CN109407086B - Aircraft trajectory generation method and system and trapping system target guiding method - Google Patents
Aircraft trajectory generation method and system and trapping system target guiding method Download PDFInfo
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- CN109407086B CN109407086B CN201811550039.5A CN201811550039A CN109407086B CN 109407086 B CN109407086 B CN 109407086B CN 201811550039 A CN201811550039 A CN 201811550039A CN 109407086 B CN109407086 B CN 109407086B
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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
The application relates to an aircraft track generation method, an aircraft track generation system and a trapping system target guiding method, wherein the generation method comprises the following steps of 1, detecting a target aircraft through a two-coordinate radar to obtain first target data, and detecting the target aircraft through a photoelectric tracker to obtain second target data; step 2, respectively preprocessing the first target data and the second target data to obtain first effective data and second effective data; step 3, carrying out time axis alignment on the first effective data and the second effective data, and fusing the aligned first effective data and the aligned second effective data to obtain observation data; and 4, processing the observation data through the adaptive Kalman filter to obtain a measurement filtering value or a predicted value of the target aircraft. The target aircraft is detected by various sensors, so that the target data dimension is increased, the target data precision and the data updating rate are improved, and a stable and continuous target track is formed for the detected target.
Description
Technical Field
The invention relates to the field of aircraft trajectory generation, in particular to an aircraft trajectory generation method and system and a trapping system target guiding method.
Background
At present, the flight path of an aircraft is detected by tracking the flight path of a target aircraft through a detection radar and processing tracking data to obtain a path value of the aircraft, and a photoelectric device is only used as an auxiliary target identification device. However, the detection radar is adopted for tracking, so that the problems of low target tracking data rate, poor reliability and poor stability exist, pitch dimensional data is lacked when the two-coordinate radar is adopted, and the generated target track data cannot realize effective guidance of the trapping device.
Disclosure of Invention
The invention provides an aircraft trajectory generation method for solving the technical problems.
The technical scheme for solving the technical problems is as follows: an aircraft trajectory generation method, comprising:
step 1, detecting a target aircraft through a two-coordinate radar to obtain first target data, and detecting the target aircraft through a photoelectric tracker to obtain second target data.
And 2, respectively preprocessing the first target data and the second target data to obtain first effective data and second effective data.
And 3, carrying out time axis alignment on the first effective data and the second effective data, and fusing the aligned first effective data and second effective data to obtain observation data.
And 4, processing the observation data through a self-adaptive Kalman filter to obtain a measurement filtering value or a predicted value of the target aircraft.
The invention has the advantages that the target aircraft is respectively detected by the two coordinate radars and the photoelectric tracker to obtain two detection data, the two detection data are fused, and the measurement filtering value or the prediction value of the target aircraft is obtained by filtering and predicting, so that the target data dimension is increased, the target data precision and the data updating rate are improved, and stable and continuous target track information is formed for the detected target. The observation data are processed through the adaptive Kalman filter to obtain a measurement filtering value or a predicted value of the target aircraft, and smoothness of the target motion track and continuity of the target motion track when detection is invalid are guaranteed.
On the basis of the technical scheme, the invention can be further improved as follows.
Further, the first target data comprises a first target distance, a first azimuth angle, a first pitch angle, and the period of the first target data is T R (ii) a The second object data comprises a second objectDistance, a second azimuth angle and a second pitch angle, wherein the period of the second target data is T S 。
The further scheme has the advantages that the target tracking accuracy is ensured by collecting the target distance, the azimuth angle and the pitch angle of the target aircraft, and the data period T is collected R And a data period T S For alignment of the two subsequent detection data.
Further, the step 2 specifically includes:
step 2.1, judging whether the first target distance or the first azimuth angle or the first pitch angle of the first target data is a outlier or not through an outlier threshold, if so, judging the first target data to be invalid, and if not, determining the first target data to be the first valid data.
And 2.2, judging whether the second azimuth angle or the second pitch angle of the second target number is a wild value or not through the wild value threshold, if so, judging the second target data to be invalid, and if not, determining the second target data to be second valid data.
