CN110597264B - Unmanned aerial vehicle counter-braking system - Google Patents
Unmanned aerial vehicle counter-braking system Download PDFInfo
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- CN110597264B CN110597264B CN201910912187.5A CN201910912187A CN110597264B CN 110597264 B CN110597264 B CN 110597264B CN 201910912187 A CN201910912187 A CN 201910912187A CN 110597264 B CN110597264 B CN 110597264B
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Abstract
The invention is suitable for the technical field of unmanned aerial vehicle analysis, and provides an unmanned aerial vehicle counter-braking system, which comprises: the ground measurement and control module plans the air route of the attack unmanned aerial vehicle according to the received position information of the target unmanned aerial vehicle to obtain a pre-planned air route, then when the position of the target unmanned aerial vehicle changes, the attack unmanned aerial vehicle replans the pre-planned air route according to the position change information of the target unmanned aerial vehicle, the information detected by the attack unmanned aerial vehicle in real time and the information monitored by the ground measurement and control module received in real time, and the target unmanned aerial vehicle is attacked after flying to an attacking area according to the replanned air route, so that the target unmanned aerial vehicle can still be attacked when the position of the target unmanned aerial vehicle changes, and the problem that tracking failure or destruction failure is possibly caused after the position of the unmanned aerial vehicle changes in the prior art can be solved.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle analysis, and particularly relates to an unmanned aerial vehicle counter-braking system.
Background
In recent years, with the rapid development of aircrafts such as unmanned planes and the like and the gradual opening of low-altitude airspace control, the phenomena of illegal ascent, black flight and the like of the low-slow small unmanned planes occur, so that the tracking and destroying of the low-slow small unmanned planes are needed to be realized so as to resist the illegal ascent of the low-slow small unmanned planes.
The low-slow small unmanned aerial vehicle has the characteristics of low flying height, low moving speed, small radar scattering area and the like, so that the difficulty in defense, attack and counter control of the low-slow small unmanned aerial vehicle is high. When "slow little" unmanned aerial vehicle is controlled in the counter-reaction among the prior art, directly trail unmanned aerial vehicle behind the position of confirming unmanned aerial vehicle, then probably lead to tracking or destroy the failure after unmanned aerial vehicle's position changes.
Disclosure of Invention
In view of this, the embodiment of the present invention provides an unmanned aerial vehicle anti-braking system, so as to solve the problem in the prior art that when the position of an unmanned aerial vehicle changes, tracking failure or destruction failure may be caused.
A first aspect of an embodiment of the present invention provides an unmanned aerial vehicle countering system, including: the system comprises a detection module, a ground measurement and control module and an attack unmanned aerial vehicle;
the detection module is used for carrying out data fusion on the acquired ground detection data and the acquired aerial detection data, carrying out target identification and target tracking on the aerial flying unmanned aerial vehicle according to the fused data, acquiring the position information of the target unmanned aerial vehicle, and sending the position information of the target unmanned aerial vehicle to the ground measurement and control module;
the ground measurement and control module is used for planning the route of the attack unmanned aerial vehicle according to the received position information of the target unmanned aerial vehicle to obtain a pre-planned route; uploading the position information of the pre-planned route and the target unmanned aerial vehicle to the attacking unmanned aerial vehicle;
the attack unmanned aerial vehicle is used for receiving the preplanned air route and the position information of the target unmanned aerial vehicle, detecting whether the position of the target unmanned aerial vehicle changes or not according to the position information of the target unmanned aerial vehicle, when the position of the target unmanned aerial vehicle changes, replanning the preplanned air route according to the position change information of the target unmanned aerial vehicle, the information detected by the attack unmanned aerial vehicle in real time and the information monitored by the ground measurement and control module in real time, and attacking the target unmanned aerial vehicle after flying to an attack area according to the replanning air route.
In an embodiment, the planning a route of an attacking drone according to the received position information of the target drone to obtain a pre-planned route includes:
planning the air route of the attack unmanned aerial vehicle according to the received position information and flight constraint information of the target unmanned aerial vehicle to obtain a pre-planned air route;
and evaluating the pre-planned route by adopting a route cost evaluation model to determine the optimal pre-planned route.
