Unmanned aerial vehicle exposure risk assessment method based on flight path and speed

Abstract

The invention relates to a flight path speed-based unmanned aerial vehicle exposure risk assessment method, which is used for assessing a flight exposure risk when an unmanned aerial vehicle flies according to a to-be-assessed flight path in a detector network, and is based on operation parameters of all detectors in the detector network and the to-be-assessed flight path of the unmanned aerial vehicle. And calculating the flight exposure risk value of the current flight path to be evaluated by using the unmanned aerial vehicle flight exposure risk evaluation model. According to the method, quantitative calculation is carried out on the unmanned aerial vehicle flight exposure risk assessment model by using the idea of curve integration and the mode of numerical integration, and the flight exposure risk value of the unmanned aerial vehicle flying according to the to-be-assessed flight path can be clearly calculated, so that the assessment of the flight exposure risk of the unmanned aerial vehicle in the detector network is realized; the flight exposure risk can be used as a flight risk quantitative index to provide support for planning of the flight path of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) flight technology, and in particular to a UAV exposure risk assessment method based on track and speed. Background Art

[0002] A drone's flight trajectory is subject to numerous constraints, both in terms of control technology and the airspace environment. It's not just the spatial path from the mission's origin to its destination; it also reflects multiple characteristics, such as its speed and attitude. Planning a drone's flight trajectory not only impacts the timeliness of the mission but also, due to the need for concealment, directly impacts flight safety and mission success. Therefore, risk assessment of planned drone flight trajectories is essential.

[0003] In a complex airspace environment, drones face multiple risk factors during mission execution, including their own risks, flight environment risks, operator risks, and the risk of being detected by detectors. Therefore, it is necessary to scientifically and effectively obtain, analyze and evaluate these risk factors during flight to provide support for drone trajectory planning.

[0004] However, existing drone trajectory planning methods generally only consider the risks posed by the drone itself, the flight environment, and the operator. They fail to systematically analyze and assess the risk of being detected by detectors during flight, leading to surveillance, interference, and attack by anti-drone systems. Surveillance, interference, and attack by anti-drone systems, including detectors, can lead to mission failure and compromise the drone's safety. Therefore, in-depth research is needed to assess the risk of detection by detectors while the drone is following a trajectory to ensure mission timeliness and flight safety. Summary of the Invention

[0005] Based on this, it is necessary to provide a drone exposure risk assessment method based on track speed to address the above technical problems. This method can effectively assess the flight exposure risk of a drone when it flies along the track to be assessed in a detector network, and can provide reliable flight risk quantitative indicators for drone track planning.

[0006] The present invention provides a UAV exposure risk assessment method based on track and speed, which is used to assess the flight exposure risk of a UAV when it flies along a track to be assessed in a detector network. The method uses a UAV flight exposure risk assessment model to calculate the flight exposure risk value of the current track to be assessed based on the operating parameters of all detectors in the detector network and the track to be assessed of the UAV.

[0007] The drone flight exposure risk assessment model is:

[0008]

[0009] Where R represents the flight exposure risk value when the UAV flies along the trajectory to be evaluated, t b Indicates the end time of the track to be evaluated, t a represents the starting time of the track to be evaluated, F(u) represents the exposure of the UAV at a certain track point in the detector network, n represents the total number of detectors in the detector network, and f(i,u) represents the number of UAVs at u on the track to be evaluated. (t) The flight exposure under detector i at point i, σ represents the standard deviation of the deviation angle, and λ i represents the signal attenuation characteristic of detector i, R i represents the detection radius of detector i, R ei represents the uncertain detection distance of detector i, θ if represents the standard illumination angle of detector i, θ ih represents the maximum error deviation angle of detector i, d iu represents the distance between the UAV and the detector i, θ iu Represents the direction angle of the UAV relative to the detector i.

