A method and system for guiding UAV heading deviation based on inertial navigation signal simulation

Abstract

This application provides a method and system for guiding UAV heading deviation based on inertial navigation signal simulation, relating to the field of UAV heading deviation guidance technology. This application constructs a baseline environmental map by collecting background radio frequency signals around the UAV's flight path and combining them with inertial navigation position data. Subsequently, a directional radio frequency beacon array is deployed to the side of the flight path, and a composite guidance field with gradually increasing intensity is generated through cooperative control. A virtual navigation beacon is constructed in the peak field strength region. The received signal strength of the UAV is monitored in real time, and abnormal enhancements are identified by comparing it with the baseline map. A virtual deflection command is calculated. This command is fused with the flight attitude, and the virtual beacon is mapped to a temporary waypoint through coordinate transformation. Finally, this waypoint is injected as a high-priority task into the flight control system to induce the UAV to deviate from its original flight path, thereby improving the accuracy and stealth of UAV heading guidance.

Description

Technical Field

[0001] This application relates to the field of UAV heading deviation guidance technology, and in particular to a UAV heading deviation guidance method and system based on inertial navigation signal simulation. Background Technology

[0002] In complex environments with dense radio signals, UAV navigation systems face severe signal interference and highly variable environmental characteristics. Multipath effects and signal blockage in such environments make it difficult to reliably receive traditional navigation signals, necessitating the development of heading control technologies that can adapt to dense radio frequency environments and possess strong anti-interference capabilities.

[0003] The current main solution to this need is a trajectory correction method based on multi-source signal fusion. This method deploys multiple fixed-location auxiliary beacon stations to collect signal strength distribution characteristics in the environment and establish a signal attenuation model database. When the UAV is flying, the system compares the received signal with the database in real time and calculates the optimal heading correction amount through a weighted fusion algorithm.

[0004] However, this approach has significant limitations. The deployment of fixed beacon stations lacks flexibility and is difficult to cope with dynamically changing signal environments. The establishment of the signal attenuation model relies on a large amount of prior data and requires long-term calibration in new environments. More importantly, the multi-source signal fusion algorithm has high computational complexity and is difficult to provide real-time and accurate heading correction for UAVs. The response lag problem is particularly prominent in rapidly changing radio-dense environments. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for guiding UAV heading deviation based on inertial navigation signal simulation, so as to solve the problems of insufficient accuracy and stealth in UAV heading guidance in the prior art.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a method for inducing UAV heading deviation based on inertial navigation signal simulation, comprising:

[0007] For a drone flying on a specific route, the background radio frequency signals around the drone's flight path are first captured and correlated with the position coordinates output by the airborne inertial navigation unit to construct a reference environment map that characterizes the inherent spatial attenuation gradient of the radio frequency signals on the specific route.

[0008] On the side deviating from the specific route, a directional radio frequency beacon array is deployed. By coordinating the transmission parameters of each beacon, a composite induction field with gradually increasing directional intensity in space is generated, and a virtual navigation beacon that does not exist physically is constructed in the intensity peak region of the composite induction field.

[0009] The strength of the radio frequency signal received by the UAV is monitored in real time. The dynamic change trend of the radio frequency signal strength is compared with the natural attenuation model represented by the reference environment map. When an abnormal enhancement phenomenon of the radio frequency signal strength that violates the natural attenuation model is identified, a virtual deflection command for heading correction is calculated.

[0010] The virtual deflection command is fused with the current flight attitude and speed information of the UAV, and the virtual navigation beacon is mapped into a temporary three-dimensional space waypoint that can be recognized by the inertial navigation system through coordinate transformation calculation.

[0011] The temporary three-dimensional space waypoint is injected into the UAV's autonomous flight control module as a high-priority navigation task, forcing it to abandon the specific route and instead perform a maneuver to fly toward the temporary three-dimensional space waypoint, thus completing the course deviation guidance for the UAV.

[0012] Optionally, the step of deploying a directional radio frequency beacon array on the side deviating from the specific flight path, generating a composite induction field with gradually increasing directional intensity in space by coordinating the transmission parameters of each beacon, and constructing a non-physical virtual navigation beacon in the intensity peak region of the composite induction field, includes:

[0013] The induced path is decomposed into a series of spatial anchor points, and a target signal strength is assigned to each spatial anchor point to define a preset intensity curve in which the directional intensity increases monotonically along the induced path.

[0014] Using the preset intensity curve as the solution target, and taking into account the deployment position of each beacon in the directional radio frequency beacon array, antenna pattern, and signal propagation attenuation, a set of beacon transmission parameters that can satisfy the preset intensity curve is derived in reverse.

[0015] The beacon transmission parameter set is solidified into a cooperative control protocol, and the transmission behavior of the directional radio frequency beacon array is uniformly controlled according to the cooperative control protocol, so that the radio frequency energy synthesized by the directional radio frequency beacon array reconstructs the preset intensity curve in space, thereby forming a composite induced field, and constructing a non-physical virtual navigation beacon in the intensity peak region of the composite induced field.

[0016] Optionally, the real-time monitoring of the radio frequency signal strength received by the UAV, comparing the dynamic trend of the radio frequency signal strength with the natural attenuation model represented by the reference environmental map, and when an abnormal enhancement phenomenon in the radio frequency signal strength that violates the natural attenuation model is identified, a virtual deflection command for heading correction is calculated, including:

[0017] The radio frequency signal strength received by the UAV is captured at fixed time intervals to form continuous signal strength segments. At the same time, the background signal strength corresponding to the current position of the UAV is retrieved from the reference environment map to form a reference strength segment.

[0018] For each time period, a dynamic feature model is established to describe the internal intensity fluctuations of the signal intensity segment and the reference intensity segment. The dynamic feature model is used to quantify the change pattern of the current signal and the natural fluctuation pattern of the background signal.

[0019] The dynamic feature model is compared with the change pattern and the natural wave pattern for conformity. If the deviation shown by the comparison result exceeds the preset judgment threshold, the specific value of the deviation is converted into a virtual deflection command containing the deflection angle and rate.

[0020] Optionally, the step of fusing the virtual deflection command with the current flight attitude and velocity information of the UAV, and mapping the virtual navigation beacon into a temporary three-dimensional space waypoint recognizable by the inertial navigation system through coordinate transformation calculations, includes:

[0021] The virtual deflection command is parsed to extract the heading deflection angle and pseudorange parameters used to estimate the distance, and the current flight speed vector of the UAV output by the UAV inertial navigation unit is retrieved simultaneously.

[0022] In a dynamic coordinate system centered on the UAV, the heading deflection angle and the flight speed vector are calculated to generate a guidance vector pointing to the virtual navigation beacon;

[0023] Based on the direction of the guiding vector, and combined with the pseudorange parameter, spatial projection calculation is performed to convert the position of the virtual navigation beacon into a temporary three-dimensional spatial waypoint that can be recognized by the inertial navigation system in the global geographic coordinate system.

[0024] Optionally, injecting the temporary three-dimensional space waypoint as a high-priority navigation task into the UAV's autonomous flight control module, forcing it to abandon the specific route and instead execute a maneuver towards the temporary three-dimensional space waypoint, thereby guiding the UAV's course deviation, includes:

[0025] The three-dimensional coordinates of the temporary three-dimensional space waypoints are compiled into navigation commands that can be directly parsed by the UAV flight control system, and the navigation commands are assigned a special identifier with the highest execution priority.

[0026] The navigation command carrying the special identifier is submitted to the mission scheduling unit of the autonomous flight control module, and the flight mission currently being executed along the specific route is interrupted by using the special identifier.

[0027] Immediately after the flight mission is interrupted, route replanning is performed, and a new set of low-level control commands is generated to directly control the rotor system of the UAV, causing the UAV to turn and fly toward the temporary three-dimensional space waypoint, thus completing the course deviation guidance of the UAV.

[0028] Optionally, the step of solidifying the beacon transmission parameter set into a cooperative control protocol, and uniformly regulating the transmission behavior of the directional radio frequency beacon array according to the cooperative control protocol, so that the radio frequency energy synthesized by the directional radio frequency beacon array reconstructs the preset intensity curve in space, thereby forming a composite induced field, and constructing a non-physical virtual navigation beacon in the intensity peak region of the composite induced field, includes:

[0029] The beacon transmission power and beam pointing data contained in the beacon transmission parameter set are structured and encapsulated according to a preset communication protocol to form a control command sequence containing timing information and beacon identity code.

[0030] The control command sequence is distributed according to the beacon identity code through a central scheduling node, and the received control command sequence is parsed by the local processing unit of each beacon in the directional radio frequency beacon array;

[0031] The local processing unit drives the antenna systems of each beacon to perform synchronous transmission based on the resolved transmit power and beam pointing data, so that the radio frequency energy of each beacon interferes and superimposes in a predetermined spatial region, generating the composite induced field that matches the preset intensity curve.

