Small approaching type unmanned aerial vehicle flight path deception feasibility evaluation and correction method and system

Abstract

The invention belongs to the technical field of electronic countermeasures, discloses a small approaching type unmanned aerial vehicle flight path deception feasibility evaluation and correction method, and discloses a flight path plan making method for implementing flight path deception interference on a radar by using a small approaching type unmanned aerial vehicle, which comprises the steps of expected flight path feasibility evaluation and expected flight path correction. The advantages of the small approaching type unmanned aerial vehicle are developed to the maximum extent. The method is suitable for flight path deception of a single unmanned aerial vehicle to a single radar and flight path deception of an unmanned aerial vehicle group to a radar net. According to the flight path plan making method for implementing flight path deception on the radar by using the unmanned aerial vehicle, power and speed constraints of the unmanned aerial vehicle are considered, feasibility evaluation is carried out on the designed expected flight path, a set of correction strategies is provided for an infeasible area, it is guaranteed that the corrected flight path is close to the original expected flight path as much as possible, and the flight path deception rate is improved. And the effect of the unmanned aerial vehicle is maximized.

Description

Technical Field

[0001] The present invention belongs to the technical field of electronic countermeasures, and particularly relates to a method and system for evaluating and correcting the feasibility of track deception based on small close-proximity unmanned aerial vehicles. Background Art

[0002] Track deception interference is an advanced deception interference pattern formed by combining various deception interference patterns. Through track deception interference, one or more false target tracks can be formed on the radar to cover our true intentions or disrupt the command decision-making and fire control of the radar side.

[0003] Early track deception interference platforms were limited to manned aircraft platforms such as electronic warfare aircraft. Due to limitations such as their own radar cross-sectional area, these interference platforms are difficult to approach the opponent's radar for operation. Therefore, the power required for interference is large, and the requirements for reconnaissance sensitivity and platform maneuverability are also high. Small close-proximity unmanned aerial vehicles (hereinafter referred to as "UAVs") are not easily detected by the opponent due to their "low and small" characteristics. When interfering with the radar system, they can penetrate into the radar danger zone and complete complex interference tasks. In addition, due to the continuous development of UAV "swarm" technology, UAVs can be connected together through networking, and each UAV has the ability to become a central node. Even in the case of losing some UAVs, they can still receive instructions normally and complete tasks, which has significant advantages.

[0004] CN202311512305 takes minimizing the path required for UAV clusters to implement track deception as the optimization goal and reasonably plans the tracks of UAV swarms. CN202411041814 addresses the problem that in the prior art, when the interference UAV resources are relatively insufficient, the "one-to-one" interference mode cannot be achieved, resulting in poor interference effects, and plans the tracks of UAV clusters to improve the interference effect. CN202311028426 aims at variable-speed antenna scanning, controls the interference release timing by measuring the position of the radar main lobe and the scanning period, and thus realizes continuous radial or oblique false tracks. CN202410762474 provides a matrix deception interference method for networked radars, which can be used to reduce the requirements for cooperation accuracy and the flight control ability of UAV nodes. CN202410215378 optimizes the UAV track planning solution model from the perspective of reducing the calculation amount. However, the existing methods do not consider the interference power limitation and maneuver speed limitation of the UAV platform, and the planned expected tracks may have infeasible regions due to insufficient interference power or insufficient UAV maneuver speed.

[0005] Through the above analysis, the problems and defects existing in the prior art are as follows: Existing methods do not consider the interference power limit and maneuvering speed limit of the UAV platform, and the planned expected flight path may have infeasible regions due to insufficient interference power or insufficient UAV maneuvering speed. Summary of the Invention

[0006] In view of the problems existing in the prior art, the present invention provides a method for evaluating and correcting the feasibility of flight path deception based on a small close-in UAV.

[0007] To avoid confusion of related concepts, the basic meanings of related terms used in the patent are clarified as follows.

[0008] Definition (expected target): A target with specific target characteristics recognized by the radar through interference with the radar by presetting parameters.

[0009] Definition (expected flight path): A false target flight path that the interferer hopes to be detected by the disturbed radar according to requirements.

[0010] Definition (corrected flight path): A feasible expected flight path after correction, which may be different from the expected flight path in the shape or speed of some regions.

[0011] Definition (coordinated flight path): When implementing flight path deception on a networked radar, the expected flight path after overall coordination of the corrected flight paths of each radar. All UAVs in the UAV group formulate a flight path deception interference plan based on this flight path.

[0012] The relevant prerequisite assumptions applicable to the present invention include: Assumption 1: The radars discussed in the patent are all land-based fixed radars and their geographical locations are known.

[0013] Assumption 2: The radars discussed in the patent are all self-transmitting and self-receiving pulse system radars.

[0014] Assumption 3: The miniaturized UAVs discussed in the patent have sufficient electronic countermeasure reconnaissance and analysis capabilities, can measure the signal parameters of the radar, and infer the corresponding scanning rules (for mechanically scanned radars).

[0015] Assumption 4: For the convenience of problem discussion, it is assumed that before a single UAV implements flight path interference on a single radar, the UAVs have all hovered at the corresponding positions of the starting points, and start to implement interference when detecting the main lobe irradiation of the radar antenna.

[0016] Assumption 5: The main lobe of the UAV antenna is always aligned with the radar.

[0017] Assumption 6: The UAV makes a circular motion around the radar when implementing interference. In fact, the UAV can plan flight paths of any shape. For the convenience of discussion, this assumption is made in the present invention, but the relevant conclusions and flight path deception methods can be easily extended to the case of any flight path of the UAV.

[0018] Assumption 7: Without considering the height factor and only considering the situation where the UAV is projected onto a two-dimensional plane, the relevant methods can be easily extended to the three-dimensional case.

[0019] Assumption 8: The UAV can complete the transition from hovering to the maximum speed or from the maximum speed to hovering in a short period of time. For the convenience of problem discussion, this time is ignored in the present invention, and this kind of ignoring usually does not affect the implementation effect of the track deception. The performance parameters of the relevant UAV platforms involved in the present invention are all referenced from the multi-rotor UAVs publicly available on the Internet.

[0020] The present invention is implemented as follows. A method for evaluating and correcting the feasibility of track deception based on a small close-proximity UAV includes: Step 1: According to the requirements of the track deception mission, determine the radar cross section (RCS) of the expected target and the expected track; determine the initial flight path of the UAV according to the principle that the UAV makes a circular motion around the radar during interference implementation.

