Unmanned aerial vehicle flight path planning method based on radio frequency fingerprints

Abstract

The invention belongs to the technical field of unmanned aerial vehicle path planning, and relates to an unmanned aerial vehicle flight path planning method based on radio frequency fingerprints. According to the invention, by constructing the multi-band radio frequency fingerprint database, the latest states of the terrain, the obstacles and the electromagnetic signals of the target area are included in real time, so that the accuracy of long-term operation and the strategy timeliness are ensured; the flight path is adjusted in real time through the flight process parameters monitored in real time, the problems that the unmanned aerial vehicle deviates from the flight path, energy consumption exceeds the standard or the collision risk is likely to be caused by dynamic changes of wind power, electric quantity, airflow and the like in the flight process can be responded, and it is guaranteed that the unmanned aerial vehicle can still fly safely in the scenes of sudden strong wind, insufficient electric quantity and the like; the manual intervention frequency and the out-of-control risk are reduced; by adaptively switching the radio frequency band of the unmanned aerial vehicle and calling the corresponding frequency band fingerprint database, the problem of link interruption caused by same frequency interference when the unmanned aerial vehicle adopts fixed frequency band communication in the flight process is solved, and communication interruption and positioning failure are avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of UAV path planning, and relates to a UAV flight path planning method based on radio frequency fingerprints. Background Technique

[0002] With the rapid development of UAV technology, its applications in military reconnaissance, logistics distribution, agricultural monitoring, environmental monitoring and other fields are becoming increasingly widespread. However, the widespread application of UAVs has also brought many challenges, such as problems like flight path planning. Especially in the low-altitude airspace, with the increase in the number of UAVs and the complex flight environment, traditional path planning methods are difficult to meet the requirements of high precision and high safety. Against this background, the UAV flight path planning method based on radio frequency fingerprints has emerged, which has very important functions and significance.

[0003] Traditional UAV flight path planning methods can meet the basic usage requirements, but there are still certain deficiencies: (1) Traditional UAV flight path planning methods mostly rely on single GNSS positioning, and the positioning accuracy drops significantly in scenarios of occlusion, multipath effect or electromagnetic interference. Moreover, they mostly adopt static environment models and cannot adapt to terrain changes, dynamic distribution of obstacles and time-varying characteristics of electromagnetic signals in real time.

[0004] Traditional UAV flight path planning methods mostly fly according to the set route during flight, thus unable to process in real time problems such as deviation from the route, excessive energy consumption or collision risk caused by dynamic changes such as wind force, battery power, air flow, etc. during flight, and further unable to reduce the out-of-control risk of UAVs in scenarios such as sudden strong wind and insufficient battery power.

[0005] (3) Traditional UAV flight path planning methods mostly use fixed frequency bands for communication, which may lead to problems such as link interruption caused by co-frequency interference, and cannot ensure the reliable transmission of control commands and data of UAVs in complex electromagnetic environments such as industrial areas and urban dense areas. Summary of the Invention

[0006] In view of this, to solve the problems raised in the above background technique, a UAV flight path planning method based on radio frequency fingerprints is proposed.

[0007] The object of the present invention can be achieved through the following technical solutions: The present invention provides a UAV flight path planning method based on radio frequency fingerprints, including: S1, three-dimensional model construction: By periodically collecting the geographical coordinates, timestamps and environmental parameters of each fingerprint point in the target area, a dynamic three-dimensional model of the target area is constructed.

[0008] S2, radio frequency fingerprint library construction: Align and store the multi-band radio frequency signal characteristic parameters of each fingerprint point in the target area collected synchronously with the dynamic three-dimensional model in space and time to construct a multi-band radio frequency fingerprint library.

[0009] S3. Initial flight path planning: Dynamically match the radio frequency signal feature information collected in real time during the flight of the target UAV with the corresponding radio frequency fingerprint database to achieve positioning, and combine the preset flight mission parameters to plan the initial flight path.

[0010] S4. Adaptive flight path optimization: Real-time monitor the current flight parameters, environmental interference parameters and current remaining battery power of the target UAV during flight, and perform dynamic comparison based on this to trigger the built-in path dynamic correction unit to generate heading angle adjustment instructions, speed adjustment instructions and altitude adjustment instructions.