The beneficial effect of adopting the further scheme is that the effectiveness of the target data is ensured by eliminating the outliers in the target data, thereby improving the prediction accuracy.
Further, the outlier threshold comprises a target distance judgment threshold, an azimuth angle judgment threshold and a pitch angle judgment threshold.
The target distance judgment threshold is as follows:
|R-R′|＞V R T S ；
the azimuth angle judgment threshold is as follows:
R'|sinA-sinA'|＞V H T S ；
the pitch angle judgment threshold is as follows:
R'|sinE-sinE'|＞V V T S ；
wherein, R is the first target distance currently detected by the two-coordinate radar or the second target distance currently detected by the photoelectric tracker, and R' is a target distance filtering value or a predicted value obtained after the target aircraft is processed in the last period; a is the first azimuth currently detected by the two-coordinate radar or the second azimuth currently detected by the photoelectric tracker, and A' is a target azimuth filtering value or a predicted value obtained by processing the target aircraft in the last period; and E is the first pitch angle currently detected by the two-coordinate radar or the second pitch angle currently detected by the photoelectric tracker, and E' is a target pitch angle filtered value or a predicted value obtained by processing the target aircraft in the last period.
V H For the maximum horizontal velocity, V, of the target aircraft detectable by the two-coordinate radar T For the maximum vertical velocity of the target aircraft that the two coordinate radar can detect,
the method has the advantages that the outliers in the target data can be effectively removed through the outlier threshold.
Further, the step 3 is specifically realized as follows:
according to the data period T R And said data period T S And carrying out time axis alignment on the first effective data and the second effective data by utilizing an interpolation extrapolation method.
The beneficial effect of adopting the above further scheme is that the first valid data and the second valid data can be aligned more effectively by interpolation extrapolation.
The invention provides an aircraft trajectory generation system for solving the technical problem.
The technical scheme is as follows: an aircraft trajectory generation system comprising:
and the target data acquisition module is used for detecting the target aircraft through the two-coordinate radar to obtain first target data and detecting the target aircraft through the photoelectric tracker to obtain second target data.
And the data preprocessing module is used for respectively preprocessing the first target data and the second target data to obtain first effective data and second effective data.
And the observation data generation module is used for carrying out time axis alignment on the first effective data and the second effective data and fusing the aligned first effective data and second effective data to obtain observation data.
And the measurement filtering value and predicted value generation module is used for processing the observation data through a self-adaptive Kalman filter to obtain a measurement filtering value or a predicted value of the target aircraft.
Further, the first target data comprises a first target distance, a first azimuth angle, a first pitch angle, and the period of the first target data is T R (ii) a The second target data comprises a second target distance, a second azimuth angle and a second pitch angle, and the period of the second target data is T S 。
Further, the data preprocessing module specifically includes:
a first validity judging unit, configured to judge whether the first target distance, the first azimuth angle, or the first pitch angle of the first target data is a outlier through an outlier threshold, if so, judge the first target data as invalid, and if not, determine that the first target data is the first valid data;
a second validity judging unit, configured to judge, through the outlier threshold, whether the second azimuth angle or the second pitch angle of the second target number is an outlier, determine, if yes, that the second target data is invalid, and if not, determine that the second target data is the second valid data.
Further, the outlier threshold comprises a target distance judgment threshold, an azimuth angle judgment threshold and a pitch angle judgment threshold.
The target distance judgment threshold is as follows:
|R-R′|＞V R T S ；
the azimuth angle judgment threshold is as follows:
R'|sinA-sinA'|＞V H T S ；
the pitch angle judgment threshold is as follows:
R'|sinE-sinE'|＞V V T S ；
wherein, R is the first target distance currently detected by the two-coordinate radar or the second target distance currently detected by the photoelectric tracker, and R' is a target distance filtering value or a predicted value obtained after the target aircraft is processed in the last period; a is the first azimuth currently detected by the two-coordinate radar or the second azimuth currently detected by the photoelectric tracker, and A' is a target azimuth filtered value or a predicted value obtained by processing the target aircraft in the last period; and E is the first pitch angle currently detected by the two-coordinate radar or the second pitch angle currently detected by the photoelectric tracker, and E' is a target pitch angle filtered value or a predicted value obtained by processing the target aircraft in the last period.
