CN111612673B

CN111612673B - Method and system for confirming threat degree of unmanned aerial vehicle to multiple places

Info

Publication number: CN111612673B
Application number: CN202010405041.4A
Authority: CN
Inventors: 姜春福; 王书峰; 闫梦龙
Original assignee: Sapai Intelligent Technology Co ltd
Current assignee: Sapai Intelligent Technology Co ltd
Priority date: 2020-05-13
Filing date: 2020-05-13
Publication date: 2023-12-15
Anticipated expiration: 2040-05-13
Also published as: CN111612673A

Abstract

The application provides a method and a system for confirming the threat degree of an unmanned aerial vehicle to multiple places, wherein the method comprises the following steps: acquiring target characteristics of different attributes of an attack target aiming at multiple places based on detection means such as radar or spectrum monitoring and infrared; according to the target distance and different target characteristics, extracting different credible characteristic weights from a target characteristic database according to the method, and continuously updating a target belief function in the detection process; and determining the credibility of target confirmation according to the target belief function value and the fuzzy algorithm strategy, forming a specific value and providing the specific value for a command control system to assist in decision-making. The application can carry out target auxiliary judgment on the unmanned aerial vehicle which possibly threatens the safety of the important target in the protection area, reduces the misjudgment probability, improves the automation and the intellectualization of the auxiliary decision of the command information system, shortens the decision time of the commander, and improves the striking efficiency of a plurality of land defense systems.

Description

Method and system for confirming threat degree of unmanned aerial vehicle to multiple places

Technical Field

The application relates to the field of threat determination methods of non-cooperative targets, in particular to a method and a system for confirming the degree of threat of unmanned aircrafts to multiple places.

Background

With the rapid development of unmanned aerial vehicle technology, some other people maliciously utilize the unmanned aerial vehicle as a new means to perform illegal activities, and the method is used for detecting military places, transmitting forbidden articles, shooting security facilities, carrying explosive articles to perform purposeful destruction activities and the like, thereby forming serious threats for the national, military and public security.

The current detection means generally adopts various detection means such as radar, spectrum monitoring and the like, and because the flying speed of the unmanned aerial vehicle is high, the unmanned aerial vehicle needs to be monitored very timely, accurately and quickly, and effective treatment means must be adopted immediately once the unmanned aerial vehicle is determined to have bad attempts, so that the supervision efficiency of the unmanned aerial vehicle is a primary aim.

At present, the supervision means of the unmanned aerial vehicle is incomplete, and a stable and reliable mature system is not applied. In order to ensure safety, some important points of military and government authorities mostly adopt a personnel duty mode, and threat intentions of different kinds of targets are manually judged through target detection means such as radar, spectrum monitoring and the like so as to provide a treatment decision. The manual judgment requires the accumulation of long-term experience of the person on duty, and is always in a state of mental stress during the period of duty, and particularly when a plurality of unmanned aerial vehicles or a large number of unmanned aerial vehicles approach to a plurality of protection areas in multiple directions at the same time, the workload of manually judging the intention of a target is huge, and the timely, accurate, quick and effective treatment is extremely difficult.

Disclosure of Invention

In order to solve the problems in the prior art, the application provides a method and a system for confirming the threat degree of an unmanned aerial vehicle to multiple places, which comprise the following steps:

acquiring multi-place coordinates and continuous coordinate data of a plurality of unmanned aircrafts in a certain time period;

and determining the threat degree of each unmanned aircraft to a plurality of places according to the change condition of the coordinates in the continuous time of the unmanned aircraft.

Preferably, the acquiring continuous coordinate data of the unmanned aerial vehicle in a certain period of time includes:

based on the set time step, detecting equipment is adopted in the set time period and GPS or Beidou positioning information is combined, so that coordinate data of the unmanned aerial vehicle are obtained;

wherein the detection device comprises a radar, spectrum monitoring or infrared device.