[0011] S5. Adaptive radio frequency band switching: Real-time monitor the signal-to-noise ratio and duty cycle of the communication link of the current frequency band of the target UAV. If it is less than the corresponding preset threshold, automatically switch to the standby radio frequency band and call the corresponding frequency band fingerprint database.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention constructs a multi-band radio frequency fingerprint database by periodically collecting the geographical coordinates, timestamps, environmental parameters and multi-band radio frequency signal feature parameters of each fingerprint point in the target area, and incorporates the latest states of the terrain, obstacles and electromagnetic signals in the target area in real time, ensuring the accuracy of long-term operation and the timeliness of strategies, and facilitating the reduction of the cost of re-modeling.

[0013] 2. After planning the initial flight path of the UAV, the present invention monitors its flight process in real time and adjusts the flight path in real time according to the monitored parameters, which helps to respond in real time to problems such as the UAV deviating from the flight path, exceeding the energy consumption standard or collision risk easily caused by dynamic changes such as wind force, battery power and air flow during flight, ensuring that the UAV can still fly safely in scenarios such as sudden strong winds and insufficient battery power, and reducing the frequency of manual intervention and the risk of out-of-control.

[0014] 3. By real-time monitoring the signal-to-noise ratio and duty cycle of the communication link of the current frequency band of the target UAV, the present invention automatically switches to the standby radio frequency band and calls the corresponding frequency band fingerprint database accordingly, solving the problem that the UAV is vulnerable to co-frequency interference and causing link interruption when using fixed-frequency communication during flight, ensuring that continuous positioning can still be based on the signal features of the new frequency band after the frequency band is switched, avoiding communication interruption and positioning failure, and ensuring the reliable transmission of control instructions and data of the UAV in complex electromagnetic environments such as industrial areas and urban dense areas. Description of the Drawings

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0016] Figure 1 It is a schematic diagram of the method implementation steps of the present invention. Specific implementation manners

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0018] Please refer to Figure 1 As shown, the present invention provides a method for unmanned aerial vehicle flight path planning based on radio frequency fingerprint, and the specific steps are as follows: S1. Three-dimensional model construction: By periodically collecting the geographical coordinates, timestamps, and environmental parameters of each fingerprint point in the target area, a dynamic three-dimensional model of the target area is constructed.

[0019] It should be explained that a fingerprint point refers to a set of unique and quantifiable characteristic parameters extracted from the radio frequency signal of a wireless device, and these parameters can uniquely identify the hardware identity or signal source of the device. Its essence is a "set of characteristic points" formed by the spatial distribution of signal characteristics (such as spectrum, time-domain waveform) or physical hardware characteristics. Each fingerprint point in the target area in the present invention refers to a building or device composed of a combination of characteristics such as geographical coordinates, timestamps, environmental parameters, and multi-band radio frequency signal characteristic parameters.

[0020] It should be further explained that the specific acquisition methods of the geographical coordinates and timestamps of each fingerprint point in the target area are as follows: Integrate a GNSS (Global Navigation Satellite System) module (such as GPS, Beidou, GLONASS, etc.) on the acquisition device (such as a mobile terminal, sensor), and then the acquisition device calculates each fingerprint point in the target area through satellite signals and outputs the longitude, latitude, and altitude of each fingerprint point in the target area, which are used as the geographical coordinates of each fingerprint point in the target area. While collecting the geographical coordinates, the current time is recorded through a software or hardware trigger mechanism and is recorded as the timestamp of each fingerprint point in the target area.

[0021] As a preferred feasible embodiment, the environmental parameters include temperature, humidity, and obstacle distribution data.

[0022] Among them, the obstacle distribution data includes the geographical coordinates and size information of each obstacle.

[0023] It should be further noted that the specific acquisition method of the geographical coordinates and size information of each obstacle is as follows: The geographical coordinates of each obstacle can be obtained from the geographical coordinates of each fingerprint point in the target area. The size information of each obstacle can be obtained through three-dimensional scanning technology.

[0024] As a preferred feasible embodiment, the specific construction process of the dynamic three-dimensional model of the target area includes: A1. Data preprocessing: Perform data cleaning and spatio-temporal alignment preprocessing on the geographical coordinates, timestamps, and environmental parameters of each fingerprint point in the target area.

[0025] Specifically, clean the collected data to remove obviously incorrect or abnormal data points, such as data points with geographical coordinates outside the target area range, incorrect timestamps, and environmental parameters significantly deviating from the normal range.

[0026] The spatio-temporal alignment process is to unify multi-source data to the same time axis according to the timestamp (such as interpolating to fill in missing time periods); convert coordinates from different sources to the same reference system (such as WGS84); interpolate discrete fingerprint point data into continuous grid data (such as Kriging interpolation).

