CN109407086A - A kind of aerial vehicle trajectory generation method, system and trapping system goal directed method - Google Patents
A kind of aerial vehicle trajectory generation method, system and trapping system goal directed method Download PDFInfo
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- CN109407086A CN109407086A CN201811550039.5A CN201811550039A CN109407086A CN 109407086 A CN109407086 A CN 109407086A CN 201811550039 A CN201811550039 A CN 201811550039A CN 109407086 A CN109407086 A CN 109407086A
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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
- G01S13/66—Radar-tracking systems; Analogous systems
- G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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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
- G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
Abstract
This application involves a kind of aerial vehicle trajectory generation method, system and trapping system goal directed methods, its generation method includes step 1, target aircraft is detected by two-coordinate radar to obtain first object data, target aircraft is detected by photoelectric tracer to obtain the second target data；Step 2, first object data and the second target data are pre-processed respectively, obtains the first valid data and the second valid data；Step 3, time shaft alignment carried out to the first valid data and the second valid data, and by after alignment the first valid data and the second valid data merge, obtain observation data；Step 4, observation data are handled by adaptive Kalman filter, obtains the measurement filter value or predicted value of target aircraft.Target aircraft is detected by multiple sensors, increases target data dimension, improves target data precision, data updating rate, the targetpath of steady and continuous is formd to detection target.
Description
Technical field
Field more particularly to a kind of aerial vehicle trajectory generation method, system are generated the present invention relates to aerial vehicle trajectory and are lured
Catch aims of systems bootstrap technique.
Background technique
Currently, to the flight path detection of aircraft being carried out by flight path of the detection radar to target aircraft
Tracking, and tracking data is handled to obtain the track value of aircraft, target identification of the optoelectronic device only as auxiliary is set
It is standby.However, being tracked using detection radar, there is a problem of that target tracking data rate is low, reliability and stability are poor, and
Lack pitching dimension data when using two-coordinate radar, the targetpath data of generation cannot achieve effectively drawing to trapping apparatus
It leads.
Summary of the invention
In order to solve the above-mentioned technical problem the present invention provides a kind of aerial vehicle trajectory generation method.
The technical scheme to solve the above technical problems is that a kind of aerial vehicle trajectory generation method, comprising:
Step 1, target aircraft is detected by two-coordinate radar to obtain first object data, passes through photoelectric tracking
Device detects the target aircraft to obtain the second target data.
Step 2, the first object data and second target data are pre-processed respectively, obtains first effectively
Data and the second valid data.
Step 3, time shaft alignment is carried out to first valid data and second valid data, and will be after alignment
First valid data and the second valid data are merged, and observation data are obtained.
Step 4, the observation data are handled by adaptive Kalman filter, obtains the target aircraft
Measurement filter value or predicted value.
The invention has the advantages that passing through two-coordinate radar and photoelectric tracer visiting to target aircraft respectively
It surveys to obtain two kinds of detection data, and fusion treatment is carried out to two kinds of detection datas, and is filtered, predicts to show that target flies
The measurement filter value or predicted value of row device improve target data precision, data update to increase target data dimension
Rate forms the targetpath information of steady and continuous to detection target.Wherein, by adaptive Kalman filter to observation number
According to being handled, the measurement filter value or predicted value of target aircraft are obtained, ensure that the smoothness and spy of target trajectory
The continuity of target trajectory when surveying invalid.
Based on the above technical solution, the present invention can also be improved as follows.
Further, the first object data include first object distance, first party parallactic angle, the first pitch angle, described
The period of one target data is TR；Second target data includes the second target range, second party parallactic angle, the second pitch angle,
The period of second target data is TS。
Beneficial effect using above-mentioned further scheme is, by acquiring the target range of target aircraft, azimuth, bowing
The elevation angle guarantees the tracking accuracy to target, by acquiring data cycle TRWith data cycle TSSo as to subsequent two kinds of detection datas
Alignment.
Further, the step 2 specifically includes:
Step 2.1, the first object distance or described first of the first object data is judged by outlier thresholding
Whether azimuth or first pitch angle are outlier, if so, being invalid by the first object data judging, if it is not, then
Determine that the first object data are first valid data.
