CN111624590B - Unmanned aerial vehicle target confirmation method and system - Google Patents
Unmanned aerial vehicle target confirmation method and system Download PDFInfo
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- CN111624590B CN111624590B CN202010403149.XA CN202010403149A CN111624590B CN 111624590 B CN111624590 B CN 111624590B CN 202010403149 A CN202010403149 A CN 202010403149A CN 111624590 B CN111624590 B CN 111624590B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/16—Spectrum analysis; Fourier analysis
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
- G01S13/62—Sense-of-movement determination
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V8/00—Prospecting or detecting by optical means
- G01V8/10—Detecting, e.g. by using light barriers
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention provides a target confirmation method and a target confirmation system for an unmanned aerial vehicle, wherein the method comprises the following steps: acquiring target characteristics of different attributes of an attack target 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 invention 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 hit efficiency of the land defense system.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicle target identification, in particular to an unmanned aerial vehicle target identification method and system.
Background
The rapid development of unmanned aerial vehicle technology makes unmanned aerial vehicle commercialization degree higher and higher, but relevant management and control measures are seriously lack, and the unmanned aerial vehicle is also utilized by lawless persons while bringing convenience to people's life, and constitutes serious threat for national security, public security, especially government land, important economic targets, important activity security and the like. However, in the ground clearance defense, the airborne unidentified flying object may include targets such as a balloon, a Kong Ming lamp, and a bird in addition to the unmanned aerial vehicle, so in order to accurately strike the non-cooperative unmanned aerial vehicle target, the ground clearance defense system must be able to accurately identify unidentified flying objects of different types and different characteristics, and provide accurate target information for the striking system.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an unmanned aerial vehicle target confirmation method, which comprises the following steps:
when a suspicious incoming target is found, continuously acquiring the distance and the target characteristics of the suspicious incoming target under various detection means;
extracting a credible feature weight from a preset target feature database according to the suspicious attack target distance and target characteristics under each detection means, and continuously updating a credible feature function value;
and determining the credibility of the suspicious incoming target as the incoming unmanned aerial vehicle based on the target belief function values under all detection means.
Preferably, the detection means comprises radar detection, spectrum monitoring and infrared detection from far to near in sequence.
Preferably, the target belief function is calculated as follows:
wherein C is the objective belief function value, a i The target confirmation coefficient is the target confirmation coefficient when the detection means I obtains the target information, and I is the number of the detection means.
Preferably, the extracting the trusted feature weight from the preset target feature database according to the target distance and the target characteristic of the suspicious attack under each detection means includes:
when the detection means is radar detection:
real-time calculating the instantaneous navigational speed and direction of the target unmanned aerial vehicle according to the two-point data;
obtaining motion characteristics from a preset target characteristic database according to the navigational speed and the navigational direction;
the motion characteristic is a corresponding confirmation coefficient of radar detection.
Preferably, the motion feature is a radar detection corresponding target unmanned aerial vehicle confirmation coefficient, including:
wherein a is 1 Target validation coefficient b of unmanned aerial vehicle corresponding to radar detection 1 B is the target speed characteristic value 2 Is the target height characteristic value.
Preferably, the extracting the trusted feature weight from the preset target feature database according to the target distance and the target characteristic of the suspicious attack under each detection means further includes:
when the detection means is spectrum monitoring:
detecting electromagnetic signal spectrum characteristics of the target unmanned aerial vehicle according to spectrum monitoring equipment;
based on a preset characteristic database of the target unmanned aerial vehicle, obtaining a corresponding confirmation coefficient of spectrum monitoring according to the spectrum characteristics of the target unmanned aerial vehicle;
wherein the spectral features include: one or more characteristics of amplitude, frequency domain, time domain, space domain and code domain.
Preferably, the calculation formula of the corresponding target unmanned aerial vehicle confirmation coefficient of the spectrum monitoring is as follows:
wherein a is 3 Identifying coefficient, a, for spectrum monitoring corresponding target unmanned aerial vehicle j For the jth spectral feature, J is the number of spectral features selected and N is the number of total spectral features.