[0027] A2. Establishment of a spatial coordinate system: Take the center point of the target area as the origin, establish a three-dimensional spatial coordinate system, and use the longitude, latitude, and altitude in the geographical coordinates as the horizontal axis, vertical axis, and Z-axis respectively, which helps to ensure that the coordinate system corresponds to the actual geographical space so as to accurately represent the position of each fingerprint point.

[0028] A3. Map fingerprint points to three-dimensional space: Map the geographical coordinates of each fingerprint point in the target area to the corresponding positions in the three-dimensional space coordinate system. Among them, for fingerprint points with the same geographical coordinates but different timestamps or environmental parameters, they are regarded as different observations at the same position under different times or environmental states.

[0029] Specifically, map the longitude, latitude, and altitude of each fingerprint point to the horizontal axis, vertical axis, and Z-axis coordinates of the three-dimensional coordinate system respectively to determine its unique position in the three-dimensional space. For example: If the geographical coordinates of a certain fingerprint point are (longitude 116.3°, latitude 39.9°, altitude 50m), then its corresponding position mapped to the three-dimensional space coordinate system is (116.3, 39.9, 50).

[0030] For fingerprint points with the same geographical coordinates but different timestamps or environmental parameters, they are regarded as "observations of the same spatial location under different times or environmental states", marked as the same position point in three-dimensional space, but associated with different time attributes or environmental parameters (such as additional time dimension labels, environmental parameter vectors). For example: The signal characteristics collected at the same location (116.3, 39.9, 50) at 10 am (timestamp t1) and 3 pm (timestamp t2) are different. When mapped to three-dimensional space, they are the same coordinate point, but the time attributes of t1 and t2 and the corresponding environmental parameters (such as temperature and humidity) are recorded respectively.

[0031] A4. Construct a dynamic model: Use the timestamps of each fingerprint point in the target area as the dynamic dimension, and adopt time series analysis methods (such as autoregressive moving average model (ARMA), Kalman filter, etc.) to describe the dynamic change law of the environmental parameters of the fingerprint points over time, and assign time attributes to the positions of each fingerprint point in three-dimensional space, so as to construct a dynamic three-dimensional model of the target area.

[0032] Specifically, use the timestamp as the dynamic dimension, assign time attributes to each fingerprint point in three-dimensional space, and form a two-dimensional index of "spatial position + time" (such as the signal characteristic change curve of a certain fingerprint point at time t).

[0033] Bind the environmental parameters (atmospheric pressure, temperature and humidity, obstacle distribution data, etc.) to the three-dimensional space coordinates, so that each fingerprint point in three-dimensional space not only represents a position, but also carries real-time environmental state information (such as the airflow intensity at a certain altitude affects the flight resistance of the drone).

[0034] S2. Construction of the radio frequency fingerprint library: Align and store the multi-band radio frequency signal characteristic parameters collected synchronously at each fingerprint point in the target area with the dynamic three-dimensional model in space-time to construct a multi-band radio frequency fingerprint library.

[0035] As a preferred feasible embodiment, the radio frequency signal characteristic information includes carrier frequency offset, received signal strength, phase noise variance, and multipath delay spread.

[0036] It should be further noted that the specific acquisition methods of the carrier frequency offset, received signal strength, phase noise variance, and multipath delay spread are as follows: The carrier frequency offset of each fingerprint point in the target area is directly detected by a high-precision frequency meter. The received signal strength of each fingerprint point in the target area is directly read by a radio frequency chip integrated with the RSSI function. The carrier phase sequence is extracted through a PLL or Costas loop , calculate the phase difference between adjacent sampling points , and further obtain the phase noise variance of each fingerprint point in the target area according to the variance calculation formula , where , is the number of each fingerprint point, is the number of fingerprint points. The time-domain signal is collected through an oscilloscope or ADC. The received signal is cross-correlated with a reference signal (such as a transmitted pulse) to find the peak of the time delay, and the signal power corresponding to each time delay is calculated. A time-delay - power curve is plotted, and the maximum time-delay difference is obtained from it to get the multipath time-delay spread of each fingerprint point in the target area.

[0037] As a preferred feasible embodiment, the specific construction process of the multi-band radio frequency fingerprint library includes: B1. Space-time alignment: While collecting the multi-band radio frequency signal characteristic parameters of each fingerprint point in the target area, record its timestamp. The timestamp should have high precision and accuracy, which can be achieved by synchronizing with the time of the Global Positioning System (GPS). The collected multi-band radio frequency signal characteristic parameters are associated with the corresponding timestamps, ensuring the consistency of data in the time dimension during subsequent storage and processing. And the collected radio frequency signal characteristic parameters are associated and stored with the geographical coordinate information of each fingerprint point in the target area in the dynamic three-dimensional model after space-time alignment.