Preferably, the determining the threat degree of each unmanned aerial vehicle to a plurality of sites according to the change condition of the coordinates in the continuous time of the unmanned aerial vehicle includes:

determining a value of an influence factor from the coordinates of the unmanned aerial vehicle and the change condition of the coordinates in a continuous time based on a preset influence factor for each land;

bringing the values of the influence factors of each unmanned aircraft into a pre-constructed threat function to determine threat degrees to a plurality of sites;

the influencing factors include: distance, trajectory and speed of unmanned aerial vehicles.

Preferably, the threat level function is calculated as follows:

wherein f _nm (w) threat functions for the nth target for the mth protected land; v represents that the nth target shares V threat factors; i is the ith threat factor of the nth target for the mth protected land; w (w) _mi Threat weight corresponding to the ith threat factor of the mth protected area.

Preferably, the determining, for each location, the value of the influencing factor from the coordinates of the unmanned aerial vehicle and the change condition of the coordinates in the continuous time based on the preset influencing factor includes:

determining the distance, track and speed of the unmanned aerial vehicle to each land based on the change condition of the coordinates of the unmanned aerial vehicle in continuous time;

determining the distance weight of the unmanned aerial vehicle to each destination based on the distance of the unmanned aerial vehicle and a weight model constructed for the distance in advance, and constructing a distance weight matrix based on the distance weights of all unmanned aerial vehicles to all destinations;

determining the track weight of the unmanned aerial vehicle to each land based on the track of the unmanned aerial vehicle, and constructing a track weight matrix based on the track weights of all unmanned aerial vehicles to all lands;

and determining a speed weight of the unmanned aerial vehicle to each destination based on the speed of the unmanned aerial vehicle, and constructing a speed weight matrix based on the speed weights of all unmanned aerial vehicles to all destinations.

Preferably, the weight model includes: a straight line model, a power function model, an ellipse model and a circle model.

Preferably, the calculation formula of the straight line model is as follows:

wherein: y is ₁ Is a distance weight; x is x ₁ Distance from unmanned aircraft to center of land; k (k) ₁ And b ₁ Is a constant term, where k ₁ Determined by the furthest effective range of the detection device b ₁ The value of (2) is 1.

Preferably, the power function model has the following formula:

y ₁ ＝a ₁ x ₁ ^-1

wherein: y is ₁ Is a distance weight; x is x ₁ Distance from unmanned aircraft to center of land; a, a ₁ Is a value associated with the minimum dead zone range of the detection device.

Preferably, the calculation formula of the ellipse model is as follows:

wherein: y is ₁ Is a distance weight; x is x ₁ Distance from unmanned aircraft to center of land; a, a ₂ Is a value associated with a maximum detection range of the detection device; b ₂ The value of (2) is 1.

Preferably, the calculation formula of the circular model is as follows:

x ₁ ² +y ₁ ² ＝R ₁ ² ，y ₁ ′＝y ₁ /R ₁

wherein: y is ₁ Is a distance weight; x is x ₁ Is an unmanned aircraftDistance to the center of the field; r is R ₁ Is a value associated with a maximum detection range of the detection device; y is ₁ ' is y ₁ Normalizing the processed value.

Preferably, the distance weight matrix of the unmanned aerial vehicle is as follows:

wherein: a is that _d Threat weight matrix for distance factor at a certain moment; w (w) _dnm Threat weights are corresponding to distance factors aiming at the mth protection places for the nth attack targets.

Preferably, the determining the trajectory weight of the unmanned aerial vehicle to each destination based on the trajectory of the unmanned aerial vehicle, and constructing the trajectory weight matrix based on the trajectory weights of all unmanned aerial vehicles to all destinations, includes:

acquiring an effective striking range for the unmanned aerial vehicle;

based on the coordinates P of the current and previous moments of each destination for each unmanned aircraft ₁ (x _t1 ，y _t1 ) And P ₀ (x _t0 ，y _t0 ) Determining the position point vector at the current moment and the previous moment;

determining threat weights based on the distance relationships between the effective strike range, the location point vector, and the center point;

accumulating the position weight of each moment on the unmanned aerial vehicle track, and normalizing to obtain the unmanned aerial vehicle track weight;

a trajectory weight matrix for all unmanned aerial vehicles to all venues is determined based on the trajectory weights for each unmanned aerial vehicle to each venue.