[0010] In one embodiment, based on the operating parameters of all detectors and the flight path of the UAV to be evaluated, a UAV flight exposure risk assessment model is used to calculate the flight exposure risk value of the current flight path to be evaluated, including the following steps:

[0011] Generate a time-position correspondence information table for the UAV based on the trajectory to be evaluated, the time-position correspondence information table including multiple different sampling moments and the UAV flight coordinate information corresponding to each moment;

[0012] The UAV flight exposure risk assessment model is converted into a UAV flight exposure risk assessment discrete model by using discretization and numerical integration methods;

[0013] The operating parameters of all detectors and the data in the drone's time-position correspondence information table are input into the drone flight exposure risk assessment discrete model to calculate the flight exposure risk value of the track to be evaluated.

[0014] In one embodiment, determining a time-position correspondence information table of a UAV based on a track to be evaluated includes the following steps:

[0015] Obtain geographic information data of the drone flight area;

[0016] 3D modeling and gridding of the UAV flight area based on geographic information data;

[0017] The gridded UAV flight area model is used to discretize the trajectory to be evaluated and obtain the time-position correspondence information table of the UAV.

[0018] In one embodiment, the discrete model for human-machine multi-detector flight exposure risk assessment is:

[0019]

[0020] Where Δt represents the sampling time interval, Indicates the total number of sampling points rounded down, f(i,u (m×Δt) ) represents the exposure of the UAV at different sampling points m relative to different detectors i, Indicates that the drone is on the track to be evaluated Flight exposure under detector i at time point, (t b -t a )modΔt represents the weighted time of the non-integral sampling at the destination.

[0021] In one embodiment, the operating parameters of the detector include the position distribution, detection direction, detection distance and signal attenuation characteristics of the detector.

[0022] In one embodiment, the time interval between any two adjacent sampling moments is less than or equal to 2 seconds. In one embodiment, the greater the flight exposure risk value, the higher the flight exposure risk of the drone when flying along the trajectory to be evaluated in the detector network.

[0023] In one embodiment, the signal coverage of each detector in the detector network is independent of each other and there is no sharing mechanism.

[0024] The beneficial effects of the present invention are as follows: the present invention takes into account factors such as the position distribution of each detector, the detection direction, the detection distance, and the signal attenuation characteristics, and first determines the exposure of the drone at a certain track point in the detector network through the relationship between the drone and the detector during flight, and then combines the exposure with the flight speed of the drone to establish a drone flight exposure risk assessment model, and then uses the curve integral method to perform numerical calculations on the model, which can clearly calculate the flight exposure risk value of the drone when flying along the track to be evaluated, thereby realizing the assessment of the drone's flight exposure risk in the detector network. The flight exposure risk can be used as a quantitative indicator of flight risk to provide support for the planning of drone flight tracks. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a schematic diagram of the UAV trajectory planning provided in an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of a single detector coverage signal provided in an embodiment of the present invention;

[0027] Figure 3This is a schematic diagram of a directional detector coverage signal plane provided in an embodiment of the present invention;

[0028] Figure 4 This is a schematic diagram of calculating the exposure risk of a drone flight provided in an embodiment of the present invention;

[0029] Figure 5 Schematic diagram of sampling points for drone trajectory planning provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0031] In one embodiment, a UAV exposure risk assessment method based on track and speed is used to assess the flight exposure risk of a UAV when it flies along a track to be assessed in a detector network. The method is based on the operating parameters of all detectors in the detector network and the UAV's track to be assessed, and uses a UAV flight exposure risk assessment model to calculate the flight exposure risk value of the current track to be assessed.

[0032] Specifically, the flight exposure risk of this embodiment refers to the probability that the drone will reach or cross the detection network composed of enemy optical detection, radio detection, acoustic detection and other detectors and be discovered by the enemy's detectors when the drone is performing reconnaissance, strike, delivery and other missions, and the detection network composed of enemy optical detection, radio detection, acoustic detection and other detectors is basically known.

[0033] The UAV mission area detector network generally includes multiple detectors distributed at different locations. It should be noted that the detectors in the detector network of this embodiment operate independently of each other and the relevant operating parameters of each detector are known.