[0038] Specifically, the specific operation of the space-time alignment is: (1) Time dimension alignment: Strictly match the timestamp of the radio frequency signal characteristic parameters collected by jiang1 with the collection timestamp of the geographical coordinates of the fingerprint points at the same position in the dynamic three-dimensional model, that is, complete the time dimension alignment.

[0039] (2) Space dimension alignment: The collected radio frequency signal characteristic parameters are strictly matched with the geographical coordinates (longitude, latitude, altitude) of the fingerprint points at the same position in the dynamic three-dimensional model, ensuring that each signal parameter corresponds to a unique spatial coordinate point (such as a certain signal parameter is collected at the coordinate point (116.3, 39.9, 50m)), that is, complete the space dimension alignment.

[0040] B2. Construct a multi-band radio frequency fingerprint library: Indexed by time and space information, store the multi-band radio frequency signal characteristic parameters collected synchronously and aligned in time and space in the database, and the database that stores the dynamic three-dimensional model of the target area and its multi-band radio frequency signal characteristic parameters together is denoted as the multi-band radio frequency fingerprint library.

[0041] Specifically, the database can use a relational database or a non-relational database, and during the storage process, the data can be compressed and encrypted to save storage space and improve data security. At the same time, in order to facilitate subsequent retrieval and application, an effective indexing mechanism needs to be established so that the radio frequency fingerprint data can be quickly and accurately queried and obtained according to time, space or other relevant conditions.

[0042] Since the environment of the target area and the activities of the unmanned aerial vehicle (UAV) may change over time, the multi-band radio frequency fingerprint database needs to be updated and maintained regularly. According to the set acquisition period and update strategy, the multi-band radio frequency signal characteristic parameters of each fingerprint point in the target area are recollected and fused or replaced with the original data. At the same time, the dynamic three-dimensional model is updated accordingly to reflect the actual changes in the target area, ensuring the timeliness and accuracy of the radio frequency fingerprint database.

[0043] In the present invention, by periodically collecting the geographical coordinates, timestamps, environmental parameters, and multi-band radio frequency signal characteristic parameters of each fingerprint point in the target area, a multi-band radio frequency fingerprint database is constructed, and the latest states of the terrain, obstacles, and electromagnetic signals in the target area are incorporated in real time, ensuring the accuracy during long-term operation and the timeliness of the strategy, and facilitating the reduction of the cost of re-modeling.

[0044] S3. Initial flight path planning: Dynamically match the radio frequency signal characteristic information during the flight of the target UAV with the corresponding radio frequency fingerprint database to achieve positioning, and combine the preset flight mission parameters to plan the initial flight path.

[0045] As a preferred feasible embodiment, the specific process of dynamically matching the radio frequency signal characteristic information during the flight of the target UAV with the radio frequency fingerprint database to achieve positioning includes: extracting the carrier frequency offset, received signal strength, phase noise variance, and multipath delay spread during the flight of the target UAV, and respectively calculating the Euclidean distance between them and the carrier frequency offset, received signal strength, phase noise variance, and multipath delay spread of each fingerprint point in the target area stored in the corresponding radio frequency fingerprint database, obtaining the Euclidean distance between each fingerprint point in the target area and the target UAV, and further screening the fingerprint point in the target area with the smallest Euclidean distance from the target UAV as the current positioning of the target UAV.

[0046] It should be further noted that the specific acquisition method of the corresponding radio frequency fingerprint database is: matching the current frequency band of the target UAV with the multi-band radio frequency fingerprint database to obtain a certain frequency band radio frequency fingerprint database consistent with the current frequency band of the target UAV, which is denoted as the corresponding radio frequency fingerprint database.

[0047] It should be further noted that the specific process of the Euclidean distance calculation is: according to the standard Euclidean distance calculation formula to obtain the Euclidean distance between each fingerprint point in the target area and the target UAV where are respectively the carrier frequency offset, received signal strength, phase noise variance, and multipath delay spread during the flight of the target UAV, are respectively the The carrier frequency offset, received signal strength, phase noise variance, and multipath delay spread of each fingerprint point , is the number of each fingerprint point, and

[0048] As a preferred feasible embodiment, the specific planning process of the initial flight path includes: extracting the mission objective, starting point, ending point, and each important fingerprint point of the target UAV from the flight mission parameters, and generating the initial flight path of the target UAV according to the dynamic three-dimensional model of the target area in the multi-band radio frequency fingerprint library and the path planning algorithm.