[0032] Optionally, for each time period, a dynamic feature model capable of depicting the internal intensity fluctuations of the signal intensity segment and the reference intensity segment is established. This dynamic feature model quantifies the change pattern of the current signal and the natural fluctuation pattern of the background signal, including:

[0033] The signal intensity segment and the reference intensity segment are projected onto a set of predefined standard waveform substrates, and a set of projection coefficients for characterizing the segment contour features is obtained through projection operations.

[0034] The projection coefficient set is reorganized according to the inherent order of the standard waveform basis to construct a dynamic feature model that can reflect the energy fluctuations and change rate within the signal strength segment;

[0035] A specific combination of key projection coefficients is extracted from the dynamic feature model, and the value of the specific combination is used as the final quantitative representation of the change pattern of the current signal and the natural fluctuation pattern of the background signal.

[0036] Secondly, this application provides a UAV heading deviation guidance system based on inertial navigation signal simulation, comprising:

[0037] The construction module is used to first capture the background radio frequency signals around the flight path of the UAV for a UAV flying on a specific route, and associate them with the position coordinates output by the airborne inertial navigation unit to construct a reference environment map that characterizes the inherent spatial attenuation gradient of the radio frequency signals of the specific route.

[0038] The deployment module is used to deploy a directional radio frequency beacon array on the side deviating from the specific route, and generate a composite induction field with gradually increasing directional intensity in space by coordinating the transmission parameters of each beacon, and construct a non-physical virtual navigation beacon in the intensity peak region of the composite induction field.

[0039] The monitoring module is used to monitor the strength of the radio frequency signal received by the UAV in real time, compare the dynamic change trend of the radio frequency signal strength with the natural attenuation model represented by the reference environment map, and when an abnormal enhancement phenomenon of the radio frequency signal strength that violates the natural attenuation model is identified, a virtual deflection command for heading correction is calculated.

[0040] The calculation module is used to fuse the virtual deflection command with the current flight attitude and speed information of the UAV, and to map the virtual navigation beacon into a temporary three-dimensional space waypoint that can be recognized by the inertial navigation system through coordinate transformation calculation.

[0041] The guidance module is used to inject the temporary three-dimensional space waypoint as a high-priority navigation task into the autonomous flight control module of the UAV, forcing it to abandon the specific route and instead perform a maneuver to fly towards the temporary three-dimensional space waypoint, thus completing the course deviation guidance for the UAV.

[0042] Thirdly, this application provides an electronic device, comprising:

[0043] Memory, used to store computer programs;

[0044] A processor, configured to execute the computer program to implement the steps of a UAV heading deviation induction method based on inertial navigation signal simulation as described in the first aspect above.

[0045] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the UAV heading deviation guidance method based on inertial navigation signal simulation as described in the first aspect above.

[0046] The UAV heading deviation guidance method based on inertial navigation signal simulation provided in this application first captures the background radio frequency (RF) signals around the UAV's flight path for a UAV flying on a specific route. This is then correlated with the position coordinates output by the onboard inertial navigation unit to construct a reference environment map characterizing the inherent spatial attenuation gradient of the RF signals along the specific flight path. This establishes an RF environment baseline for the UAV's flight area, providing a reference for subsequent abnormal signal identification and ensuring accurate signal interpretation. Next, a directional RF beacon array is deployed on the side deviating from the specific flight path. By coordinating the transmission parameters of each beacon, a composite guidance field with gradually increasing directional intensity is generated in space. A non-physical virtual navigation beacon is constructed in the peak intensity region of this composite guidance field. This allows for the precise construction of a controllable, directional RF guidance region in the target area, forming an attractive "virtual" navigation point for the UAV, providing a physical basis for heading deviation. Finally, by real-time monitoring of the RF signal strength received by the UAV, the dynamic trend of the RF signal strength is compared with the natural attenuation model represented by the reference environment map. By comparing the signal strength and identifying an abnormal increase in the radio frequency signal strength that violates the natural attenuation model, a virtual deflection command for heading correction is calculated. This allows for real-time and intelligent identification of whether the UAV is affected by external induced signals and quantification of this effect, providing a basis for subsequent heading correction decisions. By fusing the virtual deflection command with the UAV's current flight attitude and speed information, and through coordinate transformation calculations, the virtual navigation beacon is mapped into a temporary three-dimensional space waypoint recognizable by the inertial navigation system. This transforms abstract signal strength changes into concrete waypoint commands that the UAV navigation system can directly understand and execute, achieving seamless integration between the induced commands and the UAV flight control system. By injecting the temporary three-dimensional space waypoint as a high-priority navigation task into the UAV's autonomous flight control module, it forces the UAV to abandon the specific route and instead execute a maneuver towards the temporary three-dimensional space waypoint, thus completing the heading deviation guidance for the UAV. This ensures that the UAV can respond quickly and forcefully to the induced commands, achieving effective control and deviation guidance of the UAV's heading and reaching the expected goal.

[0047] Furthermore, by subdividing the induced path into a series of spatial points and setting an increasing target signal strength for each point, a desired intensity variation curve is plotted. Then, based on this curve, and considering the actual deployment location of the beacon, antenna characteristics, and signal propagation loss, the beacon transmission parameters capable of achieving this intensity curve are calculated in reverse. Finally, these parameters are transformed into a set of coordinated control commands to uniformly regulate the transmission behavior of the beacon array, enabling it to accurately reproduce the preset intensity curve in space, forming a composite induced field, and creating non-physical navigation guidance in its highest intensity region. This process enables precise shaping and control of the spatial radio frequency energy distribution, ensuring that the constructed composite induced field has high directivity and predictability. Through precise parameter back-calculation and coordinated control, the generation location and intensity distribution of the virtual navigation beacon highly match the design target, thereby providing the UAV with a stable, reliable, and imperceptible induced signal, significantly improving the accuracy and effectiveness of the induced signal. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 A flowchart illustrating a method for inducing UAV heading deviation based on inertial navigation signal simulation, provided in an embodiment of this application;

[0050] Figure 2 A schematic diagram illustrating a specific implementation of a UAV heading deviation induction method based on inertial navigation signal simulation, provided in an embodiment of this application;

[0051] Figure 3 A schematic diagram illustrating a specific implementation of a UAV heading deviation guidance method based on inertial navigation signal simulation provided in this application embodiment;

[0052] Figure 4 This is a schematic diagram of a UAV heading deviation guidance system based on inertial navigation signal simulation, provided as an embodiment of this application. Detailed Implementation

[0053] In complex environments with dense radio signals, UAV navigation systems face severe signal interference and highly variable environmental characteristics. Multipath effects and signal blockage in such environments make it difficult to reliably receive traditional navigation signals, necessitating the development of heading control technologies that can adapt to dense radio frequency environments and possess strong anti-interference capabilities. Current main solutions address this need are track correction methods based on multi-source signal fusion. This method deploys multiple fixed-location auxiliary beacon stations to collect signal strength distribution characteristics in the environment and establish a signal attenuation model database. When the UAV is in flight, the system compares the received signal with the database in real time and calculates the optimal heading correction amount through a weighted fusion algorithm. However, this approach has significant limitations: the deployment of fixed beacon stations lacks flexibility and struggles to cope with dynamically changing signal environments; the establishment of the signal attenuation model relies on a large amount of prior data, requiring lengthy calibration in new environments; and more importantly, the multi-source signal fusion algorithm has high computational complexity, making it difficult to provide real-time, accurate heading correction for UAVs, with response lag being particularly prominent in rapidly changing, dense radio environments.

[0054] To address the limitations of existing technologies in controlling the heading of unmanned aerial vehicles (UAVs) in complex radio environments, this application proposes an innovative guidance method. This method first constructs a radio frequency (RF) environmental baseline map around the UAV's flight path to accurately grasp the natural attenuation patterns of signals. Then, directional RF transmitting units are deployed in specific areas. By coordinating and controlling their transmission parameters, a directional and progressively stronger composite energy field is precisely generated in space, and a non-physical guidance point is constructed in its peak region. By monitoring changes in the signals received by the UAV in real time and comparing them with the preset baseline map, any abnormal enhancement is detected, and virtual commands are immediately generated and fused to transform the guidance point into a navigation target recognizable by the UAV, forcing it to perform a deviation maneuver. This scheme effectively overcomes the dependence on fixed infrastructure and the cumbersome data calibration of traditional methods, achieving dynamic, precise, and covert guidance of the UAV's heading, significantly improving the adaptability and effectiveness of UAV heading control in complex radio environments.