V H For the maximum horizontal velocity, V, of the target aircraft detectable by the two-coordinate radar T For the maximum vertical velocity of the target aircraft that the two-coordinate radar can detect,
further, the observation data generation module is further configured to generate the observation data according to the data period T R And said data period T S Performing time axis alignment on the first valid data and the second valid data by using interpolation extrapolation.
The invention provides a trapping system target guiding method for solving the technical problems, and aims at providing an unmanned aerial vehicle.
The technical scheme is as follows: the target guidance method comprises the aircraft trajectory prediction method, and further comprises the following steps:
and 5, taking the measured filtering value or the predicted value as a track updating point of the target unmanned aerial vehicle, forming target track guiding data according to the track updating point, and respectively guiding a detection device and a navigation device to accurately track and deceive the target unmanned aerial vehicle through the target track guiding data.
The method has the advantages that through the aircraft track generation method, effective target guide data can be provided for the unmanned aerial vehicle trapping system so as to guide detection equipment and guide deception jamming, and therefore tracking stability and trapping success rate of the target unmanned aerial vehicle are improved.
Drawings
Fig. 1 is a schematic flow chart of an aircraft trajectory generation method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating the results of time axis alignment for interpolation and extrapolation of observed data according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a target guidance method of a trapping system according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an aircraft trajectory generation system according to an embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, an aircraft trajectory generation method provided in an embodiment of the present invention includes:
step 1, detecting a target aircraft through a two-coordinate radar to obtain first target data, and detecting the target aircraft through a photoelectric tracker to obtain second target data.
And 2, respectively preprocessing the first target data and the second target data to obtain first effective data and second effective data.
And 3, carrying out time axis alignment on the first effective data and the second effective data, and fusing the aligned first effective data and the aligned second effective data to obtain observation data.
And 4, processing the observation data through a self-adaptive Kalman filter to obtain a measurement filtering value or a predicted value of the target aircraft.
Wherein, the aircraft can be unmanned aerial vehicle, helicopter, aircraft etc..
It should be noted that outliers are also referred to as extreme values or singular values, and a small portion of data points that deviate significantly from the trend exhibited by a large portion of data may be generally referred to as outlier points.
In an actual application scenario, taking an unmanned aerial vehicle as an example, firstly, a target detection and data processing system of the unmanned aerial vehicle trapping system is constructed.
Unmanned aerial vehicle traps system's target detection and data processing system, wherein: the target detection comprises two coordinate radars and a photoelectric tracker, and the data processing comprises a main control computer (comprising main control software).
The two-coordinate radar has the functions of: detecting and tracking a target, guiding a photoelectric tracker to identify the target through an image, outputting parameters including target distance, azimuth angle, antenna pitch angle and radial speed, and having a data period of T R . I.e. outputs the first target data.
The photoelectric tracker has the functions of: identifying, tracking and ranging a target, outputting parameters including target distance, azimuth angle and pitch angle, and having a data period of T S . I.e. to output the second target data.
And the master computer can function as: receiving and processing target data output by each sensor, outputting control instructions of each device and target guide data, wherein the target guide data are used for guiding two-coordinate radar, a photoelectric tracker and navigation deception jamming, and the data receiving and outputting period of a main control computer is T S 。
Then, the target data is preprocessed.
Specifically, the main control computer firstly judges the validity of the data according to the valid bit of the data returned by the sensor, and then carries out wild elimination on the target distance, the direction and the pitching data of the valid sensor. Wherein, the pitch angle of radar detection is E by taking the center of the radar antenna pitch wave beam as the pitch angle r +β/2,E r Beta is the radar antenna elevation angle and beta is the radar antenna pitch beam width.