[0049] It should be further noted that the path planning algorithm includes, but is not limited to, the A algorithm, Dijkstra algorithm, etc. These algorithms can find the shortest or optimal path from the starting point to the ending point in a given map or spatial model. For example, the A algorithm searches for the optimal path by evaluating the cost function of each node, and the cost function usually includes the estimated distance from the current node to the target node and the distance already traveled.

[0050] S4. Adaptive flight path optimization: Real-time monitor the current flight parameters, environmental interference parameters, and current remaining battery power of the target UAV during flight, and perform dynamic comparison based on this to trigger the built-in path dynamic correction unit to generate heading angle adjustment instructions, speed adjustment instructions, and altitude adjustment instructions.

[0051] As a preferred feasible embodiment, the specific triggering process of the heading angle adjustment instruction includes: extracting the current flight heading angle of the target UAV from the flight parameters, and calculating the deviation angle between it and the flight heading angle at the corresponding position in the initial flight path.

[0052] It should be further noted that the specific acquisition method of the flight heading angle at the corresponding position in the initial flight path is: matching the current position of the target UAV with each position of the target UAV during flight. If the current position of the target UAV is consistent with a certain position of the target UAV during flight, then record this position of the target UAV during flight as the corresponding position in the initial flight path of the target UAV.

[0053] Extract the wind direction and wind force of the environment where the target UAV is currently located from the environmental interference parameters, match the wind force of the environment where the target UAV is currently located with the wind force-angle mapping table to obtain the angle corresponding to the wind force of the environment where the target UAV is currently located. If the wind direction of the environment where the target UAV is currently located is opposite to the flight heading angle and the deviation angle is greater than 0, then reduce the heading angle according to the difference between the deviation angle and the angle corresponding to the wind force as the heading angle adjustment instruction of the target UAV. If the wind direction of the environment where the target UAV is currently located is the same as the flight heading angle and the deviation angle is greater than 0, then reduce the heading angle according to the sum of the deviation angle and the angle corresponding to the wind force as the heading angle adjustment instruction of the target UAV. If the wind direction of the environment where the target UAV is currently located is opposite to the flight heading angle and the deviation angle is less than 0, then increase the heading angle according to the sum of the deviation angle and the angle corresponding to the wind force as the heading angle adjustment instruction of the target UAV. If the wind direction of the environment where the target UAV is currently located is the same as the flight heading angle and the deviation angle is less than 0, then increase the heading angle according to the difference between the deviation angle and the angle corresponding to the wind force as the heading angle adjustment instruction of the target UAV.

[0054] It should be further noted that the wind force-angle mapping table is shown in Table 1 for example.

[0055] Table 1 Example of wind force-angle mapping table

[0056] Among them, the higher the wind force level, the larger the corresponding angle, reflecting the influence degree of the wind force on the UAV heading (such as increasing the angle of attack for headwind and decreasing the angle of attack for tailwind).

[0057] When adjusting the heading angle, query the mapping table through the current wind force level to obtain the "corresponding angle", combine the relationship between the wind direction and the flight direction (same direction / opposite direction), calculate the superposition or cancellation of the deviation angle and the wind force angle, and generate the final adjustment instruction. And in practical applications, the mapping table can be optimized according to the UAV model, aerodynamic characteristics and environmental test data to achieve more accurate wind force compensation.

[0058] As a preferred feasible embodiment, the specific triggering process of the speed adjustment instruction includes: extracting the wind force of the environment where the target UAV is currently located from the environmental interference parameters, extracting the current remaining power of the target UAV, calculating the power deviation value between it and the remaining power at the corresponding position in the initial flight path. If it is less than 0 and the wind force is less than or equal to the preset wind force safety threshold, then reducing the speed to the economic cruise speed as the speed adjustment instruction of the target UAV. If it is less than 0 and the wind force is greater than the preset wind force safety threshold, then reducing the speed to the set safety speed as the speed adjustment instruction of the target UAV.

[0059] As a preferred feasible embodiment, the specific triggering process of the altitude adjustment instruction includes: extracting the current flight altitude of the target UAV from the flight parameters and calculating the deviation altitude between it and the flight altitude at the corresponding position in the initial flight path.