Preferably, the number of spectral features further includes:
when the detection means is infrared detection:
detecting infrared characteristics and azimuth information of the target by infrared detection equipment;
assigning a value for the infrared characteristic value based on a preset characteristic database of the target unmanned aerial vehicle and the infrared characteristic of the target unmanned aerial vehicle;
and evaluating the size and the appearance structure of the target according to the infrared image, the current azimuth of the target and the estimated distance, and determining the confirmation coefficient corresponding to infrared detection.
Preferably, the extracting the trusted feature weight from the preset target feature database according to the target distance and the target characteristic of the suspicious attack under each detection means further includes:
at the same time, the azimuth information mean value is simultaneously provided according to various means;
and determining a target unmanned aerial vehicle confirmation coefficient after the fusion of each means based on the difference value between the position information and the azimuth information mean value determined by each means.
Preferably, after determining that the suspicious incoming attack target is the trusted degree of the incoming attack unmanned aerial vehicle, the method further comprises:
when the credibility reaches a preset threshold value, the photoelectric detection obtains final confirmation that the target can be an attack unmanned aerial vehicle, and a command for striking the target unmanned aerial vehicle is issued.
Based on the same inventive concept, the invention also provides an unmanned aerial vehicle target confirmation system, comprising:
the acquisition module is used for continuously acquiring the distance and the target characteristics of the suspicious incoming targets under various detection means when the suspicious incoming targets are found;
the credible feature determining module is used for extracting credible feature weights from a preset target feature database according to the suspicious attack target distance and target characteristics under each detection means and continuously updating the credible feature function values;
and the determining module is used for determining the credibility of the suspicious incoming target as the incoming unmanned aerial vehicle based on the target belief function values under all detection means.
Preferably, the trusted characteristic determining module includes:
radar detection sub-module: for, when the detection means is radar detection: real-time calculating the instantaneous navigational speed and direction of the target unmanned aerial vehicle according to the two-point data; obtaining motion characteristics from a preset target characteristic database according to the navigational speed and the navigational direction; the motion characteristic is a corresponding confirmation coefficient of radar detection.
Preferably, the trusted characteristic determining module further includes:
spectrum monitoring sub-module: for when the detection means is spectrum monitoring: detecting electromagnetic signal spectrum characteristics of the target unmanned aerial vehicle according to spectrum monitoring equipment; based on a preset characteristic database of the target unmanned aerial vehicle, obtaining a corresponding confirmation coefficient of spectrum monitoring according to the spectrum characteristics of the target unmanned aerial vehicle;
wherein the spectral features include: one or more characteristics of amplitude, frequency domain, time domain, space domain and code domain.
Preferably, the trusted characteristic determining module further includes:
infrared detection submodule: for when the detection means is infrared detection: detecting infrared characteristics and azimuth information of the target by infrared detection equipment; assigning a value for the infrared characteristic value based on a preset characteristic database of the target unmanned aerial vehicle and the infrared characteristic of the target unmanned aerial vehicle; and evaluating the size and the appearance structure of the target according to the infrared image, the current azimuth of the target and the estimated distance, and determining the confirmation coefficient corresponding to infrared detection.
Preferably, the trusted characteristic determining module further includes:
and (3) detecting a fusion sub-module: means for providing an azimuth information mean value simultaneously according to various means at the same time; and determining a target unmanned aerial vehicle confirmation coefficient after the fusion of each means based on the difference value between the position information and the azimuth information mean value determined by each means.
Preferably, the target belief function is calculated as follows:
wherein C is the objective belief function value, a i The target confirmation coefficient is the target confirmation coefficient when the detection means I obtains the target information, and I is the number of the detection means.
Compared with the prior art, the invention has the beneficial effects that:
the technical means provided by the invention is an unmanned aerial vehicle target confirmation method, comprising the following steps: when a suspicious incoming target is found, continuously acquiring the distance and the target characteristics of the suspicious incoming target under various detection means; extracting a credible feature weight from a preset target feature database according to the suspicious attack target distance and target characteristics under each detection means, and continuously updating a credible feature function value; the method and the system can be used for carrying out target auxiliary judgment on the unmanned aerial vehicle possibly threatening the safety of an important target in a protection area, reduce the misjudgment probability, improve the automation and the intellectualization of auxiliary decision of a command information system, shorten the decision time of a commander and improve the attack efficiency of a ground defense system.