[0021] The position information of the track discussed in the present invention is a matrix composed of a large number of coordinate points containing position coordinate information, and the speed information is represented by the method of segmentally assigning values to the expected track. Based on the above two pieces of information, the time information of the expected track can be calculated to form a complete track information matrix.

[0022] Step 2: Evaluate the feasibility of track deception.

[0023] Case 1, the interference object is a mechanically scanned radar.

[0024] ① Focus on considering the shape of the expected track, and there is no significant constraint on the track speed. At this time, electronic interference only needs to make the false dot traces captured by the radar in each antenna scanning cycle fall on the expected track, and the position where the next false dot trace appears should move a certain distance along the changing direction of the expected track accordingly. After the radar's calculation, the mutually associated false dot traces are connected one by one, and the expected track that meets the shape can be formed.

[0025] The feasible region of track deception satisfies the following formula:

[0026] In the formula, is the distance between the UAV and the radar, is the distance between the expected target and the radar; , are the interference transmission power and antenna gain of the UAV respectively; , are the radar transmission power and antenna gain respectively; Ae is the effective area of the radar receiving antenna, is the effective receiving area of the radar antenna in the direction of the UAV; is the radar cross section of the UAV; is the radar cross section of the expected target.

[0027] ② At the same time, consider the shape and speed of the expected flight path. Denote as the angular velocity of the expected flight path relative to the radar, as the angular velocity of the radar beam scanning, then at least satisfy , the expected flight path may be feasible. At this time, according to the radar scanning law and the non-radial velocity of the expected flight path, calculate the encounter point between the main lobe of the radar and the expected flight path, which is the expected trace; the intersection point of the line connecting the expected trace and the radar and the flight path of the UAV is the position where the UAV releases interference, denoted as the expected interference point. Considering the judgment conditions for the start and end of the radar flight path, reference [1] points out that the "4 / 3" logic is more commonly used in engineering, that is, the judgment condition for the start of the flight path is that at least 3 mutually correlated traces are detected by the radar within 4 consecutive antenna scanning periods. Denote the angle between the current radar antenna main lobe axis and the next expected trace as , then the encounter time can be expressed as:

[0028] Usually require

[0029] Among them, is the radar antenna scanning period.

[0030] The UAV only needs to move to the next expected interference point position within the expected interference point time interval and emit the corresponding interference signal to successfully achieve flight path deception. Considering the limited flight speed of the UAV, there may be a situation where it is impossible to reach the next interference point within the specified time due to the too far distance between adjacent interference points. Therefore, the following relationship should be satisfied between the expected flight path speed and the UAV speed: Among them, is the maximum rotational angular velocity of the UAV, and this value is related to the distance between the UAV and the radar. Therefore, when conducting flight path feasibility evaluation, this formula should be discussed in combination with the UAV interference position.

[0031] [1] An Hong, Yang Li. Radar Electronic Warfare System Modeling and Simulation [M]. Beijing: National Defense Industry Press, 2017, page 93. Case 2, the interference object is an electronically scanned radar.

[0032] To achieve main lobe azimuth false target deception, the UAV must be on the line connecting the radar and the expected target at the current moment. Figure 1 In, A and B are two traces in the expected flight path, , are the intersection points of the connecting lines between points A and B and the radar with the UAV flight path. To keep the UAV azimuth consistent with the expected track point, the UAV angular velocity and the expected track angular velocity should be the same, that is:

[0033] For a specific expected track point, as long as , the expected track point can be considered feasible, where is the maximum flight speed value of the UAV.

[0034] Case 3, the interfering object is a networked radar.

[0035] When the UAV swarm conducts track deception on the radar network, the specific method is to use multiple UAVs to simultaneously deceive the radar nodes in the radar network (usually the number of UAVs is not less than the number of radars), ensuring that the deception tracks formed in different radars are consistent in space and time to meet the information fusion requirements of the radar network. Among them, the evaluation process of the track deception feasibility of a single UAV node for a specific radar node is the same as the previous process. When using the UAV swarm to conduct track deception on the radar network, based on the deception interference of a single UAV on a single radar, the present invention considers the following constraints: ① SRC homology test constraint. That is, the expected target track points formed by interfering with different radars in the radar network meet the requirements of the radar (Space Resolution Cell, SRC) homology test, that is where A and B are the target positions detected by any two radar stations in the radar network, is the distance from the i-th radar station to target A, is the distance from the i-th radar station to target B, is the resolution unit size of the i-th radar (in the above formula, i = 1, 2, which can be extended to any number of radar cases). If at the same moment, the targets detected by any two radars in the networked radar meet the above formula, it is considered that the expected target track points at this position meet the homology test requirements, as shown in Figure 2 .

[0036] ② UAV cooperation constraint.

[0037] Since multiple UAVs are selected to conduct track deception on the networked radar, and the UAVs move in the same time and adjacent spaces. At this time, if the distance between the radars in the radar network is large, due to the close-in interference of the UAVs, there will be no overlap of the flight routes between adjacent UAVs; if the distance between the radars in the radar network is small, the tracks between the UAVs may overlap, which may cause the UAVs to collide with each other. At this time, attention should be paid to adjusting the flight radius of the UAVs around the radar to avoid collisions.

[0038] This step will give the feasible region and the infeasible region of the expected flight path. If there is no infeasible region in the output result, jump to step 5; otherwise, proceed downward.

[0039] Step 3: Correction of the infeasible region of the expected flight path.

[0040] For the correction of the infeasible region of the expected flight path, the present invention provides two correction strategies, which can be selected according to actual needs. Generally, correction strategy 1 is preferably recommended. When correction strategy 1 cannot meet the requirements, correction strategy 2 is used. Of course, in actual operation, correction strategy 2 can also be directly used.

[0041] Correction strategy 1: Adjust the flight path of the unmanned aerial vehicle (UAV).

[0042] Approach the original expected flight path as closely as possible, that is, keep the parameters of the original expected flight path unchanged, adjust the flight path of the UAV, and increase the feasible region of the expected flight path by approaching the radar with the UAV.

