Unmanned aerial vehicle course deviation induction method and system based on inertial navigation signal simulation

Abstract

The invention provides an unmanned aerial vehicle course deviation induction method and system based on inertial navigation signal simulation, and relates to the technical field of unmanned aerial vehicle course deviation induction. Background radio frequency signals around a flight path of an unmanned aerial vehicle are collected, and inertial navigation position data are combined to construct a reference environment map; then deploying a directional radio frequency beacon array on the side of the air route, generating a composite induction field with gradually increased intensity through cooperative control, and constructing a virtual navigation beacon in a field intensity peak region; monitoring the received signal strength of the unmanned aerial vehicle in real time, comparing the received signal strength with a reference map to identify abnormal enhancement, and resolving a virtual deflection instruction; the instruction is fused with the flight attitude, and the virtual beacon is mapped into a temporary waypoint through coordinate transformation; and finally, injecting the waypoint as a high-priority task into a flight control system to induce the unmanned aerial vehicle to deviate from the original route, so that the accuracy and the concealment of course induction of the unmanned aerial vehicle can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of UAV heading deviation induction, and particularly relates to a UAV heading deviation induction method and system based on inertial navigation signal simulation. BACKGROUND

[0002] In a complex environment with dense radio signals, the UAV navigation system faces technical challenges such as serious signal interference and complex and variable environmental characteristics. The multipath effect and signal shielding phenomenon in such an environment make it difficult for traditional navigation signals to be stably received, and it is urgent to develop a heading control technology that can adapt to dense radio frequency environments and has strong anti-interference capability.

[0003] The main solution to this demand at present is a track correction method based on multi-source signal fusion. This method deploys multiple auxiliary beacon stations at fixed positions, collects signal strength distribution characteristics in the environment, and establishes 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 solution has obvious limitations. The deployment of fixed beacon stations lacks flexibility and is difficult to respond to dynamically changing signal environments. The establishment of the signal attenuation model relies on a large amount of prior data and requires a long time to calibrate in a new environment. 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

[0005] The present application aims to provide a UAV heading deviation induction method and system based on inertial navigation signal simulation to solve the problem of insufficient accuracy and concealment of UAV heading induction in the prior art.

[0006] To solve the above technical problems, in a first aspect, the present application provides a UAV heading deviation induction method based on inertial navigation signal simulation, comprising: For a UAV flying on a specific route, first capture the background radio frequency signals around the flight path of the UAV, and associate the position coordinates output by the on-board inertial navigation unit to construct a reference environment map that depicts the inherent spatial attenuation gradient of the radio frequency signals on the specific route; On one side deviating from the specific route, a directional radio frequency beacon array is arranged, the emission parameters of each beacon are cooperatively controlled to generate a composite induction field with a directional intensity gradient in space, and a virtual navigation beacon that does not exist physically is constructed in the intensity peak area of the composite induction field; Real-time monitoring of the unmanned aerial vehicle received radio frequency signal strength, the dynamic trend of the radio frequency signal strength and the natural attenuation model characterized by the reference environment map are compared, when the radio frequency signal strength is identified as abnormal enhancement phenomenon contrary to the natural attenuation model, the virtual deflection instruction for the heading correction is calculated; The virtual deflection instruction is fused with the current flight attitude and speed information of the unmanned aerial vehicle, and the virtual navigation beacon is mapped to a temporary three-dimensional space navigation point recognizable by the inertial navigation system through coordinate transformation operation; The temporary three-dimensional space navigation point is injected into the autonomous flight control module of the unmanned aerial vehicle as a navigation task with high priority, forcing it to abandon the specific route and execute the maneuver of flying to the temporary three-dimensional space navigation point, thereby inducing the deviation of the unmanned aerial vehicle.

[0007] Optionally, the directional radio beacon array is arranged on one side deviating from the specific route, and a composite induction field with intensity gradually increasing in a directional manner in space is generated by cooperatively controlling the transmission parameters of each beacon, and a virtual navigation beacon is constructed in the intensity peak area of the composite induction field, including: The induction path is divided into a series of spatial anchor points, and each spatial anchor point is assigned a target signal strength to define a preset intensity curve with monotonically increasing intensity along the direction of the induction path; The preset intensity curve is used as a solution target, and the deployment position, antenna pattern and signal propagation attenuation of each beacon in the directional radio beacon array are comprehensively considered to reversely deduce a set of beacon transmission parameters that can meet the preset intensity curve; The beacon transmission parameter set is solidified into a set of cooperative control protocols, and the transmission behavior of the directional radio beacon array is uniformly regulated according to the cooperative control protocol, so that the radio frequency energy synthesized by the directional radio beacon array reconstructs the preset intensity curve in space, thereby forming a composite induction field, and a virtual navigation beacon is constructed in the intensity peak area of the composite induction field.

[0008] Optionally, the real-time monitoring of the radio frequency signal strength received by the unmanned aerial vehicle, the dynamic trend of the radio frequency signal strength and the natural attenuation model characterized by the reference environment map are compared, when the radio frequency signal strength is identified as abnormal enhancement phenomenon contrary to the natural attenuation model, the virtual deflection instruction for the heading correction is calculated, including: The radio frequency signal strength received by the unmanned aerial vehicle is intercepted at fixed time intervals to form continuous signal strength segments, and the background signal strength corresponding to the current position of the unmanned aerial vehicle is retrieved from the reference environment map to form a reference intensity segment; For each time period, a dynamic characteristic model is established to depict the internal intensity fluctuation, and the dynamic characteristic model is used to quantify the current signal change pattern and the background signal natural fluctuation pattern; The dynamic characteristic model is compared with the change pattern and the natural fluctuation pattern. If the comparison result shows that the deviation degree exceeds the preset judgment threshold, the specific value of the deviation degree is converted into a virtual deflection instruction including a deflection angle and a rate.

[0009] Optionally, the virtual deflection instruction is fused with the current flight attitude and speed information of the unmanned aerial vehicle, and the virtual navigation beacon is mapped to a temporary three-dimensional space waypoint recognizable by an inertial navigation system through coordinate transformation operation, including: The virtual deflection instruction is analyzed to extract a heading deflection angle and a pseudo-range parameter for estimating a distance, and the current flight speed vector of the unmanned aerial vehicle output by the inertial navigation unit of the unmanned aerial vehicle is synchronously called; In a dynamic coordinate system centered on the unmanned aerial vehicle, the heading deflection angle and the flight speed vector are operated to generate a guide vector pointing to the virtual navigation beacon; Based on the pointing direction of the guide vector, the position of the virtual navigation beacon is converted to a temporary three-dimensional space waypoint recognizable by an inertial navigation system in a global geographic coordinate system through space projection calculation.

[0010] Optionally, the temporary three-dimensional space waypoint is injected into the autonomous flight control module of the unmanned aerial vehicle as a navigation task with high priority, forcing it to abandon the specific route and execute a maneuvering action to fly to the temporary three-dimensional space waypoint, thereby inducing the heading deviation of the unmanned aerial vehicle, including: The three-dimensional coordinates of the temporary three-dimensional space waypoint are compiled into navigation instructions that can be directly analyzed by the unmanned aerial vehicle flight control system, and the navigation instructions are given a special identifier with the highest execution priority; The navigation instructions carrying the special identifier are submitted to the task scheduling unit of the autonomous flight control module to interrupt the flight task along the specific route currently being executed; Immediately after interrupting the flight task, a new set of bottom layer control instructions is generated to directly control the rotor system of the unmanned aerial vehicle, so that the unmanned aerial vehicle turns to fly to the temporary three-dimensional space waypoint, thereby inducing the heading deviation of the unmanned aerial vehicle.

[0011] Optionally, 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 regulated 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 a virtually existing virtual navigation beacon is constructed in the intensity peak area of the composite induced field, including: The beacon transmission power and beam pointing data contained in the beacon transmission parameter set are structured and packaged according to a preset communication protocol to form a control instruction sequence containing timing information and beacon identity codes; The control instruction sequence is distributed according to the beacon identity codes by a central scheduling node, and the control instruction sequence received is parsed by a 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 according to the parsed transmission power and beam pointing data, so that the radio frequency energy of each beacon interferes and superimposes in a predetermined spatial region to generate the composite induced field consistent with the preset intensity curve.

