optical fiber communication is connected between the ground wireless communication terminal and the remote ground station. The remote ground station deploys a data receiving system and a remote control system; The wireless communication terminal on the drone wirelessly transmits the return data to the ground wireless communication terminal. The return data includes the drone observation image, drone remote control data and combined navigation and positioning information. The ground wireless communication terminal receives the return data and transmits it to the remote ground station via optical fiber communication.

10. The method for autonomous navigation and positioning of unmanned aerial vehicles in a satellite-denied environment according to claim 9, characterized in that: In step 7, the process of the UAV terminal executing the UAV remote control command from the remote ground terminal includes: The UAV remote control instructions include UAV flight control instructions, UAV mission load control instructions and UAV power supply instructions; The UAV airborne wireless communication terminal receives the UAV flight control command, and the UAV terminal controls the UAV's flight altitude, flight speed, flight direction, take-off and landing according to the UAV flight control command; The UAV airborne wireless communication terminal receives the UAV mission payload control command, and the UAV terminal controls the UAV's gimbal angle, optical camera focal length, and remote photography according to the UAV mission payload control command; The UAV onboard wireless communication terminal receives the UAV power supply command, and the UAV end remotely controls the power supply of the UAV mission payload and the combined navigation equipment according to the UAV power supply command.

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