Step 2.2, the second party parallactic angle or described second of second number of targets is judged by the outlier thresholding
Whether pitch angle is outlier, if so, second target data is determined as in vain, if not, it is determined that second target
Data are second valid data.
Beneficial effect using above-mentioned further scheme is, by rejecting the outlier in target data, to guarantee target data
Validity, to improve prediction accuracy.
Further, the outlier thresholding includes that target range judges that thresholding, azimuth judge that thresholding and pitch angle judge door
Limit.
The target range judges that thresholding is as follows:
| R-R ' | > VRTS；
The azimuth judges that thresholding is as follows:
R'| sinA-sinA'| > VHTS；
The pitch angle judges that thresholding is as follows:
R'| sinE-sinE'| > VVTS；
Wherein, R be the two-coordinate radar currently detect the first object distance or the photoelectric tracer it is current
Second target range of detection, R' are that the target range obtained after the processing of upper a cycle to the target aircraft is filtered
Wave number or predicted value；A is that the first party parallactic angle that the two-coordinate radar currently detects or the photoelectric tracer are currently visited
The second party parallactic angle surveyed, A' is the azimuth of target filter value handled in upper a cycle the target aircraft
Or predicted value；E is that first pitch angle that the two-coordinate radar currently detects or the photoelectric tracer currently detect
Second pitch angle, E' are the target pitch angle filter value handled in upper a cycle the target aircraft or pre-
Measured value.
VHThe maximum horizontal speed of target aircraft, V can be detected for the two-coordinate radarTFor the two coordinates thunder
Up to the maximum vertical speed that can detect target aircraft,
Beneficial effect using above-mentioned further scheme is, by above-mentioned outlier thresholding, can effectively to reject in target data
Outlier.
Further, the specific implementation of the step 3 are as follows:
According to the data cycle TRWith the data cycle TSUsing interpolation extrapolation to first valid data and institute
It states the second valid data and carries out time shaft alignment.
Beneficial effect using above-mentioned further scheme is, can be more effectively to the first valid data by interpolation extrapolation
It is aligned with the second valid data.
In order to solve the above-mentioned technical problem the present invention provides a kind of aerial vehicle trajectory generation system.
Its technical solution is as follows: a kind of aerial vehicle trajectory generation system, comprising:
Target data acquisition module obtains first object number for being detected by two-coordinate radar to target aircraft
According to being detected to obtain the second target data to the target aircraft by photoelectric tracer.
Data preprocessing module, for being located in advance to the first object data and second target data respectively
Reason, obtains the first valid data and the second valid data.
Data generation module is observed, for carrying out time shaft pair to first valid data and second valid data
Standard, and by after alignment the first valid data and the second valid data merge, obtain observation data.
Measure filter value and predicted value generation module, by adaptive Kalman filter to the observation data at
Reason, obtains the measurement filter value or predicted value of the target aircraft.
Further, the first object data include first object distance, first party parallactic angle, the first pitch angle, described
The period of one target data is TR；Second target data include the second target range, second party parallactic angle, the second pitch angle,
The period of second target data is TS。
Further, the data preprocessing module specifically includes:
First effective judging unit, for judged by outlier thresholding the first objects of the first object data away from
From or the first party parallactic angle or first pitch angle whether be outlier, if so, being by the first object data judging
In vain, if not, it is determined that the first object data are first valid data；
Second effective judging unit, for judging the second orientation of second number of targets by the outlier thresholding
Whether angle or second pitch angle are outlier, if so, second target data is determined as in vain, if not, it is determined that
Second target data is second valid data.
Further, the outlier thresholding includes that target range judges that thresholding, azimuth judge that thresholding and pitch angle judge door
Limit.
The target range judges that thresholding is as follows:
| R-R ' | > VRTS；
The azimuth judges that thresholding is as follows:
R'| sinA-sinA'| > VHTS；
The pitch angle judges that thresholding is as follows:
R'| sinE-sinE'| > VVTS；
Wherein, R be the two-coordinate radar currently detect the first object distance or the photoelectric tracer it is current
Second target range of detection, R' are that the target range obtained after the processing of upper a cycle to the target aircraft is filtered
Wave number or predicted value；A is that the first party parallactic angle that the two-coordinate radar currently detects or the photoelectric tracer are currently visited
The second party parallactic angle surveyed, A' is the azimuth of target filter value handled in upper a cycle the target aircraft
Or predicted value；E is that first pitch angle that the two-coordinate radar currently detects or the photoelectric tracer currently detect
Second pitch angle, E' are the target pitch angle filter value handled in upper a cycle the target aircraft or pre-
Measured value.