In an embodiment, the flight constraint information includes drone body performance constraint information and external environment constraint information;
the unmanned aerial vehicle body performance constraint information comprises at least one of a maximum voyage, a maximum climbing angle, a minimum step length and a minimum turning radius;
the external environment constraint information includes at least one of an atmospheric threat, a radar threat, a missile threat, an antiaircraft gun threat and a terrain collision threat.
In one embodiment, when a threat signal is detected during the process that the attacking unmanned aerial vehicle flies to the attack area, an emergent threat section route in the pre-planned routes is determined;
performing neighborhood search according to the burst threat section route and the information monitored by the ground measurement and control module received in real time to determine an updated route;
evaluating the updated route by adopting a route cost evaluation model to determine an optimal route;
correcting the route of the sudden threat section according to the optimal route to obtain an updated route;
and repeatedly correcting the preplanned air route according to the mode of updating the air route until the pre-planned air route flies to the attack area.
In an embodiment, the track cost evaluation model is:
wherein C represents a airline cost value, l i Represents the length of the ith flight path, h i Representing the altitude of the attacking drone, said f TAi A threat index representing the ith route, w 1 W to 2 And said w 3 Respectively represent the weighting systemsAnd (4) counting.
In an embodiment, after the attacking unmanned aerial vehicle flies into the attacking area, situation information of the target unmanned aerial vehicle is acquired;
analyzing according to the situation information of the target unmanned aerial vehicle and the situation information of the target unmanned aerial vehicle to obtain an analysis result;
obtaining an attack track according to the analysis result;
and obtaining an optimal autonomous attack track according to the attack track and the maneuvering flight action information of the attack track.
In an embodiment, the attacking unmanned aerial vehicle is further configured to evaluate a damage condition of the target unmanned aerial vehicle, obtain an evaluation result, and determine an attacking behavior according to the evaluation result.
In an embodiment, when the evaluation result is greater than or equal to a first threshold value, the target unmanned aerial vehicle flies to an attack area again and then attacks again;
and when the evaluation result is smaller than the first threshold value, starting a self-destruction impact program to impact the target unmanned aerial vehicle.
In one embodiment, after the attacking unmanned aerial vehicle starts a self-destruction impact program, analyzing an image of the target unmanned aerial vehicle shot by machine vision arranged on the attacking unmanned aerial vehicle to obtain the current speed and position of the target unmanned aerial vehicle;
predicting the speed and the position of the target unmanned aerial vehicle at the next moment according to the current speed and the position of the target unmanned aerial vehicle;
performing tracking delay compensation on the target unmanned aerial vehicle according to the predicted speed and position of the target unmanned aerial vehicle at the next moment to obtain the position of the target unmanned aerial vehicle at the impact moment;
and impacting the target unmanned aerial vehicle according to the position of the target unmanned aerial vehicle at the impact moment.
In an embodiment, when the position of the target unmanned aerial vehicle is not changed, the target unmanned aerial vehicle is attacked after being rescheduled to fly to an attack area according to the preplanned air route.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: planning the route of the attack unmanned aerial vehicle according to the received position information of the target unmanned aerial vehicle through a ground measurement and control module to obtain a pre-planned route, then the attacking unmanned aerial vehicle detects whether the position of the target unmanned aerial vehicle changes according to the received position information of the target unmanned aerial vehicle, when the position of the target unmanned aerial vehicle changes, the attacking unmanned aerial vehicle replans the pre-planned route according to the position change information of the target unmanned aerial vehicle, the information detected by the attacking unmanned aerial vehicle in real time and the information monitored by the ground measurement and control module received in real time, and the target unmanned aerial vehicle is attacked after flying to an attack area according to the re-planned air route, thereby realizing that the target unmanned aerial vehicle can still be attacked when the position of the target unmanned aerial vehicle is changed, the problem that tracking failure or destruction failure is possibly caused after the position of the unmanned aerial vehicle is changed in the prior art can be solved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic diagram of a drone countering system provided by an embodiment of the invention;
fig. 2 is a schematic diagram of a process of attacking an unmanned aerial vehicle to correct a pre-planned route according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a drone countering system provided by another embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 1 is a schematic view of an unmanned aerial vehicle countering system provided in an embodiment of the present invention, which is detailed as follows.