Drawings
Fig. 1 is a flow chart of unmanned aerial vehicle target validation according to the present invention;
fig. 2 is a block diagram of a target quick acknowledgement strategy and algorithm of the unmanned aerial vehicle of the present invention;
fig. 3 is a block diagram of a drone target validation system of the present invention.
Detailed Description
For a better understanding of the present invention, reference is made to the following description, drawings and examples.
Example 1: unmanned aerial vehicle target confirmation method as shown in figure 1
S1: when a suspicious incoming target is found, continuously acquiring the distance and the target characteristics of the suspicious incoming target under various detection means;
s2: extracting a credible feature weight from a preset target feature database according to the suspicious attack target distance and target characteristics under each detection means, and continuously updating a credible feature function value;
s3: and determining the credibility of the suspicious incoming target as the incoming unmanned aerial vehicle based on the target belief function values under all detection means.
As shown in fig. 2, different detection means are different in detection distance. And according to the distance of the detection distance, analyzing according to the sequence of radar detection, spectrum monitoring, infrared detection and photoelectric detection. In practical system applications, it is also embodied in this order.
The objective validation function is:
wherein C is a i In order to obtain the target confirmation coefficient when the detection means I obtains the target information, I is the number of detection means, and the detection means adopted in the embodiment are 4 kinds, and specific strategies are as follows:
(1) Radar device
The ground clearance defense system firstly adopts radar detection and is responsible for long-distance target detection. Radar detection typically outputs information about the current distance, azimuth, altitude, etc. of the target.
1) Calculating the navigational speed: real-time calculating the instantaneous navigational speed and direction of the target through the two-point data;
2) Motion feature assignment: searching a target characteristic database according to the speed and direction, and extracting a motion characteristic value b from the database according to the target speed characteristics 1 And let the motion characteristic value a 1 ＝b 1 ；
3) If the radar used can also provide altitude information at the same time, the radar is characterized by the motion b 1 On the basis, according to the height characteristics, the motion characteristic value b is extracted from a database 2 And let the motion characteristic value a 1 ＝(b 1 +b 2 )；
4) According to the speed and track change in a certain period of time T, the similarity of the speed, track and various targets is determined, and a is further adjusted 1 。
Examples: at a certain moment, the radar detection finds that the navigational speed of a certain target is 16m/s, the altitude is 300m, and the motion characteristic table of the database is (for example, the characteristic table is formulated and adjusted according to specific conditions in actual use):
TABLE 1 target navigational speed movement characteristic value table
| Speed of navigation (m/s) | Motion characteristic value b 1 |
| (0,5] | 0.20 |
| (5,10] | 0.30 |
| (10,15] | 0.50 |
| (15,20] | 0.40 |
| (20,25] | 0.20 |
| (25,30] | 0.10 |
| (30,35] | 0.05 |
| >35 | 0.01 |
TABLE 2 target altitude mixture motion characteristic value table (forbidden in the flying zone)
TABLE 3 target altitude mixture motion characteristic value table (in non-forbidden femto zone)
And determining characteristic values of the database tables, accumulating daily data, and counting to obtain the probability of representing the target as the unmanned aerial vehicle target. And carrying out adaptive correction according to the characteristics of specific models by using the characteristic value tables corresponding to the targets of different models.
The target validation coefficients for both cases can be extracted from the table:
forbidden flyover: a, a 1 ＝0.40+0.01＝0.41
Non-forbidden flyareas: a, a 1 ＝0.40+0.50＝0.90
(2) Spectrum monitoring
The detection distance of the frequency spectrum monitoring means is similar to the detection distance of the radar, specific target information can be identified according to electromagnetic spectrum characteristics, and the defect that the radar detects small targets with electromagnetic characteristics can be overcome. The frequency spectrum monitoring can provide characteristic information of electromagnetic characteristic target amplitude domain, frequency domain, time domain, space domain, code domain and the like.