[0012] Optionally, for the signal intensity segment and the reference intensity segment in each time period, a dynamic characteristic model capable of depicting internal intensity fluctuation is established, and the current signal change pattern and the background signal natural fluctuation pattern are quantified through the dynamic characteristic model, including: The signal intensity segment and the reference intensity segment are projected onto a set of pre-defined standard waveform bases, and a set of projection coefficient sets for representing segment profile characteristics are obtained through projection operation; The projection coefficient sets are reorganized according to the inherent order of the standard waveform bases to construct a dynamic characteristic model capable of reflecting the internal energy fluctuation and change rate of the signal intensity segment; A specific combination of key projection coefficients is extracted from the dynamic characteristic model, and the numerical value of the specific combination is used as the final quantitative representation of the current signal change pattern and the background signal natural fluctuation pattern.

[0013] In a second aspect, the present application provides a UAV heading deviation induction system based on inertial navigation signal simulation, including: The construction module is used for capturing the background radio frequency signal around the flight path of the UAV first, and associating the position coordinates output by the on-board inertial navigation unit to construct a reference environment map depicting the inherent spatial attenuation gradient of the radio frequency signal of the specific route; The deployment module is configured to deploy a directional radio frequency beacon array on one side deviated from the specific route, to generate a composite induced field with a gradually increasing intensity in a directional pattern in space by cooperatively controlling the transmission parameters of each beacon, and to construct a non-physically existing virtual navigation beacon in the intensity peak area of the composite induced field. The monitoring module is configured to monitor the radio frequency signal strength received by the UAV in real time, to compare the dynamic change trend of the radio frequency signal strength with a natural attenuation model represented by the reference environment map, and to calculate a virtual deflection instruction for course correction when identifying an abnormal enhancement phenomenon of the radio frequency signal strength deviating from the natural attenuation model. The operation module is configured to fuse the virtual deflection instruction with the current flight attitude and speed information of the UAV, to map the virtual navigation beacon to a temporary three-dimensional space waypoint recognizable by an inertial navigation system through coordinate transformation operation. The induction module is configured to inject the temporary three-dimensional space waypoint as a navigation task with high priority into the autonomous flight control module of the UAV, to force it to abandon the specific route and to execute a maneuvering action of flying to the temporary three-dimensional space waypoint, thereby completing the course deviation induction of the UAV.

[0014] In a third aspect, the present application provides an electronic device, comprising: a memory configured to store a computer program; a processor configured to execute the computer program to implement the steps of the method for inducing course deviation of a UAV based on inertial navigation signal simulation according to the first aspect.

[0015] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executable by a processor to implement the steps of the method for inducing course deviation of a UAV based on inertial navigation signal simulation according to the first aspect.

[0016] The unmanned aerial vehicle heading deviation induction method based on inertial navigation signal simulation provided in the application, by aiming at the unmanned aerial vehicle flying on a specific route, first captures the background radio frequency signal around the flight path of the unmanned aerial vehicle, and associates the position coordinates output by the airborne inertial navigation unit, to construct a reference environment map depicting the inherent spatial attenuation gradient of the specific route radio frequency signal, which can establish the radio frequency environment baseline of the unmanned aerial vehicle flight area, provide a reference for subsequent abnormal signal identification, and ensure the accuracy of signal interpretation; by arranging a directional radio frequency beacon array on one side deviating from the specific route, by cooperatively controlling the transmission parameters of each beacon, a composite induction field with directional intensity gradually increasing in space is generated, and a virtual navigation beacon that does not exist physically is constructed in the intensity peak area of the composite induction field, which can accurately construct a controllable and directional radio frequency induction area in the target area, and form a "virtual" navigation point that is attractive to the unmanned aerial vehicle, providing a physical basis for heading deviation; by monitoring the radio frequency signal strength received by the unmanned aerial vehicle 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 the abnormal enhancement phenomenon of the radio frequency signal strength deviating from the natural attenuation model is identified, the virtual deflection instruction for heading correction is calculated, which can intelligently identify whether the unmanned aerial vehicle is affected by external induction signals in real time, and quantify the influence, providing a decision basis for subsequent heading correction; by fusing the virtual deflection instruction with the current flight attitude and speed information of the unmanned aerial vehicle, through coordinate transformation operation, the virtual navigation beacon is mapped to a temporary three-dimensional space navigation point recognizable by the inertial navigation system, which can convert the abstract signal strength change into a concrete navigation point instruction that can be directly understood and executed by the unmanned aerial vehicle navigation system, realizing seamless docking of the induction instruction and the unmanned aerial vehicle flight control system; by injecting the temporary three-dimensional space navigation point into the autonomous flight control module of the unmanned aerial vehicle as a navigation task with high priority, forcing it to abandon the specific route and execute the maneuvering action of flying to the temporary three-dimensional space navigation point, completing the heading deviation induction of the unmanned aerial vehicle, which can ensure that the unmanned aerial vehicle can quickly and forcibly respond to the induction instruction, realize effective control and deviation induction of the unmanned aerial vehicle heading, and achieve the expected target.

[0017] Further, by subdividing the induction path into a series of spatial points and setting an incremental target signal strength for each point, an expected intensity variation curve is drawn. Then, according to this curve and taking into account the actual deployment location of the beacons, antenna characteristics and signal propagation loss, the beacon transmission parameters that can achieve the intensity curve are reversely calculated. Finally, these parameters are converted into a set of coordinated control instructions to uniformly regulate the transmission behavior of the beacon array, so that it accurately reproduces the preset intensity curve in space, forms a composite induction field, and forms a non-physically existing navigation guide in the area with the highest intensity. This process can achieve fine shaping and control of the spatial radio frequency energy distribution, ensuring that the composite induction field constructed has high directivity and predictability. Through accurate parameter reverse calculation and coordinated control, the generation position and intensity distribution of the virtual navigation beacon are highly consistent with the design target, thereby providing a stable, reliable and difficult-to-detect induction signal for the UAV, significantly improving the accuracy and effectiveness of the induction. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0019] Figure 1 A flowchart of a UAV heading deviation induction method based on inertial navigation signal simulation provided by an embodiment of the present application; Figure 2 A specific implementation schematic diagram of a UAV heading deviation induction method based on inertial navigation signal simulation provided by an embodiment of the present application; Figure 3 A specific implementation schematic diagram of a UAV heading deviation induction method based on inertial navigation signal simulation provided by an embodiment of the present application; Figure 4 A structural schematic diagram of a UAV heading deviation induction system based on inertial navigation signal simulation provided by an embodiment of the present application. DETAILED DESCRIPTION

[0020] In complex environments with dense radio signals, the unmanned aerial vehicle navigation system faces the technical challenges of serious signal interference and complex and variable environmental characteristics. The multipath effect and signal shielding phenomenon in such environments make it difficult for traditional navigation signals to be stably received, and it is urgent to develop a heading control technology that can adapt to dense radio frequency environments and has strong anti-interference capability. The main solution to this demand at present is a track correction method based on multi-source signal fusion. This method deploys multiple auxiliary beacon stations at fixed positions to collect the signal strength distribution characteristics in the environment and establish a signal attenuation model database. When the unmanned aerial vehicle 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. However, this solution has obvious limitations. The deployment of fixed beacon stations lacks flexibility and is difficult to respond to dynamically changing signal environments. The establishment of the signal attenuation model relies on a large amount of prior data and requires a long time to calibrate in a new environment. More importantly, the multi-source signal fusion algorithm has high computational complexity, making it difficult to provide real-time and accurate heading correction for unmanned aerial vehicles, and the response lag problem is particularly prominent in rapidly changing radio-dense environments.

[0021] In view of the limitations of the above prior art in controlling the heading of unmanned aerial vehicles in complex radio environments, an innovative induction method is proposed. This method first constructs a radio frequency environment reference map around the flight path of the unmanned aerial vehicle to accurately grasp the natural attenuation law of the signal. Then, directional radio frequency transmitting units are deployed in a specific area to accurately generate a composite energy field with directivity and gradually increasing intensity in space by coordinating and controlling its transmission parameters, and to construct a non-physically existing guide point in the peak area. By monitoring the changes in the signals received by the unmanned aerial vehicle in real time and comparing them with the preset reference map, as soon as an abnormal enhancement is found, a virtual command is generated and fused to convert the guide point into a navigational target recognizable by the unmanned aerial vehicle and force it to perform a deviation maneuver. This solution effectively overcomes the dependence of traditional methods on fixed infrastructure and the tediousness of data calibration, realizes dynamic, accurate and covert induction of the heading of unmanned aerial vehicles, and significantly improves the adaptability and effectiveness of the heading control of unmanned aerial vehicles in complex radio environments.