As shown in fig. 1, an unmanned aerial vehicle countering system may include: the system comprises a detection module 101, a ground measurement and control module 102 and an attack unmanned aerial vehicle 103;
the detection module 101 is configured to perform data fusion on the acquired ground detection data and the acquired aerial detection data, perform target identification and target tracking on an aerial unmanned aerial vehicle according to the fused data, acquire position information of a target unmanned aerial vehicle, and send the position information of the target unmanned aerial vehicle to the ground measurement and control module 102;
the ground measurement and control module 102 is configured to plan a route of an attacking unmanned aerial vehicle according to the received position information of the target unmanned aerial vehicle, and obtain a pre-planned route; uploading the position information of the pre-planned route and the target unmanned aerial vehicle to the attacking unmanned aerial vehicle 103;
the attack unmanned aerial vehicle 103 is used for receiving the preplanned air route and the position information of the target unmanned aerial vehicle, detecting whether the position of the target unmanned aerial vehicle changes according to the position information of the target unmanned aerial vehicle, when the position of the target unmanned aerial vehicle changes, replanning the preplanned air route according to the position change information of the target unmanned aerial vehicle, the information detected by the attack unmanned aerial vehicle in real time and the information monitored by the ground measurement and control module in real time, and attacking the target unmanned aerial vehicle after flying to an attack area according to the replanning air route.
And when the position of the target unmanned aerial vehicle is not changed, replanning according to the preplanned air route to fly to an attack area and then attacking the target unmanned aerial vehicle.
According to the unmanned aerial vehicle counter-control system, the ground measurement and control module plans the air route of the attack unmanned aerial vehicle according to the received position information of the target unmanned aerial vehicle to obtain a pre-planned air route, then the attack unmanned aerial vehicle detects whether the position of the target unmanned aerial vehicle changes according to the received position information of the target unmanned aerial vehicle, when the position of the target unmanned aerial vehicle changes, the position change information of the target unmanned aerial vehicle, the information detected by the attack unmanned aerial vehicle in real time and the information received by the ground measurement and control module in real time replanning the pre-planned air route, and the target unmanned aerial vehicle is attacked after flying to an attacking area according to the replanned air route, so that the target unmanned aerial vehicle can still be attacked when the position of the target unmanned aerial vehicle changes, and the problem that the unmanned aerial vehicle possibly causes tracking failure or destroy failure after the position of the unmanned aerial vehicle changes in the prior art can be solved.
The method is a primary link for realizing accurate striking of the unmanned aerial vehicle with low speed and small speed by accurately identifying and tracking the target in real time and acquiring the state information of the target. "Low-slow-small" drone refers to a drone with low flying height (typically 50-1000 meters), slow flying speed (less than 200 km/h), small radar reflection area (less than 2 square meters). The low-slow small unmanned aerial vehicle has the characteristics of flexible take-off and landing, easiness in carrying and operation, low cost, strong maneuverability, good concealment, difficulty in finding and treating, small combat casualties and the like, and particularly, some commercial unmanned aerial vehicles are relatively mature in technology, easy to obtain and transform and relatively large in harm. The working characteristics of the low-slow small unmanned aerial vehicle comprise: the flight height is low, and the air route is not fixed; the lifting is simple, and the penetration resistance is strong; the reflection area is small, and radar detection is difficult; the flight speed is slow, and clutter is hidden. These characteristics of "low slow small" drones make it more difficult to control their defenses, strikes and reflexes.