VHThe maximum horizontal speed of target aircraft, V can be detected for the two-coordinate radarTFor the two coordinates thunder
Up to the maximum vertical speed that can detect target aircraft,
Further, the observation data generation module is also used to according to the data cycle TRWith the data cycle TSBenefit
Time shaft alignment is carried out to first valid data and second valid data with interpolation extrapolation.
In order to solve the above-mentioned technical problem the present invention provides a kind of trapping system goal directed method, be nothing for target
It is man-machine.
Technical solution are as follows: the goal directed method includes above-mentioned aerial vehicle trajectory prediction technique, further includes:
Step 5, point is updated using the measurement filter value or predicted value as the track of the UAV targets, and according to institute
It states track update point and forms targetpath guidance data, guide data to guide detecting devices respectively by the targetpath, lead
Boat equipment is accurately tracked to the UAV targets and cheating interference.
The beneficial effect is that can be provided effectively for unmanned plane trapping system by above-mentioned aerial vehicle trajectory generation method
Goal directed data to guide detecting devices and navigation cheating interference, thus improve to the tracking stability of UAV targets and
Trap success rate.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of aerial vehicle trajectory generation method provided in an embodiment of the present invention；
Fig. 2 is that the result after the time shaft alignment provided in an embodiment of the present invention that observation data are carried out with interpolation extrapolation is illustrated
Figure；
Fig. 3 is a kind of flow diagram of trapping system goal directed method provided in an embodiment of the present invention；
Fig. 4 is the structural schematic diagram that a kind of aerial vehicle trajectory provided in an embodiment of the present invention generates system.
Specific embodiment
The principle and features of the present invention will be described below with reference to the accompanying drawings, and the given examples are served only to explain the present invention, and
It is non-to be used to limit the scope of the invention.
As shown in Figure 1, a kind of aerial vehicle trajectory generation method provided in an embodiment of the present invention, comprising:
Step 1, target aircraft is detected by two-coordinate radar to obtain first object data, passes through photoelectric tracking
Device detects the target aircraft to obtain the second target data.
Step 2, the first object data and second target data are pre-processed respectively, obtains first effectively
Data and the second valid data.
Step 3, time shaft alignment carried out to first valid data and the second valid data, and will be described in after alignment
First valid data and second valid data are merged, and observation data are obtained.
Step 4, the observation data are handled by adaptive Kalman filter, obtains the target aircraft
Measurement filter value or predicted value.
Wherein, aircraft can be unmanned plane, helicopter, aircraft etc..
It should be noted that outlier is also referred to as extremum or singular value, usually can be in by substantial deviation major part data
The sub-fraction data point of existing variation tendency is known as outlier.
In practical application scene, by taking unmanned plane as an example, firstly, the target acquisition and data of building unmanned plane trapping system
Processing system.
The target acquisition and data processing system of unmanned plane trapping system, in which: target acquisition includes two-coordinate radar, light
Electric tracing device, data processing include main control computer (containing main control software).
The function of two-coordinate radar are as follows: target is detected, is tracked, and guides photoelectric tracer by image to target
It is identified, output parameter has target range, azimuth, antenna elevation angle, radial velocity, and the data period is TR.That is output the
One target data.
The function of photoelectric tracer are as follows: target is identified, angleonly tracking and ranging, output parameter have target range,
Azimuth, pitch angle, data period are TS.Export the second target data.
And the function of main control computer can be with are as follows: the target data of each sensor output receives, processing, exports each equipment control
System instruction and goal directed data, goal directed data are used to guide two-coordinate radar, photoelectric tracer and navigation cheating interference,
Main control computer data receiver and output period are TS。
Then, target data is pre-processed.