It should be noted that the sensor is a two-coordinate radar and a photoelectric tracker.
And when one-dimensional data in the radar data is judged to be the wild value, the radar data is invalid, and when one-dimensional data in the azimuth or pitch data of the photoelectric tracker is judged to be the wild value, the photoelectric tracker data is invalid.
Wherein, the threshold for judging the outlier is as follows:
distance judgment threshold
|R-R'|＞V R T S (1)
Azimuth angle judgment threshold
R'|sinA-sinA'|＞V H T S (2)
Threshold is judged to angle of pitch
R'|sinE-sinE'|＞V V T S (3)
Wherein R, A, E is the current sensor measurement value, R ', A ', E ' are the target track three-dimensional data output after the last time main control computer processing, V H Maximum horizontal velocity, V, of a detectable object of the system T For the maximum vertical velocity at which the system can detect the target,
the maximum speed at which the system can detect the object.
In addition, the above V H 、V T 、V R Are system prior data.
And obtaining the first valid data and the second valid data through the preprocessing.
Secondly, the sensor target data is aligned and fused, namely the first valid data and the second valid data are aligned and fused.
Optionally, according to the data period T R And a data period T S And carrying out time axis alignment on the two coordinate radars and the photoelectric tracker by utilizing an interpolation extrapolation method.
Specifically, the method comprises the following steps:
1. converting radar survey data from a geodetic coordinate system to a geodetic rectangular coordinate system (north heaven):
2. by T S Extrapolating radar data for a data period:
wherein x is Rn 、y Rn 、z Rn Is the observed value, x, of the nth cycle of the radar Rni 、y Rni 、z Rni Respectively, the ith extrapolated value, T, of the nth cycle of the radar R ＝5T S Therefore, i =1 to 4.
3. Converting radar data from a rectangular (north heaven-east) earth coordinate system to a spherical coordinate system
4. When the radar and the photoelectricity are both effective, data compression and fusion are carried out, and when only one sensor is effective, the data of the sensor is directly adopted to generate observation data of the main control computer in the current processing time.
Wherein R is ZS 、A ZS 、E ZS Respectively the distance, azimuth and pitch data R observed at the current processing time of the master control I 、A I 、E I Respectively are distance, azimuth and pitch data R 'observed on the current processing time of the photoelectricity' R 、A′ R 、E′ R Respectively are distance, azimuth and pitching data of the radar after extrapolation processing in the current processing time.
Respectively, range, azimuth and pitch of the radarMeasure the error variance, < >>
The difference is the photoelectric distance, direction and pitching measurement error variance.
The time of the time axis alignment of the interpolation and extrapolation of the sensor target data by the above steps is shown in fig. 2.
And when the radar and photoelectric collected data are both effective, data compression and fusion are carried out to obtain observation data.
It should be noted that interpolation extrapolation is prior art, and the protection of the present invention is not focused on interpolation extrapolation itself, so it is not further described here.
And finally, constructing an adaptive Kalman filter to process the observation data so as to predict and filter the target track.
Specifically, the method comprises the following steps:
1. converting the main control observation data from a large earth coordinate system to a large earth rectangular coordinate system (north heaven);
2. and predicting and filtering the target track by using an adaptive Kalman filter.
First, Z (k) = [ x yz ]]Is determined as a 3-dimensional observation vector,
is a 6-dimensional state vector, k represents a discrete time instant, where x, y, z are positions in the target rectangular coordinate system, and->
Based on the speed of the target in the x, y, z direction, respectively>
The acceleration of the target in the x, y and z directions respectively.
Initial valuation
Based on sensor initializationAnd obtaining an observed value, and performing the next prediction:
the state prediction equation is:
wherein the transition matrix phi 1 (k) Comprises the following steps:
the measurement prediction value is:
wherein the observation matrix H (k) is:
the prediction covariance matrix is:
P(k+1/k)＝Φ 1 (k)P(k/k)Φ 1 T (k)+Q(k) (11)
wherein Q (k) is a 6-dimensional system noise variance.