In this embodiment, as shown in fig. 3, the detection module 101 may include an aerial detection sub-module 1011 and a ground detection sub-module 1012, and a cooperative detection mode of the aerial detection sub-module 1011 and the ground detection sub-module 1012 may be adopted, a radar detection device is used to search for a distant aerial target, a target drone is found and then a photoelectric detection device is guided to realize a panoramic view search, and then detection information obtained by a plurality of devices such as the radar detection device and the photoelectric detection device is subjected to error correction, time unification, feature extraction, target identification, target tracking, and the like, so as to complete identification and tracking of a target of a "low-slow-small" drone.
Optionally, as shown in fig. 3, the aerial detection submodule 1011 may obtain aerial detection data of the unmanned aerial vehicle, the ground detection submodule 1012 may obtain ground detection data of the unmanned aerial vehicle, and the aerial detection data and the ground detection data have different angles for describing the position of the unmanned aerial vehicle, so that in order to obtain accurate position information of the unmanned aerial vehicle, data fusion may be performed on the aerial detection data and the ground detection data, and the fused data is processed, so that more accurate position information of the target unmanned aerial vehicle may be obtained.
Optionally, the detection module 101 included in the drone countermeasure system may accomplish the identification and tracking of "low slow small" drone targets. The detection module 101 obtains the position information of the target drone, and may include: firstly, a target model and a target database are established, then an interested target is identified in a shot video image and the position of the target is detected, the current state and the next state of the target are estimated and predicted, and the state of a tracking mechanism is adjusted according to the prediction result, so that the real-time identification and tracking of the interested target are completed.
Optionally, the target recognition may include image segmentation, image feature extraction, and target feature matching. And the video sensor images the reconnaissance area to obtain a video frame, divides the video frame into a plurality of sub-images, performs feature extraction on each sub-image, matches the sub-image with the target features in the database, completes target identification after matching is successful, and otherwise, continues to repeat the processes of dividing the video, performing feature matching and the like to form a target identification cycle.
Target tracking may include several components, such as target identification, target state estimation, target prediction, and mechanism adjustment. After the target recognition is completed, the position of the target in the image needs to be detected, the current state of the target is estimated, wherein the current state of the target can include longitude and latitude, posture, speed and the like, and the state of the target at the next moment is predicted. And adjusting the flight track and the attitude of the unmanned aerial vehicle according to the prediction result, so that the target is kept at the center of the video image. And after the adjustment, re-detecting the target near the predicted position, and completing a target tracking loop.
Optionally, in the target identification stage, the image may be segmented by performing superpixel segmentation on the image. Due to the influence of factors such as illumination, noise, visual angle and the like, the image segmentation technology is difficult to completely separate the target from the background. The super pixel can be regarded as the minimum segmentation unit of the image, the image is segmented into a plurality of sub-images taking the super pixel as a unit, and then the super pixel units are combined to obtain the complete target image.
Optionally, after the image is segmented, the image is subjected to feature extraction, and a mode of describing the shape of the super pixel unit may be adopted. Features used for image target recognition should have illumination, scale, rotation, tilt invariance. Area, perimeter, etc. have illumination and rotation invariance, but not scale and tilt invariance. The super-pixel shape and the super-pixel unit have strong corresponding relation, so that the super-pixel shape can be used as the characteristic of the super-pixel unit, and the super-pixel shape can be combined into the shape of a target. Therefore, the shape of the super pixel unit is described, so that the shape obtained by adopting the super pixel unit description has illumination, scale, rotation and inclination invariance, and the extraction of the super pixel characteristics is completed.
Optionally, after the image features are extracted, the image features may be matched with the target features so as to determine whether the extracted image features are features of the target unmanned aerial vehicle. Since the superpixel unit is the smallest image segmentation unit and the shape of the object is basically unchanged in each image, even if the superpixel units of the images segmented by illumination and the like are different, the superpixel units can be respectively combined with the shape of the object. Therefore, when a super pixel unit of an image can be matched with a super pixel unit of an object, it can be determined that the object is included in the image. If the super pixel units can not be matched, the combination of the super pixel units can be found as a new target feature through a machine learning method.