Specifically, main control computer carries out data validity judgement according to the significance bit of sensor echo back data first, so
Afterwards the target range of effective sensor, orientation, pitching data are carried out picking open country.Wherein, with radar antenna pitching beam center work
It is E for the pitch angle of radar detectionr+β/2,ErFor the radar antenna elevation angle, β is radar antenna pitching beam angle.
It should be noted that sensor is two-coordinate radar and photoelectric tracer.
When there is one-dimensional data to be judged as outlier in radar data, then radar data is invalid, when photoelectric tracer orientation or bow
Face upward photoelectric tracer data invalid when the wherein one-dimensional datas of data is judged as outlier.
Wherein, outlier judges thresholding are as follows:
Distance Judgment thresholding
| R-R'| > VRTS (1)
Azimuth judges thresholding
R'| sinA-sinA'| > VHTS (2)
Pitch angle judges thresholding
R'| sinE-sinE'| > VVTS (3)
Wherein, R, A, E are current sensor measured value, and R', A', E' are to export after last moment main control computer is handled
Targetpath three-dimensional data, VHFor the maximum horizontal speed of the detectable target of system, VTFor the maximum perpendicular of the detectable target of system
Speed,For the maximum speed of the detectable target of system.
It should be noted that above-mentioned VH、VT、VRIt is system priori data.
By above-mentioned pretreatment, the first valid data and the second valid data can be obtained.
Secondly, being aligned to the first valid data and the second valid data to sensor target data alignment, fusion
And fusion.
It is alternatively possible to according to the data cycle TRWith data cycle TSUsing interpolation extrapolation to two-coordinate radar and
Photoelectric tracer carries out time shaft alignment.
Specifically, comprising the following steps:
1, GPR Detection Data is transformed into earth right angle coordinate system (Bei Tiandong) from big terrestrial coordinate system:
2, with TSRadar data is extrapolated for the data period:
Wherein, xRn、yRn、zRnIt is the observation in n-th of period of radar, xRni、yRni、zRniIt is n-th of period of radar respectively
I-th of extrapolated value, TR=5TS, therefore i=1~4.
3, radar data is transformed into big terrestrial coordinate system from earth right angle coordinate system (Bei Tiandong)
4, data compression, fusion are carried out when radar, photoelectricity are effective, only one sensor directlys adopt this when effective
Sensing data generates the observation data on main control computer current processing time.
Wherein, RZS、AZS、EZSDistance, orientation, the pitching data respectively observed on master control current processing time, RI、AI、
EIDistance, orientation, the pitching data respectively observed on photoelectricity current processing time, R 'R、A′R、E′RWhen respectively currently processed
Between upper distance, orientation, pitching data of the radar after extrapolation process.The respectively distance of radar, side
Position and pitching measurement error variance,The respectively distance, orientation of photoelectricity, pitching measurement error variance.
After the time shaft alignment that through the above steps sensor target data are carried out with interpolation extrapolation, the time of alignment is such as
Shown in Fig. 2.
And data compression, fusion can be carried out by the acquisition data of radar and photoelectricity when effective through the above steps,
To obtain observation data.
It should be noted that interpolation extrapolation is the prior art, and key protection point of the present invention does not lie in interpolation extrapolation sheet
Body, therefore more narrations are not done herein.
Finally, building adaptive Kalman filter handles to predict targetpath, filter observation data.
Specifically, comprising the following steps:
1, master control observation data are transformed into earth right angle coordinate system (Bei Tiandong) from big terrestrial coordinate system；
2, using adaptive Kalman filter to targetpath prediction, filtering.
Firstly, Z (k)=[x y z] is determined as 3 dimension observation vectors,For
6 dimension state vectors, k indicate that discrete time is instantaneous, and wherein x, y, z is the position under target rectangular coordinate system,Respectively
Target x, y, z direction speed,Respectively acceleration of the target in x, y, z direction.
Initial valuationIt is obtained according to sensor initial observation value, carries out next step prediction:
Status predication equation are as follows:
Wherein transfer matrix Φ1(k) are as follows:
Measure predicted value are as follows:
Wherein, observing matrix H (k) are as follows:
Predict covariance matrix are as follows:
P (k+1/k)=Φ1(k)P(k/k)Φ1 T(k)+Q(k) (11)
Wherein, Q (k) maintains system noise variance for 6.