Preferably, the calculation formula of the track weight is as follows:

y ₂ ′＝y ₂ /R ₂

wherein: y is ₂ Is a track weight; x is x ₂ To be centered to P ₁ P ₀ The distance of the determined straight line; r is R ₂ A value related to the maximum effective range of the treatment means; y is ₂ ' is y ₂ Normalizing the processed value.

Preferably, the track weight matrix has the following formula:

wherein: a is that _t Threat weight matrix for track factor at a certain moment; w (w) _tnm Threat weights for the nth attack target against the track factors of the mth protected land.

Preferably, the speed weight of the unmanned aerial vehicle is calculated as follows:

wherein: y is ₃ Is a speed weight; x is x ₃ The current speed of the unmanned aircraft is the current speed of the unmanned aircraft; mu is a speed value related to the target average flying speed of the unmanned plane and the like; σ is the speed difference associated with the target average flight speed.

Preferably, the speed weight matrix of the unmanned aerial vehicle is as follows:

wherein: a is that _v Threat weight matrix for a speed factor at a certain moment; w (w) _vnm Threat weights for the nth attack target against the mth protection place's speed factor.

Based on the same inventive concept, the application also provides a system for determining the threat degree of a non-cooperative target of low-altitude flight to a land, which comprises the following steps:

the data acquisition module is used for acquiring multi-place coordinates and continuous coordinate data of a plurality of unmanned aircrafts in a certain time period;

and the threat determination module is used for determining the threat degrees of each unmanned aerial vehicle to a plurality of places according to the change condition of the coordinates in the continuous time of the unmanned aerial vehicle.

Compared with the prior art, the application has the beneficial effects that:

the technical scheme provided by the application is a method and a system for confirming the threat degree of an unmanned aircraft to multiple places, wherein the method comprises the following steps: acquiring multi-place coordinates and continuous coordinate data of a plurality of unmanned aircrafts in a certain time period; according to the change condition of coordinates in the continuous time of the unmanned aerial vehicles, the threat degrees of each unmanned aerial vehicle to a plurality of places are determined, and the target auxiliary judgment is carried out on the unmanned aerial vehicles possibly threatening the important target safety in the protection area by using a plurality of means in the technical scheme provided by the application, so that the misjudgment probability is reduced, the automation and the intellectualization of the auxiliary decision of a command information system are improved, the decision time of a commander is shortened, and the hit efficiency of a plurality of land defense systems is improved;

the method and the device can be used for protecting a plurality of places, sequencing threat degrees of a plurality of suspicious targets approaching the places, accurately determining whether the suspicious targets are attack targets, and reducing the false positive probability.

Drawings

FIG. 1 is a flow chart of a validation of how much threat the unmanned aerial vehicle of the present application is presented to;

FIG. 2 is a schematic diagram of the target attack situation and defense area deployment of the present application;

FIG. 3 is a ladder model of the present application;

FIG. 4 is a straight line model of the present application;

FIG. 5 is a power function model in an embodiment provided by the present application;

FIG. 6 is an elliptical model in an embodiment provided by the present application;

FIG. 7 is a circular model in an embodiment provided by the present application;

FIG. 8 is a graph of target point and reference range under trajectory factors of the present application;

FIG. 9 is a segment curve of a threat weight calculation model in an embodiment provided by the application;

FIG. 10 is a normal distribution model of speed-threat weights in an embodiment provided by the application;

FIG. 11 is a block diagram of an unmanned aerial vehicle multi-threat level verification system of the present application.

Detailed Description

For a better understanding of the present application, reference is made to the following description, drawings and examples.

In order to solve the problem of threat judgment of the existing unmanned aerial vehicle to the land, particularly the problem of threat sequencing of a plurality of or a large number of 'low-low' targets when the targets are simultaneously approaching, the application identifies and judges potential targets with potential maliciousness from a target group, obtains threat intention expression values of the potential targets, and provides quantitative basis for commander decision and treatment.