1) Detecting, by the spectrum monitoring device, electromagnetic signal spectrum characteristics of the target;
2) Searching a target feature database, inquiring the database according to one or more features of a target amplitude, a frequency domain, a time domain, a space domain and a code domain, and extracting a frequency spectrum feature value a A 、a F 、a T 、a S Or a C And order(0≤a j 1.ltoreq.1, 1.ltoreq.N.ltoreq.5), wherein a j Represents a A 、a F 、a T 、a S Or a C One or N of the above.
Examples: for drone targets, spectrum probing is typically most focused on the frequency characteristics of its remote link, map link, or navigation link. The operating frequency of these signals is typically between 300MHz-6 GHz. In addition to the navigation link, the more common remote control and graphic communication links have working frequencies of 2.4GHz and 4.8GHz, and other frequencies of 1.4GHz, 1.2GHz, 900MHz, 800MHz and 433MHz, and other frequencies used by self-refitting unmanned aerial vehicles. Therefore, if the frequency is detected by the frequency spectrum detection system, the occurrence of suspected unmanned aerial vehicle targets is indicated to a great extent, and other information such as unmanned aerial vehicle types can be obtained to a certain extent.
TABLE 4 target frequency characteristic value Table
Therefore, if the spectrum detection system detects that the target has 2.4GHz or 5.8GHz, the target is considered to have a high probability of being an unmanned aerial vehicle target, and the corresponding target confirmation coefficient is obtained by querying the database frequency characteristic value table:
a 2 ＝0.90
if the frequency spectrum monitoring system extracts other frequency spectrum characteristics of the suspicious signals at the same time, inquiring a corresponding characteristic value table by adopting the same method, adding and averaging a plurality of frequency spectrum characteristics to obtain a 3 Used as the final value of (c).
(3) Infrared detection
The infrared detection distance is relatively close, and a connection coverage is formed in the distance with the radar and the frequency spectrum monitoring. The electromagnetic environment is complex, and the electromagnetic interference can be used as an emergency means for supplementing when a large amount of electromagnetic interference exists. The infrared detection can provide target azimuth information according to the infrared characteristics and the position of the turntable, and estimate the distance and the size of the target.
1) Detecting infrared characteristics and azimuth information of the target by infrared detection equipment;
2) Searching a target characteristic database, and assigning an infrared characteristic value a according to the target infrared characteristic 3 ；
3) According to the infrared image, the current azimuth of the target and the estimated distance, the size and the appearance structure of the target are estimated, and the infrared characteristic value a is adjusted 3 。
The unmanned plane target infrared radiation characteristics need to be considered, the influence of local atmospheric attenuation and background radiation at the time is needed, the target radiation intensity calculation is also related to the posture of the target, and certain characteristics are even related to the specific target. For an unmanned aerial vehicle target, the infrared characteristic of the unmanned aerial vehicle target is generally characterized by a set of characteristic quantities through image segmentation, target extraction and characteristic analysis:
1) Complexity (Complexity): the ratio of the number of boundary pixels to the total number of target pixels.
2) Aspect ratio (Length/Width): the ratio of the length to the width of the target minimum bounding rectangle.
3) Mean Contrast (Mean Contrast): ratio of the target gray average to the background gray average.
4) Maximum brightness (Maximum Birghtness): the maximum gray value of the target pixel point is the target brightest point gray value.
5) Contrast (contrast Ratio): the ratio of the target brightest pixel gray value to the target darkest pixel gray value.
6) Ratio of number of partially brightest Pixels/Total number of target Pixels (Raito Birght Pixels/Total Pixels): the ratio of the number of pixels within 10% less than the target brightest point brightness to the target total number of pixels.
7) Compactness (Compactness): the ratio between the number of pixels of the target and the number of pixels within the rectangle surrounding the target.
The 7 feature quantities reflect the geometric and physical characteristics of the target and the relation between the target and the background, and if the geometric and physical characteristics and the relation between the target and the background are comprehensively considered, a more comprehensive description of the infrared features of the target can be obtained. In specific application, an infrared characteristic database aiming at a certain type of unmanned aerial vehicle or other targets to be detected is formulated according to the statistical result of the larger sample.