The innovation covariance matrix is:
S(k+1)＝H(k+1)P(k+1/k)H T (k+1)+R(k+1) (12)
wherein R (k) is a 3-dimensional observed noise variance.
The gain matrix is:
K(k+1)＝P(k+1/k)H T (k+1)S -1 (k+1) (13)
the state filter estimates are:
the measured filtered value is:
the estimation error covariance matrix is:
and solving the measurement predicted value according to the formula.
Specifically, when the sensor does not have data updating at the processing time, namely the data of the observation sensor is invalid, track prediction extrapolation is carried out to obtain a measurement prediction value of a formula (10) as a target track updating point; and when the sensor data is updated, namely the data of the observation sensor is effective, track filtering is carried out, and the measurement filtering value of the formula (15) is obtained and is used as a target track updating point.
It should be noted that the adaptive kalman filter is a prior art, and since the protection of the present invention is not focused on the adaptive kalman filter itself, it is not further described here.
In summary, the aircraft trajectory generation method of the above embodiment has the following advantages:
(1) The target aircraft is detected through the two coordinate radars and the photoelectric tracker respectively to obtain two kinds of detection data, the two kinds of detection data are fused and subjected to filtering prediction to obtain a measurement filtering value or a predicted value of the target aircraft, so that the target data dimension is increased, the target data precision and the data updating rate are improved, and stable and continuous target track information is formed for the detected target.
(2) And processing the observation data through the adaptive Kalman filter to obtain a measurement filtering value or a predicted value of the target aircraft, so that smoothness of target motion track prediction and continuity of the target motion track when detection is invalid are ensured.
(3) And eliminating the outlier in the target data through the outlier threshold to ensure the validity of the target data, thereby improving the prediction accuracy.
As shown in fig. 3, the present embodiment provides a trapping system target guiding method, which is directed to a target that is an unmanned aerial vehicle, and the target guiding method includes the aircraft trajectory generating method of the above embodiment, further including:
and 5, taking the measured filtering value or the predicted value as a track updating point of the target unmanned aerial vehicle, forming target track guiding data according to the track updating point, and respectively guiding detection equipment and navigation equipment to accurately track and deceive the target unmanned aerial vehicle through the target track guiding data.
In practical application, the unmanned aerial vehicle is taken as an example, the target track update points obtained by the embodiment are used for forming target track guide data, and the guide detection device, the preprocessing device and the navigation interference device are used for carrying out deception interference on the target unmanned aerial vehicle.
According to the trapping system target guiding method, through the aircraft track prediction method, the problems of small target data dimension, low data rate, poor reliability and poor stability of a single sensor are solved, pitching guiding of a two-coordinate radar is solved, target data precision, data updating rate and smoothness of a target motion track are improved, stable and continuous target track information is formed for a detected target, effective target guiding data are provided for target detection and navigation deception interference of an unmanned aerial vehicle trapping system, and accordingly tracking stability and trapping success rate of a target unmanned aerial vehicle are improved.
As shown in fig. 4, the present embodiment provides an aircraft trajectory generation system, including:
and the target data acquisition module is used for detecting the target aircraft through the two-coordinate radar to obtain first target data and detecting the target aircraft through the photoelectric tracker to obtain second target data.
And the data preprocessing module is used for respectively preprocessing the first target data and the second target data to obtain first effective data and second effective data.
And the observation data generation module is used for carrying out time axis alignment on the first effective data and the second effective data and fusing the aligned first effective data and the aligned second effective data to obtain observation data.
And the measurement filtering value and predicted value generation module is used for processing the observation data through an adaptive Kalman filter to obtain a measurement filtering value or a predicted value of the target aircraft.
Optionally, the first target data includes a first target distance, a first azimuth angle, a first pitch angle, and a period of the first target data is T R (ii) a The second target data comprises a second target distance, a second azimuth angle and a second pitch angle, and the period of the second target data is T S 。
The target distance, the azimuth angle and the pitch angle of the target aircraft are collected to ensure the tracking accuracy of the target, and the data period T is collected R And a data period T S For alignment of the two subsequent detection data.