The present application sets the following preconditions:

1. position coordinates of at least two points of the incoming target can be obtained and can be trusted;

2. the position coordinates of all the incoming targets can be obtained without considering targets in the blind area of the detection means or missed or false alarms by the detection system;

3. the position coordinates of the target include or can be resolved at least horizontal coordinates X, Y;

4. the region is a circular region with the position of the treatment means as the center and the effective range of the treatment means as the radius;

the above attack targets are understood to be non-cooperative targets flying at low altitudes.

Example 1:

the application provides a method for confirming the threat degree of an unmanned aircraft to multiple places, which is shown in fig. 1 and comprises the following steps:

s1: acquiring multi-place coordinates and continuous coordinate data of a plurality of unmanned aircrafts in a certain time period;

s2, determining threat degrees of each unmanned aerial vehicle to a plurality of places according to the change condition of coordinates in the continuous time of the unmanned aerial vehicle.

The method comprises the following steps:

there are multiple incoming targets and multiple protections are required, as schematically shown in fig. 2. In the figure, the area (O) is protected by 1 part of detection equipment and 3 parts of protection area (C) ₁ 、O ₂ ，O ₃ ) 4 attack targets (M) ₁ 、M ₂ 、M ₃ 、M ₄ ) For illustration, wherein R is _l Corresponding to the detection distance of the detection equipment.

Setting a threat function for each target for each guard

Wherein f _nm (w) threat functions for the nth target for the mth protected land; v represents that the nth target shares V threat factors; i is the ith threat factor of the nth target for the mth protected land; w (w) _mi Threat weight corresponding to the ith threat factor of the mth protected area;

among the N incoming targets and the M protection sites, each incoming target corresponds to each protection site, and the threat function comprises the influences of distance factors, track factors and speed factors. Thus, a plurality of incoming targets may mathematically represent an N x M dimensional matrix, denoted as a, against a plurality of threats to protect against the locality.

The determination of each element in matrix a is described below by taking the determination of 1 attack target for 1 protection place threat weight as an example. The threat weights of N attack targets aiming at M protection places can be expanded, and the method and the process are the same.

The composition of threat functions of the attack targets to protect the ground is emphasized by considering distance factors, track factors and speed factors. The threat weight determination method and calculation model of each factor are as follows:

distance factor (one)

The distance here refers to the linear distance of the "slow and small" target such as the unmanned plane from the center point of the destination, and changes as the target moves. According to the effective range of various means of system configuration, the effective range is divided into Q (Q is more than or equal to 1) distance ranges from the central point outwards. In general, the range of distances generally includes the range of radar detection R _r Electronic interference range R _d Or laser striking range R _l Other e.g. spectral monitoring range R _f Gun strike range R _g Etc. These threat influencing factors may include one or more at the same time, combined and set different distance threat weights according to the requirements. In processing, the weight table is adopted for inquiring and determining. After a system configuration is determined, a table of distance threat weights is also determined.

In actual use, according to the specific situation and the requirement, according to the distance x from the non-cooperative target to the destination, the following models can be adopted to determine or calculate threat weights related to the distance:

1) Ladder model

As shown in fig. 3, the ladder model is provided with a fixed weight with reference to the effective ranges of the respective detection means and treatment means, for example, in table 1.

TABLE 1 distance factor threat weight table (example)

The ladder model is suitable for low-altitude defense scenes with simpler defense requirements and without the important emphasis of a certain protection element by a defense system. The following assignment principles are followed when applied:

the equipment category is determined, and according to the configuration condition of detection and treatment equipment of a certain system and the effective range of the configured equipment, the equipment category is assigned one by one according to the distance sequence: the farther the effective range is, the more abundant the response time of the defense system is, the less the possibility of the target to the ground threat is, and the smaller the corresponding threat weight is; the closer the effective range, the greater the likelihood that the non-cooperative target threatens the ground, the greater the corresponding weight, and the higher the defense system combat readiness level.

And determining threat weights under the ladder model, and adopting different assignment strategies according to actual conditions. In general, the system configuration scheme, the protection requirement, the important points of tasks, the system response and the threat judgment standard are considered to carry out assignment, so that the comprehensive consideration of the system at the time, the local situation and the current situation is reflected. Under the conditions of N attack targets and M protection places, N multiplied by M distance factor threat weight tables are required to be set for selection. The following model is the same as this.