Let the characteristic quantity F of the target T t (t=1, 2, …, 7), which in turn is our defined 7 feature quantities, with which the feature vector F of the object is composed t In the form of
F t ＝[F 1 F 2 … F 7 ] T
F t Is a 7-dimensional vector, and is used for classifying and identifying the target based on the 7-dimensional vector.
Form feature vector F t The various characteristic quantities of (a) are relatively complex, have proportional quantities and also have non-proportional quantities. Note that the more the corresponding components between the two sample vectors are similar, the more similar the two samples are, thus the following method is proposed to obtain the most similar (nearest neighbor) samples.
The method comprises the following steps:
(1) Normalizing the maximum brightness in the feature quantity 4) to a value between [0,1], namely:
thus, the value definition field is converted from the range [0,255] to [0,1], consistent with the other feature quantity definition fields.
(2) Comparing 7 feature quantities of the target measured in real time with corresponding feature quantity reference values in an infrared feature database established in advance, and calculating the total standard deviation:
wherein F is i For actually measuring the infrared characteristic value, U i Is the reference value of the corresponding item in the infrared characteristic database.
After normalization, 0.ltoreq.σ.ltoreq.1 can be ensured.
(3) Checking an infrared characteristic coefficient table to determine a 3 ：
TABLE 5 target frequency characteristic value Table
(4) Data fusion/validation
The radar detection, the spectrum monitoring and the infrared detection can provide the azimuth information of the target. In actual application, the target is far and near, and azimuth information provided by a radar system, a spectrum monitoring system, an infrared detection system and the like can be obtained successively. The system performs step-by-step verification according to the azimuth information, calculates an azimuth mean value according to the azimuth information provided by the three means simultaneously, and provides azimuth guidance for photoelectric detection.
Examples: target azimuth information obtained by three means of radar detection, spectrum monitoring and infrared detection is set as A respectively 1 ，A 2 And A 3 Average value thereof related to time of day
A＝(A 1 +A 2 +A 3 )/3
A is used as the average azimuth determined by three detection means, and provides a target azimuth reference value for an operator in the step of photoelectric detection. Under the control of the command system, the photoelectric equipment is turned to the azimuth A, the searching range is reduced, and the target is quickly searched for manual confirmation.
(5) Fuzzy algorithm identification
The obtained a 1 、a 2 、a 3 Adding to obtain
And inputting the C value into a fuzzy algorithm identifier, and obtaining a target reference result by the fuzzy algorithm identifier according to a step function, and providing the target reference result for a commander to make decision reference.
Examples: apart from a known above 1 (non forbidden femto zone), a 2 In addition, there is a redA, determining external characteristic coefficient table 3 =0.85, then the objective validation function value
C＝(a 1 +a 2 +a 3 )/3＝(0.9+0.9+0.85)/3＝0.8833
The fuzzy algorithm identifier respectively gives three levels of conclusions of almost no, probably and very probably according to the size of the target validation function value, namely [0,0.3], [0.3,0.6] and [0.6,1.0], and operators pay different attention in the photoelectric detection validation stage according to the specific value and the conclusions of the target validation function.
(6) Photoelectric detection
The photoelectric detection distance is relatively short, and the purpose of the photoelectric detection distance is to ensure reliability after the target reference result is obtained, finally confirm the target reference result through photoelectric detection, and then issue a striking command to implement striking operation.
Example 2
As shown in fig. 3: the invention also provides an unmanned aerial vehicle target confirmation system for realizing the method, which comprises the following steps:
the acquisition module is used for continuously acquiring the distance and the target characteristics of the suspicious incoming targets under various detection means when the suspicious incoming targets are found;
the credible feature determining module is used for extracting credible feature weights from a preset target feature database according to the suspicious attack target distance and target characteristics under each detection means and continuously updating the credible feature function values;
and the determining module is used for determining the credibility of the suspicious incoming target as the incoming unmanned aerial vehicle based on the target belief function values under all detection means.
The trusted characteristic determination module comprises:
radar detection sub-module: for, when the detection means is radar detection: real-time calculating the instantaneous navigational speed and direction of the target unmanned aerial vehicle according to the two-point data; obtaining motion characteristics from a preset target characteristic database according to the navigational speed and the navigational direction; the motion characteristic is a corresponding confirmation coefficient of radar detection.