[0060] Extract the air flow condition of the environment where the target UAV is currently located from the environmental interference parameters. If it is an updraft and the deviation altitude is greater than 0, then record reducing the flight altitude as the altitude adjustment instruction of the target UAV. If it is a downdraft and the deviation altitude is greater than 0, then record maintaining the flight altitude as the altitude adjustment instruction of the target UAV. If it is an updraft and the deviation altitude is less than 0, then record maintaining the flight altitude as the altitude adjustment instruction of the target UAV. If it is a downdraft and the deviation altitude is less than 0, then record increasing the flight altitude as the altitude adjustment instruction of the target UAV.

[0061] By planning the initial flight path of the UAV and then monitoring its flight process in real time, and adjusting the flight path in real time according to the monitored parameters, the present invention helps to respond in real time to problems such as the UAV deviating from the flight path, exceeding the energy consumption standard, or having a collision risk easily caused by dynamic changes such as wind force, power, and air flow during the flight process, ensures that the UAV can still fly safely in scenarios such as sudden strong winds and insufficient power, and reduces the frequency of manual intervention and the risk of out-of-control.

[0062] S5. Adaptive radio frequency band switching: Monitor the signal-to-noise ratio and duty cycle of the communication link of the current frequency band of the target UAV in real time. If they are less than the corresponding preset thresholds, automatically switch to the standby radio frequency band and call the corresponding frequency band fingerprint library.

[0063] It should be further noted that the specific acquisition method of the signal-to-noise ratio and duty cycle of the communication link of the current frequency band of the target UAV is: directly obtain the signal-to-noise ratio and duty cycle of the communication link of its current frequency band through reading the device driver or protocol stack interface according to the built-in function of the communication module of the target UAV.

[0064] As a preferred feasible embodiment, the specific process of the adaptive radio frequency band switching includes: comparing the signal-to-noise ratio and duty cycle of the communication link of the current frequency band of the target UAV with their corresponding preset thresholds respectively. If the signal-to-noise ratio or duty cycle of the communication link of the target UAV is less than its corresponding preset threshold, automatically switch to the segment with the highest signal-to-noise ratio and the lowest duty cycle among the candidate frequency bands as the standby radio frequency band, and at the same time call the corresponding frequency band fingerprint library from the multi-band radio frequency fingerprint library.

[0065] Specifically, by comparing the signal-to-noise ratio (SNR) and duty cycle of the current frequency band with the preset thresholds in real time, the availability of the frequency band is accurately judged. When the signal quality does not meet the standards (such as data distortion caused by too low SNR or channel congestion caused by too high duty cycle), the automatic switching mechanism is triggered to avoid communication interruption or positioning failure caused by interference in a single frequency band (directly corresponding to the core objective of "ensuring the stability of the communication link" in the document), realizing the coordinated adaptation of "frequency band - environment - positioning" and improving the global adaptability in complex scenarios.

[0066] It should be further noted that the further operations performed by the target UAV after the adaptive radio frequency band switching include: (1) Synchronizing the parameters of the newly switched standby frequency band (such as carrier frequency, bandwidth) to the communication module and positioning system of the target UAV to ensure the consistency of subsequent signal acquisition and matching with the radio frequency fingerprint library.

[0067] (2) Based on the signal characteristics of the new frequency band (such as the received signal strength and phase noise variance collected in real time), calculate the Euclidean distance with the corresponding frequency band fingerprint library to determine the current position of the UAV and avoid positioning deviation caused by frequency band switching.

[0068] (3) Combine the signal quality (such as the improved SNR after switching) and environmental parameters (such as the propagation characteristics of the signal in this frequency band at the current altitude) in the new frequency band to adjust the flight altitude, speed or heading angle. For example, if the signal in the new frequency band is blocked by obstacles at low altitude, the flight altitude is automatically increased to optimize signal reception.

[0069] (4) Continue to use the SNR and duty cycle monitoring mechanism to avoid signal deterioration in the new frequency band due to environmental changes (such as the approach of a mobile interference source), forming a dynamic cycle of "switching → monitoring → re-switching" (enhancing the system self-adaptability).

[0070] By real-time monitoring of the SNR and duty cycle of the communication link of the current frequency band of the target UAV, this invention automatically switches to the standby radio frequency band and calls the corresponding frequency band fingerprint library, solving the problem that the UAV is vulnerable to co-channel interference and link interruption when using a fixed frequency band for communication during flight, ensuring continuous positioning based on the signal characteristics of the new frequency band after frequency band switching, avoiding communication interruption and positioning failure, and ensuring reliable transmission of control commands and data for the UAV in complex electromagnetic environments such as industrial areas and urban dense areas.