[0022] In order for those skilled in the art to better understand the present application, the present application will be further described in detail below in conjunction with the drawings and specific embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0023] The core of the present application is to provide an unmanned aerial vehicle heading deviation induction method based on inertial navigation signal simulation, and a flowchart of a specific embodiment thereof is shown in Figure 1 The method comprises: S101, for a specific route of the unmanned aerial vehicle, first capture the background radio frequency signal around the flight path of the unmanned aerial vehicle, and associate the position coordinates output by the airborne inertial navigation unit, build the reference environment map depicting the specific route radio frequency signal inherent spatial decay gradient; In the above scheme, the background radio frequency signal refers to various radio signals naturally existing in the environment within the flight area of the unmanned aerial vehicle, such as signals from communication base stations, broadcast television, wireless network equipment, etc. The airborne inertial navigation unit is a device on the unmanned aerial vehicle for measuring its own position, speed and attitude, which can provide accurate coordinate information of the unmanned aerial vehicle in space. The reference environment map is a data set that records the radio frequency signal strength at different positions around the specific flight route and its spatial variation rule, which is used to describe the inherent attenuation characteristics of the radio signals in this area.

[0024] In the embodiments of the present application, first, the unmanned aerial vehicle flies on the predetermined flight route, and the radio frequency signal receiving device carried by it continuously captures the radio signals in the surrounding environment. The strength of these signals will naturally attenuate with the change of the position of the unmanned aerial vehicle, for example, the farther away from the signal source, the weaker the signal usually is. At the same time, the inertial navigation system on the unmanned aerial vehicle will output the accurate position coordinates of the unmanned aerial vehicle at the current moment, for example, longitude, latitude and height information.

[0025] Secondly, the system will associate the radio frequency signal strength data captured by the unmanned aerial vehicle at a specific position with the position coordinates output by the inertial navigation system corresponding to the position. This means that the signal strength value received at a certain time point is associated with the spatial position information of the unmanned aerial vehicle at the time point.

[0026] Finally, by collecting the associated radio frequency signal strength and position coordinate data of the unmanned aerial vehicle at different position points during the entire flight process on the specific route, the system will use data analysis and modeling techniques, for example, using signal propagation model or machine learning algorithm, to construct a reference environment map. This map can clearly show how the radio frequency signals around the specific route naturally attenuate in space, forming a gradient or terrain map of signal strength changing with distance, for example, the map will show that the signal strength gradually weakens in a certain area of the route, while the signal strength remains stable in another area.

[0027] S102, on one side deviating from the specific route, a directional radio frequency beacon array is arranged, a composite induced field with intensity gradually increasing in a direction in space is generated by cooperatively controlling the emission parameters of each beacon, and a non-physically existing virtual navigation beacon is constructed in the intensity peak area of the composite induced field; Optionally, step S102 can specifically include the following steps: S1021, decompose the induction path into a series of spatial anchors, and assign a target signal strength to each spatial anchor to define a preset intensity curve that monotonically increases in direction along the induction path; S1022, take the preset intensity curve as a solution, comprehensively consider the deployment position, antenna pattern and signal propagation attenuation of each beacon in the directional radio beacon array, and reversely deduce a set of beacon transmission parameters that can meet the preset intensity curve; S1023, solidify the set of beacon transmission parameters into a set of cooperative control protocols, and uniformly regulate the transmission behavior of the directional radio beacon array according to the cooperative control protocols, so that the directional radio beacon array synthesizes radio energy to reconstruct the preset intensity curve in space, thereby forming a composite induction field, and constructing a non-physically existing virtual navigation beacon in the intensity peak area of the composite induction field.

[0028] Among them, step S1023 can specifically include the following process: structurally package each beacon transmission power and beam pointing data contained in the set of beacon transmission parameters according to a preset communication protocol to form a control instruction sequence containing timing information and beacon identity code; distribute the control instruction sequence according to the beacon identity code through a central scheduling node, and parse the received control instruction sequence by the local processing unit of each beacon in the directional radio beacon array; drive the antenna system of each beacon to perform synchronous transmission according to the parsed transmission power and beam pointing data through the local processing unit, so that the radio energy of each beacon interferes and superimposes in the predetermined spatial area to generate the composite induction field consistent with the preset intensity curve.

[0029] In the above scheme, directional radio beacon array refers to a group of devices capable of transmitting radio signals in specific directions, which work together to achieve a specific purpose. Cooperative control refers to the coordination between multiple beacon devices to adjust their transmission behavior according to unified instructions. Transmission parameters refer to the adjustable attributes of the beacon's transmitted signal, such as power, frequency, and phase. Composite induced field refers to the area of radio signal intensity distribution formed in space after multiple beacons transmit signals together, characterized by gradually increasing signal intensity in a specific direction in space. Virtual navigation beacon is a non-physical, virtual location composed of radio signal intensity peaks, used to guide the UAV. Induced path is a pre-planned trajectory that the UAV follows after deviating from the original flight path. Spatial anchor points are selected key location points on the induced path. Target signal intensity is the expected radio signal intensity value at each spatial anchor point. The preset intensity curve describes the ideal pattern of how the signal intensity gradually changes from weak to strong along the induced path. Antenna pattern describes the graphical representation of the antenna's ability to transmit or receive signals in different directions. Signal propagation attenuation is the phenomenon that the intensity of a radio signal decreases with increasing distance when propagating in space. Beacon transmission parameter set contains all the specific transmission settings required by the beacons to achieve the preset intensity curve. Cooperative control protocol is a set of rules or instructions that specify how the beacon array should act together and adjust the transmission parameters.

[0030] In the embodiments of the present application, first, in order to effectively guide the UAV, an induced path needs to be pre-planned through step S1021. This path is the trajectory that the UAV follows after deviating from the original flight path. The determination of this path is usually based on the comprehensive consideration of the current flight path of the UAV, the target deviation direction, and environmental obstacles. Once the induced path is determined, the system will decompose it into a series of discrete spatial anchor points. The selection of these anchor points takes into account the length and complexity of the induced path, ensuring that the entire induction process is fully covered. Subsequently, for each such spatial anchor point, the system will specify a target signal intensity value. These target intensity values exhibit a monotonic increasing trend along the direction of the induced path, which means that the closer to the end of the induction, the stronger the signal intensity that the UAV is expected to experience. In this way, a preset intensity curve is defined, depicting the ideal pattern of how the signal intensity experienced by the UAV on the induced path should gradually change from weak to strong in an ideal state, providing a clear blueprint for subsequent signal generation. For example, if the induced path extends from point A to point B, the signal intensity at point A may be set to a lower value, and the signal intensity at point B may be set to a higher value, while the target intensity of each anchor point in the middle of the path is set in order from low to high, forming a smooth signal intensity rising gradient.

[0031] Secondly, with the pre-set intensity curve meticulously planned in step S1021 as the final goal to be achieved, the system will conduct a complex reverse derivation process through step S1022. The core of this process is to calculate how to make the actual emitted signal accurately reproduce this pre-set curve in space. In the calculation, the system will comprehensively consider multiple key factors: first, the accurate deployment position of each beacon in the directional radio beacon array, because the position of the beacon directly affects its signal coverage and intensity distribution; second, the antenna pattern of each beacon, which describes the energy distribution characteristics of the beacon antenna in different directions, for example, some antennas may be more suitable for concentrated emission of signals in a certain direction; finally, the law of signal propagation attenuation, that is, the intensity of the radio signal naturally weakens with the increase of distance, the obstruction of obstacles, etc. By inputting these complex physical parameters and environmental factors into professional optimization algorithms or simulation models, the system can reversely derive a set of optimal beacon emission parameters. This set of parameters contains specific emission power, frequency, phase, and antenna pointing information for each beacon in the array, ensuring that they work together to accurately synthesize a composite signal field in space that perfectly matches the pre-set intensity curve. For example, if the pre-set curve requires a particularly high signal strength in a certain area, the system may calculate that multiple beacons need to simultaneously emit high-power signals to that area and adjust their phases to achieve signal superposition enhancement.

[0032] Finally, through step S1023, this set of beacon emission parameters accurately calculated in step S1022 will be solidified into a set of coordinated control protocols. This protocol clearly specifies when and how each beacon emits signals. Subsequently, according to this coordinated control protocol, the entire directional radio beacon array will uniformly regulate their emission behavior. This means that each beacon will strictly emit signals according to the parameters specified in the protocol, and their emission actions are accurately synchronized and coordinated. Through this precise coordinated emission, the radio frequency energy synthesized by the beacon array can reconstruct the pre-set intensity curve in space. The reconstructed signal field has a high consistency with the pre-set curve in intensity distribution, forming a composite induced field with specific directivity and gradually increasing intensity. In this composite induced field, the area with the highest and most concentrated signal intensity naturally forms a non-physical virtual navigation beacon. This virtual beacon will become the strongest signal source perceived by the UAV during flight, thereby guiding it to deviate in that direction.