[0034] Specifically, detector operating parameters include their location, detection direction, detection range, and signal attenuation characteristics. Signal coverage for each detector in the detector network is independent and has no sharing mechanism. Detection direction includes the standard illumination angle and maximum error deviation angle, and detection range includes the detection radius and uncertainty detection range.

[0035] For example, the trajectory of the drone to be evaluated is as follows: Figure 1 As shown, Figure 1 In the figure, lines 1 and 2 represent two tracks to be evaluated, sn represents the starting point of the track to be evaluated, and dn represents the end point of the track to be evaluated. There are four detectors: detector A, detector B, detector C, and detector D. The spatial coordinates of the drone during flight and the speed at each coordinate can be determined based on the track to be evaluated.

[0036] Specifically, the drone flight exposure risk assessment model in this embodiment is:

[0037]

[0038] Where R represents the flight exposure risk value when the UAV flies along the trajectory to be evaluated, t b Indicates the end time of the track to be evaluated, t a represents the starting time of the track to be evaluated, F(u) represents the exposure of the UAV at a certain track point in the detector network, n represents the total number of detectors in the detector network, and f(i,u) represents the number of UAVs at u on the track to be evaluated. (t) The flight exposure under the detector i at point θ, σ represents the standard deviation of the deflection angle, that is, the deflection angle is at θ if -θ ih and θ if +θ ih The standard deviation between i represents the signal attenuation characteristic of detector i, R i represents the sensing radius of detector i, R ei represents the uncertain perception distance of detector i, θ if represents the standard illumination angle of detector i, θ ih represents the maximum error deviation angle of detector i, d iu represents the distance between the UAV and the detector i, θ iu represents the direction angle of the UAV relative to the detector i, and s represents the standard deviation of the normal distribution.

[0039] The larger the flight exposure risk value R, the higher the flight exposure risk of the drone when flying along the trajectory to be assessed in the detector network. It should be noted that for any track segment, its flight exposure risk value should be equal to the sum of the risk values ​​of the segmented track using any form of discretization. In other words, the calculation of flight exposure risk is not affected by track discretization.

[0040] In this embodiment, the process of establishing the drone flight exposure risk assessment model is as follows:

[0041] (1) Analyze the physical properties of the detector, such as its location distribution, detection direction, detection distance, and signal attenuation characteristics, and complete the calculation modeling of the drone exposure under a single independent detector.

[0042] The exposure degree of a drone under an independent detector refers to the relative degree to which a drone is detected by a single detector without considering the exposure time factor and its cumulative effect.

[0043] like Figure 2 and Figure 3As shown, the coordinates of the UAV flight area (x i ,y i ,z i ) is equipped with a detector i, whose illumination angle is The detection radius, uncertain detection distance, standard illumination angle and maximum error deviation angle are known respectively, then at time t, the position of the object at u in space is (t) =(x (t) ,y (t) ,z (t) )’s direction vector relative to the detector i for:

[0044]

[0045] The distance and direction angle of the drone relative to the detector i are:

[0046]

[0047] in:

[0048]

[0049] Considering the characteristics of the detector detection radius, uncertain detection distance, standard illumination angle and error offset angle, it can be seen that the detector has a deterministic coverage area (such as Figure 3 The E area shown is within the detection radius and the standard illumination angle), the deterministic non-coverage area (such as Figure 3 Area A shown is outside the uncertain sensing distance or error offset angle) and non-deterministic coverage area (such as Figure 3 The B / C / D areas shown are located within the uncertain sensing distance and the error offset angle. This embodiment establishes a coverage probability model as shown in formula (6) based on the directional detector network signal coverage model. Among them, the piecewise functions of f(i,u) represent Figure 3 The five regions A / B / C / D / E in λ i represents the signal attenuation characteristic of detector i.

[0050]

[0051] f(i,u) is u (t) The exposure of the UAV at point i under detector i.

[0052] (2) The exposure model of the UAV at a certain point in the detector network is completed by using the joint probability density method.