[0071] The above content is only an example and illustration of the concept of this invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined by this invention, they should all fall within the protection scope of this invention.

Claims

1. A method for unmanned aerial vehicle flight path planning based on radio frequency fingerprint, characterized in that: Including: S1. Three-dimensional model construction: By periodically collecting the geographical coordinates, timestamps, and environmental parameters of each fingerprint point in the target area, a dynamic three-dimensional model of the target area is constructed; S2. Multi-band RF fingerprint library construction: The multi-band RF signal characteristic parameters of each fingerprint point in the target area collected synchronously are stored in space-time alignment with the dynamic three-dimensional model to construct a multi-band RF fingerprint library; S3. Initial flight path planning: The RF signal characteristic information during the flight of the target UAV collected in real time is dynamically matched with the corresponding RF fingerprint library for positioning, and the initial flight path is planned in combination with the preset flight mission parameters; S4. Adaptive flight path optimization: The current flight parameters, environmental interference parameters, and current remaining battery power of the target UAV during flight are monitored in real time, and dynamic comparison is performed accordingly to trigger the built-in path dynamic correction unit to generate heading angle adjustment instructions, speed adjustment instructions, and altitude adjustment instructions; S5. Adaptive RF band switching: The signal-to-noise ratio and duty cycle of the communication link of the current band of the target UAV are monitored in real time. If they are less than the corresponding preset thresholds, it automatically switches to the standby RF band and calls the fingerprint library of the corresponding band.

2. The method for unmanned aerial vehicle flight path planning based on radio frequency fingerprint according to claim 1, wherein: The environmental parameters include temperature, humidity, and obstacle distribution data; Among them, the obstacle distribution data includes the geographical coordinates and size information of each obstacle; The RF signal characteristic information includes carrier frequency offset, received signal strength, phase noise variance, and multipath delay spread; The initial flight path includes the flight altitude, flight speed, flight heading angle, and remaining battery power corresponding to each position of the target UAV during flight.

3. The method for unmanned aerial vehicle flight path planning based on radio frequency fingerprint according to claim 2, wherein: The specific construction process of the dynamic three-dimensional model of the target area includes: A1. Data preprocessing: The geographical coordinates, timestamps, and environmental parameters of each fingerprint point in the target area are preprocessed by data cleaning and space-time alignment; A2. Spatial coordinate system establishment: Taking the center point of the target area as the origin, a three-dimensional spatial coordinate system is established, and the longitude, latitude, and altitude in the geographical coordinates are used as the horizontal axis, vertical axis, and Z axis respectively; A3. Three-dimensional space mapping of fingerprint points: The geographical coordinates of each fingerprint point in the target area are mapped to the corresponding positions in the three-dimensional spatial coordinate system; A4. Constructing a dynamic model: Using the timestamp of each fingerprint point in the target area as the dynamic dimension, the time series analysis method is used to describe the dynamic change law of the environmental parameters of the fingerprint points over time, and time attributes are assigned to the positions of each fingerprint point in three-dimensional space, thereby constructing a dynamic three-dimensional model of the target area.

4. A method for planning the flight path of an unmanned aerial vehicle based on radio frequency fingerprint according to claim 2, characterized in that: The specific construction process of the multi-band RF fingerprint library includes: B1. Space-time alignment: While collecting the multi-band RF signal characteristic parameters of each fingerprint point in the target area, its timestamp is recorded, and the collected RF signal characteristic parameters are associated and stored after space-time alignment with the geographical coordinate information of each fingerprint point in the dynamic three-dimensional model; B2. Constructing a multi-band RF fingerprint library: Indexed by time and space information, the multi-band RF signal characteristic parameters collected synchronously and space-time aligned are stored in the database, and the database that stores the dynamic three-dimensional model of the target area and its multi-band RF signal characteristic parameters together is denoted as the multi-band RF fingerprint library.

5. The method for planning a UAV flight path based on radio frequency fingerprints according to claim 2, characterized in that: The specific process of dynamically matching the RF signal feature information of the target UAV during flight with the RF fingerprint database to achieve positioning includes: Extract the carrier frequency offset, received signal strength, phase noise variance, and multipath delay spread during the flight of the target UAV, and calculate the Euclidean distances between them and the carrier frequency offset, received signal strength, phase noise variance, and multipath delay spread of each fingerprint point in the corresponding target area stored in the RF fingerprint database, respectively, to obtain the Euclidean distances between each fingerprint point in the target area and the target UAV. Further, screen out the fingerprint point in the target area with the smallest Euclidean distance from the target UAV, and use it as the current positioning of the target UAV.