[0033] In practical applications, for example in a UAV flight mission, a UAV that is currently performing a route task needs to be guided from the original route to a backup route in response to sudden airspace regulations. First, through step S1021, the operator will plan a smooth induction path on the flight map according to the current location of the UAV and the location of the backup route, which extends from the vicinity of the original route to the backup route. Then, several spatial anchor points are uniformly selected on the induction path, for example, a point is set every 50 meters. Then, set the target signal strength of these anchor points to be increasing, for example, the starting point is set to a relatively low signal strength value, and the terminal point is set to a relatively high signal strength value, thereby forming a preset intensity curve, which indicates the signal strength trend that the UAV should feel during the guidance process.

[0034] Subsequently, through step S1022, the system will use a special simulation and optimization software to input the preset intensity curve defined in step S1021 as the target. At the same time, the accurate geographic location information of the multiple directional radio frequency beacons deployed on the ground, and the detailed technical parameters of each beacon antenna, such as its signal coverage range and directivity, are input. The software also considers the signal propagation characteristics in the current environment, such as whether there are tall buildings blocking the signal, or whether there are multipath effects. Based on this information, the software will run complex algorithms, such as genetic algorithms or particle swarm optimization algorithms, to perform reverse deduction and calculate the optimal set of transmission parameters for each beacon. This includes the power size, signal frequency, phase, and accurate pointing angle of the antenna that each beacon needs to transmit, to ensure that all beacons work together to accurately generate a composite signal field that conforms to the preset intensity curve on the induction path.

[0035] Then, through step S1023, the set of beacon transmission parameters calculated in step S1022 is converted into a set of executable cooperative control protocols, which is like writing a detailed operating manual for each beacon. This protocol will be loaded into the central control system of the beacon array. Then, the control system will accurately and uniformly regulate the transmission behavior of all beacons according to the cooperative control protocol. This means that each beacon will transmit signals at the calculated power, frequency, and antenna pointing at the predetermined time point, 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 directivity and gradually increasing intensity. In this composite induction field, the area with the highest and most concentrated signal intensity naturally forms a non-physical virtual navigation beacon, which will become the strongest signal source perceived by the UAV during flight, thereby guiding it to deviate in that direction.

[0036] Finally, when the UAV enters this composite induction field, its on-board receiving equipment will perceive an abnormal increase in signal strength, and adjust its heading according to the signal strength gradient towards the virtual navigation beacon. For example, during flight, the UAV's signal receiver will continuously monitor the surrounding radio frequency signal strength. As it gradually approaches the induction path, it will find that the signal strength in a certain direction is significantly higher than the natural attenuation strength it should have on the original flight path. This abnormal signal enhancement will trigger the UAV's heading correction mechanism, causing it to automatically adjust its flight attitude and fly in the direction of the strongest signal strength, thus achieving a smooth transition from the original flight path to the standby flight path and finally completing the heading deviation induction.

[0037] The overall scheme of step S102 achieves precise, flexible and covert induction of UAV heading in complex radio environments by precisely planning the signal strength variation on the induction path and using 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 in signal interference and environmental changes, significantly improving the adaptability and reliability of UAV heading control.

[0038] S103, real-time monitoring of the radio frequency signal strength received by the UAV, comparing 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 increase in the radio frequency signal strength is identified, calculating a virtual deflection instruction for heading correction; Optionally, step S103 can specifically include the following steps, as shown in Figure 2 S1031, taking the radio frequency signal strength received by the UAV at fixed time intervals to form continuous signal strength segments, and simultaneously retrieving the background signal strength corresponding to the current position of the UAV from the reference environment map to form a reference strength segment; S1032, for each signal strength segment and reference strength segment within a time period, establishing a dynamic characteristic model that can depict the internal strength fluctuation, and quantifying the current signal change pattern and background signal natural fluctuation pattern through the dynamic characteristic model; ​The step S1032 can specifically include the following processes: projecting the signal intensity segment and the reference intensity segment onto a set of predefined standard waveform bases, and obtaining a set of projection coefficients for characterizing the profile features of the segment through projection operation; reorganizing the set of projection coefficients according to the inherent order of the standard waveform bases to construct a dynamic feature model capable of reflecting the internal energy fluctuation and change rate of the signal intensity segment; and extracting a specific combination of key projection coefficients from the dynamic feature model, and taking the numerical value of the specific combination as the final quantitative characterization of the change pattern of the current signal and the natural fluctuation pattern of the background signal.

[0039] S1033, comparing the dynamic feature model with the change pattern and the natural fluctuation pattern for compliance, and if the deviation degree shown by the comparison result exceeds a preset judgment threshold, converting the specific value of the deviation degree into a virtual deflection instruction containing a deflection angle and a rate.

[0040] In the above scheme, the radio frequency signal intensity refers to the strength of the radio signal received by the unmanned aerial vehicle. The dynamic change trend refers to the law of continuous change of signal intensity with time or spatial position. The reference environment map is a data model that describes the natural attenuation law of radio frequency signals in a specific route area. The natural attenuation model is a mathematical or statistical model in the reference environment map that reflects the normal weakening of signals with distance and environmental factors without interference. The compliance comparison refers to comparing the real-time monitored signal change with the preset natural attenuation model to determine whether they are consistent. The abnormal enhancement phenomenon refers to the case that the real-time signal intensity is significantly higher than the predicted value of the natural attenuation model, which usually implies the existence of external induced signals. The virtual deflection instruction is a non-physical instruction calculated by the system according to the signal abnormality for adjusting the flight direction and speed of the unmanned aerial vehicle. The signal intensity segment refers to the sequence of radio frequency signal intensity data continuously collected within a certain time period. The reference intensity segment is the expected signal intensity data sequence corresponding to the current position and time of the unmanned aerial vehicle extracted from the reference environment map. The dynamic feature model is a mathematical or statistical model for describing the fluctuation, change rate, peak value, etc. of signal intensity within a period of time. The change pattern refers to the specific form of the signal intensity actually received by the unmanned aerial vehicle within a period of time. The natural fluctuation pattern refers to the expected fluctuation mode of the signal intensity in the reference environment map under normal circumstances. The deviation degree refers to the difference between the actual signal change and the natural fluctuation mode. The judgment threshold is a preset value for judging whether the signal deviation degree reaches the critical value of triggering the instruction.

[0041] In the embodiments of the present application, first, through step S1031, the radio frequency receiving device carried by the unmanned aerial vehicle will continuously intercept and record the radio signal strength in the surrounding environment at very fixed and frequent time intervals during the flight process. It is equivalent to taking a snapshot of the current received signal strength every short period of time, and organizing these continuous snapshot data points in chronological order to form short signal strength segments. Each segment represents the signal strength change sequence experienced by the unmanned aerial vehicle within a certain time window. At the same time, the high-precision navigation system on the unmanned aerial vehicle will obtain its accurate current position information in real time, and according to this position information, the system will accurately retrieve the background signal strength data corresponding to the current position and time of the unmanned aerial vehicle from the pre-established reference environment map. These background signal strength data will also be organized into reference intensity 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 unmanned aerial vehicle should receive at the current location and in the normal environment, and it reflects the natural attenuation law of the signal without external induction.

[0042] Subsequently, through step S1032, for each signal strength segment obtained in step S1031, i.e. the actual received signal data of the unmanned aerial vehicle, and the corresponding reference intensity segment, i.e. the predicted normal signal data in the reference map, the system will use advanced data analysis techniques, such as time series analysis, wavelet analysis or statistical modeling methods, to establish a dynamic characteristic model that accurately depicts the internal intensity fluctuation changes of each of them. This model not only records the instantaneous intensity value of the signal, but more importantly, captures the dynamic characteristics of the signal, such as change trend, fluctuation frequency, change rate, peak and valley, etc. within a period of time. By constructing such a model, the system can deeply quantify the change pattern of the current signal actually received by the unmanned aerial vehicle, for example, whether the signal suddenly appears a sharp enhancement, how fast the enhancement is, how long the enhancement lasts, and whether the fluctuation pattern is abnormal; at the same time, it can also accurately quantify the natural fluctuation pattern of the background signal, for example, how the signal strength will change slowly with distance or environmental factors under normal circumstances, and what is the normal fluctuation range and frequency. For example, if the actual signal segment shows that the signal strength rises sharply from negative 80 units to negative 60 units in a short period of time, and the reference segment shows that the signal strength is slowly decreasing or fluctuating slightly around negative 80 units, then the two dynamic characteristic models will clearly reveal this significant difference.

[0043] Finally, through step S1033, the system will compare the dynamic characteristic model established in step S1032, representing the actual signal change, with the natural fluctuation pattern of the background signal, representing the normal expectation, for precise compliance comparison. This comparison process will use statistical methods or pattern recognition algorithms to evaluate the similarity or difference between the two. If the comparison result shows that the deviation between the two, i.e. the difference between the actual signal change and the normal expectation, exceeds the pre-set judgment threshold, it means that the UAV may have been interfered by external induced signals and has shown abnormal enhancement. Once this abnormality is identified, the system will immediately calculate the specific value of this deviation, for example, how many units the actual signal strength is higher than expected, or how many times faster its change rate is than expected. Subsequently, the system will convert this specific value into a virtual deflection command containing the deflection angle and rate. This command will accurately tell the UAV which direction and how fast it should adjust to respond to this abnormal signal, thereby guiding the UAV to deviate towards the induced signal source.

[0044] In actual application, for example, a UAV is performing a routine power line inspection task and is flying along a pre-set route. First, through step S1031, the radio frequency receiver on the UAV will continuously collect the radio signal strength data it receives at a frequency of 20 times per second, and form a signal strength segment of 20 data points per second. At the same time, the UAV will send its real-time accurate position information to the on-board processing unit, which will extract the radio signal strength data that the position should have under normal circumstances from the pre-established reference environment map of the area, and generate a corresponding reference strength segment. For example, if the UAV is flying over an open farmland, the reference strength segment will show a smooth curve of signal strength slowly decaying with distance, with a very small fluctuation range.

[0045] Subsequently, through step S1032, the UAV system will use advanced signal processing algorithms, such as Kalman filtering and trend prediction models, to establish dynamic characteristic models for each of the signal strength segments collected within one second and the corresponding reference strength segments. These models will capture key characteristics such as the average strength, maximum and minimum values, change slope, and short-term fluctuation frequency of the signal within this second. Through these models, the system can clearly quantify the change pattern of the actual signal received by the current UAV, for example, if an external induced signal suddenly intervenes, the actual signal strength may suddenly rise from -75 units to -50 units within a short period of time, showing a rapid and continuous enhancement pattern, with a change slope much greater than normal; 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.

[0046] Then, through step S1033, the system will compare the dynamic feature model of the actual signal with the natural fluctuation pattern of the background signal for consistency. The comparison algorithm will calculate the difference between the two, for example, by comparing the characteristic 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 the preset judgment threshold, the system will determine that an abnormal enhancement has occurred. At this time, the system will immediately calculate a virtual deflection instruction based on the degree and direction of this deviation. For example, if the actual signal is significantly enhanced in front of the right side of the drone, and the enhancement amplitude reaches 20 units, the system may calculate a deflection of 15 degrees to the right and adjust it at a speed of 1 meter per second to guide the drone to fly in the direction of signal enhancement.

[0047] Finally, this virtual deflection command is sent to the drone's flight control system, prompting it to adjust its course and move in the direction of the abnormally strong signal. For example, upon receiving a command to deflect 15 degrees to the right, the drone's flight control system immediately adjusts its control surfaces and engine thrust, turning its nose to the right and initiating flight in the new direction. This, in response to the external guidance signal, corrects its course and ultimately guides the drone safely off its intended route, avoiding potential risks.

[0048] The overall solution in step S103, described above, rapidly and accurately identifies abnormal signal enhancements by meticulously monitoring the drone's received RF signals in real time and intelligently comparing them with a pre-set natural environment model. This approach effectively avoids misjudgments and precisely generates virtual instructions for course correction based on the characteristics of abnormal signals. This ensures that the drone can respond promptly and effectively to external guidance in complex radio environments, achieving precise course deviation control.

[0049] S104, integrating the virtual deflection instruction with the current flight attitude and speed 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 operation; Optionally, step S104 may specifically include the following steps: S1041, parsing the virtual deflection instruction, extracting the heading deflection angle and the pseudo-range parameter for estimating the distance, and synchronously retrieving the current flight speed vector of the UAV output by the UAV inertial navigation unit; S1042. In a dynamic coordinate system centered on the UAV, calculate the heading deflection angle and the flight speed vector to generate a guidance vector pointing to the virtual navigation beacon; S1043, based on the pointing of the guide vector, combined with the pseudo-range parameter, the position of the virtual navigation beacon is converted into a temporary three-dimensional space waypoint in the global geographic coordinate system recognizable by the inertial navigation system.

[0050] In the above scheme, the virtual deflection instruction is an internal control signal generated by the UAV during flight according to the abnormal change of the environment signal, which contains the heading information that the UAV needs to adjust and the pseudo-range parameter used to estimate the target distance. The heading deflection angle is the angle size and direction that the current flight direction of the UAV needs to adjust. The pseudo-range parameter is a value used to indirectly calculate the distance, which is not a direct distance measurement value, but a distance-related quantity calculated through signal characteristics. Flight attitude refers to the attitude of the UAV in space, including pitch, roll and yaw angles, reflecting the inclination and orientation of the UAV relative to the ground. Flight speed information refers to the speed and direction of the current flight of the UAV. The inertial navigation system is a navigation system that measures and calculates the position, velocity and attitude of an object using the principle of inertia, which can navigate independently of external signals. The temporary three-dimensional space waypoint is a temporary target position point set for the UAV in three-dimensional space, which is a navigation instruction that the inertial navigation system can understand and execute.

[0051] In the embodiments of the present application, first, through step S1041, the control system inside the UAV will receive the virtual deflection instruction generated in the previous step. This instruction is not a direct navigation coordinate, but an abstract correction signal. The UAV needs to deeply analyze this signal, just like deciphering an encrypted telegram. The core of the analysis is to identify and extract two key information: one is the heading deflection angle, which clearly indicates how many degrees the UAV needs to deflect to the left or right, and the direction of deflection; the second is the pseudo-range parameter, which is a value that indirectly reflects the target distance, which may be calculated based on signal strength, signal arrival time difference and other factors, used for subsequent estimation of target position. While analyzing the instruction, the UAV will also actively request its built-in inertial unit to obtain the flight speed vector of the UAV at the current time. This speed vector contains the speed and accurate direction information of the current flight of the UAV, for example, the UAV is currently flying at a speed of 15 meters per second in the northeast direction. The synchronous acquisition of these information is the basis for ensuring the accuracy of subsequent calculation.

[0052] Subsequently, through step S1042, the UAV constructs a dynamic, real-time updated local coordinate system with itself as the reference point. In this coordinate system centered on the UAV, the system will perform precise mathematical operations on the heading deflection angle extracted in step S1041 and the flight speed vector obtained synchronously. This operation process is not simply addition and subtraction, but involves vector rotation and synthesis, aiming to integrate the intention of the deflection angle into the current motion state of the UAV. Through this operation, the system can generate a guide vector pointing to the virtual navigation beacon. This guide vector is a quantity with direction and size, which clearly indicates the direction of the virtual navigation beacon relative to the current position of the UAV. This process ensures that even if the UAV is constantly moving, the relative position of the target beacon can be accurately determined. For example, the UAV is currently flying north, and the speed vector points north. If the heading deflection angle requires a 10-degree right deflection, then in the coordinate system centered on the UAV, the guide vector will point to the direction 10 degrees to the right of the current heading of the UAV, and its length is associated with the flight speed vector, indicating a relative guide direction.

[0053] Finally, through step S1043, the UAV, based on the direction of the guide vector generated in step S1042, combines the pseudo-range parameter extracted in step S1041, and performs a series of complex spatial projection calculations. This process is like accurately positioning a point in three-dimensional space according to a direction and an estimated distance. The pseudo-range parameter plays a key role here, which is used to estimate the distance between the virtual navigation beacon and the UAV. By combining this relative distance information with the direction of the guide vector and performing coordinate transformation operations, the UAV can accurately convert the relative position of the virtual navigation beacon determined in its local coordinate system into a temporary three-dimensional space waypoint that the inertial navigation system can recognize in the global geographic coordinate system. This waypoint is a specific coordinate containing longitude, latitude and height information, which is a navigation target that the inertial navigation system can directly understand and use to adjust the flight path. This conversion process is a key step in realizing the correction of the UAV's heading, which converts abstract deflection instructions into executable navigation instructions. For example, the guide vector points 10 degrees to the right of the front of the UAV, and the pseudo-range parameter is calculated to estimate that the virtual navigation beacon is about 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 north of the current position at the same height, which is a temporary three-dimensional space waypoint that the inertial navigation system can recognize.