Optionally, in the target tracking stage, the target tracking may be converted into a process of reasoning the posterior probability density of the target state under the bayesian filtering framework. And optimizing the distance between the target template and the candidate region through an iterative process by using a method of non-parameter probability density estimation, thereby reducing the target search region. And judging whether mean shift and Kalman filtering algorithm are used or linear prediction is carried out according to specific conditions, and carrying out target tracking in an interactive mode. Aiming at the target track tracking control problem, a hidden Markov-based moving target track tracking control algorithm can be adopted, namely a hidden Markov model is established according to geographical position information, target moving speed and other information in a flight area, and then optimal solution is carried out on an optimal path and an optimal state probability by utilizing a Viterbi decoding algorithm, so that real-time tracking of the moving target track is realized.
In order to reduce the calculation workload of detecting the target in the next frame image, the motion state of the moving target needs to be estimated in advance to obtain the approximate range of target recognition in the next frame image. The target state prediction can be carried out by adopting a Bayes filtering method, the Bayes filtering method is a statistical method based on probability theory, and because the image pixels are discrete, the representation of the probability density is often discrete. The target state prediction is often associated with the solution of the discrete probability extremum, and the fuzzy theory can fuzzify and serialize the discrete problem, thereby improving the accuracy of the solution of the discrete probability extremum and simultaneously improving the accuracy of the target state prediction.
The unmanned aerial vehicle counter system can plan and modify the task target of the decision-making system in real time or near real time according to the detected situation change, so that a feasible flight track for completing the task is automatically generated, a guidance and scheduling instruction is generated according to the formed track and the current state of the airplane, the airplane is controlled to accurately track the generated track, and the target is tracked.
Optionally, as shown in fig. 3, the detection module is used to perform target identification and target tracking on the unmanned aerial vehicle flying in the air, and then position information of the target unmanned aerial vehicle can be acquired, and the position information of the target unmanned aerial vehicle is sent to the ground measurement and control module, so that the ground measurement and control module further plans a route close to the target unmanned aerial vehicle, and an attacking unmanned aerial vehicle is used to realize attack.
The unmanned aerial vehicle flight path planning is a flight plan for completing tasks satisfactorily, is one of key technologies of the task planning, and the task planning is realized by the flight path planning. Considering the dynamic randomness of the target unmanned aerial vehicle, the unmanned aerial vehicle is required to have the capability of not only pre-planning a static track, but also correcting the track in real time, so that the unmanned aerial vehicle can track the target in real time and smoothly complete a predetermined task. According to the execution steps of the flight path planning, a complete flight planning process can be divided into different levels: the method comprises the steps of firstly planning before a mission, wherein all information in the planning is static, and planning on a ground control station before an attack unmanned aerial vehicle takes off is carried out under the determined battlefield environment by statically referring to a preplanned flight path. Optionally, the ground measurement and control module plans according to the position information of the target unmanned aerial vehicle sent by the detection module, so as to obtain a pre-planned route.
Optionally, the obtaining a pre-planned route by the ground measurement and control module may include: planning the route of the attack unmanned aerial vehicle according to the received position information and flight constraint information of the target unmanned aerial vehicle to obtain a pre-planned route; and evaluating the pre-planned route by adopting a route cost evaluation model to determine the optimal pre-planned route.
Optionally, the flight constraint information includes unmanned aerial vehicle body performance constraint information and external environment constraint information. Unmanned aerial vehicle body performance constraint mainly is relevant with the structural design and the dynamic behavior of unmanned aerial vehicle body, and unmanned aerial vehicle body performance constraint information includes at least one in biggest journey, the biggest angle of climbing, minimum step length and the minimum turning radius. The external environmental constraint information includes at least one of an atmospheric threat, a radar threat, a missile threat, an antiaircraft gun threat, and a terrain collision threat.
Optionally, the track evaluation is an important component in the track planning. All factors influencing the track performance need to be comprehensively considered, all indexes are quantized and calculated, index weights influencing the track comprehensive performance are determined, and the calculation of the comprehensive indexes, the selection of the track and other work are completed. Especially in a hostile environment, the performance of the flight path directly affects the ability of the unmanned aerial vehicle to complete the mission.