[0055] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0056] The core of this application is to provide a method for guiding UAV heading deviation based on inertial navigation signal simulation. A flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0057] S101. For a drone flying on a specific route, first capture the background radio frequency signals around the flight path of the drone, and associate them with the position coordinates output by the airborne inertial navigation unit to construct a reference environment map that characterizes the inherent spatial attenuation gradient of the radio frequency signals on the specific route.

[0058] In the above scheme, background radio frequency signals refer to various naturally occurring radio signals in the environment within the UAV's flight area, such as signals from communication base stations, broadcast television, wireless networks, and other devices. The onboard inertial navigation unit (INS) is a device on the UAV used to measure its own position, velocity, and attitude, providing precise coordinate information of the UAV in space. The baseline environment map is a dataset that records the radio frequency signal strength at different locations around a specific flight path and its spatial variation, used to depict the inherent attenuation characteristics of radio signals in that area.

[0059] In this embodiment, the drone first flies along a predetermined flight path, while its onboard radio frequency signal receiving device continuously captures radio signals from the surrounding environment. The strength of these signals naturally attenuates as the drone's position changes; for example, the farther away from the signal source, the weaker the signal typically becomes. Simultaneously, the drone's inertial navigation system outputs the drone's precise current position coordinates in real time, such as longitude, latitude, and altitude information.

[0060] Secondly, the system associates the radio frequency signal strength data captured by the drone at a specific location with the position coordinates output by the corresponding inertial navigation system. This means that the signal strength value received at a certain point in time is correlated with the spatial location information of the drone at that point in time.

[0061] Finally, by collecting radio frequency signal strength and location coordinate data associated with different points along the entire flight path of the UAV, the system utilizes data analysis and modeling techniques, such as signal propagation models or machine learning algorithms, to construct a baseline environmental map. This map clearly shows how radio frequency signals naturally attenuate in space around the specific flight path, forming a gradient or topographic map of signal strength changes with distance. For example, the map may show that signal strength gradually weakens in one section of the flight path, while remaining stable in another section.

[0062] S102. On the side deviating from the specific route, a directional radio frequency beacon array is deployed. By coordinating the transmission parameters of each beacon, a composite induction field with gradually increasing directional intensity in space is generated. A virtual navigation beacon that does not exist physically is constructed in the intensity peak region of the composite induction field.

[0063] Optionally, step S102 may specifically include the following steps:

[0064] S1021. Decompose the induced path into a series of spatial anchor points, and assign a target signal strength to each spatial anchor point to define a preset intensity curve in which the directional intensity increases monotonically along the induced path.

[0065] S1022. Taking the preset intensity curve as the solution target, and comprehensively considering the deployment position of each beacon in the directional radio frequency beacon array, the antenna pattern, and the signal propagation attenuation, a set of beacon transmission parameters that can satisfy the preset intensity curve is derived in reverse.

[0066] S1023. The beacon transmission parameter set is solidified into a set of cooperative control protocols, and the transmission behavior of the directional radio frequency beacon array is uniformly controlled according to the cooperative control protocols, so that the radio frequency energy synthesized by the directional radio frequency beacon array reconstructs the preset intensity curve in space, thereby forming a composite induced field, and constructing a non-physical virtual navigation beacon in the intensity peak region of the composite induced field.

[0067] Specifically, step S1023 may include the following process: The beacon transmission power and beam pointing data contained in the beacon transmission parameter set are structured and encapsulated according to a preset communication protocol to form a control command sequence containing timing information and beacon identification codes; the control command sequence is distributed according to the beacon identification codes through a central scheduling node, and the received control command sequence is parsed by the local processing unit of each beacon in the directional radio frequency beacon array; the local processing unit drives the antenna system of each beacon to perform synchronous transmission based on the parsed transmission power and beam pointing data, so that the radio frequency energy of each beacon interferes and superimposes within a predetermined spatial region, generating the composite induced field that matches the preset intensity curve.

[0068] In the above scheme, a directional radio frequency beacon array refers to a group of devices capable of transmitting radio signals in a specific direction, working together to achieve a specific purpose. Coordinated control refers to the cooperation among multiple beacon devices, adjusting their respective transmission behaviors according to unified instructions. Transmission parameters refer to adjustable attributes such as the power, frequency, and phase of the beacon's transmitted signal. A composite induced field is the radio signal strength distribution area formed in space after multiple beacons jointly transmit signals, characterized by a gradual increase in signal strength along a specific direction in space. A virtual navigation beacon is a non-physical virtual location composed of peak radio signal strength, used to guide the UAV. The induced path is a pre-planned trajectory for the UAV after it deviates from its original flight path. Spatial anchor points are selected key locations on the induced path. The target signal strength is the desired radio signal strength value at each spatial anchor point. The preset strength curve describes the ideal pattern of how the signal strength gradually changes from weak to strong along the induced path. The antenna pattern is a graphical representation of the antenna's ability to transmit or receive signals in different directions. Signal propagation attenuation is the phenomenon where the strength of a radio signal decreases with increasing distance as it propagates in space. The beacon transmission parameter set contains all the specific transmission settings required for all beacons to achieve a preset intensity profile. The coordinated control protocol is a set of rules or instructions that govern how the beacon arrays act in unison and adjust their transmission parameters.

[0069] In this embodiment, firstly, through step S1021, to effectively guide the drone, a guidance path needs to be pre-planned. This path is the trajectory the drone will follow after deviating from its original flight path. The determination of this path is typically based on a comprehensive consideration of the drone's current flight path, the direction of target deviation, and environmental obstacles. Once the guidance path is determined, the system decomposes it into a series of discrete spatial anchor points. The selection of these anchor points takes into account the length and complexity of the guidance path to ensure sufficient coverage of the entire guidance process. Subsequently, for each such spatial anchor point, the system assigns a target signal strength value. These target strength values ​​exhibit a monotonically increasing trend along the direction of the guidance path, meaning that the closer to the endpoint of the guidance, the stronger the signal strength expected to be felt by the drone. In this way, a preset strength curve is defined, depicting the ideal pattern of signal strength gradually changing from weak to strong that the drone should feel on the guidance path under ideal conditions, providing a clear blueprint for subsequent signal generation. For example, if the induced path extends from point A to point B, the signal strength at point A may be set to a lower value, the signal strength at point B may be set to a higher value, and the target strengths of each anchor point in the middle of the path may be set sequentially from low to high, forming a smooth signal strength gradient.

[0070] Secondly, through step S1022, using the preset intensity curve carefully planned in step S1021 as the final solution objective, the system performs a complex reverse derivation process. The core of this process is calculating how to accurately reproduce this preset curve in space using the actually transmitted signal. During the calculation, the system comprehensively considers several key factors: first, the precise deployment location of each beacon in the directional RF beacon array, as the beacon's location directly affects its signal coverage and intensity distribution; second, the antenna pattern of each beacon, which describes the energy distribution characteristics of the signal transmitted by the beacon antenna in different directions; for example, some antennas may be more suitable for concentrating signal transmission in a specific direction; and finally, the law of signal propagation attenuation, i.e., the intensity of a radio signal naturally weakens as distance increases and obstacles obstruct its propagation through the air. By inputting these complex physical parameters and environmental factors into a professional optimization algorithm or simulation model, the system can reverse-engineer an optimal set of beacon transmission parameters. This parameter set contains specific information such as the transmit power, frequency, phase, and antenna pointing of each beacon in the array, ensuring that when they work together, they can accurately synthesize a composite signal field in space that perfectly matches the preset intensity curve. For example, if the preset curve requires a particularly high signal strength in a certain area, the system may calculate that multiple beacons need to simultaneously transmit high-power signals into that area and adjust their phases to achieve signal superposition and enhancement.

[0071] Finally, through step S1023, the beacon transmission parameter set precisely calculated in step S1022 is solidified into a cooperative control protocol. This protocol clearly specifies when and how each beacon transmits a signal. Subsequently, according to this cooperative control protocol, the entire directional radio frequency beacon array will uniformly regulate its transmission behavior. This means that each beacon will strictly transmit signals according to the parameters specified in the protocol, and their transmission actions are precisely synchronized and coordinated. Through this precise cooperative transmission, the radio frequency energy synthesized by the beacon array can reconstruct the previously set preset intensity curve in space. The intensity distribution of this reconstructed signal field is highly consistent with the preset curve, thus forming a composite induced field with specific directionality and gradually increasing intensity. In this composite induced field, the area with the highest and most concentrated signal strength naturally constructs a non-physical virtual navigation beacon. This virtual beacon becomes the strongest signal source perceived by the UAV during flight, thereby guiding it to deviate in that direction.