[0053] In a detector network containing multiple detectors, if the cooperation between detectors is not considered, it is assumed that the signal coverage of each detector is independent of each other. According to the joint probability distribution formula, it can be known that in u(t) The probability F(u) that a point drone is covered by n detector signals is shown in the following formula, and F(u) is defined as the drone exposure degree of a point in the detector network.

[0054]

[0055] (3) Taking the UAV spatial trajectory and speed as the object, complete the establishment of a model for UAV flight exposure risk.

[0056] The exposure risk of drone flight needs to be based on the exposure of the drone in the detector network, considering the exposure of the drone's complete flight trajectory from the starting point to the end point in time and space. That is, the slower the drone's flight speed in the enemy detection network, the longer it stays in the air, so the drone's flight speed is inversely proportional to the flight exposure risk. This embodiment is based on the idea of ​​curve integration. Assume that the drone is at a track point u with a length of ds at time t. (t) , the speed is v(t), then the exposure risk factor of the micro-segment can be defined as follows:

[0057]

[0058] like Figure 4 As shown in Figure 2, for trajectory C, through curve integration, the flight exposure risk R of the drone is:

[0059]

[0060] in, and Substituting into formula (9), we can get

[0061]

[0062] In one embodiment, a flight exposure risk value of a current flight track to be evaluated is calculated using a UAV flight exposure risk assessment model based on the operating parameters of all detectors and the flight track to be evaluated of the UAV, including the following steps:

[0063] S101: Generate a time-position correspondence information table for a UAV based on the track to be evaluated, where the time-position correspondence information table includes multiple different sampling moments and UAV flight coordinate information corresponding to each moment.

[0064] like Figure 5 As shown, Figure 5 yes Figure 1 Schematic diagram of the discretized sampling of the evaluated track routes 1 and 2. Since the calculation of flight exposure risk is not affected by track discretization, to simplify the calculation, this embodiment discretizes the evaluated track and selects the flight times and corresponding coordinate information of some points as model input for calculation.

[0065] The interval between any two adjacent sampling moments is less than or equal to 2s.

[0066] Specifically, determining the time-position correspondence information table of the UAV based on the track to be evaluated includes the following steps:

[0067] S1011. Obtain geographic information data of the drone's flight area. The geographic information data must at least include the location data of each detector in the detector network.

[0068] S1012. Perform three-dimensional modeling and gridding of the UAV flight area based on geographic information data.

[0069] Convert geographic coordinate system into spatial rectangular coordinate system during 3D modeling.

[0070] S1013. Discretize the track to be evaluated using the gridded UAV flight area model to obtain a time-position correspondence information table for the UAV.

[0071] The purpose of gridding is to discretize the trajectory to be evaluated. For example, the UAV flight area, i.e., the UAV classification airspace, is divided into 10m×10m×10m grids.

[0072] S102. Convert the UAV flight exposure risk assessment model into a UAV flight exposure risk assessment discrete model by using discretization and numerical integration.

[0073] The drone flight exposure risk assessment model is difficult to complete through integration, so the model is discretized by discretization and numerical integration to make the model calculation easier.

[0074] S103: Input the operating parameters of all detectors and the data in the time-position correspondence information table of the UAV into the UAV flight exposure risk assessment discrete model to calculate and obtain the flight exposure risk value of the track to be assessed.

[0075] Specifically, the discrete model for drone flight exposure risk assessment is:

[0076]

[0077] Where Δt represents the sampling time interval, Indicates the total number of sampling points rounded down, f(i,u (m×Δt) ) represents the exposure of the UAV at different sampling points m relative to different detectors i, Indicates that the drone is on the track to be evaluated Flight exposure under detector i at time point, (t b -t a)modΔt represents the weighted time of the non-integral sampling at the destination.

[0078] For example, if the flight time is 86 seconds and the sampling time is 3 seconds, the weighted time of the non-integrated sampling at the destination is 2 seconds.

[0079] This embodiment uses software programming to calculate the flight exposure risk value R. The pseudo code for calculating the flight exposure risk value R is as follows:

[0080]

[0081]

[0082] Using software programming to calculate the flight exposure risk value R can improve the calculation efficiency of the flight exposure risk value R.