The track evaluation is a complex decision-making system consisting of a plurality of factors which are mutually related and mutually restricted. In order to represent the comprehensive performance of the flight path, influence factors of all aspects need to be converted into dimensionless values which can be directly compared according to a certain standard, then the weight of each single index in the comprehensive index is determined, and finally the dimensionless value of the comprehensive index of the representative flight path is obtained. The evaluation of the tracks is essentially to solve the comprehensive characteristic value of each track, so as to select the track with the optimal comprehensive characteristic value. The track cost evaluation model may be:
wherein C represents a airline cost value, l i Indicating the length of the ith flight path, l i By shortening the total length of the flight path, the flight time of the unmanned aerial vehicle in an enemy control area is reduced, so that the risk coefficient of the unmanned aerial vehicle is reduced, and the oil consumption is saved; h is i Represents the altitude, h, of the attacking drone i By reducing the height of the unmanned aerial vehicle, the hiding effect of the terrain and ground clutter are utilized to achieve the hiding purpose, so that the probability of being discovered by an enemy radar and destroyed by a ground defense system is reduced; f is TAi Threat index, f, representing the ith route TAi Limiting the unmanned aerial vehicle not to be too close to the known ground threat, so that the unmanned aerial vehicle flies through an area with smaller threat as much as possible; said w 1 The above-mentioned w 2 And said w 3 Respectively, represent weighting coefficients.
There is usually a range of action for the threat, and the pre-planned route of the drone should avoid the threat as much as possible, i.e. bypass these areas. These threats may be changing and it is difficult to obtain information in advance about the exact number of threats in the planned space, as well as the type, location, coverage, threat strength, etc. of each threat. In addition, even if the threat source is the same, the threat level to the drone may vary according to the difference in the received warning signal. Therefore, when the attacking unmanned aerial vehicle flies, threat signals need to be detected in real time, the route of the sudden threat section is determined according to threat information obtained continuously, and the preplanned route is corrected repeatedly until the attacking unmanned aerial vehicle finally reaches an attack area.
Optionally, as shown in fig. 2, in the process from takeoff to flying to the attack area, when a threat signal is detected, the process of modifying the pre-planned route by the attacking drone may include the following steps.
And 202, performing neighborhood search according to the sudden threat segment air route and the information monitored by the ground measurement and control module and received in real time, and determining an updated air route.
Because the threat is sudden, the newly generated track algorithm is required to be real-time and efficient so as to avoid the threat, the sudden threat section is used as the leading bee track according to the characteristic that the swarm algorithm has neighborhood search, and the honey bee gathering and following bees only need to perform neighborhood search on the sudden threat section of the reference track without searching other track sections, so that the search time can be saved, and the new airline can be quickly determined.
Optionally, the ground measurement and control module may measure and control the flight state and the flight environment of the attacking unmanned aerial vehicle in real time, and send the measured and controlled information to the attacking unmanned aerial vehicle in real time, so that the attacking unmanned aerial vehicle can perform accurate route correction.
And 203, evaluating the updated route by adopting a track cost evaluation model to determine an optimal route.
Optionally, the track cost evaluation model may be:
when C is minimum, the obtained course is optimal.
And 204, correcting the route of the sudden threat section according to the optimal route to obtain an updated route.
And repeatedly correcting the preplanned air route according to the mode of updating the air route until the pre-planned air route flies to the attack area.
And after the attacking unmanned aerial vehicle flies into the attacking area, locking the target unmanned aerial vehicle and acquiring the situation information of the target unmanned aerial vehicle. And analyzing according to the situation information of the target unmanned aerial vehicle and the situation information of the target unmanned aerial vehicle to obtain an analysis result, namely, the attack unmanned aerial vehicle carries out situation evaluation and tactical decision to obtain a decision result. And then, acquiring an attack track according to the analysis result, namely inputting the decision result into a calculation system of the line to be attacked, and calculating to obtain the attack track suitable for attacking the unmanned aerial vehicle. And then obtaining an optimal autonomous attack track according to the attack track and the maneuvering flight action information of the attack track. And after the attacking unmanned aerial vehicle enters the attack area, the fire control system on the attacking unmanned aerial vehicle is quickly controlled to lock the target and fire the weapon.