New breath covariance matrix are as follows:
S (k+1)=H (k+1) P (k+1/k) HT(k+1)+R(k+1) (12)
Wherein, the 3 dimension observation noise variances of R (k).
Gain matrix are as follows:
K (k+1)=P (k+1/k) HT(k+1)S-1(k+1) (13)
State filtering valuation are as follows:
Measure filter value are as follows:
Valuation error co-variance matrix are as follows:
Measurement predicted value is solved according to above-mentioned formula.
Specifically, carve sensor when treated when there is no a data update i.e. observation sensor data invalid when, carry out track
Prediction extrapolation, the measurement predicted value for obtaining formula (10) update point as targetpath；It is seen when there is sensing data update
When survey sensing data is effective, track filtering is carried out, the measurement filter value for obtaining formula (15) updates point as targetpath.
It should be noted that adaptive Kalman filter is the prior art, since key protection point of the invention is not lain in
Adaptive Kalman filter itself, therefore more narrations are not done herein.
In conclusion a kind of aerial vehicle trajectory generation method of above-described embodiment, has the advantage that
(1) respectively target aircraft detect to obtain two kinds of spy by two-coordinate radar and photoelectric tracer
Measured data, and fusion treatment is carried out to two kinds of detection datas, and carry out the measurement filter value that filter forecasting obtains target aircraft
Or predicted value improves target data precision, data updating rate to increase target data dimension, is formed to detection target
The targetpath information of steady and continuous.
(2) observation data are handled by adaptive Kalman filter, obtains the measurement filtering of target aircraft
Value or predicted value, the continuity of target trajectory when ensure that the smoothness of target trajectory prediction and detecting invalid.
(3) outlier in target data is rejected by outlier thresholding, guarantees the validity of target data, to improve prediction
Accuracy.
As shown in figure 3, be unmanned plane for target the present embodiment provides a kind of trapping system goal directed method,
Goal directed method includes a kind of aerial vehicle trajectory generation method of above-described embodiment, further includes:
Step 5, point is updated using the measurement filter value or predicted value as the track of the UAV targets, and according to institute
It states track update point and forms targetpath guidance data, guide data to guide detecting devices respectively and lead by the targetpath
Boat equipment is accurately tracked to the UAV targets and cheating interference.
In practical applications, equally by taking unmanned plane as an example, point is updated using the targetpath that above-described embodiment obtains and is formed
Targetpath guides data, and guidance detecting devices, pre-processing device and navigation jamming equipment cheat UAV targets
Interference.
The trapping system goal directed method of the present embodiment solves single biography by above-mentioned aerial vehicle trajectory prediction technique
The problem that sensor target data dimension is few, data transfer rate is low, reliability and stability are poor, the pitching for solving two-coordinate radar are drawn
It leads, improves the smoothness of target data precision, data updating rate and target trajectory, stable company is formd to detection target
Continuous targetpath information provides effective goal directed number for the target acquisition and navigation cheating interference of unmanned plane trapping system
According to improve the tracking stability and trapping success rate to UAV targets.
As shown in figure 4, the present embodiment provides a kind of aerial vehicle trajectories to generate system, comprising:
Target data acquisition module obtains first object number for being detected by two-coordinate radar to target aircraft
According to being detected to obtain the second target data to the target aircraft by photoelectric tracer.
Data preprocessing module, for being located in advance to the first object data and second target data respectively
Reason, obtains the first valid data and the second valid data.
Data generation module is observed, for carrying out time shaft pair to first valid data and second valid data
Standard, and by after alignment first valid data and second valid data merge, obtain observation data.
Measure filter value and predicted value generation module, by adaptive Kalman filter to the observation data at
Reason, obtains the measurement filter value or predicted value of the target aircraft.
Optionally, the first object data include first object distance, first party parallactic angle, the first pitch angle, described
The period of one target data is TR；Second target data includes the second target range, second party parallactic angle, the second pitch angle,
The period of second target data is TS。
By acquiring target range, the azimuth, pitch angle of target aircraft, guarantees the tracking accuracy to target, lead to
Cross acquisition data cycle TRWith data cycle TSSo as to the alignment of subsequent two kinds of detection datas.