[0043] Denote the flight radius of the UAV around the radar as . When adjusting , it can be adjusted according to the step size , and there is

[0044] It should be noted that shall not be less than its safety distance , and there is

[0045] Among them, is set by the user according to experience. After changing one step size each time, execute step 2, re-evaluate the feasibility of the expected flight path until there is no infeasible region in the evaluation result, and then jump to step 5; if the infeasible region of the expected flight path cannot be eliminated by adjusting the flight path of the UAV, jump to step 4.

[0046] Correction strategy 2: Adjust the speed of the expected flight path.

[0047] Keep the shape of the original expected flight path as unchanged as possible, and achieve correction by reducing the speed of the expected flight path in the infeasible region.

[0048] It should be noted that when interfering with the radar network, for a UAV swarm, each UAV has its specific corrected flight path. The faster the expected flight path speed, the more difficult it is for the UAV to achieve. Each UAV must mainly splice the local corrected flight paths with a slower speed of the corrected expected flight path to form a unified corrected flight path, so that this flight path is feasible for any UAV in the UAV swarm.

[0049] Step 4: Overall adjustment of the flight track and formulation of the flight track deception implementation plan Implementation conditions: The feasibility of the expected flight track has been evaluated, and there is no infeasible area in the expected flight track or the infeasible area becomes feasible after correction.

[0050] Calculate the time delay parameter of the UAV according to the relationship among the radar, the expected interference point of the UAV, and the expected echo , there is

[0051]

[0052]

[0053] Calculate the interference power parameter of the UAV , there is Apply the classical Doppler frequency shift formula to calculate the Doppler frequency shift parameter of the interference signal emitted by the UAV. When the UAV hovers for interference, only the relative velocity between the expected flight track and the radar needs to be considered when calculating the Doppler frequency shift; when the UAV interferes while moving, there is a relative velocity between the UAV itself and the radar, and this velocity should also be taken into account when calculating the Doppler frequency shift to ensure that the false target echo formed has the relative velocity characteristics of the expected flight track in the view of the radar.

[0054] For mechanical scanning radars and phased array radars, only the UAV flight path and interference parameter scheme formed after correction need to be adopted.

[0055] For networked radars, if correction strategy 2 is used during the correction process of the infeasible area of the expected flight track, it is necessary to first conduct overall adjustment of the flight track to form a unified feasible expected flight track, and then formulate the UAV flight path and interference parameter schemes respectively. [[ID=зо]]

[0056] So far, the flight track deception plan based on small close-in UAVs has been formulated. The specific implementation of flight track deception can be carried out according to the plan.

[0057] Combined with the above technical solutions and the technical problems solved, please analyze the advantages and positive effects of the technical solution to be protected by the present invention from the following aspects: First, the present invention discloses a method for formulating a pre-plan for using small close-in UAVs to deceive the radar flight track, including the feasibility evaluation of the expected flight track and the correction of the expected flight track. The core of the present invention is to start from the actual maximum flight speed and maximum interference power constraints of the UAV platform to evaluate the feasibility of the expected flight track; for the infeasible area in the flight track, on the premise that the corrected flight track should approximate the expected flight track as much as possible, the expected flight track is corrected to obtain an actually feasible corrected flight track.

[0058] This invention discloses a method for developing a trajectory plan for radar track deception using a small, close-in drone. The method includes feasibility assessment and correction of the expected trajectory to maximize the advantages of small, close-in drones. This method is applicable to both single-drone trajectory deception of a single radar and drone swarms of radars.

[0059] The present invention proposes a method for formulating a trajectory plan for using a drone to deceive radar. The method takes into account the power and speed constraints of the drone, conducts a feasibility assessment on the designed expected trajectory, and provides a set of correction strategies for infeasible areas to ensure that the corrected trajectory is as close as possible to the original expected trajectory, thereby maximizing the effectiveness of the drone.

[0060] Second, the technical solution of the present invention provides a method for formulating a plan for implementing track deception interference of a small close-in UAV, taking the UAV interference power and maneuvering speed constraints into consideration, filling the technical gap in the feasibility assessment and correction of the expected track.

[0061] The technical solution of the present invention first determines the expected trajectory based on user or mission requirements, then conducts a feasibility assessment considering relevant constraints, and makes corrections to infeasible areas, properly compromises and resolves the contradiction between the satisfaction of the false trajectory with mission requirements and the physical feasibility of the drone.

[0062] Reference [2] points out that the existing track deception methods can be divided into two categories according to the problem-solving ideas. One is the inverse problem method, that is, first designing a false track and then solving the UAV track; the other is the forward problem method, first considering the UAV track and then generating a false track.

[0063] In the inverse problem approach, a false trajectory is designed to meet the mission requirements, but it does not reflect the concept of a feasible working area. Given the relevant constraints of the drone, the resulting drone trajectory may not be achievable. This type of solution also does not provide a targeted correction and adjustment method for the infeasible area of the expected trajectory. In the forward problem approach, a false trajectory can be designed to meet the relevant constraints of the drone to facilitate physical realization, but the false trajectory designed in this way may be significantly different from the mission requirements.

[0064] The technical method of the present invention overcomes the above technical biases. First, the expected trajectory is determined according to the mission requirements, and then the feasibility is evaluated by considering the relevant constraints of the UAV. At the same time, a method for correcting infeasible areas is provided under the premise of meeting the mission requirements as much as possible.

[0065] [2] Bai Peng, Wang Yubing, Liang Xiaolong, Zhang Jiaqiang, Wang Weijia. A review of drone track deception on radar network[J]. Acta Aeronautica et Aeronautica Sinica, 2020, 41(10): 023912. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is the flow chart of the method for evaluating and correcting the feasibility of small close - in UAV track deception provided by the embodiments of the present invention.

[0067] Figure 2 It is the structural block diagram of the system for evaluating and correcting the feasibility of small close - in UAV track deception provided by the embodiments of the present invention.

[0068] Figure 3 It is the basic flow chart of the method provided by the embodiments of the present invention.

[0069] Figure 4 It is the scenario description diagram of implementing track deception on a phased - array radar provided by the embodiments of the present invention.

[0070] Figure 5 It is the schematic diagram of SRC homology inspection provided by the embodiments of the present invention.

[0071] Figure 6 It is the radar antenna pattern provided by the embodiments of the present invention.

[0072] Figure 7 It is the flow chart of the expected track planning provided by the embodiments of the present invention.

[0073] Figure 8 It is the hand - drawn track diagram provided by the embodiments of the present invention.

[0074] Figure 9 It is the fitted track curve graph provided by the embodiments of the present invention.