Spectrum monitoring sub-module: for when the detection means is spectrum monitoring: detecting electromagnetic signal spectrum characteristics of the target unmanned aerial vehicle according to spectrum monitoring equipment; based on a preset characteristic database of the target unmanned aerial vehicle, obtaining a corresponding confirmation coefficient of spectrum monitoring according to the spectrum characteristics of the target unmanned aerial vehicle;
wherein the spectral features include: one or more characteristics of amplitude, frequency domain, time domain, space domain and code domain.
Infrared detection submodule: for when the detection means is infrared detection: detecting infrared characteristics and azimuth information of the target by infrared detection equipment; assigning a value for the infrared characteristic value based on a preset characteristic database of the target unmanned aerial vehicle and the infrared characteristic of the target unmanned aerial vehicle; and evaluating the size and the appearance structure of the target according to the infrared image, the current azimuth of the target and the estimated distance, and determining the confirmation coefficient corresponding to infrared detection.
And (3) detecting a fusion sub-module: means for providing an azimuth information mean value simultaneously according to various means at the same time; and determining a target unmanned aerial vehicle confirmation coefficient after the fusion of each means based on the difference value between the position information and the azimuth information mean value determined by each means.
Wherein the target belief function is calculated as:
wherein C is the objective belief function value, a i The target confirmation coefficient is the target confirmation coefficient when the detection means I obtains the target information, and I is the number of the detection means.
It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
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 invention 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 invention are intended to be included within the scope of the present invention as defined by the appended claims.
Claims (10)
1. A method for unmanned aerial vehicle target validation, comprising:
when a suspicious incoming target is found, continuously acquiring the distance and the target characteristics of the suspicious incoming target under various detection means;
extracting target confirmation coefficients from a preset target feature database according to the suspicious attack target distance and target characteristics under each detection means, and continuously updating the target belief function values;
determining the credibility of the suspicious attack target as the attack unmanned plane based on the target belief function values under all detection means;
the detection means sequentially comprises radar detection, spectrum monitoring, infrared detection and photoelectric detection from far to near;
the extracting the target confirmation coefficient from the preset target feature database according to the target distance and the target characteristic of the suspicious attack under each detection means comprises the following steps:
when the detection means is radar detection:
real-time calculating the instantaneous navigational speed and direction of the target unmanned aerial vehicle according to the two-point data;
obtaining motion characteristics from a preset target characteristic database according to the navigational speed and the navigational direction;
the motion characteristics are target confirmation coefficients corresponding to radar detection;
extracting a target confirmation coefficient from a preset target feature database according to the target distance and the target characteristics of suspicious attack under each detection means, and further comprising:
when the detection means is spectrum monitoring:
detecting electromagnetic signal spectrum characteristics of the target unmanned aerial vehicle according to spectrum monitoring equipment;
based on a preset characteristic database of the target unmanned aerial vehicle, obtaining a target confirmation coefficient corresponding to spectrum monitoring according to the spectrum characteristic of the target unmanned aerial vehicle;
the objective confidence function value is calculated as follows:
wherein (1)>For the objective confidence function value +.>Is a detection meansiTarget confirmation coefficient when obtaining target information, +.>The number of detection means.
2. The method of claim 1, wherein the motion feature is a radar detection of a corresponding target validation factor, comprising:
wherein, the liquid crystal display device comprises a liquid crystal display device,a 1 corresponding target validation coefficients are detected for the radar,b 1 as a characteristic value of the target speed,b 2 is the target height characteristic value.
3. The method of claim 1, wherein the spectral features comprise: one or more characteristics of amplitude, frequency domain, time domain, space domain and code domain.
4. The method of claim 1, wherein the calculation formula of the corresponding target acknowledgement coefficient for the spectrum monitoring is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,a 3 identifying coefficients for the corresponding targets for spectral monitoring, +.>For the jth spectral feature,Jfor the number of spectral features selected,Nis the number of total spectral features.
5. The method according to claim 1, wherein the extracting the target validation coefficients from the predetermined target feature database according to the target distance and the target characteristics of the suspicious attack under each detection means comprises:
when the detection means is infrared detection:
detecting infrared characteristics and azimuth information of the target by infrared detection equipment;
assigning a value for the infrared characteristic value based on a preset characteristic database of the target unmanned aerial vehicle and the infrared characteristic of the target unmanned aerial vehicle;
and evaluating the size and the appearance structure of the target according to the infrared image, the current azimuth of the target and the estimated distance, and determining a target confirmation coefficient corresponding to infrared detection.
6. The method of claim 1, further comprising, after determining that the suspected incoming target is a trustworthiness of an incoming drone:
when the credibility reaches a preset threshold value, the photoelectric detection obtains final confirmation that the suspicious incoming target is the incoming unmanned aerial vehicle, and a command for striking the target unmanned aerial vehicle is issued.
7. An unmanned aerial vehicle target validation system, comprising:
the acquisition module is used for continuously acquiring the distance and the target characteristics of the suspicious incoming target under various detection means when the suspicious incoming target is found;
the credible feature determining module is used for extracting a target confirmation coefficient from a preset target feature database according to the suspicious attack target distance and target characteristics under each detection means and continuously updating the target belief function value;
the determining module is used for determining the credibility of the suspicious incoming target as the incoming unmanned plane based on the target belief function values under all detection means;
the detection means sequentially comprises radar detection, spectrum monitoring, infrared detection and photoelectric detection from far to near;
the extracting the target confirmation coefficient from the preset target feature database according to the target distance and the target characteristic of the suspicious attack under each detection means comprises the following steps:
when the detection means is radar detection:
real-time calculating the instantaneous navigational speed and direction of the target unmanned aerial vehicle according to the two-point data;
obtaining motion characteristics from a preset target characteristic database according to the navigational speed and the navigational direction;
the motion characteristics are target confirmation coefficients corresponding to radar detection;
extracting a target confirmation coefficient from a preset target feature database according to the target distance and the target characteristics of suspicious attack under each detection means, and further comprising:
when the detection means is spectrum monitoring:
detecting electromagnetic signal spectrum characteristics of the target unmanned aerial vehicle according to spectrum monitoring equipment;
based on a preset characteristic database of the target unmanned aerial vehicle, obtaining a target confirmation coefficient corresponding to spectrum monitoring according to the spectrum characteristic of the target unmanned aerial vehicle;
the objective confidence function value is calculated as follows:
wherein (1)>For the objective confidence function value +.>Is a detection meansiTarget confirmation coefficient when obtaining target information, +.>The number of detection means.
8. The system of claim 7, wherein the trusted feature determination module comprises:
radar detection sub-module: for, when the detection means is radar detection: real-time calculating the instantaneous navigational speed and direction of the target unmanned aerial vehicle according to the two-point data; obtaining motion characteristics from a preset target characteristic database according to the navigational speed and the navigational direction; the motion characteristic is a target confirmation coefficient corresponding to radar detection.
9. The system of claim 8, wherein the trusted feature determination module further comprises:
spectrum monitoring sub-module: for when the detection means is spectrum monitoring: detecting electromagnetic signal spectrum characteristics of the target unmanned aerial vehicle according to spectrum monitoring equipment; based on a preset characteristic database of the target unmanned aerial vehicle, obtaining a target confirmation coefficient corresponding to spectrum monitoring according to the spectrum characteristic of the target unmanned aerial vehicle;
wherein the spectral features include: one or more characteristics of amplitude, frequency domain, time domain, space domain and code domain.
10. The system of claim 9, wherein the trusted feature determination module further comprises:
infrared detection submodule: for when the detection means is infrared detection: detecting infrared characteristics and azimuth information of the target by infrared detection equipment; assigning a value for the infrared characteristic value based on a preset characteristic database of the target unmanned aerial vehicle and the infrared characteristic of the target unmanned aerial vehicle; and evaluating the size and the appearance structure of the target according to the infrared image, the current azimuth of the target and the estimated distance, and determining a target confirmation coefficient corresponding to infrared detection.
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