[0083] The method of this embodiment can effectively evaluate the flight exposure risk of a UAV under the detector network when the operating parameters of each detector in the detector network are known, and can provide a more reliable flight risk quantitative indicator for the UAV's trajectory planning.

[0084] The above-described embodiments merely represent several implementation methods of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for assessing the exposure risk of unmanned aerial vehicles based on track and speed, characterized in that: Used to assess the flight exposure risk of a UAV when flying along a track to be assessed in a detector network. The method uses a UAV flight exposure risk assessment model to calculate the flight exposure risk value of the current track to be assessed based on the operating parameters of all detectors in the detector network and the track to be assessed. The UAV flight exposure risk assessment model is: Where R represents the flight exposure risk value when the UAV flies along the trajectory to be evaluated, t b Indicates the end time of the track to be evaluated, t a represents the starting time of the track to be evaluated, F(u) represents the exposure of the UAV at a certain track point in the detector network, n represents the total number of detectors in the detector network, and f(i,u) represents the number of UAVs at u on the track to be evaluated. (t) The flight exposure under detector i at point i, σ represents the standard deviation of the deviation angle, and λ i represents the signal attenuation characteristic of detector i, R i represents the detection radius of detector i, R ei represents the uncertain detection distance of detector i, θ if represents the standard illumination angle of detector i, θ ih represents the maximum error deviation angle of detector i, d iu represents the distance between the UAV and the detector i, θ iu Represents the direction angle of the UAV relative to the detector i.

2. The UAV exposure risk assessment method based on track and speed according to claim 1 is characterized in that: Based on the operating parameters of all detectors in the detector network and the UAV's flight path to be assessed, the UAV flight exposure risk assessment model is used to calculate the flight exposure risk value of the current flight path to be assessed, including the following steps: Generate a time-position correspondence information table of the UAV based on the track to be evaluated, wherein the time-position correspondence information table includes multiple different sampling moments and the UAV flight coordinate information corresponding to each moment; The UAV flight exposure risk assessment model is converted into a UAV flight exposure risk assessment discrete model by using discretization and numerical integration; The operating parameters of all detectors and the data in the time-position correspondence information table of the UAV are input into the UAV flight exposure risk assessment discrete model to calculate the flight exposure risk value of the track to be assessed.

3. The method for assessing the exposure risk of unmanned aerial vehicles based on track and speed according to claim 2, wherein: Determining a time-position correspondence information table of the UAV based on the track to be evaluated includes the following steps: Obtain geographic information data of the drone flight area; Performing three-dimensional modeling and gridding of the UAV flight area based on the geographic information data; The track to be evaluated is discretized using a gridded UAV flight area model to obtain a time-position correspondence information table of the UAV.

4. The method for assessing the exposure risk of unmanned aerial vehicles based on track and speed according to claim 2, wherein: The discrete model for drone flight exposure risk assessment is: Where Δt represents the sampling time interval, Indicates the total number of sampling points rounded down, f(i,u (m×Δt) ) represents the exposure of the UAV at different sampling points m relative to different detectors i, Indicates that the drone is on the track to be evaluated Flight exposure under detector i at time point, (t b -t a )modΔt represents the weighted time of the non-integral sampling at the destination.

5. The UAV exposure risk assessment method based on track and speed according to claim 3 or 4, characterized in that: The operating parameters of the detector include the position distribution of the detector, the detection direction, the detection distance and the signal attenuation characteristics.

6. The method for assessing the exposure risk of unmanned aerial vehicles based on track and speed according to claim 5, characterized in that: The interval between any two adjacent sampling times is less than or equal to 2s.

7. The method for assessing the exposure risk of unmanned aerial vehicles based on track and speed according to claim 6, characterized in that: The larger the flight exposure risk value is, the higher the flight exposure risk of the UAV when flying along the trajectory to be evaluated in the detector network.

8. The method for assessing the exposure risk of unmanned aerial vehicles based on track and speed according to claim 7, characterized in that: The signal coverage of each detector in the detector network is independent of each other and there is no information sharing mechanism.

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