[0072] In practical applications, such as during a drone flight mission, to cope with sudden airspace control changes, it is necessary to guide a drone currently performing a flight path from its original route to an alternative route. First, through step S1021, the operator plans a smooth guidance path on the flight map based on the drone's current position and the location of the alternative route. This path extends from near the original route to the alternative route. Next, several spatial anchor points are evenly selected along this guidance path, for example, one point every 50 meters. Then, an increasing target signal strength is set for these anchor points; for example, the starting point is set to a relatively low signal strength value, while the ending point is set to a relatively high signal strength value, thus forming a preset strength curve. This curve indicates the trend of signal strength changes that the drone should experience during the guidance process.

[0073] Subsequently, in step S1022, the system utilizes specialized simulation and optimization software, taking the preset intensity curve defined in step S1021 as the target input. Simultaneously, it inputs the precise geographical location information of multiple directional radio frequency beacons deployed on the ground, as well as the detailed technical parameters of each beacon antenna, such as its signal coverage and directivity. The software also considers the signal propagation characteristics of the current environment, such as whether tall buildings obstruct the signal or whether multipath effects exist. Based on this information, the software runs complex algorithms, such as those based on genetic algorithms or particle swarm optimization, to perform reverse derivation and calculate the optimal set of transmission parameters for each beacon. This includes the required transmission power, signal frequency, phase, and precise pointing angle of the antenna for each beacon, ensuring that when all beacons work together, a composite signal field conforming to the preset intensity curve can be accurately generated along the induced path.

[0074] Next, in step S1023, the beacon transmission parameter set calculated in step S1022 is transformed into an executable cooperative control protocol, much like creating a detailed operation manual for each beacon. This protocol is loaded into the central control system of the beacon array. Then, the control system precisely and uniformly regulates the transmission behavior of all beacons according to this cooperative control protocol. This means that each beacon will transmit signals at a predetermined time with calculated power, frequency, and antenna pointing, and their transmissions are highly synchronized and coordinated. Through this precise cooperative transmission, the radio frequency energy synthesized by the beacon array can reconstruct the previously set preset intensity curve in space, thereby forming a composite induction field with specific directionality and gradually increasing intensity. In this composite induction field, the area with the highest and most concentrated signal strength naturally constructs a non-physical virtual navigation beacon. This virtual beacon becomes the strongest signal source perceived by the UAV during flight, thus guiding it to deviate in that direction.

[0075] Finally, when the drone enters this complex induction field, its onboard receiver detects an abnormal increase in signal strength and adjusts its course towards the virtual navigation beacon based on the signal strength gradient. For example, during flight, the drone's signal receiver continuously monitors the surrounding radio frequency signal strength. As it approaches the induction path, it detects that the signal strength in a certain direction is significantly higher than the natural attenuation it should have on its original flight path. This abnormal signal enhancement triggers the drone's course correction mechanism, causing it to automatically adjust its flight attitude and fly towards the direction with the strongest signal strength, thus achieving a smooth transition from the original flight path to the alternative route and ultimately completing the course deviation induction.

[0076] The overall scheme in step S102 above achieves precise, flexible, and covert guidance of UAV heading in complex radio environments by accurately planning signal strength changes along the induced path and utilizing multi-beacon cooperative control technology. This method can dynamically construct a controllable signal guidance area in space, effectively overcoming the limitations of traditional navigation methods under signal interference and environmental changes, and significantly improving the adaptability and reliability of UAV heading control.

[0077] S103. Monitor the strength of the radio frequency signal received by the UAV in real time, compare the dynamic change trend of the radio frequency signal strength with the natural attenuation model represented by the reference environment map, and when an abnormal enhancement phenomenon of the radio frequency signal strength that violates the natural attenuation model is identified, calculate the virtual deflection command for heading correction.

[0078] Optionally, step S103 may specifically include the following steps, such as Figure 2 As shown:

[0079] S1031. The radio frequency signal strength received by the UAV is intercepted at fixed time intervals to form continuous signal strength segments. At the same time, the background signal strength corresponding to the current position of the UAV is retrieved from the reference environment map to form a reference strength segment.

[0080] S1032. For the signal intensity segment and the reference intensity segment within each time period, establish a dynamic feature model that can describe the internal intensity fluctuations, and quantify the change pattern of the current signal and the natural fluctuation pattern of the background signal through the dynamic feature model.

[0081] Specifically, step S1032 may include the following process: projecting the signal intensity segment and the reference intensity segment onto a set of predefined standard waveform substrates, and obtaining a set of projection coefficients to characterize the segment contour features through projection operations; reorganizing the set of projection coefficients according to the inherent order of the standard waveform substrates to construct a dynamic feature model that can reflect the energy fluctuations and change rates within the signal intensity segment; extracting specific combinations of key projection coefficients from the dynamic feature model, and using the values ​​of the specific combinations as the final quantitative representation of the change pattern of the current signal and the natural fluctuation pattern of the background signal.

[0082] S1033. The dynamic feature model is compared with the change pattern and the natural wave pattern for conformity. If the deviation shown by the comparison result exceeds the preset judgment threshold, the specific value of the deviation is converted into a virtual deflection command containing the deflection angle and rate.

[0083] In the above scheme, radio frequency (RF) signal strength refers to the strength of the radio signal received by the UAV. Dynamic change trend refers to the continuous change in signal strength over time or spatial location. The baseline environment map is a pre-established data model describing the natural attenuation of RF signals within a specific flight path area. The natural attenuation model is a mathematical or statistical pattern in the baseline environment map reflecting the normal weakening of signals with distance and environmental factors under interference-free conditions. Conformity comparison refers to comparing real-time monitored signal changes with the preset natural attenuation model to determine if they are consistent. Anomaly enhancement refers to a situation where the real-time signal strength is significantly higher than the predicted value of the natural attenuation model, usually indicating the presence of external induced signals. Virtual deflection command is a non-physical command calculated by the system based on signal anomalies, used to adjust the UAV's flight direction and speed. A signal strength segment refers to a sequence of RF signal strength data continuously collected within a specific time period. A reference strength segment is a sequence of expected signal strength data extracted from the baseline environment map, corresponding to the UAV's current position and time. The dynamic characteristic model is a mathematical or statistical model used to describe the fluctuation, rate of change, peak value, and other characteristics of signal strength over a period of time. The variation pattern refers to the specific manifestation of the signal strength actually received by the UAV over a period of time. The natural fluctuation pattern refers to the expected fluctuation pattern of signal strength under normal conditions in the baseline environmental map. The degree of deviation refers to the magnitude of the difference between the actual signal variation and the natural fluctuation pattern. The judgment threshold is a preset value used to determine whether the degree of signal deviation reaches the critical value for triggering the command.

[0084] In this embodiment, firstly, through step S1031, during flight, the onboard radio frequency receiving equipment of the UAV continuously captures and records the radio signal strength in the surrounding environment at very fixed and frequent time intervals. This is equivalent to taking a snapshot of the currently received signal strength every short period and organizing these continuous snapshot data points in chronological order to form short signal strength segments. Each segment represents the sequence of signal strength changes experienced by the UAV within a specific time window. Simultaneously, the high-precision navigation system on the UAV acquires its precise current location information in real time. Based on this location information, the system accurately retrieves background signal strength data corresponding to the UAV's current location and time from a pre-established reference environment map. This background signal strength data is also organized into reference strength segments that match the length and time interval of the real-time signal segments. This reference segment represents the signal strength change pattern that the UAV should receive under normal conditions and at the current location, reflecting the natural attenuation law of the signal when there is no external inducement.

[0085] Subsequently, in step S1032, for each signal strength segment obtained in step S1031—that is, the signal data actually received by the UAV—and the corresponding reference strength segment—that is, the normal signal data predicted in the baseline spectrum—the system employs advanced data analysis techniques, such as time series analysis, wavelet analysis, or statistical modeling methods, to establish a dynamic characteristic model that can accurately depict the internal intensity fluctuations of each segment. This model not only simply records the instantaneous intensity value of the signal, but more importantly, it captures the dynamic characteristics of the signal over a period of time, such as its trend, fluctuation frequency, rate of change, peak value, and trough value. By constructing such a model, the system can deeply quantify the changing patterns of the signal actually received by the UAV, for example, whether the signal suddenly shows a sharp increase, how fast the increase is, how long this increase lasts, and whether its fluctuation pattern is abnormal; it can also accurately quantify the natural fluctuation patterns of the background signal, for example, how the signal strength changes slowly with distance or environmental factors under normal conditions, and what its normal fluctuation range and frequency are. For example, if an actual signal segment shows that the signal strength rises sharply from -80 units to -60 units in a short period of time, while a reference segment shows that the signal strength decreases slowly or fluctuates slightly around -80 units, then these two dynamic characteristic models will clearly reveal this significant difference.