Optionally, the data preprocessing module specifically includes:
a first validity judging unit, configured to judge whether the first target distance, the first azimuth angle, or the first pitch angle of the first target data is a outlier through an outlier threshold, if so, judge the first target data as invalid, and if not, determine that the first target data is the first valid data;
a second validity judging unit, configured to judge, through the outlier threshold, whether the second azimuth angle or the second pitch angle of the second target number is an outlier, determine, if yes, that the second target data is invalid, and if not, determine that the second target data is the second valid data.
By eliminating outliers in the target data, the effectiveness of the target data is guaranteed, and therefore the prediction accuracy is improved.
Optionally, the outlier threshold includes a target distance determination threshold, an azimuth determination threshold, and a pitch determination threshold.
The target distance judgment threshold is as follows:
|R-R′|＞V R T S ；
the azimuth angle judgment threshold is as follows:
R'|sinA-sinA'|＞V H T S ；
the pitch angle judgment threshold is as follows:
R'|sinE-sinE'|＞V V T S ；
wherein, R is the first target distance currently detected by the two-coordinate radar or the second target distance currently detected by the photoelectric tracker, and R' is a target distance filtered value or a predicted value obtained after the target aircraft is processed in step 4 in the previous period; a is the first azimuth currently detected by the two-coordinate radar or the second azimuth currently detected by the photoelectric tracker, and A' is a target azimuth filtering value or a target azimuth predicted value obtained by processing the target aircraft in the step 4 in the previous period; and E is the first pitch angle currently detected by the two-coordinate radar or the second pitch angle currently detected by the photoelectric tracker, and E' is a target pitch angle filtered value or a predicted value obtained by processing the target aircraft in the step 4 in the last period.
V H For the maximum horizontal velocity, V, of the target aircraft detectable by the two-coordinate radar T For the maximum vertical velocity of the target aircraft that the two coordinate radar can detect,
by the threshold of the outlier, the outlier in the target data can be effectively removed.
Optionally, the observation data generating module is further configured to generate the observation data according to the data period T R And a data period T S Performing time axis alignment on the first valid data and the second valid data by using interpolation extrapolation.
The first valid data and the second valid data can be aligned more efficiently by interpolation extrapolation.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (4)
1. An aircraft trajectory generation method, comprising:
step 1, detecting a target aircraft through a two-coordinate radar to obtain first target data, and detecting the target aircraft through a photoelectric tracker to obtain second target data;
the first target data comprises a first target distance, a first azimuth angle and a first pitch angle, and the period of the first target data is T R (ii) a The second target data comprises a second target distance, a second azimuth angle and a second pitch angle, and the period of the second target data is T S ；
Step 2, respectively preprocessing the first target data and the second target data to obtain first effective data and second effective data;
the step 2 specifically comprises:
step 2.1, judging whether the first target distance, the first azimuth angle or the first pitch angle of the first target data is a outlier or not through an outlier threshold, if so, judging the first target data to be invalid, and if not, determining the first target data to be the first valid data;
step 2.2, judging whether the second azimuth angle or the second pitch angle of the second target data is a outlier or not through the outlier threshold, if so, judging the second target data to be invalid, and if not, determining the second target data to be the second valid data;
the outlier threshold comprises a target distance judgment threshold, an azimuth angle judgment threshold and a pitch angle judgment threshold;
the target distance judgment threshold is as follows:
|R-R'|＞V R T S ；
the azimuth angle judgment threshold is as follows:
R'|sinA-sinA'|＞V H T S ；
the pitch angle judgment threshold is as follows:
R'｜sinE-sinE'|＞V V T S ；
wherein, R is the first target distance currently detected by the two-coordinate radar or the second target distance currently detected by the photoelectric tracker, and R' is a target distance filtering value or a predicted value obtained after the target aircraft is processed in a last period; a is the first azimuth currently detected by the two-coordinate radar or the second azimuth currently detected by the photoelectric tracker, and A' is a target azimuth filtered value or a predicted value obtained by processing the target aircraft in the last period; e is the first pitch angle currently detected by the two-coordinate radar or the second pitch angle currently detected by the photoelectric tracker, and E' is a target pitch angle filtered value or a predicted value obtained by processing the target aircraft in the last period;
V H for the maximum horizontal velocity, V, of the target aircraft detectable by the two-coordinate radar T For the maximum vertical velocity of the target aircraft that the two coordinate radar can detect,
step 3, carrying out time axis alignment on the first effective data and the second effective data, and fusing the aligned first effective data and the aligned second effective data to obtain observation data;
and 4, processing the observation data through a self-adaptive Kalman filter to obtain a measurement filtering value or a predicted value of the target aircraft.
2. The aircraft trajectory generation method according to claim 1, wherein the step 3 is implemented by:
according to the data period T R And said data period T S Performing time axis alignment on the first valid data and the second valid data by using interpolation extrapolation.
3. A trapping system target guidance method, which is directed to a target being an unmanned aerial vehicle, comprising the aircraft trajectory generation method of any one of claims 1-2, further comprising:
and 5, taking the measured filtering value or the predicted value as a track updating point of the target unmanned aerial vehicle, forming target track guiding data according to the track updating point, and guiding a detection device to accurately track the target unmanned aerial vehicle and guiding a navigation interference device to perform deception interference on the target unmanned aerial vehicle through the target track guiding data.
4. An aircraft trajectory generation system, comprising:
the target data acquisition module is used for detecting a target aircraft through a two-coordinate radar to obtain first target data and detecting the target aircraft through a photoelectric tracker to obtain second target data;
the first target data comprises a first target distance, a first azimuth angle and a first pitch angle, and the period of the first target data is T R (ii) a The second target data comprises a second target distance, a second azimuth angle and a second pitch angle, and the period of the second target data is T S ；
The data preprocessing module is used for respectively preprocessing the first target data and the second target data to obtain first effective data and second effective data;
the data preprocessing module specifically comprises:
a first validity judging unit, configured to judge whether the first target distance, the first azimuth angle, or the first pitch angle of the first target data is a outlier through an outlier threshold, if so, judge the first target data as invalid, and if not, determine that the first target data is the first valid data;
a second validity judging unit, configured to judge whether the second azimuth angle or the second pitch angle of the second target data is a outlier through the outlier threshold, if yes, determine that the second target data is invalid, and if not, determine that the second target data is the second valid data;
the outlier threshold comprises a target distance judgment threshold, an azimuth angle judgment threshold and a pitch angle judgment threshold;
the target distance judgment threshold is as follows:
|R-R'|＞V R T S ；
the azimuth angle judgment threshold is as follows:
R'|sinA-sinA'|＞V H T S ；
the pitch angle judgment threshold is as follows:
R'|sinE-sinE'|＞V V T S ；
wherein, R is the first target distance currently detected by the two-coordinate radar or the second target distance currently detected by the photoelectric tracker, and R' is a target distance filtering value or a predicted value obtained after the target aircraft is processed in the last period; a is the first azimuth currently detected by the two-coordinate radar or the second azimuth currently detected by the photoelectric tracker, and A' is a target azimuth filtering value or a predicted value obtained by processing the target aircraft in the last period; and E is the first pitch angle currently detected by the two-coordinate radar or the second pitch angle currently detected by the photoelectric tracker, and E' is a target pitch angle filtered value or a predicted value obtained by processing the target aircraft in the last period.
V H For the maximum horizontal velocity, V, of the target aircraft detectable by the two-coordinate radar T For the maximum vertical velocity of the target aircraft that the two-coordinate radar can detect,
the observation data generation module is used for carrying out time axis alignment on the first effective data and the second effective data and fusing the aligned first effective data and second effective data to obtain observation data;
and the measurement filtering value and predicted value generation module is used for processing the observation data through a self-adaptive Kalman filter to obtain a measurement filtering value or a predicted value of the target aircraft.
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