And after the transmission is finished, the attacking unmanned aerial vehicle evaluates the damage condition of the target unmanned aerial vehicle through airborne reconnaissance equipment to obtain an evaluation result, and determines the attacking behavior according to the evaluation result.
Optionally, the evaluation result may be a damage value of the target unmanned aerial vehicle, and the first threshold may be a damage threshold, that is, when the damage value of the target unmanned aerial vehicle is smaller than the damage threshold, it indicates that the weapon attacking the unmanned aerial vehicle has no effect, and when the damage value of the target unmanned aerial vehicle is greater than or equal to the damage threshold, it indicates that the weapon attacking the unmanned aerial vehicle has a certain effect, but an ideal damage effect is not achieved, and therefore, attack needs to be performed again.
When the evaluation result is larger than or equal to a first threshold value, flying to an attack area again and then attacking the target unmanned aerial vehicle again;
and when the evaluation result is smaller than the first threshold value, starting a self-destruction impact program to impact the target unmanned aerial vehicle.
Optionally, after the attacking unmanned aerial vehicle starts a self-destruction impact program, analyzing an image of the target unmanned aerial vehicle shot by machine vision arranged on the attacking unmanned aerial vehicle to obtain the current speed and position of the target unmanned aerial vehicle; predicting the speed and the position of the target unmanned aerial vehicle at the next moment according to the current speed and the position of the target unmanned aerial vehicle to realize state estimation of the target unmanned aerial vehicle, and performing tracking delay compensation on the target unmanned aerial vehicle according to the predicted speed and the predicted position of the target unmanned aerial vehicle at the next moment to obtain the position of the target unmanned aerial vehicle at the impact moment; and impacting the target unmanned aerial vehicle according to the position of the target unmanned aerial vehicle at the impacting moment.
The method is characterized in that the speed of the target is quickly estimated by using an adaptive estimator, the position of the target is predicted, and a control instruction at the next moment is predicted according to the position and attitude information of the unmanned aerial vehicle at the current moment, so that the target tracking delay is compensated, the position of the target unmanned aerial vehicle can be accurately predicted, and accurate and effective impact is carried out.
According to the unmanned aerial vehicle counter-braking system, the pre-planned route planned according to the position information of the target unmanned aerial vehicle is utilized by the ground measurement and control module, the attacking unmanned aerial vehicle approaches the target unmanned aerial vehicle according to the pre-planned route and attacks when the position of the target unmanned aerial vehicle is not changed, the pre-planned route is updated in real time according to the detected real-time threat, the detected self situation and the information and other information monitored by the ground measurement and control module and received in real time when the position of the target unmanned aerial vehicle is changed, the optimal route is obtained through the route cost evaluation model, and therefore the attacking behavior can be implemented according to the corrected route approaching the target unmanned aerial vehicle. In addition, after the unmanned aerial vehicle is attacked, secondary attack or self-destruction impact can be carried out according to the attack result, and the ideal effect of destroying the target unmanned aerial vehicle is finally achieved.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (8)
1. An unmanned aerial vehicle countering system, characterized in that includes: the system comprises a detection module, a ground measurement and control module and an attack unmanned aerial vehicle;
the detection module is used for carrying out data fusion on the acquired ground detection data and the acquired aerial detection data, carrying out target identification and target tracking on the aerial flying unmanned aerial vehicle according to the fused data, acquiring the position information of the target unmanned aerial vehicle, and sending the position information of the target unmanned aerial vehicle to the ground measurement and control module;
the ground measurement and control module is used for planning the route of the attack unmanned aerial vehicle according to the received position information of the target unmanned aerial vehicle to obtain a pre-planned route; uploading the pre-planned air route and the position information of the target unmanned aerial vehicle to the attacking unmanned aerial vehicle;
the attack unmanned aerial vehicle is used for receiving the preplanned air route and the position information of the target unmanned aerial vehicle, detecting whether the position of the target unmanned aerial vehicle changes according to the position information of the target unmanned aerial vehicle, replanning the preplanned air route according to the position change information of the target unmanned aerial vehicle, the information detected by the attack unmanned aerial vehicle in real time and the information monitored by the ground measurement and control module received in real time when the position of the target unmanned aerial vehicle changes, and attacking the target unmanned aerial vehicle after flying to an attack area according to the replanning air route;
the attack unmanned aerial vehicle is also used for evaluating the damage condition of the target unmanned aerial vehicle to obtain an evaluation result and determining an attack behavior according to the evaluation result; when the evaluation result is larger than or equal to a first threshold value, flying to an attack area again and then attacking the target unmanned aerial vehicle again; when the evaluation result is smaller than the first threshold value, starting a self-destruction impact program to impact the target unmanned aerial vehicle;
the target unmanned aerial vehicle is a low-slow small unmanned aerial vehicle.