2) Linear model

As shown in fig. 4, the linear model calculates a value according to the relation y= -kx+b according to the distance between the devices. In the linear model, threat weights of non-cooperative targets are related to distance linearity, threat urgency degree changes are linear, equipment spare combat degrees are not distinguished, and the method is suitable for a defense system which is stable in performance of detection equipment, high in system reliability and smooth in linkage. Specifically, let b=1, and the linear model expression chooses:

y＝-kx+1

when b=1, the threat weight interval can be ensured to be limited to [0,1], and the threat weight interval is consistent with other factors, so that the influence of one factor covers the influence of the other factor due to different value standards is avoided.

The value of k is determined according to the furthest effective range k of the detection equipment ₀ And (5) determining. For example: the effective range of radar detection in a certain system is 5km, then the method takesThe linear model type is changed into:

y＝-0.2x+1

thus, as the target approaches the center distance, the threat weight also increases gradually, but remains between [0,1 ].

It should be noted that the calculation model for determining the weight is continuous in the defined domain, but in actual use, the target coordinate data is usually obtained at a certain time interval T, and the threat weight thus calculated is also discrete at a certain time interval T. These discrete threat weights, i.e. the threat weights that we need to correspond to a certain element at a certain time t. The following calculation models are the same as this.

3) Power function model

The power function model, as shown in FIG. 5, is based on the effective range of each device, according to the relation y=ax ^-1 (x > 0) calculating the value. According to the weight determined by the power function model, the activation of each device in the system can represent an assignment strategy with greater urgency from far to near. Particularly, when an attack target enters the effective range of the treatment means (the range is generally smaller than 1 km), the threat weight changes more severely when the attack target is closer to the center, and the threat degree of the attack target to the center point and the treatment urgency are more truly represented. The power function model is suitable for a system with a rapid response speed of a treatment means and rapid treatment of an attack target, and once the system enters the effective range of the treatment means, threat weights are rapidly increased, and the system is prompted to immediately implement treatment after determining the target.

Considering that it is generally about 200-300m from the center, it is already a blind spot of a radar or other detection means. For stability, the smallest value of the distance x when the power function is adopted may be 200m (e.g. a radar blind area of a certain type) from the center, i.e. 0.2km, and the farthest distance still takes the farthest range of the detection device, e.g. 5km. Thus, to ensure that the threat weight does not exceed 1 at maximum, the power function is taken as a=0.2:

y＝0.2x ^-1

if the target distance is less than 200m, the threat weight value is unchanged y=1.

4) Elliptical model

In the elliptical model, as shown in FIG. 6, the effective ranges of the devices are related by the relationAnd (5) calculating. With the model, the distance is longer, and the distance is longerThe threat weight changes faster and more violently near the center of the plot, while the weight changes gradually and gently near the center of the plot. The model can emphasize importance of a target found by a far end in actual application, is suitable for a system which emphasizes early warning importance or requires longer preparation time for treatment means, such as a scene of manual patrol before going out and manual treatment at the back end.

In practical application, the maximum value of a is a value according to the furthest detection distance of a detection system in the system, and the maximum value of b is a value of 1 according to the normalization requirement. If the detection distance of the radar in the system is 5km at the most, a=5 is taken, and the relation becomes:

0.04x ² +y ² ＝1(5≥x＞0，1≥y＞0)

thus, the threat weight y is limited between [0,1] and satisfies the normalization principle.

5) Round model

The circular model is a special elliptical model as shown in fig. 7. According to the effective range of each device and the relation x ² +y ² ＝R ² (x > 0, y > 0). Similar to the elliptical model, the weight changes faster and more strongly with longer distance in the circular model, while the weight changes gently near the center of the plot. The round model can also play a role in emphasizing the importance of finding a target at a far end in the system, and is suitable for the conditions of taking early warning as a main part and needing longer preparation time for treatment means.

In practical application, the data processing of the circular model is slightly different from that of the elliptical model. In the circular model, the maximum value of R is the value according to the furthest detection distance of a detection system in the system. If the detection distance of the radar in the system is 5km at the most, r=5 is taken, and the relation becomes:

x ² +y ² ＝25(5≥x＞0，5≥y＞0)

normalization is also required, and y, calculated from the different distances x, is normalized to:

the y' calculation here represents the threat weight of the target, so that the threat weight y is also limited between 0,1 under the round model.