[0075] Figure 10 It is the schematic diagram of the expected track used in the present invention provided by the embodiments of the present invention.

[0076] Figure 11 It is the track deception feasible region graph when the UAV is 10 km away from the radar provided by the embodiments of the present invention.

[0077] Figure 12 It is the track deception feasible region graph when the UAV is 15 km away from the radar provided by the embodiments of the present invention.

[0078] Figure 13 It is the graph of the meeting point of the main lobe of the antenna and the expected track ( = 5 s, = 0.9 Mach) provided by the embodiments of the present invention.

[0079] Figure 14 It is the feasible region graph of the UAV implementing track deception on a mechanical scanning radar (the radius of the UAV flying around the radar is 5 km) provided by the embodiments of the present invention.

[0080] Figure 15It is a feasibility evaluation diagram for a drone to implement track deception on a phased array radar provided by an embodiment of the present invention.

[0081] Figure 16 It is a corrected track diagram (correction strategy 1) for a drone to implement track deception on a mechanical scanning radar provided by an embodiment of the present invention.

[0082] Figure 17 It is a corrected track diagram (correction strategy 2) for a drone to implement track deception on a mechanical scanning radar provided by an embodiment of the present invention.

[0083] Figure 18 It is a corrected track diagram (correction strategy 1) for a drone to implement track deception on a phased array radar provided by an embodiment of the present invention.

[0084] Figure 19 It is a corrected track diagram (correction strategy 2) for a drone to implement track deception on a phased array radar provided by an embodiment of the present invention.

[0085] Figure 20 It is a schematic diagram of a scenario for a drone to implement track deception on a networked radar provided by an embodiment of the present invention.

[0086] Figure 21 It is a feasible region diagram for a drone to implement track deception on a mechanical scanning radar in a radar network provided by an embodiment of the present invention.

[0087] Figure 22 It is a corrected track (correction strategy 1) for a drone to implement track deception on a mechanical scanning radar in a radar network provided by an embodiment of the present invention.

[0088] Figure 23 It is a corrected track (correction strategy 2) for a drone to implement track deception on a mechanical scanning radar in a radar network provided by an embodiment of the present invention.

[0089] Figure 24 It is a feasible region diagram for a drone to implement track deception on a phased array radar in a radar network provided by an embodiment of the present invention.

[0090] Figure 25 It is a corrected track (correction strategy 1) for a drone to implement track deception on a phased array radar in a radar network provided by an embodiment of the present invention.

[0091] Figure 26 It is a corrected track (correction strategy 2) for a drone to implement track deception on a phased array radar in a radar network provided by an embodiment of the present invention.

[0092] Figure 27 It is an implementation plan for a drone to deceive the track of a networked radar (correction strategy 1) provided by an embodiment of the present invention.

[0093] Figure 28 This is the unified adjusted track chart after adopting the correction strategy 2 provided by the embodiment of the present invention.

[0094] Figure 29 This is the implementation plan diagram of the UAV's track deception against the networked radar (the UAV's circumferential flight radius is 3 km) provided by the embodiment of the present invention. Detailed implementation manners

[0095] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0096] The basic meanings of the relevant terms used in the patent are clarified as follows.

[0097] Definition (expected target): By presetting parameters, interfering with the radar to make the radar identify a target with specific target characteristics.

[0098] Definition (expected track): The false target track that the interfering party formulates according to the needs and hopes to make the disturbed radar detect.

[0099] Definition (corrected track): The expected track that is feasible after correction, and there may be differences in the shape or speed of some areas compared with the expected track.

[0100] Definition (unified adjusted track): When implementing track deception against the networked radar, the expected track after overall coordination of the corrected tracks of each radar, and all UAVs in the UAV group formulate track deception interference plans based on this track.

[0101] The relevant prerequisite assumptions applicable to the present invention include: Assumption 1: The radars discussed in the patent are all land-based fixed radars and their geographical locations are known.

[0102] Assumption 2: The radars discussed in the patent are all self-oscillating and self-receiving pulse system radars.

[0103] Assumption 3: The miniaturized UAVs discussed in the patent have sufficient electronic countermeasure reconnaissance and analysis capabilities, can measure the signal parameters of the radar, and infer the corresponding scanning rules (for mechanically scanned radars).

[0104] Assumption 4: For the convenience of problem discussion, it is assumed that before a single UAV implements track interference against a single radar, the UAVs have all hovered at the corresponding positions of the starting points, and start to implement interference when detecting the main lobe illumination of the radar antenna.

[0105] Assumption 5: The main lobe of the UAV antenna is always aligned with the radar.

[0106] Hypothesis 6: When implementing interference, the UAV moves in a circular motion around the radar. In fact, the UAV can plan flight trajectories of any shape. For the convenience of discussion, this hypothesis is made in the present invention, but the relevant conclusions and trajectory deception methods can be easily extended to the case of any flight trajectory of the UAV.

[0107] Hypothesis 7: Without considering the altitude factor and only considering the situation of the UAV projected in the two-dimensional plane, the relevant methods can be easily deduced to the three-dimensional situation.

[0108] Hypothesis 8: The UAV can complete the transition from hovering to the maximum speed or from the maximum speed to hovering in a short time. For the convenience of problem discussion, this time is ignored in the present invention, and this kind of ignoring usually does not affect the implementation effect of trajectory deception. The performance parameters of the relevant UAV platforms involved in the present invention are all referenced from the multi-rotor UAVs publicly available on the Internet.

[0109] As Figure 1 、 Figure 3 shown, the present invention is implemented as follows. A method for evaluating and correcting the feasibility of trajectory deception based on a small close-proximity UAV includes: S1: According to the requirements of the trajectory deception mission, determine the radar cross-sectional area of the expected target and the expected trajectory; determine the initial flight path of the UAV according to the principle that the UAV moves in a circular motion around the radar when implementing interference.

[0110] The position information of the trajectory discussed in the present invention is a matrix composed of a large number of coordinate points containing position coordinate information, and the speed information is represented by the method of segmentally assigning values to the expected trajectory. According to the above two pieces of information, the time information of the expected trajectory can be calculated to form a complete trajectory information matrix.

[0111] S2: Evaluate the feasibility of trajectory deception.

[0112] Case 1, the interference object is a mechanical scanning radar.

[0113] ① Focus on considering the shape of the expected trajectory, and there is no significant constraint on the trajectory speed. At this time, the electronic interference only needs to make the false dot plots captured by the radar in each antenna scanning cycle fall on the expected trajectory, and the position where the next false dot plot appears should move a certain distance along the changing direction of the expected trajectory. After the radar's calculation, the mutually correlated false dot plots are connected one by one, and the expected trajectory that meets the shape can be formed.

[0114] The feasible region of trajectory deception satisfies the following formula:

[0115] In the formula, is the distance between the UAV and the radar, is the distance between the expected target and the radar; 、 are the interference transmission power and antenna gain of the UAV, respectively; and are the radar transmission power and antenna gain, respectively; Ae is the effective area of the radar receiving antenna, is the effective receiving area of the radar antenna in the direction of the UAV; is the radar cross section of the UAV; is the radar cross section of the expected target.

[0116] ② At the same time, consider the shape and speed of the expected track. Denote as the angular velocity of the expected track relative to the radar, as the angular velocity of the radar beam scanning, then at least satisfy , the expected track is likely to be feasible. At this time, according to the radar scanning law and the non-radial velocity of the expected track, calculate the meeting point of the radar main lobe and the expected track, which is the expected trace; the intersection of the line connecting the expected trace and the radar and the UAV flight path is the position where the UAV releases interference, denoted as the expected interference point. Considering the judgment conditions for the start and end of the radar track, Reference [1] points out that the "4 / 3" logic is more commonly used in engineering, that is, the judgment condition for the start of the track is that at least 3 mutually correlated traces are detected by the radar within 4 consecutive antenna scanning periods. Denote the angle between the current radar antenna main lobe axis and the next expected trace as , then the meeting time can be expressed as:

[0117] Usually require

[0118] where is the radar antenna scanning period.

[0119] The UAV only needs to move to the next expected interference point position within the expected interference point time interval and emit the corresponding interference signal to successfully achieve track deception. Considering the limited flight speed of the UAV, there may be a situation where it is impossible to reach the next interference point within the specified time due to the too far distance between adjacent interference positions. Therefore, the following relationship should be satisfied between the expected track speed and the UAV speed: where is the maximum rotational angular velocity of the UAV, and this value is related to the distance between the UAV and the radar. Therefore, when conducting track feasibility evaluation, this formula should be discussed in combination with the UAV interference position.

[0120] [1] An Hong, Yang Li. Radar Electronic Warfare System Modeling and Simulation [M]. Beijing: National Defense Industry Press, 2017, p. 93. Case 2, the object of interference is an electronic scanning radar.

[0121] To achieve main lobe azimuth false target deception, the UAV must be on the line connecting the radar and the expected target at the current moment. Figure 4 In the figure, A and B are two point traces in the expected flight path. 、 are the intersection points of the lines connecting points A and B with the radar and the UAV flight path. To keep the UAV in the same azimuth as the expected point trace, the angular velocity of the UAV and the angular velocity of the expected flight path should be the same, that is:

[0122] For a specific expected point trace, as long as , it can be considered that the expected point trace is feasible, where is the maximum flight speed value of the UAV.

[0123] Case 3, the object of interference is a netted radar.

[0124] When the UAV swarm conducts flight path deception on the radar network, the specific method is to use multiple UAVs to simultaneously deceive the radar nodes in the radar network (usually the number of UAVs is not less than the number of radars), ensuring that the deception flight paths formed in different radars are consistent in space and time to meet the information fusion requirements of the radar network. Among them, the evaluation process of the flight path deception feasibility of a single UAV node for a specific radar node is the same as the aforementioned process. When using the UAV swarm to conduct flight path deception on the radar network, based on the deception interference of a single UAV on a single radar, the present invention considers the following constraints: ① SRC homologous test constraint. That is, the expected target point traces formed by interfering with different radars in the radar network meet the requirements of the radar (Space Resolution Cell, SRC) homologous test, that is where A and B are the target positions detected by any two radar stations in the radar network, is the distance from the i-th radar station to target A, is the distance from the i-th radar station to target B, is the resolution unit size of the i-th radar (in the above formula, i = 1, 2, which can be extended to any number of radar cases). If at the same moment, the targets detected by any two radars in the netted radar meet the above formula, it is considered that the expected target point trace at this position meets the homologous test requirements, as shown in Figure 5 .

[0125] ② UAV cooperation constraint.

[0126] Since multiple UAVs are selected to implement track deception on the networked radar, and the UAVs move in the same time and adjacent spaces. At this time, if the distance between radars in the radar network is large, due to the close-in interference of the UAVs, there will be no overlap of flight routes between adjacent UAVs; if the distance between radars in the radar network is small, the tracks between UAVs may overlap, which may cause the UAVs to collide with each other. At this time, attention should be paid to adjusting the flight radius of the UAVs around the radar to avoid collisions.

[0127] This step will give the feasible area and infeasible area of the expected track. If there is no infeasible area in the output result, jump to S4; otherwise, execute downward.

[0128] S3: Correction of the infeasible area of the expected track.

[0129] For the correction of the infeasible area of the expected track, the present invention provides two correction strategies, which can be selected according to actual needs. Generally, correction strategy 1 is preferably recommended. When correction strategy 1 cannot meet the requirements, correction strategy 2 is used. Of course, in actual operation, correction strategy 2 can also be directly used.

[0130] Correction strategy 1: Adjust the flight path of the UAV.

[0131] Approach the original expected track as much as possible, that is, keep the parameters of the original expected track unchanged, adjust the flight path of the UAV, and increase the feasible area of the expected track by approaching the radar with the UAV.

[0132] Denote the flight radius of the UAV around the radar as . When adjusting , it can be adjusted according to the step size , and there is

[0133] Attention should be paid not to be less than its safety distance , and there is

[0134] Among them, is set by the user according to experience. After changing one step size each time, execute step 2 to re-evaluate the feasibility of the expected track until there is no infeasible area in the evaluation result, and then jump to step 5; if the infeasible area of the expected track cannot be eliminated by adjusting the flight path of the UAV, then jump to step 4.

[0135] Correction strategy 2: Adjust the speed of the expected track.

[0136] Keep the shape of the original expected track as unchanged as possible, and achieve correction by reducing the speed of the expected track in the infeasible area.

[0137] It should be noted that when interfering with a radar network, for a swarm of drones, each drone has its specific corrected flight path. The faster the expected flight path speed, the more difficult it is for the drone to achieve. Each drone must mainly splice local corrected flight paths with a slower corrected expected flight path speed to form a unified corrected flight path, making this flight path feasible for any drone within the swarm of drones.

[0138] S4: Unified adjustment of the flight path and formulation of the implementation plan for flight path deception Implementation conditions: The feasibility of the expected flight path has been evaluated, and there is no infeasible area in the expected flight path or the infeasible area becomes feasible after correction.

[0139] Calculate the time delay parameter of the drone according to the relationship among the radar, the expected interference point of the drone, and the expected echo , there is

[0140]

[0141]

[0142] Calculate the interference power parameter of the drone , there is Apply the classical Doppler frequency shift formula to calculate the Doppler frequency shift parameter of the interference signal emitted by the drone. When the drone hovers for interference, only the relative speed between the expected flight path and the radar needs to be considered when calculating the Doppler frequency shift; when the drone interferes while moving, there is a relative speed between the drone itself and the radar, and this speed should also be taken into account when calculating the Doppler frequency shift to ensure that the false target echo formed indeed has the relative speed characteristics of the expected flight path in the view of the radar.

[0143] For mechanical scanning radars and phased array radars, only the flight path of the drone and the interference parameter scheme formed after correction are needed.

[0144] For a networked radar, if correction strategy 2 is used during the correction process of the infeasible area of the expected flight path, it is necessary to first perform unified adjustment of its flight path to form a unified feasible expected flight path, and then formulate the flight path of the drone and the interference parameter scheme respectively.

[0145] So far, the formulation of the flight path deception scheme based on small close-in drones is completed. The specific implementation of flight path deception can be carried out according to the scheme.

[0146] Such as Figure 2As shown, a small close - in UAV track deception feasibility evaluation and correction system for implementing the method of small close - in UAV track deception feasibility evaluation and correction as described in any one of claims 1 - 6 provided by an embodiment of the present invention is characterized in that the small close - in UAV track deception feasibility evaluation and correction system includes: A track determination module, configured to determine the radar cross - sectional area and the expected track of the expected target according to the track deception task requirements; and determine the initial flight path of the UAV according to the principle that the UAV makes a circular motion around the radar during interference implementation. An evaluation module, configured to perform track deception feasibility evaluation. A correction module, configured to correct the non - feasible area of the expected track. A formulation module, configured to perform overall adjustment of the track and formulate a track deception implementation plan.

[0147] The small close - in UAV track deception feasibility evaluation and correction system proposed by an embodiment of the present invention is constructed on the basis of the theory of electromagnetic deception and target feature synthesis. The overall system adopts a modular architecture and specifically includes: - Track determination module: Based on the equivalent characteristic modeling of the radar cross - sectional area (Radar Cross Section, RCS) of the deception target in the task requirements, combined with radar observation parameters (azimuth angle, elevation angle, beam width, etc.), first establish an ideal track trajectory model of the expected target. To achieve the dynamic stability of the deception signal, this module introduces a diffraction point equidistant rotation algorithm during trajectory generation, and accordingly plans the initial flight path of the small UAV to meet the constraint condition of "consistent trajectory under the radar perspective". The dynamic constraint model of the UAV and the minimum turning radius of the flight platform are considered during path calculation to generate an initial interference route that meets physical constraints.

[0148] - Evaluation module: Based on the radar signal fusion model and the target motion analysis model (Target Motion Analysis, TMA), perform a quantitative evaluation of the feasibility of track deception. By constructing a spatio - temporal scattering feature matrix of the radar received signal and introducing an error perception metric function (such as time - delay error, path deviation residual), determine whether the currently planned path can be truly mapped as the expected target track in the radar system echo, so as to realize the robustness discrimination of the deception strategy and the classification of deception quality levels.

[0149] - Correction module: When there are deception failure areas in the track due to maneuverability limitations or spatial terrain constraints, this module interpolates or reconstructs track segments in the infeasible areas based on the Dynamic Programming algorithm or the local gradient search algorithm. During the correction process, the Line-of-Sight (LOS) occlusion analysis and the optimal viewing angle maintenance criterion are integrated to avoid time-frequency mismatches between the deception path and the target features when they appear in the radar observation window, thereby enhancing the anti-identification ability of the deception track under high-resolution radar detection.

[0150] - Formulation module: The finally formed track is calibrated for spatio-temporal consistency through the Trajectory Harmonization Framework, and combined with the mission environment (terrain, distribution of electromagnetic interference sources, etc.) and platform limitations (speed, payload capacity, etc.), a track deception implementation plan is comprehensively formulated. The output of the plan includes a track navigation instruction set, an attitude control instruction sequence, and a deception window scheduling time sequence, providing a standardized data interface for actual flight execution.

[0151] An embodiment of the present invention also provides a computer device for implementing the above track deception evaluation and correction system. The computer device includes a memory and a processor, where: - The computer program pre-set in the memory contains the scheduling logic of multiple task modules and a trajectory solving function, covering a simulation control unit based on the target dynamic model; - The processor calls the track generation, simulation evaluation, path correction, and final output modules based on the task scheduling mechanism, and adopts a multi-threaded concurrent mechanism and an asynchronous processing framework to improve processing efficiency; - The overall system follows the module-to-module data interface standard (such as the UAVCAN or MAVLink protocol) to ensure good portability and real-time performance when the system runs on embedded devices or tactical ground terminals.

[0152] This embodiment also discloses a computer-readable storage medium storing the above computer program. This medium supports running in a tactical embedded system, a flight control system, or a simulation platform. During the running of the program, the track deception simulation and decision-making logic module is called to implement the following key processing procedures: - Execute the track fitting and pseudo-track synthesis algorithm; - Implement the deception effect simulation based on the radar scattering model; - Start the trajectory distortion dynamic adjustment module for real-time correction; - Output the track deception feasibility level and implementation parameters.

[0153] The medium can be encapsulated with anti-interference solid-state storage chips (such as SPI NOR Flash) to meet the high reliability requirements for temperature resistance, shock resistance, and electromagnetic interference resistance in the combat environment.

[0154] The present invention also provides an information data processing terminal, which serves as the core control carrier of the track deception evaluation system and integrates a high-speed data acquisition module, a graphical task configuration interface, and a flight control instruction output unit. When the terminal is running: - Real-time receive the flight state data of the unmanned aerial vehicle and the external radar situation information; - Based on the built-in core of the track deception algorithm, complete task modeling, path simulation, and multi-scheme evaluation and comparison; - Output standard format control data including track point sequences, attitude adjustment parameters, and interference execution timings, which can be seamlessly docked with various small unmanned aerial vehicle platforms.

[0155] The terminal supports a human-machine interface (HMI) and an automated deployment interface (API), has good operability and tactical adaptability, and can be deployed and run in a tactical command unit or a portable ground station.

[0156] In the embodiment of the present invention, the radar-related parameter settings are as follows: the transmit power is 0.1 MW, the main lobe gain of the transmit antenna is 36 dB, the main lobe gain of the receive antenna is 30 dB, and the sensitivity of the radar receiver is -100 dBmW; the radar antenna pattern is as Figure 6 shown: In the embodiment of the present invention, the unmanned aerial vehicle-related parameter settings are as follows: the transmit power of the unmanned aerial vehicle is 10 W, the gain of the transmit antenna is 1 dB; the maximum flight speed is 20 m / s.

[0157] In the embodiment of the present invention, the initial parameter settings of the expected track are as follows: the RCS is 10 , and the magnitude of the track speed is 0.9 Mach.

[0158] Step 1: Determine the expected track For any arbitrarily drawn expected track, the method used in the present invention is to first fit it to obtain an expected track formed by a smooth curve, and then take points on it to obtain the coordinate information of the expected track and map it to the coordinate system determined by the corresponding start and end point positions, thereby obtaining the complete coordinate information of the expected track. For the convenience of discussion, the extreme points of the expected track are used as the speed change points below, but it is easy to generalize to the case of changing the speed at any point on the track. The flow chart for determining the expected track is as Figure 7 shown.

[0159] For a track drawn casually as Figure 8 shown, its fitted curve can be obtained as Figure 9As shown, the coordinate information matrix of the expected trajectory can be obtained and mapped to the corresponding coordinate system, and then the corresponding speed information is assigned to it, and finally the expected trajectory information matrix is formed.

[0160] Figure 10 The expected trajectory adopted in the embodiment of the present invention given according to the foregoing method.

[0161] Step 2: Feasibility evaluation of the expected trajectory.

[0162] Case 1: The interfering object is a mechanical scanning radar.

[0163] ① Focus on the shape of the expected trajectory, and there is no significant constraint on the trajectory speed.

[0164] The feasible region ranges for the UAV to implement trajectory deception at 1 km and 40 km from the radar are respectively as Figure 11 、 12 shown. The area inside the "trajectory deception feasible region" curve in the figure is the feasible region of the expected trajectory.

[0165] ② Consider both the shape and speed of the expected trajectory Assume that the radar antenna scanning period is 5 s and the expected trajectory speed is 0.9 Mach. The meeting points of the main lobe of the radar antenna and the expected trajectory are as Figure 13 shown.

[0166] When the UAV interferes at 5 km from the radar, the feasibility of the expected trajectory is evaluated, and Figure 14 is obtained.

[0167] Case 2: The interfering object is a phased array radar. It only needs to satisfy that the azimuth of the UAV is always consistent with the azimuth of the expected target. The maximum speed of the UAV is 20 m / s, and it moves in a circular motion at 5 km from the radar. When the expected trajectory speed is 0.9 Mach, the feasibility evaluation result is as Figure 15 shown.

[0168] Case 3: The interfering object is a radar network.

[0169] Use 2 UAVs and adopt the method of a single UAV interfering with a single radar to simplify the many-to-many problem into a one-to-one problem. Therefore, the feasibility evaluation of implementing trajectory deception on the radar network needs to be carried out for each radar separately. Then, according to the subsequent steps, corrections and overall planning are carried out to form a single expected trajectory that satisfies all the radars in the radar network.

[0170] Step 3: Correction of the infeasible region of the expected trajectory Implementation conditions: The feasibility of the expected trajectory has been evaluated and there is an infeasible region in the expected trajectory.

[0171] Implementation steps: Based on the results of the feasibility assessment of track deception, following the correction criterion of "the corrected track should approximate the expected track as closely as possible", two correction strategies are adopted.

[0172] Correction strategy 1: Approximate the original expected track as closely as possible, that is, keep the parameters of the original expected track unchanged and change the movement track of the UAV. That is, the correction is achieved by the UAV approaching the radar.

[0173] Correction strategy 2: Keep the shape of the original expected track unchanged as much as possible and achieve the correction by changing the speed of the expected track.

[0174] Generally, correction strategy 1 is preferred first. If correction strategy 1 still cannot meet the interference requirements, correction strategy 2 is considered. At the same time, in order to follow the expected track correction criterion as much as possible, it can be considered to first use correction strategy 1 for correction, so that the UAV is at the minimum safe distance for interference, so as to reduce the change range of the expected track parameters by correction strategy 2 and better meet the requirements of the criterion.

[0175] ① Perform correction on the infeasible area of the expected track of the mechanical scanning radar. When adopting correction strategy 1, the radius of the UAV flying around the radar is reduced from 5 km to 2.97 km, meeting the feasibility requirements, as Figure 16 shown.

[0176] When adopting correction strategy 2, the speed of the infeasible area of the original expected track is reduced, and the feasibility requirements can also be met, as Figure 17 shown.

[0177] ② Perform correction on the infeasible area of the expected track of the phased array radar. When adopting correction strategy 1, reducing the radius of the UAV flying around the radar to 2.65 km can meet the feasibility requirements, as Figure 18 shown. When adopting correction strategy 2, reducing the speed of the infeasible area in segments can also meet the feasibility requirements, as Figure 19 shown.

[0178] ③ Perform correction on the infeasible area of the expected track of the networked radar.

[0179] Consisting of a mechanical scanning radar and a phased array radar in a network. In a two-dimensional coordinate system, assume that the mechanical scanning radar is located at the coordinate (0, 0) km and the phased array radar is located at the coordinate (10, 15) km. Use two UAVs to perform track deception on the two radars at a distance of 3 km from the two radars respectively. The expected track scenario is as Figure 20 shown.

[0180] Perform track deception on the mechanical scanning radar, and the results of the feasibility assessment of the expected track are as Figure 21 shown. The results after correcting the expected track of the mechanical scanning radar by adopting correction strategy 1 are as Figure 22The result after correcting the expected track of the mechanical scanning radar using correction strategy 2 is as shown in Figure 23 as shown.