[0086] Finally, in step S1033, the system performs a precise conformity comparison between the dynamic feature model established in step S1032, representing the actual signal change, and the natural fluctuation pattern of the background signal, representing the normal expectation. This comparison process uses statistical methods or pattern recognition algorithms to assess the similarity or difference between the two. If the comparison result shows that the degree of deviation between the two—that is, the difference between the actual signal change and the normal expectation—exceeds a pre-set judgment threshold, it means that the drone may have been interfered with by an external induced signal, resulting in an abnormal enhancement phenomenon. Once this anomaly is identified, the system immediately calculates the specific value of this deviation, for example, how many units higher the actual signal strength is than expected, or how many times faster its rate of change is than expected. Subsequently, the system converts this specific value into a virtual deflection command containing the deflection angle and rate. This command precisely tells the drone which direction and at what speed to adjust in response to this abnormal signal, thereby guiding the drone to deviate from the direction of the induced signal source.

[0087] In practical applications, for example, a drone performing a routine power line inspection mission, flying along a pre-set route, firstly, through step S1031, the radio frequency receiver on the drone continuously collects the received radio signal strength data at a frequency of 20 times per second, and combines the 20 data points per second into a signal strength segment. Simultaneously, the drone sends its precise location information to the onboard processing unit in real time. Based on the drone's current location, this unit extracts the radio frequency signal strength data that should exist at that location under normal conditions from a pre-established reference environmental map of the area, and generates a corresponding reference strength segment. For example, if the drone is flying over an open farmland, the reference strength segment will show a smooth curve where the signal strength gradually decreases with distance, with very small fluctuations.

[0088] Subsequently, through step S1032, the UAV system uses advanced signal processing algorithms, such as Kalman filtering and trend prediction models, to establish dynamic characteristic models for the signal strength segments and corresponding reference strength segments collected every second. These models capture key features of the signal within that second, such as the average strength, maximum and minimum values, slope of change, and short-term fluctuation frequency. Through these models, the system can clearly quantify the changing pattern of the signal actually received by the UAV. For example, if an external induced signal suddenly intervenes, the actual signal strength may rise sharply from -75 units to -50 units in a short period of time, exhibiting a rapid and continuous strengthening pattern, with a slope of change much greater than under normal circumstances; while the natural fluctuation pattern of the background signal may show that, under normal circumstances, the signal strength only fluctuates slightly around -75 units, or slowly decreases at a rate of 0.1 units per second.

[0089] Next, in step S1033, the system compares the dynamic characteristic model of the actual signal with the natural fluctuation pattern of the background signal. The comparison algorithm calculates the difference between the two, for example, by comparing the feature vector distance or correlation coefficient of the two models within a specific time window. If the comparison result shows that the difference between the intensity change of the actual signal and the normal background signal exceeds a preset judgment threshold, the system will determine that an abnormal enhancement phenomenon has occurred. At this time, the system will immediately calculate a virtual deflection command based on the degree and direction of this deviation. For example, if the actual signal shows a significant enhancement to the right front of the drone, and the enhancement magnitude reaches 20 units, the system may calculate a command to deflect 15 degrees to the right and adjust at a rate of 1 meter per second to guide the drone to fly in the direction of signal enhancement.

[0090] Finally, this virtual yaw command is sent to the UAV's flight control system, prompting the UAV to adjust its course and move in the direction of the abnormally strong signal. For example, after receiving a command to yaw 15 degrees to the right, the UAV's flight control system will immediately adjust the UAV's control surfaces and engine thrust, causing its nose to turn to the right and begin flying in the new direction. This responds to external guidance signals, corrects its course, and ultimately guides the UAV safely away from its original route, avoiding potential risks.

[0091] The overall solution in step S103 described above, by real-time and precise monitoring of the radio frequency signals received by the UAV and intelligently comparing them with a preset natural environment model, can quickly and accurately identify abnormal signal enhancement phenomena. This method effectively avoids misjudgment and can accurately generate virtual commands for heading correction based on the characteristics of abnormal signals, thereby ensuring that the UAV can respond to external inducements in a timely and effective manner in complex radio environments and achieve precise heading deviation control.

[0092] S104. The virtual deflection command is fused with the current flight attitude and speed information of the UAV, and the virtual navigation beacon is mapped into a temporary three-dimensional space waypoint that can be recognized by the inertial navigation system through coordinate transformation calculation.

[0093] Optionally, step S104 may specifically include the following steps:

[0094] S1041. Analyze the virtual deflection command, extract the heading deflection angle and pseudorange parameters used to estimate the distance, and simultaneously retrieve the current flight speed vector of the UAV output by the UAV inertial navigation unit.

[0095] S1042. In a dynamic coordinate system centered on the UAV, the heading deflection angle and the flight speed vector are calculated to generate a guidance vector pointing to the virtual navigation beacon;

[0096] S1043. Based on the direction of the guidance vector, and combined with the pseudorange parameter, perform spatial projection calculation to convert the position of the virtual navigation beacon into a temporary three-dimensional spatial waypoint that can be recognized by the inertial navigation system in the global geographic coordinate system.

[0097] In the above scheme, the virtual yaw command is an internal control signal generated by the UAV during flight based on abnormal changes in environmental signals. It contains the heading information that the UAV needs to adjust and pseudorange parameters used to estimate the target distance. The heading yaw angle refers to the magnitude and direction of the angle that the UAV needs to adjust its current flight direction. The pseudorange parameter is a numerical value used to indirectly calculate distance; it is not a direct distance measurement but a distance-related quantity derived from signal characteristics. Flight attitude refers to the UAV's attitude in space, including pitch, roll, and yaw angles, reflecting the UAV's tilt and orientation relative to the ground. Flight speed information refers to the magnitude and direction of the UAV's current flight speed. An inertial navigation system (INS) is a navigation system that uses inertial principles to measure and calculate the position, velocity, and attitude of an object; it can navigate independently of external signals. A temporary 3D waypoint is a target position point temporarily set for the UAV in 3D space; this point represents the navigation command that the INS can understand and execute.

[0098] In this embodiment, firstly, through step S1041, the UAV's internal control system receives the virtual deflection command generated in the previous step. This command is not a direct navigation coordinate, but an abstract correction signal. The UAV needs to perform in-depth analysis of this signal, much like deciphering an encrypted telegram. The core of the analysis lies in identifying and extracting two key pieces of information: first, the heading deflection angle, which clearly indicates how many degrees the UAV needs to deflect to the left or right, and the direction of the deflection; second, the pseudorange parameter, which is a value that indirectly reflects the target distance. It may be calculated based on a combination of factors such as signal strength and signal arrival time difference, and is used for subsequent estimation of the target position. While analyzing the command, the UAV also actively requests its built-in inertial navigation unit to synchronously acquire the UAV's current flight velocity vector. This velocity vector contains the magnitude of the UAV's current flight speed and precise directional information; for example, the UAV is currently flying northeast at a speed of 15 meters per second. The synchronous acquisition of this information is the foundation for ensuring the accuracy of subsequent calculations.

[0099] Subsequently, through step S1042, the UAV constructs a dynamic, real-time updated local coordinate system with itself as the reference point. Within this UAV-centered coordinate system, the system performs precise mathematical calculations on the heading deflection angle extracted in step S1041 and the synchronously acquired flight velocity vector. This calculation is not a simple addition or subtraction but involves vector rotation and synthesis, aiming to integrate the intended deflection angle into the UAV's current motion state. Through this calculation, the system can generate a guidance vector pointing to the virtual navigation beacon. This guidance vector is a quantity with direction and magnitude, clearly indicating the direction of the virtual navigation beacon relative to the UAV's current position. This process ensures that even when the UAV is constantly moving, the relative orientation of the target beacon can be accurately determined. For example, if the UAV is currently flying due north, and the velocity vector points due north, and the heading deflection angle requires a 10-degree rightward deflection, then in the UAV-centered coordinate system, the guidance vector will point 10 degrees to the right of the UAV's current heading, its length associated with the flight velocity vector, representing a relative guidance direction.

[0100] Finally, in step S1043, the UAV, based on the direction of the guidance vector generated in step S1042 and combined with the pseudorange parameters extracted in step S1041, performs a series of complex spatial projection calculations. This process is like precisely locating a point in three-dimensional space based on a direction and an estimated distance. The pseudorange parameters play a crucial role here; they are used to estimate the distance between the virtual navigation beacon and the UAV. By combining this relative distance information with the direction of the guidance vector and performing coordinate transformation calculations, the UAV can accurately convert the relative position of the virtual navigation beacon, determined in its local coordinate system, into a temporary three-dimensional waypoint that the inertial navigation system can recognize in the global geographic coordinate system. This waypoint is a specific coordinate containing longitude, latitude, and altitude information, and is a navigation target that the inertial navigation system can directly understand and use to adjust its flight path. This conversion process is a key step in realizing the UAV's heading correction, transforming abstract deflection commands into executable navigation commands. For example, if the guidance vector points 10 degrees to the right of the UAV's front, the pseudorange parameters estimate that the virtual navigation beacon is approximately 50 meters away from the UAV. Through spatial projection and coordinate transformation, the UAV converts this relative position into a specific coordinate in the global geographic coordinate system. For example, a point 50 meters east-northeast of the current position with a constant altitude is a temporary three-dimensional waypoint that the inertial navigation system can recognize.

[0101] In practical applications, a drone is performing a complex reconnaissance mission when its sensors suddenly detect an abnormal signal. The system then generates a virtual deflection command. First, through step S1041, the drone's internal flight control computer immediately parses this virtual deflection command. It precisely extracts from the command the required 15-degree leftward yaw angle and a pseudorange parameter for distance estimation, for example, indicating a target distance of approximately 80 meters. Simultaneously, the flight control computer also obtains the drone's current flight velocity vector from its inertial navigation unit (IMU) in real time, assuming the drone is flying stably south at a speed of 25 meters per second. This crucial data is rapidly integrated, preparing for subsequent calculations.

[0102] Next, in step S1042, the flight control computer establishes a dynamic, real-time updated local three-dimensional coordinate system centered on the UAV itself. Within this coordinate system, it performs precise vector calculations with the previously resolved 15-degree leftward heading deflection angle and the current southward flight speed vector. This calculation simulates how the UAV's relative motion direction would change if adjusted according to the deflection angle at its current speed. Through this calculation, the system generates a guidance vector that clearly points to the virtual navigation beacon. This vector not only indicates the direction but also implicitly contains relative speed information, ensuring the accuracy of the target's bearing.

[0103] Subsequently, in step S1043, the flight control computer uses the direction indicated by this newly generated guidance vector as a reference, combined with the previously acquired pseudorange parameters, to perform spatial projection calculations. This process is analogous to marking a point in a three-dimensional coordinate system based on a direction and an estimated distance. The pseudorange parameters are used here to accurately estimate the straight-line distance between the virtual navigation beacon and the UAV. Through a series of complex coordinate transformation algorithms, the flight control computer accurately converts the relative position of the virtual navigation beacon, determined in the UAV's local coordinate system, into a temporary three-dimensional spatial waypoint that the inertial navigation system can directly recognize and execute in the global geographic coordinate system of the Earth. This waypoint contains precise longitude, latitude, and altitude information, and is a navigation target that the inertial navigation system can directly use to adjust the UAV's flight path. Once this waypoint is determined, the inertial navigation system immediately adjusts the UAV's flight attitude and speed, enabling it to fly precisely towards this temporary waypoint, thereby correcting its heading.

[0104] The overall solution in step S104 described above can efficiently transform the heading correction requirements caused by abnormal external environmental signals into navigation commands that can be directly executed by the UAV's inertial navigation system. Through precise analysis of virtual deflection commands and combined with the UAV's real-time flight status, it constructs a guidance direction in a dynamic coordinate system and ultimately generates precise temporary three-dimensional waypoints through spatial projection and coordinate transformation. This enables the UAV to respond quickly and accurately to environmental changes, achieve smooth heading adjustments, effectively avoid potential interference or dangers, ensure the smooth execution of flight missions, and significantly improve the UAV's autonomous navigation and risk avoidance capabilities in complex electromagnetic environments.

[0105] S105. The temporary three-dimensional space waypoint is injected into the autonomous flight control module of the UAV as a high-priority navigation task, forcing it to abandon the specific route and instead perform a maneuver to fly towards the temporary three-dimensional space waypoint, thereby completing the course deviation guidance for the UAV.

[0106] Optionally, step S105 may specifically include the following steps:

[0107] S1051. Compile the three-dimensional coordinates of the temporary three-dimensional space waypoint into navigation instructions that can be directly parsed by the UAV flight control system, and assign a special identifier with the highest execution priority to the navigation instructions;

[0108] S1052. Submit the navigation command carrying the special identifier to the mission scheduling unit of the autonomous flight control module, and use the special identifier to interrupt the currently executing flight mission along the specific route.

[0109] S1053. Immediately after the flight mission is interrupted, route replanning is performed, and a new set of low-level control commands is generated to directly control the rotor system of the UAV, causing the UAV to turn and fly toward the temporary three-dimensional space waypoint, thus completing the heading deviation guidance of the UAV.

[0110] In the above scheme, a temporary 3D waypoint is a target location temporarily set in 3D space that the UAV's inertial navigation system can identify and execute. Navigation commands are commands that the UAV's flight control system can directly understand and execute, used to guide the UAV to a specific location or perform specific actions. Special identifiers are markers assigned to navigation commands, indicating that the command has the highest execution priority and can forcibly interrupt the currently executing task. The autonomous flight control module is the core system within the UAV responsible for managing and executing flight tasks; it includes a task scheduling unit and route replanning functions. The task scheduling unit is a component of the autonomous flight control module, responsible for receiving, sorting, and allocating various flight tasks. A specific flight path is a pre-set, currently executing flight path for the UAV. Low-level control commands are fine-grained operational commands that directly act on the UAV hardware (such as the rotor system), used to achieve attitude and position control of the UAV. The rotor system is a key component providing lift and thrust for the UAV, controlling its flight by adjusting its rotational speed and tilt angle.

[0111] In this embodiment, firstly, through step S1051, the UAV system receives the precise three-dimensional coordinate information of the temporary three-dimensional spatial waypoint calculated in the previous step. To enable the UAV to understand and execute this new target, the system needs to compile these coordinates. This compilation process is akin to translating human-understandable geographical coordinates (such as longitude, latitude, and altitude) into specific format digital instructions that the UAV flight control system can directly recognize and process. This typically involves data encoding, format conversion, and verification steps to ensure the accuracy and completeness of the instructions. More importantly, while generating this navigation instruction, the system assigns it a special identifier with the highest execution priority. This identifier acts like an emergency pass, explicitly telling the UAV's internal task management system that this instruction must be executed immediately, with a higher priority than all other ongoing or queued tasks, thus laying the foundation for subsequent forced heading deviation guidance.

[0112] Subsequently, through step S1052, this compiled navigation command, carrying a special identifier indicating the highest priority, is immediately submitted to the task scheduling unit within the UAV's autonomous flight control module. The task scheduling unit acts as the traffic control center for UAV flight missions, responsible for receiving, evaluating, and distributing all flight commands. Upon receiving this navigation command with the special identifier, it immediately recognizes its urgency. Using this special identifier, the task scheduling unit forcibly interrupts the UAV's current flight mission along a specific route. This interruption is not a simple pause; rather, it means the task scheduling unit immediately stops sending commands related to the current route to the UAV's underlying control system and clears or suspends the current route mission's status, ensuring the UAV no longer continues flying along the original path, thus freeing up complete control to execute new directional maneuvers.

[0113] Finally, through step S1053, after the current flight mission is forcibly interrupted, the UAV system immediately initiates its internal route replanning program within milliseconds. This program uses the newly injected temporary 3D space waypoint as the new sole target and quickly calculates the optimal and most direct flight path from the UAV's current position to that target point. This calculation process considers the UAV's current attitude, speed, remaining battery power, and environmental factors to ensure the feasibility and safety of the path. Once the new route planning is complete, the system generates a new set of refined low-level control commands based on this new path. These commands directly affect the UAV hardware, especially the micro-operation commands of the rotor system, such as precisely adjusting the rotational speed, tilt angle, and control surface deflection angle of each rotor. Through the precise control of these low-level commands, the UAV will be forcibly changed in flight attitude and direction, causing it to quickly turn and begin maneuvering towards the temporary 3D space waypoint, thereby completing the course deviation guidance for the UAV and achieving real-time and precise control of the UAV's flight path.

[0114] In practical applications, a drone is performing an important area monitoring mission, flying along a pre-programmed complex route. Suddenly, the system detects the need for emergency heading guidance and generates a precise temporary 3D waypoint. First, through step S1051, the drone's internal navigation computer immediately compiles the precise coordinates of this temporary 3D waypoint—for example, a specific point approximately 150 meters ahead of the current position at a constant altitude—into a binary navigation command that the drone's flight control system can directly recognize and process. Simultaneously, to ensure this command takes immediate effect, the system assigns it a special highest-priority identifier, essentially labeling it "Top Secret, Execute Immediately," ensuring its absolute priority among all commands.

[0115] Next, through step S1052, this compiled navigation command, marked with a special highest priority identifier, is rapidly and forcibly submitted to the task scheduling unit within the UAV's autonomous flight control module. Upon receiving this command, the task scheduling unit immediately identifies its highest priority and decisively interrupts the area monitoring task currently being performed by the UAV. This means the UAV will immediately cease flying along its original monitoring route, all flight commands related to the monitoring task will be suspended or cleared, and the UAV will completely exit its original mission mode, preparing to execute a new guided flight task, ensuring no delays or interference.

[0116] Subsequently, through step S1053, the moment the current monitoring task is forcibly interrupted, the UAV system immediately activates its internal route replanning algorithm. This algorithm uses the newly injected temporary 3D waypoint as the new sole target and quickly calculates the optimal and safest flight path from the UAV's current position to that target point. Once the new route planning is complete, the system generates a series of fine-grained low-level control commands based on this new path. These commands directly affect the UAV's rotor system. For example, it precisely adjusts the rotational speed and tilt angle of each rotor, as well as the UAV's attitude control parameters, enabling the UAV to quickly change its flight direction and attitude, forcibly turn, and begin flying towards the temporary 3D waypoint. Through this series of rapid and precise actions, the UAV is successfully induced to deviate from its original flight path, achieving the expected heading correction and effectively responding to the emergency.

[0117] The overall scheme of step S105 described above ensures that, in emergency or necessary situations, the UAV can be forcibly deviated from the preset route and redirected to perform a specific guided flight mission. It achieves precise and mandatory control over the UAV's heading by converting temporary waypoints into high-priority navigation commands, interrupting existing tasks using special markers, and then rapidly replanning the route and generating low-level control commands. This enables the UAV to respond quickly to external guidance in complex environments, effectively avoid potential risks, or perform special missions, significantly improving the UAV's adaptability and mission execution flexibility in dynamic environments.

[0118] The following is a complete embodiment for steps S101 to S105:

[0119] like Figure 3As shown, firstly, through step S101, a drone performs a routine patrol mission, flying stably along a preset route. To establish an accurate radio frequency (RF) environmental benchmark, the drone's onboard RF receiver continuously captures various background RF signals around the flight path during flight, such as those from communication base stations, radio broadcasts, and ambient noise. Simultaneously, the drone's inertial navigation unit outputs high-precision position coordinate information, including longitude, latitude, and altitude. These captured RF signal strength data are precisely correlated with the corresponding position coordinates and transmitted to the onboard processing unit. The processing unit uses this data and, through complex algorithms such as fitting a signal strength attenuation model with distance, constructs a detailed benchmark environmental map. This map accurately depicts the inherent attenuation law of RF signal strength along this specific route under normal conditions, providing a reference for subsequent identification of abnormal signals.

[0120] Secondly, in step S102, an array of multiple directional radio frequency beacons is pre-deployed on one side of the UAV's preset flight path. These beacons do not emit signals randomly, but are coordinated and controlled by a central control system to precisely adjust parameters such as the transmission power, frequency, and phase of each beacon. The purpose of this coordinated control is to cleverly generate a composite induction field in space. This induction field is characterized by a gradually increasing directional trend in its radio frequency signal strength, meaning the signal strength gradually increases in a specific direction. By precisely adjusting the parameters of the beacon array, a non-physical virtual navigation beacon can be constructed in a specific area of ​​this composite induction field, such as the point of highest signal strength. Although this virtual beacon has no physical form, its signal characteristics are sufficient to simulate a real navigation source, providing a target for subsequently guiding the UAV away from its flight path.

[0121] Next, through step S103, during flight, the UAV's onboard radio frequency receiver continuously and in real-time monitors the strength of the received radio frequency signal. This real-time data is constantly compared with the reference environment map constructed in step S101. This comparison process is like the UAV constantly asking itself: Does the signal strength I am currently receiving conform to the normal attenuation pattern I recorded before? When the UAV identifies an abnormal increase in the received radio frequency signal strength that violates the natural attenuation model represented by the reference environment map—for example, the signal strength in a certain area suddenly exceeds expectations—this indicates that the UAV may be subject to interference from an external induced field. At this time, the intelligent processing unit inside the UAV immediately starts the calculation program, and calculates a virtual deflection command for heading correction based on the amplitude, direction, and other characteristics of the abnormal signal increase. This command includes the heading angle that the UAV needs to adjust and the pseudorange parameter used to estimate the distance.

[0122] Then, in step S104, the UAV system deeply fuses the virtual deflection command calculated in step S103 with the UAV's current flight attitude and velocity information. This fusion process involves complex coordinate transformation calculations, aiming to convert the abstract deflection command into specific spatial coordinates that the UAV's inertial navigation system can understand. Through these calculations, the system can accurately map the non-physical virtual navigation beacon constructed in step S102 into a temporary three-dimensional spatial waypoint that the inertial navigation system can recognize in the global geographic coordinate system. This waypoint is a specific coordinate containing longitude, latitude, and altitude information, and is a precise target that the inertial navigation system can directly use to adjust the UAV's flight path, thus providing a clear navigation basis for the UAV to perform maneuvers that deviate from the flight path.

[0123] Finally, in step S105, the UAV system immediately injects the temporary 3D waypoint generated in step S104 as a navigation task with the highest execution priority into the UAV's autonomous flight control module. This injection process forces the UAV to abandon its currently executing specific route task, regardless of how important or urgent the task is. Upon receiving this high-priority task, the autonomous flight control module immediately initiates route replanning, calculates an optimal path from the current position to the temporary 3D waypoint, and generates a new set of low-level control commands. These commands directly manipulate the UAV's rotor system, such as adjusting the rotational speed and tilt angle of each rotor, causing the UAV to quickly turn and execute maneuvers toward the temporary waypoint. Through this series of rapid and forced operations, the UAV is successfully induced to deviate from its original route, achieving the expected heading deviation inducement.

[0124] The UAV heading deviation guidance method based on inertial navigation signal simulation provided in this application constructs a refined radio frequency environment benchmark, cleverly utilizes a directional radio frequency beacon array to build a non-physical virtual navigation beacon, and monitors abnormal changes in the UAV's received signals in real time. Once an anomaly is detected, the system can quickly calculate heading correction commands and convert them into temporary waypoints recognizable by the inertial navigation system. Finally, through high-priority task injection and forced route replanning, the UAV is effectively guided to deviate from its original route, thereby significantly improving the stealth, accuracy, and real-time performance of UAV route guidance in complex radio environments, effectively addressing potential threats or achieving specific mission objectives.

[0125] Figure 4 This application provides a schematic diagram of a specific implementation of a UAV heading deviation guidance system based on inertial navigation signal simulation, as shown in the following embodiment. Figure 4 The system may include:

[0126] The construction module 41 is used to first capture the background radio frequency signals around the flight path of the UAV for a UAV flying on a specific route, and associate them with the position coordinates output by the airborne inertial navigation unit to construct a reference environment map that characterizes the inherent spatial attenuation gradient of the radio frequency signals of the specific route.

[0127] The deployment module 42 is used to deploy a directional radio frequency beacon array on the side deviating from the specific route, and generate a composite induction field with gradually increasing directional intensity in space by coordinating the transmission parameters of each beacon, and construct a non-physical virtual navigation beacon in the intensity peak region of the composite induction field.

[0128] Monitoring module 43 is used to monitor the strength of radio frequency signals received by the UAV in real time, compare the dynamic change trend of the radio frequency signal strength with the natural attenuation model represented by the reference environment map, and when an abnormal enhancement phenomenon of the radio frequency signal strength that violates the natural attenuation model is identified, a virtual deflection command for heading correction is calculated.

[0129] The calculation module 44 is used to fuse the virtual deflection command with the current flight attitude and speed information of the UAV, and to map the virtual navigation beacon into a temporary three-dimensional space waypoint that can be recognized by the inertial navigation system through coordinate transformation calculation.

[0130] The guidance module 45 is used to inject the temporary three-dimensional space waypoint as a high-priority navigation task into the autonomous flight control module of the UAV, forcing it to abandon the specific route and instead perform a maneuver to fly towards the temporary three-dimensional space waypoint, thereby completing the course deviation guidance of the UAV.

[0131] The UAV heading deviation guidance system based on inertial navigation signal simulation in this application embodiment is used to implement the aforementioned UAV heading deviation guidance method based on inertial navigation signal simulation. Therefore, the specific implementation of the UAV heading deviation guidance system based on inertial navigation signal simulation can be found in the embodiment section of the UAV heading deviation guidance method based on inertial navigation signal simulation above. The specific implementation can be referred to the description of the corresponding embodiments, and will not be repeated here.

[0132] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described UAV heading deviation guidance methods based on inertial navigation signal simulation.

[0133] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the above-described methods for induction of UAV heading deviation based on inertial navigation signal simulation.

[0134] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0135] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the embodiments of the UAV heading deviation induction method based on inertial navigation signal simulation described above.

[0136] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0137] The foregoing has provided a detailed description of a method and system for inducing UAV heading deviation based on inertial navigation signal simulation, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for inducing unmanned aerial vehicle (UAV) heading deviation based on inertial navigation signal simulation, characterized in that, include: For a drone flying on a specific route, the background radio frequency signals around the drone's flight path are first captured and correlated with the position coordinates output by the airborne inertial navigation unit to construct a reference environment map that characterizes the inherent spatial attenuation gradient of the radio frequency signals on the specific route. On the side deviating from the specific route, a directional radio frequency beacon array is deployed. By coordinating the transmission parameters of each beacon, a composite induction field with gradually increasing directional intensity in space is generated, and a virtual navigation beacon that does not exist physically is constructed in the intensity peak region of the composite induction field. The strength of the radio frequency signal received by the UAV is monitored in real time. The dynamic change trend of the radio frequency signal strength is compared with the natural attenuation model represented by the reference environment map. When an abnormal enhancement phenomenon of the radio frequency signal strength that violates the natural attenuation model is identified, a virtual deflection command for heading correction is calculated. The virtual deflection command is fused with the current flight attitude and speed information of the UAV, and the virtual navigation beacon is mapped into a temporary three-dimensional space waypoint that can be recognized by the inertial navigation system through coordinate transformation calculation. The temporary three-dimensional space waypoint is injected into the UAV's autonomous flight control module as a high-priority navigation task, forcing it to abandon the specific route and instead perform a maneuver to fly toward the temporary three-dimensional space waypoint, thus completing the course deviation guidance for the UAV.

2. The method according to claim 1, characterized in that, The method involves deploying a directional radio frequency beacon array on a side deviating from the specific flight path, generating a composite induction field with gradually increasing directional intensity in space by coordinating the transmission parameters of each beacon, and constructing a non-physical virtual navigation beacon within the peak intensity region of the composite induction field, including: The induced path is decomposed into a series of spatial anchor points, and a target signal strength is assigned to each spatial anchor point to define a preset intensity curve in which the directional intensity increases monotonically along the induced path. Using the preset intensity curve as the solution target, and taking into account the deployment position of each beacon in the directional radio frequency beacon array, antenna pattern, and signal propagation attenuation, a set of beacon transmission parameters that can satisfy the preset intensity curve is derived in reverse. The beacon transmission parameter set is solidified into a cooperative control protocol, and the transmission behavior of the directional radio frequency beacon array is uniformly controlled according to the cooperative control protocol, so that the radio frequency energy synthesized by the directional radio frequency beacon array reconstructs the preset intensity curve in space, thereby forming a composite induced field, and constructing a non-physical virtual navigation beacon in the intensity peak region of the composite induced field.

3. The method according to claim 1, characterized in that, The system monitors the intensity of the radio frequency (RF) signal received by the UAV in real time, compares the dynamic trend of the RF signal intensity with the natural attenuation model represented by the reference environmental map, and when an abnormal enhancement of the RF signal intensity that violates the natural attenuation model is identified, calculates a virtual deflection command for heading correction, including: The radio frequency signal strength received by the UAV is captured at fixed time intervals to form continuous signal strength segments. At the same time, the background signal strength corresponding to the current position of the UAV is retrieved from the reference environment map to form a reference strength segment. For each time period, a dynamic feature model is established to describe the internal intensity fluctuations of the signal intensity segment and the reference intensity segment. The dynamic feature model is used to quantify the change pattern of the current signal and the natural fluctuation pattern of the background signal. The dynamic feature model is compared with the change pattern and the natural wave pattern for conformity. If the deviation shown by the comparison result exceeds the preset judgment threshold, the specific value of the deviation is converted into a virtual deflection command containing the deflection angle and rate.

4. The method according to claim 1, characterized in that, The step of fusing the virtual deflection command with the current flight attitude and velocity information of the UAV, and mapping the virtual navigation beacon into a temporary three-dimensional space waypoint recognizable by the inertial navigation system through coordinate transformation calculations, includes: The virtual deflection command is parsed to extract the heading deflection angle and pseudorange parameters used to estimate the distance, and the current flight speed vector of the UAV output by the UAV inertial navigation unit is retrieved simultaneously. In a dynamic coordinate system centered on the UAV, the heading deflection angle and the flight speed vector are calculated to generate a guidance vector pointing to the virtual navigation beacon; Based on the direction of the guiding vector, and combined with the pseudorange parameter, spatial projection calculation is performed to convert the position of the virtual navigation beacon into a temporary three-dimensional spatial waypoint that can be recognized by the inertial navigation system in the global geographic coordinate system.

5. The method according to claim 1, characterized in that, The step of injecting the temporary three-dimensional space waypoint as a high-priority navigation task into the UAV's autonomous flight control module, forcing it to abandon the specific route and instead execute a maneuver towards the temporary three-dimensional space waypoint, thereby guiding the UAV to deviate from its course, includes: The three-dimensional coordinates of the temporary three-dimensional space waypoints are compiled into navigation commands that can be directly parsed by the UAV flight control system, and the navigation commands are assigned a special identifier with the highest execution priority. The navigation command carrying the special identifier is submitted to the mission scheduling unit of the autonomous flight control module, and the flight mission currently being executed along the specific route is interrupted by using the special identifier. Immediately after the flight mission is interrupted, route replanning is performed, and a new set of low-level control commands is generated to directly control the rotor system of the UAV, causing the UAV to turn and fly toward the temporary three-dimensional space waypoint, thus completing the course deviation guidance of the UAV.

6. The method according to claim 2, characterized in that, The step of solidifying the beacon transmission parameter set into a cooperative control protocol, and uniformly regulating the transmission behavior of the directional radio frequency beacon array according to the cooperative control protocol, so that the radio frequency energy synthesized by the directional radio frequency beacon array reconstructs the preset intensity curve in space, thereby forming a composite induced field, and constructing a non-physical virtual navigation beacon in the intensity peak region of the composite induced field, includes: The beacon transmission power and beam pointing data contained in the beacon transmission parameter set are structured and encapsulated according to a preset communication protocol to form a control command sequence containing timing information and beacon identity code. The control command sequence is distributed according to the beacon identity code through a central scheduling node, and the received control command sequence is parsed by the local processing unit of each beacon in the directional radio frequency beacon array; The local processing unit drives the antenna systems of each beacon to perform synchronous transmission based on the resolved transmit power and beam pointing data, so that the radio frequency energy of each beacon interferes and superimposes in a predetermined spatial region, generating the composite induced field that matches the preset intensity curve.

7. The method according to claim 3, characterized in that, For each time period, a dynamic feature model is established for the signal intensity segment and the reference intensity segment, capable of depicting the internal intensity fluctuations. This dynamic feature model quantifies the change pattern of the current signal and the natural fluctuation pattern of the background signal, including: The signal intensity segment and the reference intensity segment are projected onto a set of predefined standard waveform substrates, and a set of projection coefficients for characterizing the segment contour features is obtained through projection operations. The projection coefficient set is reorganized according to the inherent order of the standard waveform basis to construct a dynamic feature model that can reflect the energy fluctuations and change rate within the signal strength segment; A specific combination of key projection coefficients is extracted from the dynamic feature model, and the value of the specific combination is used as the final quantitative representation of the change pattern of the current signal and the natural fluctuation pattern of the background signal.

8. A UAV heading deviation guidance system based on inertial navigation signal simulation, characterized in that, include: The construction module is used to first capture the background radio frequency signals around the flight path of the UAV for a UAV flying on a specific route, and associate them with the position coordinates output by the airborne inertial navigation unit to construct a reference environment map that characterizes the inherent spatial attenuation gradient of the radio frequency signals of the specific route. The deployment module is used to deploy a directional radio frequency beacon array on the side deviating from the specific route, and generate a composite induction field with gradually increasing directional intensity in space by coordinating the transmission parameters of each beacon, and construct a non-physical virtual navigation beacon in the intensity peak region of the composite induction field. The monitoring module is used to monitor the strength of the radio frequency signal received by the UAV in real time, compare the dynamic change trend of the radio frequency signal strength with the natural attenuation model represented by the reference environment map, and when an abnormal enhancement phenomenon of the radio frequency signal strength that violates the natural attenuation model is identified, a virtual deflection command for heading correction is calculated. The calculation module is used to fuse the virtual deflection command with the current flight attitude and speed information of the UAV, and to map the virtual navigation beacon into a temporary three-dimensional space waypoint that can be recognized by the inertial navigation system through coordinate transformation calculation. The guidance module is used to inject the temporary three-dimensional space waypoint as a high-priority navigation task into the autonomous flight control module of the UAV, forcing it to abandon the specific route and instead perform a maneuver to fly towards the temporary three-dimensional space waypoint, thus completing the course deviation guidance for the UAV.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the UAV heading deviation guidance method based on inertial navigation signal simulation as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the implementation of the UAV heading deviation guidance method based on inertial navigation signal simulation as described in any one of claims 1 to 7.

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