In the M protection places, the geographic environment, the social environment, the protection requirements, the key points and the like of each protection place are possibly different, the types, the characteristics and the capacities of the deployed treatment means are possibly different, and the threat effects on different protection places are possibly different for the same attack target, so that in order to more approximate to the real threat situation, different calculation models are selected according to the actual requirements when evaluating the threat degree of one attack target on the local. The above 5 calculation models are designed for this requirement.

From this, N attack targets can be obtained through expansion to the distance factor threat weight matrix of M protection places at a certain moment:

wherein: a is that _d Threat weight matrix for distance factor at a certain moment; w (w) _dnm The threat weight of the distance factor corresponding to the mth attack target aiming at the mth protection place is given by y ₁ Or y ₁ ' is specifically determined by the weight model selected.

(II) trajectory factor

The trajectory factor mainly evaluates the accumulated intention of a certain target for a period of time and can also be understood as the comprehensive intention of the target for a period of time. In actual use, according to the track points of the front and rear points of a certain target provided by a radar or other detection means, if the connecting line direction of the two points to the center of the defense area, the target is considered to have possible attack intention at the moment, and the threat function is enhanced; conversely, if the two points continue in a direction away from the center of the area, then the target is considered to have a possible intent to depart from at that point, contributing to the abatement of the threat function. The representation of the likelihood of such attack or distancing, the overall intent will be represented by a concrete value over time, and the system may also evaluate the intent of the target attack at each moment in real time by continuously updating threat weights, as shown in fig. 8.

Let a certain time t ₁ Obtaining t through a detection system ₁ At the previous time t ₀ Two-point coordinate P of (2) ₁ (x ₁ ，y ₁ ) And P ₀ (x ₀ ，y ₀ ) The vector between two points is

1) Determining a range R, and discussing the range R based on the range, such as selecting the effective laser striking range (such as 1 km) as a reference;

2) From P ₀ To P ₁ Making a straight line according toDistance relationship to the center point to determine threat weights:

a) Let O be ₁ To the point ofIs x, then according to the constraint

Threat weights y are obtained as in fig. 9.

Thus, when x is more than or equal to 0 and less than or equal to R, the threat weight y is positive, which indicates that the target plays a positive role in threat to the land at the moment; when 2R is more than or equal to x and is more than R, the threat weight y is negative, which means that the target plays a negative role in threat to the land at the moment; when x is more than 2R, the threat weight value takes a constant-R.

b) Normalizing, and making y '=y/R, so as to ensure that threat weight |y' | is less than or equal to 1. But note that the weights themselves are signed.

For each t on a certain target track ₁ At the previous time t ₀ All of the above processes are completed to obtain threat weight y 'of either positive or negative or 0' _i The data is processed as follows:

1) Will y' _i Continuously accumulating to obtain the weight sum of the target until the current moment:

2) W (t) is normalized. y' _i Recording accumulation times T in the continuous accumulation process, and carrying out normalization processing to obtain threat weight at the moment every time one W (T) is obtained

After the treatment, threat weight |w| caused by the track factors is less than or equal to 1.

As can be seen from FIG. 8, the direction of a target may be constantly changing in its flight path over time, e.g. M ₃ . Thus, in the whole process, at a certain moment, the direction of the target track changes along with time, the threat weight of the target is increased, decreased or unchanged, but the final weight is related to the duration of the flight track, and the change degree of the threat degree along with time is represented. If a target is always facing the center O ₁ Flying, the weight sum of which is expected to be increased, also shows the larger and larger attack intention. If the trajectory of a target is mostly far from the central zone, its weight is also continuously decreasing, indicating its smaller and smaller attack intent. The processing method can comprehensively evaluate the attack intention of a target and avoid the large influence of single-step jump on the weight. The longer the observation time, the more reliable the intention judgment.