6. The method for planning a UAV flight path based on radio frequency fingerprint according to claim 5, wherein: The specific process of planning the initial flight path includes: Extract the mission objective, starting point, ending point, and each important fingerprint point of the target UAV from the flight mission parameters, and generate the initial flight path of the target UAV according to the dynamic three-dimensional model of the target area in the multi-band RF fingerprint database and the path planning algorithm.

7. A method for unmanned aerial vehicle flight path planning based on radio frequency fingerprint according to claim 2, characterized in that: The specific process of triggering the heading angle adjustment instruction includes: Extract the current flight heading angle of the target UAV from the flight parameters, and calculate the deviation angle between it and the flight heading angle at the corresponding position in the initial flight path; Extract the wind direction and wind force of the environment where the target UAV is currently located from the environmental interference parameters, match the wind force of the environment where the target UAV is currently located with the wind force-angle mapping table to obtain the angle corresponding to the wind force of the environment where the target UAV is currently located. If the wind direction of the environment where the target UAV is currently located is opposite to the flight heading angle and the deviation angle is greater than 0, then reduce the heading angle according to the difference between the deviation angle and the angle corresponding to the wind force as the heading angle adjustment instruction for the target UAV. If the wind direction of the environment where the target UAV is currently located is the same as the flight heading angle and the deviation angle is greater than 0, then reduce the heading angle according to the sum of the deviation angle and the angle corresponding to the wind force as the heading angle adjustment instruction for the target UAV. If the wind direction of the environment where the target UAV is currently located is opposite to the flight heading angle and the deviation angle is less than 0, then increase the heading angle according to the sum of the deviation angle and the angle corresponding to the wind force as the heading angle adjustment instruction for the target UAV. If the wind direction of the environment where the target UAV is currently located is the same as the flight heading angle and the deviation angle is less than 0, then increase the heading angle according to the difference between the deviation angle and the angle corresponding to the wind force as the heading angle adjustment instruction for the target UAV.

8. The method for unmanned aerial vehicle flight path planning based on radio frequency fingerprint according to claim 2, wherein: The specific process of triggering the speed adjustment instruction includes: Extract the wind force of the environment where the target UAV is currently located from the environmental interference parameters, extract the current remaining power of the target UAV, and calculate the power deviation value between it and the remaining power at the corresponding position in the initial flight path. If it is less than 0 and the wind force is less than or equal to the preset wind force safety threshold, then reduce the speed to the economic cruise speed as the speed adjustment instruction for the target UAV. If it is less than 0 and the wind force is greater than the preset wind force safety threshold, then reduce the speed to the set safety speed as the speed adjustment instruction for the target UAV.

9. The method for unmanned aerial vehicle flight path planning based on radio frequency fingerprint according to claim 2, wherein: The specific process of triggering the altitude adjustment instruction includes: Extract the current flight altitude of the target UAV from the flight parameters, and calculate the deviation altitude between it and the flight altitude at the corresponding position in the initial flight path; Extract the airflow condition of the current environment of the target UAV from the environmental interference parameters. If it is an updraft and the deviation height is greater than 0, record the instruction to lower the flight height as the height adjustment instruction of the target UAV. If it is a downdraft and the deviation height is greater than 0, record the instruction to maintain the flight height as the height adjustment instruction of the target UAV. If it is an updraft and the deviation height is less than 0, record the instruction to maintain the flight height as the height adjustment instruction of the target UAV. If it is a downdraft and the deviation height is less than 0, record the instruction to increase the flight height as the height adjustment instruction of the target UAV.

10. A method for unmanned aerial vehicle flight path planning based on radio frequency fingerprint according to claim 1, characterized in that: The specific process of the adaptive radio frequency band switching includes: Compare the signal-to-noise ratio and duty cycle of the communication link of the current frequency band of the target UAV with their corresponding preset thresholds respectively. If the signal-to-noise ratio or duty cycle of the communication link of the target UAV is less than its corresponding preset threshold, automatically switch to the segment with the highest signal-to-noise ratio and the lowest duty cycle among the candidate frequency bands as the backup radio frequency band, and at the same time call the corresponding frequency band fingerprint library from the multi-band radio frequency fingerprint library.

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