2. The drone countermeasure system of claim 1, wherein the planning of the route of the attacking drone according to the received position information of the target drone, obtaining a pre-planned route, comprises:
planning the air route of the attack unmanned aerial vehicle according to the received position information and flight constraint information of the target unmanned aerial vehicle to obtain a pre-planned air route;
and evaluating the pre-planned route by adopting a route cost evaluation model to determine the optimal pre-planned route.
3. The drone countermeasure system of claim 2, wherein the flight constraint information includes drone body performance constraint information and external environment constraint information;
the unmanned aerial vehicle body performance constraint information comprises at least one of a maximum voyage, a maximum climbing angle, a minimum step length and a minimum turning radius;
the external environment constraint information includes at least one of an atmospheric threat, a radar threat, a missile threat, an antiaircraft gun threat and a terrain collision threat.
4. The drone opposing system of claim 1, wherein during flight of the attacking drone to the aggressable zone, when a threat signal is detected, a sudden threat segment course of the pre-planned course is determined;
performing neighborhood search according to the burst threat section route and the information monitored by the ground measurement and control module received in real time to determine an updated route;
evaluating the updated route by adopting a route cost evaluation model to determine an optimal route;
correcting the route of the sudden threat section according to the optimal route to obtain an updated route;
and repeatedly correcting the preplanned air route according to the mode of updating the air route until the pre-planned air route flies to the attack area.
5. An unmanned aerial vehicle countering system according to claim 2 or 4, wherein the track cost assessment model is:
wherein C represents a airline cost value, l i Represents the length of the ith flight path, h i Representing the altitude of the attacking drone, said f TAi A threat index representing the ith route, w 1 W to 2 And said w 3 Respectively, represent weighting coefficients.
6. The drone countermeasure system of claim 1, wherein the target drone acquires situational information after the attacking drone has flown into the aggressable region;
analyzing according to the situation information of the target unmanned aerial vehicle and the situation information of the target unmanned aerial vehicle to obtain an analysis result;
obtaining an attack track according to the analysis result;
and obtaining an optimal autonomous attack track according to the attack track and the maneuvering flight action information of the attack track.
7. The unmanned aerial vehicle countering system of claim 1,
after the attacking unmanned aerial vehicle starts a self-destruction impact program, analyzing an image of the target unmanned aerial vehicle shot by machine vision arranged on the attacking unmanned aerial vehicle to obtain the current speed and position of the target unmanned aerial vehicle;
predicting the speed and the position of the target unmanned aerial vehicle at the next moment according to the current speed and the position of the target unmanned aerial vehicle;
performing tracking delay compensation on the target unmanned aerial vehicle according to the predicted speed and position of the target unmanned aerial vehicle at the next moment to obtain the position of the target unmanned aerial vehicle at the impact moment;
and impacting the target unmanned aerial vehicle according to the position of the target unmanned aerial vehicle at the impact moment.
8. The drone opposing system of claim 1, wherein when the location of the target drone has not changed, the target drone is attacked after being rescheduled for flight to an assaultable area according to the pre-planned route.
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