Optionally, the data preprocessing module specifically includes:
First effective judging unit, for judged by outlier thresholding the first objects of the first object data away from
From or the first party parallactic angle or first pitch angle whether be outlier, if so, being by the first object data judging
In vain, if not, it is determined that the first object data are first valid data；
Second effective judging unit, for judging the second orientation of second number of targets by the outlier thresholding
Whether angle or second pitch angle are outlier, if so, second target data is determined as in vain, if not, it is determined that
Second target data is second valid data.
By rejecting the outlier in target data, guarantee the validity of target data, to improve prediction accuracy.
Optionally, the outlier thresholding includes that target range judges that thresholding, azimuth judge that thresholding and pitch angle judge door
Limit.
The target range judges that thresholding is as follows:
| R-R ' | > VRTS；
The azimuth judges that thresholding is as follows:
R'| sinA-sinA'| > VHTS；
The pitch angle judges that thresholding is as follows:
R'| sinE-sinE'| > VVTS；
Wherein, R be the two-coordinate radar currently detect the first object distance or the photoelectric tracer it is current
Second target range of detection, R' are the target obtained after the processing of upper a cycle step 4 to the target aircraft
Distance Filter value or predicted value；A is the first party parallactic angle or the photoelectric tracer that the two-coordinate radar currently detects
The second party parallactic angle currently detected, A' are the target obtained to the target aircraft in the processing of upper a cycle step 4
Azimuth filter value or predicted value；E is first pitch angle or the photoelectric tracking that the two-coordinate radar currently detects
Second pitch angle that device currently detects, E' are the mesh obtained to the target aircraft in the processing of upper a cycle step 4
Mark pitch angle filter value or predicted value.
VHThe maximum horizontal speed of target aircraft, V can be detected for the two-coordinate radarTFor the two coordinates thunder
Up to the maximum vertical speed that can detect target aircraft,
By above-mentioned outlier thresholding, the outlier in target data can be effectively rejected.
Optionally, the observation data generation module is also used to according to the data cycle TRWith data cycle TSUsing interior
It inserts extrapolation and time shaft alignment is carried out to first valid data and second valid data.
More effectively the first valid data and the second valid data can be aligned by interpolation extrapolation.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all in spirit of the invention and
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of aerial vehicle trajectory generation method characterized by comprising
Step 1, target aircraft is detected by two-coordinate radar to obtain first object data, passes through photoelectric tracer pair
The target aircraft is detected to obtain the second target data；
Step 2, the first object data and second target data are pre-processed respectively, obtains the first valid data
With the second valid data；
Step 3, time shaft alignment carried out to first valid data and second valid data, and by first after alignment
Valid data and the second valid data are merged, and observation data are obtained；
Step 4, the observation data are handled by adaptive Kalman filter, obtains the survey of the target aircraft
Measure filter value or predicted value.
2. aerial vehicle trajectory generation method according to claim 1, which is characterized in that the first object data include the
One target range, first party parallactic angle, the first pitch angle, the period of the first object data are TR；Second target data
Period including the second target range, second party parallactic angle, the second pitch angle, second target data is TS。
3. aerial vehicle trajectory generation method according to claim 2, which is characterized in that the step 2 specifically includes:
Step 2.1, the first object distance or the first orientation of the first object data are judged by outlier thresholding
Whether angle or first pitch angle are outlier, if so, by the first object data judging be it is invalid, if not, it is determined that
The first object data are first valid data；
Step 2.2, the second party parallactic angle of second target data is judged by the outlier thresholding or described second bowed
Whether the elevation angle is outlier, if so, second target data is determined as in vain, if not, it is determined that second number of targets
According to for second valid data.
4. aerial vehicle trajectory generation method according to claim 3, which is characterized in that the outlier thresholding include target away from
From judging that thresholding, azimuth judge that thresholding and pitch angle judge thresholding；
The target range judges that thresholding is as follows:
| R-R ' | > VRTS；
The azimuth judges that thresholding is as follows:
R'| sinA-sinA'| > VHTS；
The pitch angle judges that thresholding is as follows:
R'| sinE-sinE'| > VVTS；
Wherein, R be the two-coordinate radar currently detect the first object distance or the photoelectric tracer currently detect
Second target range, R' be to the target aircraft after the processing of upper a cycle obtained target range filter value
Or predicted value；A is that the first party parallactic angle that the two-coordinate radar currently detects or the photoelectric tracer currently detect
The second party parallactic angle, A' are the azimuth of target filter value handled in upper a cycle the target aircraft or pre-
Measured value；E is first pitch angle that currently detects of the two-coordinate radar or the photoelectric tracer currently detect it is described
Second pitch angle, E' are the target pitch angle filter value handled in upper a cycle the target aircraft or prediction
Value；
VHThe maximum horizontal speed of target aircraft, V can be detected for the two-coordinate radarTFor the two-coordinate radar institute
The maximum vertical speed of target aircraft can be detected,
5. aerial vehicle trajectory generation method according to claim 2, which is characterized in that the specific implementation of the step 3 are as follows:
According to the data cycle TRWith the data cycle TSUsing interpolation extrapolation to first valid data and described
Two valid data carry out time shaft alignment.
6. a kind of trapping system goal directed method is unmanned plane for target, which is characterized in that including claim 1-5
Described in any item aerial vehicle trajectory generation methods, further includes:
Step 5, point is updated using the measurement filter value or predicted value as the track of the UAV targets, and according to the boat
Mark update point formed targetpath guidance data, by the targetpath guide data guide detecting devices to the target without
It is man-machine accurately to be tracked, navigation jamming equipment is guided to carry out cheating interference to the UAV targets.
7. a kind of aerial vehicle trajectory generates system characterized by comprising
Target data acquisition module obtains first object data for being detected by two-coordinate radar to target aircraft,
The target aircraft is detected by photoelectric tracer to obtain the second target data；
Data preprocessing module is obtained for pre-processing respectively to the first object data and second target data
To the first valid data and the second valid data；
Data generation module is observed, for carrying out time shaft alignment to first valid data and second valid data,
And by after alignment the first valid data and the second valid data merge, obtain observation data；
Filter value and predicted value generation module are measured, the observation data are handled by adaptive Kalman filter,
Obtain the measurement filter value or predicted value of the target aircraft.
8. aerial vehicle trajectory according to claim 7 generates system, which is characterized in that the first object data include the
One target range, first party parallactic angle, the first pitch angle, the first object data period be TR；Second target data
Period including the second target range, second party parallactic angle, the second pitch angle, second target data is TS。
9. aerial vehicle trajectory according to claim 8 generates system, which is characterized in that the data preprocessing module is specific
Include:
First effective judging unit, for judged by outlier thresholding the first object data the first object distance or
Whether the first party parallactic angle or first pitch angle are outlier, if so, by the first object data judging be it is invalid,
If not, it is determined that the first object data are first valid data；
Second effective judging unit, for judging the second party parallactic angle of second target data by the outlier thresholding
Or whether second pitch angle is outlier, if so, second target data is determined as in vain, if not, it is determined that institute
Stating the second target data is second valid data.
10. aerial vehicle trajectory according to claim 9 generates system, which is characterized in that the outlier thresholding includes target
Distance Judgment thresholding, azimuth judge that thresholding and pitch angle judge thresholding；
The target range judges that thresholding is as follows:
| R-R ' | > VRTS；
The azimuth judges that thresholding is as follows:
R'| sinA-sinA'| > VHTS；
The pitch angle judges that thresholding is as follows:
R'| sinE-sinE'| > VVTS；
Wherein, R be the two-coordinate radar currently detect the first object distance or the photoelectric tracer currently detect
Second target range, R' be to the target aircraft after the processing of upper a cycle obtained target range filter value
Or predicted value；A is that the first party parallactic angle that the two-coordinate radar currently detects or the photoelectric tracer currently detect
The second party parallactic angle, A' are the azimuth of target filter value handled in upper a cycle the target aircraft or pre-
Measured value；E is first pitch angle that currently detects of the two-coordinate radar or the photoelectric tracer currently detect it is described
Second pitch angle, E' are the target pitch angle filter value handled in upper a cycle the target aircraft or prediction
Value；
VHThe maximum horizontal speed of target aircraft, V can be detected for the two-coordinate radarTFor the two-coordinate radar institute
The maximum vertical speed of target aircraft can be detected,
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