Therefore, the track factor threat weight matrix of N attack targets at a certain moment aiming at M protection sites can be obtained through expansion:

wherein: a is that _t For a rail at a certain momentTrace factor threat weight matrix; w (w) _tnm Threat weights for the nth attack target against the track factors of the mth protected land.

(III) speed factor

For a 'low-low' target such as an unmanned plane, if the target has an attack intention, the faster the target is, the more beneficial the target is to be achieved, and conversely, for a defense system, the higher the threat level of the target to a defense area is, the more the warning level needs to be improved, and the quick response capability of the system is improved.

The threat weight considering the speed factor is corrected based on a normal distribution model as a threat weight calculation function, as shown in fig. 10. Specifically, in addition to military unmanned aerial vehicles, the maximum speed of a common unmanned aerial vehicle in the market is about 10-20m/s, so if a certain target speed falls near the area, the threat weight setting is increased to highlight the threat possibility. The goal of a lesser or higher speed is that the likelihood of the drone becomes smaller, with a corresponding decrease in threat weight. Value example:

the normal distribution function has the original function:

where x is the velocity v, and the weight function is set to have a maximum value of 1

Taking μ=16, σ=8, then

In this way, from the flight speed of the uncooperative target, its threat weight W (v) to the ground can be determined, with the maximum value of the weight not exceeding 1. Note that here the weight calculation simply borrows the expression of the normal distribution function, but does not borrow the original mathematical meaning of the normal distribution.

Through expansion, a speed factor threat weight matrix of N attack targets at a certain moment aiming at M protection sites can be obtained:

wherein: a is that _v Threat weight matrix for a speed factor at a certain moment; w (w) _vnm The threat weight of the speed factor aiming at the mth protected place for the nth attack target is given by W (v).

Thus, in summary, the threat level matrix a of N attack targets for M protection places is expressed as:

matrix a is the final result of threat weights. The result can provide decision support for the command control center commander to comprehensively grasp the battlefield situation. For each guard site, only the threat weights of all targets locally related, i.e. the corresponding column value elements in matrix a, need be of interest.

Example 2:

in order to implement the method of the application, the application also provides a system for determining the threat level of the non-cooperative targets of the low-altitude flight to the ground, which comprises the following steps:

the data module is used for acquiring multi-place coordinates and continuous coordinate data of a plurality of unmanned aircrafts in a certain time period;

The data module is specifically used for:

The threat determination module is specifically configured to:

The threat level function is calculated as follows:

The determining, for each plot, a value of an influence factor from the coordinates of the unmanned aerial vehicle and the change condition of the coordinates over a continuous time based on the influence factor set in advance includes:

The weight model comprises: a straight line model, a power function model, an ellipse model and a circle model.

The calculation formula of the straight line model is as follows:

y ₁ ＝-k ₁ x ₁ +b ₁

The power function model has the following calculation formula:

y ₁ ＝a ₁ x ₁ ^-1

The calculation formula of the ellipse model is as follows:

The calculation formula of the circular model is as follows:

x ₁ ² +y ₁ 2＝R ₁ ² ，y ₁ ′＝y ₁ /R ₁

wherein: y is ₁ Is a distance weight; x is x ₁ Distance from unmanned aircraft to center of land; r is R ₁ Is a value associated with a maximum detection range of the detection device; y is ₁ ' is y ₁ Normalizing the processed value.

The distance weight matrix of the unmanned aerial vehicle is as follows:

The determining the track weight of the unmanned aerial vehicle to each destination based on the track of the unmanned aerial vehicle, and constructing a track weight matrix based on the track weights of all unmanned aerial vehicles to all destinations comprises:

acquiring an effective striking range for the unmanned aerial vehicle;

based on the coordinates P of the current and previous moments of each destination for each unmanned aircraft ₁ (x _t1 ，y _t1 ) And P ₀ (x _t0 ，y _to ) Determining the position point vector at the current moment and the previous moment;

The calculation formula of the track weight is as follows:

y ₂ ′＝y ₂ /R ₂

The track weight matrix has the following formula:

The speed weight calculation formula of the unmanned aerial vehicle is as follows:

The speed weight matrix of the unmanned aerial vehicle is as follows:

wherein: a is that _v Threat weight matrix for a speed factor at a certain moment; w (w) _vnm Threat weight of speed factor aiming at mth protected place for nth attack target is given as y ₃ 。

It will be apparent that the described embodiments are some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The foregoing is illustrative of the present application and is not to be construed as limiting thereof, but rather as providing for the use of additional embodiments and advantages of all such modifications, equivalents, improvements and similar to the present application are intended to be included within the scope of the present application as defined by the appended claims.