[0181] Another UAV implements track deception on the electronically scanned radar. The result of the feasibility assessment of the expected track is as shown in Figure 24 as shown. The result after correcting the expected track of the electronically scanned radar using correction strategy 1 is as shown in Figure 25 as shown. The result after correcting the expected track of the electronically scanned radar using correction strategy 2 is as shown in Figure 26 as shown.

[0182] Step 3: Unified adjustment of the track and formulation of the track deception implementation plan Implementation conditions: The feasibility of the expected track has been evaluated, and there is no infeasible area in the expected track or the infeasible area becomes feasible after correction.

[0183] Implementation steps: For the mechanical scanning radar and the phased array radar, only the UAV track and interference scheme formed after correction are needed.

[0184] For the networked radar, if correction strategy 2 is adopted, it is necessary to first conduct unified adjustment of its track to form a unified and feasible expected track, and then formulate interference and movement plans respectively.

[0185] For the case of correction strategy 1, on the basis of Figure 22 and Figure 25 , conduct unified adjustment of the track and formulate the track deception implementation plan. The obtained result is as shown in Figure 26 as shown.

[0186] For the case of correction strategy 2, on the basis of Figure 23 and Figure 26 , conduct unified adjustment of the track and formulate the track deception implementation plan. The obtained results are as shown in Figure 27 , Figure 28 , Figure 29 as shown.

Claims

1. A method for evaluating and correcting the feasibility of a small close - in UAV trajectory deception, characterized in that, It includes the following steps: Step 1: Construct an expected target track information matrix, including a target position sequence and a speed sequence generated based on radar cross-section modeling, and generate a time sequence by dividing the spatial distance by the speed to form complete three-dimensional trajectory data; Step 2: Based on the principle that the UAV makes a circular motion with equal angular velocity around the radar, generate an initial flight path, combine the main lobe scanning characteristics of the radar to calculate the expected echo position, and determine the intersection point of the line connecting to the expected echo on the UAV trajectory as the expected interference point; Step 3: Construct a multi-type track deception feasibility evaluation model for mechanical scanning radar, electronically scanned radar, and networked radar, including multiple inequality constraints among parameters such as interference power, radar antenna gain, the distances between the radar, the target, and the UAV, the radar cross-sections of the target and the UAV, the effective area of the radar receiving antenna, and the azimuth direction gain; Step 4: For the track segment with infeasible echoes, use the path iteration method with the circumvention radius as the control variable for correction, or restore the feasibility of the track by adjusting the local track speed; Step 5: Calculate the round-trip time delay difference of the interference signal based on the distance difference between the radar and the UAV and the radar and the target, deduce the estimated values of the interference transmission power and Doppler frequency shift, form a set of matching parameters, and complete the unified adjustment of the track deception parameters and the implementation path configuration based on this; 2. The method according to claim 1, characterized in that, In the above Step 1, the expected target track information matrix includes a position sequence composed of discrete three-dimensional coordinate points. The speed sequence is assigned by piecewise linear interpolation, and the time sequence is obtained by dividing each section of the distance by the corresponding speed to construct a track sample set with time consistency and position continuity.

3. The method according to claim 1, characterized in that In the above Step 2, for the deception feasibility evaluation of the mechanical scanning radar, a criterion based on the relationship between the main lobe angular velocity of the antenna and the angular velocity of the expected track is constructed. The difference between the main lobe angular velocity and the track angular velocity is used to calculate the time required for the main lobe to scan from the current position to the next expected echo, and the track is determined with the upper limit that this time does not exceed three scanning periods; if the condition that at least three out of four consecutive scanning periods successfully identify the echo is met, the track segment constructed by the radar is considered valid.

4. The method according to claim 1, characterized in that In the above Step 2, for the networked radar, a joint criterion including homology test constraints and multi-UAV circumvention coordination constraints is constructed. The former is determined by comparing whether the ranging difference of the same target at two positions by different radar stations in the radar network is less than the set threshold of their respective spatial resolution units; the latter optimizes by adjusting the circumvention radii of multiple UAVs to prevent track overlap or flight conflict, especially using the discrete radius difference strategy in the case of a small distance between radar stations.

5. The method according to claim 1, wherein, The path correction strategy in the above Step 3 includes a monotonically decreasing iterative method with the circumvention radius as the variable. In each round of iteration, the current radius is reduced by a fixed step distance, and at the same time, the lower limit threshold is set as the safety radius, which is set by the user based on the task risk assessment experience; if the path adjustment still cannot cover the feasible region of all expected echoes, switch to the track speed adjustment strategy, and restore the feasibility by reducing the speed in the infeasible region to lengthen the echo appearance time window.

6. The method according to claim 1, wherein In step 5, the time delay difference is obtained by subtracting the round-trip propagation time corresponding to the distance between the target and the radar from the round-trip propagation time between the UAV and the radar; the interference transmission power is obtained by multiplying the radar transmission power by its antenna gain, dividing the result by the UAV antenna gain, multiplying by the radar cross section of the expected target and the square of the distance between the UAV and the radar, then dividing by the fourth power of the target distance, and simultaneously multiplying by the ratio of the effective area and the directional gain of the radar receiving antenna; the Doppler frequency shift is determined only by the radial relative velocity between the target and the radar in the case of static interference, and in the case of moving interference, the radial velocity component between the UAV itself and the radar needs to be additionally added.

7. A small close - in UAV track deception feasibility evaluation and correction system for implementing the small close - in UAV track deception feasibility evaluation and correction method according to any one of claims 1 - 6, characterized in that, The small close-in UAV track deception feasibility evaluation and correction system includes: A track determination module, configured to determine the radar cross section of the expected target and the expected track according to the requirements of the track deception task; and determine the initial flight path of the UAV according to the principle that the UAV makes a circular motion around the radar during interference implementation. An evaluation module, configured to evaluate the feasibility of track deception. A correction module, configured to correct the infeasible area of the expected track. A formulation module, configured to perform unified adjustment of the track and formulate a track deception implementation plan.

8. A computer device, characterized in that, The computer device includes a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor executes the steps of the small close-in UAV track deception feasibility evaluation and correction method according to any one of claims 1-6.

9. A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor executes the steps of the small close-in UAV track deception feasibility evaluation and correction method according to any one of claims 1-6.

10. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the small close-in UAV track deception feasibility evaluation and correction system according to claim 7.

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