[0054] In practical application, an unmanned aerial vehicle is performing a complex reconnaissance task, when its sensors suddenly detect an abnormal signal, the system immediately generates a virtual deflection command. First, through step S1041, the flight control computer inside the unmanned aerial vehicle immediately analyzes this virtual deflection command. It accurately extracts from the command a heading deflection angle of 15 degrees to the left, as well as a pseudo-range parameter for distance estimation, for example, indicating that the target is approximately 80 meters away. At the same time, the flight control computer also obtains the current flight velocity vector of the unmanned aerial vehicle from the inertial navigation unit (IMU) in real time, assuming that the unmanned aerial vehicle is flying steadily at a speed of 25 meters per second in the positive south direction. These key data are quickly integrated to prepare for subsequent calculations.

[0055] Then, through step S1042, the flight control computer establishes a dynamic, real-time updated local three-dimensional coordinate system centered on the unmanned aerial vehicle itself. In this coordinate system, it performs precise vector operations on the previously analyzed heading deflection angle of 15 degrees to the left and the current flight velocity vector in the positive south direction. This operation process simulates how the relative motion direction of the unmanned aerial vehicle will change if it adjusts according to the deflection angle at the current speed. Through this calculation, the system generates a clear guide vector pointing to the virtual navigation beacon, which not only indicates the direction, but also implicitly contains relative speed information, ensuring the accuracy of the target position.

[0056] Subsequently, through step S1043, the flight control computer performs spatial projection calculations based on the direction indicated by the newly generated guide vector, combined with the previously obtained pseudo-range parameter. This process is like marking a point in a three-dimensional coordinate system according to a direction and an estimated distance. The pseudo-range parameter is used here to accurately estimate the straight-line distance between the virtual navigation beacon and the unmanned aerial vehicle. Through a series of complex coordinate transformation algorithms, the flight control computer accurately converts the relative position of the virtual navigation beacon in the local coordinate system of the unmanned aerial vehicle into a temporary three-dimensional spatial waypoint in the global geographical coordinate system of the Earth that the inertial navigation system can directly recognize and execute. This waypoint contains accurate longitude, latitude, and altitude information and is a navigation target that the inertial navigation system can directly use to adjust the flight path of the unmanned aerial vehicle. Once this waypoint is determined, the inertial navigation system will immediately adjust the flight attitude and speed of the unmanned aerial vehicle to accurately fly to this temporary waypoint, thereby achieving the correction of the heading.

[0057] The overall scheme of step S104 can efficiently convert the need for course correction caused by external environmental signal abnormalities into navigation instructions that can be directly executed by the UAV inertial navigation system. By accurately analyzing the virtual deflection instructions and combining the real-time flight status of the UAV, it constructs a guide direction in the dynamic coordinate system, and finally generates accurate temporary three-dimensional space waypoints through spatial projection and coordinate transformation. This enables the UAV to quickly and accurately respond to environmental changes, smoothly adjust the course, effectively avoid potential interference or danger, ensure the smooth progress of the flight mission, and significantly improve the autonomous navigation and risk avoidance capabilities of the UAV in complex electromagnetic environments.

[0058] S105, inject the temporary three-dimensional space waypoint as a navigation task with high priority into the autonomous flight control module of the UAV, force it to abandon the specific flight route and execute a maneuver to fly to the temporary three-dimensional space waypoint, and complete the course deviation induction of the UAV.

[0059] Optionally, step S105 can specifically include the following steps: S1051, compile the three-dimensional coordinates of the temporary three-dimensional space waypoint into navigation instructions that can be directly analyzed by the UAV flight control system, and assign a special identifier with the highest execution priority to the navigation instructions; S1052, submit the navigation instructions carrying the special identifier to the task scheduling unit of the autonomous flight control module, and use the special identifier to interrupt the flight task currently being executed along the specific flight route; S1053, immediately after interrupting the flight task, re-plan the flight route and generate a new set of bottom-layer control instructions to directly control the rotor system of the UAV, so that the UAV turns to fly to the temporary three-dimensional space waypoint, and completes the course deviation induction of the UAV.

[0060] In the above scheme, the temporary three-dimensional space waypoint is a target position point temporarily set in three-dimensional space that can be recognized and executed by the UAV inertial navigation system. The navigation instruction is a command that the UAV flight control system can directly understand and execute, used to guide the UAV to fly to a specific location or perform a specific action. The special identifier is a label assigned to the navigation instruction, indicating that the instruction has the highest execution priority and can forcibly interrupt the currently executing task. The autonomous flight control module is the core system inside the UAV responsible for managing and executing flight tasks, which includes a task scheduling unit and a route re-planning function. The task scheduling unit is a component of the autonomous flight control module, responsible for receiving, sorting, and distributing various flight tasks. The specific route is the pre-set flight path of the UAV being executed. The underlying control instruction is a fine operation command directly acting on the UAV hardware (such as the rotor system), used to realize the attitude and position control of the UAV. The rotor system is the key component of the UAV to provide lift and thrust, and its rotation speed and inclination are adjusted to control the flight of the UAV.

[0061] In the embodiments of the present application, first, through step S1051, the UAV system will receive the accurate three-dimensional coordinate information of the temporary three-dimensional space waypoint calculated in the previous step. In order to let the UAV understand and execute this new target, the system needs to compile these coordinates. This compilation process is like translating human understandable geographic coordinates (such as longitude, latitude, and altitude) into specific format digital instructions that the UAV flight control system can directly recognize and process. This usually involves data encoding, format conversion, and verification steps to ensure the accuracy and integrity of the instructions. More importantly, while generating this navigation instruction, the system will mark it with a special identifier with the highest execution priority. This identifier is like an emergency pass, which clearly tells 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, thereby laying the foundation for subsequent forced heading deviation induction.

[0062] Subsequently, through step S1052, the navigation instruction that has been compiled and carries the highest priority special identification will be immediately submitted to the task scheduling unit inside the UAV autonomous flight control module. The task scheduling unit is the traffic control center of the UAV flight task, which is responsible for receiving, evaluating and distributing all flight instructions. When it receives this navigation instruction with special identification, it will immediately recognize its emergency attribute. With this special identification, the task scheduling unit will forcibly interrupt the UAV's current flight task along a specific route. This interruption is not simply a pause, but means that the task scheduling unit will immediately stop sending instructions related to the current route to the UAV's underlying control system, and clear or suspend the status of the current route task, ensuring that the UAV no longer continues to fly along the original path, thus giving full control to execute the new induced action.

[0063] Finally, through step S1053, after the current flight task is forcibly interrupted, the UAV system will immediately start the internal route replanning program in milliseconds. This program will take the temporary three-dimensional space waypoint just injected as the new and only target, and quickly calculate an optimal and most direct flight path from the current position of the UAV to the target point. This calculation process will take into account the current attitude, speed, remaining power of the UAV and environmental factors, etc., to ensure the feasibility and safety of the path. Once the new route is planned, the system will generate a new set of fine-grained underlying control instructions based on this new path. These instructions are micro-operation commands that directly act on the UAV hardware, especially the rotor system, such as precisely adjusting the speed, angle of inclination, rudder deflection angle, etc. of each rotor. Through the precise control of these underlying instructions, the UAV will be forced to change its flight attitude and direction, making it quickly turn and begin to execute the maneuver of flying to the temporary three-dimensional space waypoint, thus completing the deviation induction of the UAV and achieving immediate and precise control of the UAV's flight path.

[0064] In actual application, a UAV is executing an important regional monitoring task, flying according to a pre-set complex route. Suddenly, the system detects the need for emergency heading induction and generates an accurate temporary three-dimensional space waypoint. First, through step S1051, the UAV's internal navigation computer immediately compiles the accurate coordinates of this temporary three-dimensional space waypoint, such as a specific point located about 150 meters in front of the current position at the same height, into binary navigation instructions that the UAV flight control system can directly recognize and process. At the same time, in order to ensure that this instruction can take effect immediately, the system will assign it a special identification with the highest priority, which is like giving it a "top secret, execute immediately" label, ensuring that it has absolute priority among all instructions.

[0065] Then, through step S1052, this navigation instruction that has been compiled and carries the highest priority special identifier will be quickly and forcefully submitted to the task scheduling unit inside the UAV autonomous flight control module. Upon receiving this instruction, the task scheduling unit will immediately recognize its highest priority and without hesitation interrupt the regional monitoring task that the UAV is currently performing. This means that the UAV will immediately stop flying according to the original monitoring flight path, all flight instructions related to the monitoring task will be paused or cleared, and the UAV will completely break away from the original task mode to prepare for the new induced flight task, ensuring that there is no delay or interference.

[0066] Subsequently, through step S1053, at the moment when the current monitoring task is forcibly interrupted, the UAV system will immediately start the internal route re-planning algorithm. This algorithm will take the just-injected temporary three-dimensional space waypoint as the new and only target, quickly calculate an optimal and safest flight path from the current position of the UAV to this target point. Once the new route planning is completed, the system will generate a series of fine bottom-level control instructions based on this new path, which directly act on the rotor system of the UAV. For example, it will precisely adjust the rotation speed, inclination angle of each rotor, and attitude control parameters of the UAV, so that the UAV can quickly change the flight direction and attitude, forcibly turn and start flying towards that temporary three-dimensional space waypoint. Through this series of rapid and precise actions, the UAV successfully deviates from the original flight path, achieves the expected heading correction, and effectively responds to the emergency situation.

[0067] The overall scheme of step S105 described above can ensure that in emergency or necessary situations, the UAV can be forced to deviate from the preset flight path and turn to perform specific induced flight tasks. It converts the temporary waypoint into a high-priority navigation instruction, interrupts the existing task using a special identifier, and then performs rapid route re-planning and generates bottom-level control instructions, thereby achieving precise and forced control of the UAV's heading. This enables the UAV to quickly respond to external inducement in complex environments, effectively avoiding potential risks or performing special tasks, significantly improving the adaptability and task execution flexibility of the UAV in dynamic environments.

[0068] The following is a complete embodiment for steps S101-S105: As Figure 3As shown, first, by step S101, a drone is performing a routine patrol task, flying steadily along a preset route. To establish a precise radio frequency environment benchmark, the drone's onboard radio frequency receiver continuously captures various background radio frequency signals around the flight path during flight, such as from communication base stations, radio broadcasts, environmental noise, etc. At the same time, the drone's inertial navigation unit outputs high-precision position coordinate information, including longitude, latitude, and altitude. These captured radio frequency signal strength data are accurately associated with the corresponding position coordinates and transmitted to the onboard processing unit. The processing unit uses these data to construct a detailed benchmark environment map through complex algorithms, such as signal strength and distance attenuation model fitting. This map accurately depicts the inherent attenuation law of radio frequency signal strength with spatial position along this specific route under normal circumstances, providing a reference for subsequent abnormal signal recognition.

[0069] Second, by step S102, an array composed of multiple directional radio frequency beacons is pre-deployed on one side of the drone's preset route. These beacons are not randomly emitting signals, but are cooperatively controlled by a central control system to precisely adjust parameters such as transmission power, frequency, and phase of each beacon. The purpose of this cooperative control is to cleverly generate a composite induced field in space. The characteristic of this induced field is that its radio frequency signal strength shows a gradually increasing trend in a certain direction, i.e., the signal strength gradually increases in a certain direction. By precisely adjusting the parameters of the beacon array, a virtual navigation beacon that does not physically exist can be constructed in a specific area of the composite induced field, such as the point of highest signal strength. This virtual beacon, although not physical, has signal characteristics sufficient to simulate a real navigation source, providing a target for subsequent induced drone deviation from the route.

[0070] Then, by step S103, the drone's onboard radio frequency receiver continuously and real-time monitors the radio frequency signal strength it receives during flight. These real-time data are constantly compared with the benchmark environment map constructed in step S101 for conformity. This comparison process is like the drone constantly asking itself: whether the current received signal strength conforms to the previously recorded normal attenuation pattern. When the drone identifies that the received radio frequency signal strength has deviated from the natural attenuation model represented by the benchmark environment map, such as a sudden signal strength far exceeding expectations in a certain area, it indicates that the drone may be disturbed by an external induced field. At this time, the intelligent processing unit inside the drone will immediately start the calculation program to calculate a virtual deflection instruction for course correction according to the characteristics such as the amplitude and direction of the signal abnormal enhancement. This instruction contains the heading angle that the drone needs to adjust and the pseudo-range parameter for distance estimation.

[0071] Then, through step S104, the UAV system will deeply fuse the virtual deflection instruction calculated in step S103 with the current flight attitude and speed information of the UAV. This fusion process involves complex coordinate transformation operations, aiming to convert the abstract deflection instruction into a specific spatial coordinate that the inertial navigation system can understand. Through these operations, the system can accurately map the non-physical virtual navigation beacon constructed in step S102 into a temporary three-dimensional space 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, which is an accurate target that the inertial navigation system can directly use to adjust the flight path of the UAV, thus providing a clear navigation basis for the UAV to perform off-course maneuvering.

[0072] Finally, through step S105, the UAV system will inject the temporary three-dimensional space waypoint generated in step S104 into the autonomous flight control module of the UAV as a navigation task with the highest execution priority. This injection process will force the UAV to abandon the specific route task it is currently performing, regardless of how important or urgent the task is. After receiving this high-priority task, the autonomous flight control module will immediately start route re-planning, calculate an optimal path from the current location to the temporary three-dimensional space waypoint, and generate a new set of bottom-level control instructions. These instructions directly control the rotor system of the UAV, such as adjusting the speed and inclination of each rotor, so that the UAV quickly turns and performs a maneuvering action to fly to the temporary waypoint. Through this series of rapid and forced operations, the UAV successfully deviates from the original route, achieving the expected heading deviation induction.

[0073] The UAV heading deviation induction method based on inertial navigation signal simulation provided by the present application constructs a fine radio frequency environment reference, skillfully uses a directional radio frequency beacon array to construct a non-physical virtual navigation beacon, and monitors the abnormal changes of the received signal of the UAV in real time. Once an anomaly is detected, the system can quickly calculate the heading correction instruction and convert it into a temporary waypoint that the inertial navigation system can recognize. Finally, through high-priority task injection and forced route re-planning, the UAV is effectively guided to deviate from the original route, thereby significantly improving the concealment, accuracy and real-time performance of the UAV route induction in a complex radio environment, effectively dealing with potential threats or achieving specific task objectives.

[0074] Figure 4 A specific implementation structure diagram of a UAV heading deviation induction system based on inertial navigation signal simulation provided by an embodiment of the present application is shown in Figure 4 The system can include: The constructing module 41 is configured to first capture the background radio frequency signals around the flight path of the UAV flying on a specific route, and associate the position coordinates output by the airborne inertial navigation unit, to construct a reference environment map depicting the inherent spatial attenuation gradient of the radio frequency signals of the specific route; The deploying module 42 is configured to deploy a directional radio frequency beacon array on one side deviating from the specific route, to generate a composite induced field with a spatially directional intensity gradually increasing by cooperatively controlling the transmission parameters of each beacon, and to construct a non-physically existing virtual navigation beacon in the intensity peak area of the composite induced field; The monitoring module 43 is configured to monitor the radio frequency signal strength received by the UAV in real time, to compare the dynamic change trend of the radio frequency signal strength with the natural attenuation model represented by the reference environment map, and to calculate a virtual deflection instruction for heading correction when identifying an abnormal enhancement phenomenon of the radio frequency signal strength deviating from the natural attenuation model; The operation module 44 is configured to fuse the virtual deflection instruction with the current flight attitude and speed information of the UAV, to map the virtual navigation beacon to a temporary three-dimensional space waypoint recognizable by the inertial navigation system through coordinate transformation operation; The inducing module 45 is configured to inject the temporary three-dimensional space waypoint as a navigation task with high priority into the autonomous flight control module of the UAV, to force it to abandon the specific route and instead perform a maneuvering action of flying to the temporary three-dimensional space waypoint, to complete the heading deviation induction of the UAV.

[0075] The UAV heading deviation induction system based on inertial navigation signal simulation of the embodiments of the present application is used to implement the foregoing UAV heading deviation induction method based on inertial navigation signal simulation, and therefore the specific embodiments in the UAV heading deviation induction system based on inertial navigation signal simulation can be seen from the foregoing embodiment part of the UAV heading deviation induction method based on inertial navigation signal simulation, and the specific embodiments can be referred to the description of the corresponding embodiment part, which will not be described here again.

[0076] The present 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 the UAV heading deviation induction method based on inertial navigation signal simulation.

[0077] The present application also provides a computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the steps of the UAV heading deviation induction method based on inertial navigation signal simulation.

[0078] In one example embodiment, the computer readable storage medium described above can include, but is not limited to, a U disk, a read-only memory, a random access memory, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store computer programs.

[0079] Embodiments of the present application also provide a computer program product, which comprises a computer program, and the computer program, when executed by a processor, implements the steps in any of the above-mentioned embodiments of the method for inducing deviation of a UAV heading based on inertial navigation signal simulation.

[0080] Those skilled in the art will further appreciate that the functions of the examples described herein, including any related steps of a method, can be implemented using electronic hardware, computer software, or any combination of the two. To clearly illustrate this interchangeability of hardware and software, various examples have been described herein generally in terms of their functionality, which is the manner in which the various examples are described herein. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

[0081] The above provides a method and system for inducing deviation of a UAV heading based on inertial navigation signal simulation. The principles and implementation modes of the present application are described herein by applying specific examples, and the above description of the examples is only used to help understand the method of the present application and its core idea. It should be pointed out that, for those skilled in the art, some improvements and modifications can be made to the present application without departing from the principles of the present application, and these improvements and modifications also fall within the protection scope of the present application.

Claims

1. A method for inducing UAV heading deviation based on inertial navigation signal simulation, characterized in that: include: For a drone flying a specific route, the system first captures the background RF signals around the drone's flight path and correlates them with the position coordinates output by the onboard inertial navigation unit to construct a baseline environmental map that depicts the inherent spatial attenuation gradient of the RF signals along the specific route. On the side deviating from the specific route, an array of directional radio frequency beacons is deployed, and by cooperatively controlling the transmission parameters of each beacon, a composite induction field with gradually increasing directional intensity in space is generated, and a non-physical virtual navigation beacon is constructed in the intensity peak area of ​​the composite induction field; monitoring the radio frequency signal strength received by the drone in real time, comparing the dynamic change trend of the radio frequency signal strength with the natural attenuation model represented by the reference environment map for consistency, and calculating a virtual deflection instruction for course correction when an abnormal increase in the radio frequency signal strength that violates the natural attenuation model is identified; The virtual deflection instruction is integrated 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 recognizable by the inertial navigation system through coordinate transformation operation; The temporary three-dimensional space waypoint is injected into the autonomous flight control module of the UAV as a navigation task with high priority, forcing it to abandon the specific route and instead execute a maneuver to fly to the temporary three-dimensional space waypoint, thereby completing the heading deviation induction of the UAV.

2. The method according to claim 1, characterized in that The method comprises deploying a directional radio frequency beacon array on the side deviating from the specific route, generating a composite induction field with gradually increasing directional intensity in space by collaboratively controlling the transmission parameters of each beacon, and constructing a non-physical virtual navigation beacon in the intensity peak area of ​​the composite induction field, including: Decomposing the induced path into a series of spatial anchor points, and assigning a target signal strength to each spatial anchor point to define a preset intensity curve with monotonically increasing directional intensity along the induced path; Taking the preset strength curve as the solution target, comprehensively considering the deployment position, antenna pattern and signal propagation attenuation of each beacon in the directional radio frequency beacon array, reversely deriving a set of beacon transmission parameters that can meet the preset strength curve; The beacon transmission parameter set is solidified into a set of collaborative control protocols, and the transmission behavior of the directional radio frequency beacon array is uniformly regulated according to the collaborative 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 induction field, and constructing a non-physically existing virtual navigation beacon in the intensity peak area of ​​the composite induction field.

3. The method according to claim 1, characterized in that The real-time monitoring of the radio frequency signal strength received by the drone, comparing the dynamic change trend of the radio frequency signal strength with the natural attenuation model represented by the reference environment map, and calculating a virtual deflection instruction for course correction when an abnormal increase in the radio frequency signal strength that violates the natural attenuation model is identified, includes: Intercepting the radio frequency signal strength received by the drone at fixed time intervals to form continuous signal strength segments, and simultaneously retrieving the background signal strength corresponding to the current position of the drone from the reference environment map to form a reference strength segment; For the signal intensity segments and the reference intensity segments within each time period, a dynamic feature model capable of describing internal intensity fluctuations is established, and the dynamic feature model is used to quantify the changing form of the current signal and the natural fluctuation form of the background signal; The dynamic feature model is compared with the change form and the natural fluctuation form for consistency. If the deviation degree shown by the comparison result exceeds the preset judgment threshold, the specific value of the deviation degree is converted into a virtual deflection instruction including the deflection angle and rate.

4. The method according to claim 1, wherein The step of fusing the virtual deflection instruction with the current flight attitude and speed information of the UAV and mapping the virtual navigation beacon into a temporary three-dimensional space waypoint recognizable by an inertial navigation system through coordinate transformation operation includes: Parsing the virtual deflection command, extracting the heading deflection angle and pseudo-range parameters for estimating the distance, and synchronously calling the current flight speed vector of the UAV output by the UAV inertial navigation unit; In a dynamic coordinate system centered on the UAV, calculating the heading deflection angle and the flight speed vector to generate a guidance vector pointing to the virtual navigation beacon; Based on the direction of the guidance vector and in combination with the pseudorange parameter, a spatial projection calculation is performed to convert the position of the virtual navigation beacon into a temporary three-dimensional space waypoint identifiable by the inertial navigation system in a global geographic coordinate system.

5. The method according to claim 1, wherein The temporary three-dimensional space waypoint is injected into the autonomous flight control module of the UAV as a navigation task with a high priority, forcing the UAV to abandon the specific route and instead execute a maneuver to fly to the temporary three-dimensional space waypoint, thereby completing the heading deviation induction of the UAV, including: Compiling the three-dimensional coordinates of the temporary three-dimensional space waypoint into a navigation instruction that can be directly parsed by the UAV flight control system, and assigning a special identifier with the highest execution priority to the navigation instruction; Submitting the navigation instruction carrying the special identifier to the task scheduling unit of the autonomous flight control module, and using the special identifier to interrupt the currently executing flight mission along the specific route; After the flight mission is interrupted, the route is immediately replanned and a new set of low-level control instructions are generated to directly control the rotor system of the UAV, so that the UAV turns to fly toward the temporary three-dimensional space waypoint, thereby completing the heading deviation induction of the UAV.

6. The method according to claim 2, characterized in that The beacon transmission parameter set is solidified into a set of collaborative control protocols, and the transmission behavior of the directional radio frequency beacon array is uniformly regulated according to the collaborative 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 induction field, and constructing a non-physical virtual navigation beacon in the intensity peak area of ​​the composite induction field, including: The beacon transmission parameter set includes the transmission power and beam pointing data of each beacon, and is structured and encapsulated according to a preset communication protocol to form a control instruction sequence including timing information and beacon identity code; The control instruction sequence is distributed by a central scheduling node according to the beacon identity code, and the local processing unit of each beacon in the directional radio frequency beacon array parses the received control instruction sequence; The local processing unit drives the antenna system of each beacon to perform synchronous transmission based on the analyzed transmission power and beam pointing data, so that the radio frequency energy of each beacon interferes and superimposes in a predetermined spatial area, generating the composite induced field that matches the preset intensity curve.

7. The method according to claim 3, characterized in that The method includes establishing a dynamic feature model capable of describing internal intensity fluctuations for the signal intensity segments and the reference intensity segments within each time period, and quantifying the changing form of the current signal and the natural fluctuation form of the background signal through the dynamic feature model, including: Projecting the signal intensity segment and the reference intensity segment onto a set of predefined standard waveform bases, and obtaining a projection coefficient set for characterizing segment profile features through a projection operation; Reorganizing the projection coefficient set according to the intrinsic order of the standard waveform basis to construct a dynamic characteristic model capable of reflecting the energy fluctuation and change rate within the signal intensity 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 changing form of the current signal and the natural fluctuation form of the background signal.

8. A UAV heading deviation guidance system based on inertial navigation signal simulation, characterized in that: include: A construction module is configured to capture background radio frequency signals around the flight path of a UAV flying a specific route, correlate these signals with the position coordinates output by an onboard inertial navigation unit, and construct a baseline environmental map that depicts the inherent spatial attenuation gradient of the radio frequency signals along the specific route. a deployment module for deploying a directional radio frequency beacon array on a side deviating from the specific route, generating a composite induction field with gradually increasing directional intensity in space by collaboratively controlling the transmission parameters of each beacon, and constructing a non-physical virtual navigation beacon in the intensity peak area of ​​the composite induction field; a monitoring module for monitoring the radio frequency signal strength received by the drone in real time, comparing the dynamic change trend of the radio frequency signal strength with the natural attenuation model represented by the reference environment map for consistency, and calculating a virtual deflection instruction for course correction when an abnormal increase in the radio frequency signal strength that violates the natural attenuation model is identified; a calculation module, configured to fuse the virtual deflection instruction with the current flight attitude and speed information of the UAV, and map the virtual navigation beacon into a temporary three-dimensional space waypoint recognizable by an inertial navigation system through coordinate transformation calculation; The induction 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 to the temporary three-dimensional space waypoint, thereby completing the heading deviation induction of the UAV.

9. An electronic device, characterized in that: include: memory for storing computer programs; A processor, configured to implement the steps of the UAV heading deviation induction 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, which, when executed by a processor, can implement the UAV heading deviation induction method based on inertial navigation signal simulation as described in any one of claims 1 to 7.

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