Claims

1. A method for confirming the threat level of an unmanned aerial vehicle to multiple sites, comprising:

determining threat degrees of each unmanned aircraft to a plurality of places according to the change condition of coordinates in the continuous time of the unmanned aircraft;

the obtaining continuous coordinate data of the unmanned aerial vehicle within a certain time period comprises the following steps:

wherein the detection device comprises a radar, spectrum monitoring or infrared device;

the method for determining the threat degree of each unmanned aerial vehicle to a plurality of places according to the change condition of coordinates in the continuous time of the unmanned aerial vehicle comprises the following steps:

the influencing factors include: distance, trajectory and speed of unmanned aerial vehicle;

the threat level function is calculated as follows:

2. The method of claim 1, wherein the weight model comprises: a straight line model, a power function model, an ellipse model and a circle model.

3. The method of claim 2, wherein the linear model is calculated as:

y ₁ ＝-k ₁ x ₁ +b ₁

4. The method of claim 2, wherein the power function model is calculated as:

y ₁ ＝a ₁ x ₁ ^-1

5. The method of claim 2, wherein the elliptical model is calculated as:

wherein: y is ₁ Is a distance weight; x is x ₁ Distance from unmanned aircraft to center of land; a, a ₂ Is a value associated with a maximum detection range of the detection device; b ₂ The value of (2) is 1;

the calculation formula of the circular model is as follows:

x ₁ ² +y ₁ ² ＝R ₁ ² ，y ₁ ^′ ＝y ₁ /R ₁

wherein: y is ₁ Is a distance weight; x is x ₁ Distance from unmanned aircraft to center of land; r is R ₁ Is a value associated with a maximum detection range of the detection device; y is ₁ ^′ Is y ₁ Normalizing the processed value;

the distance weight matrix of the unmanned aerial vehicle is as follows:

wherein: a is that _d Threat weight matrix for distance factor at a certain moment; w (w) _dnm Threat weights of distance factors corresponding to the mth protected area for the nth attack target;

acquiring an effective striking range for the unmanned aerial vehicle;

based on the coordinates P of the current and previous moments of each destination for each unmanned aircraft ₁ (x _t1 ,y _t1 ) And P ₀ (x _t0 ,y _t0 ) Determining the position point vector at the current moment and the previous moment;

determining a track weight matrix of all unmanned aerial vehicles to all sites based on the track weight of each unmanned aerial vehicle to each site;

the calculation formula of the track weight is as follows:

y ₂ ^′ ＝y ₂ /R ₂

wherein: y is ₂ Is a track weight; x is x ₂ To center to the groundTo P ₁ P ₀ The distance of the determined straight line; r is R ₂ A value related to the maximum effective range of the treatment means; y is ₂ ^′ Is y ₂ Normalizing the processed value;

the track weight matrix has the following formula:

wherein: a is that _t Threat weight matrix for track factor at a certain moment; w (w) _tnm Threat weights for the nth attack target aiming at the track factors of the mth protected land;

wherein: y is ₃ Is a speed weight; x is x ₃ The current speed of the unmanned aircraft is the current speed of the unmanned aircraft; mu is a speed value related to the target average flying speed of the unmanned plane and the like; sigma is the speed difference associated with the target average flight speed;

the speed weight matrix of the unmanned aerial vehicle is as follows:

6. A system for determining the threat level of a non-cooperative target of low-altitude flight to a ground for implementing the unmanned aerial vehicle to multi-ground threat level confirmation method according to any of claims 1 to 5, comprising:

the threat determination module is used for determining threat degrees of each unmanned aircraft to a plurality of places according to the change condition of coordinates in the continuous time of the unmanned aircraft;

the threat level function is calculated as follows: