CN103235291A - Method for filtering flight path - Google Patents

Method for filtering flight path Download PDF

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CN103235291A
CN103235291A CN2013101439492A CN201310143949A CN103235291A CN 103235291 A CN103235291 A CN 103235291A CN 2013101439492 A CN2013101439492 A CN 2013101439492A CN 201310143949 A CN201310143949 A CN 201310143949A CN 103235291 A CN103235291 A CN 103235291A
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measured value
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historical data
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CN103235291B (en
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王耀兴
刘永刚
李涛
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Sichuan Jiuzhou ATC Technology Co Ltd
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Abstract

The invention relates to the technical field of secondary radar surveillance. The invention discloses a method for filtering a flight path, which specifically comprises the following steps of detecting and tracking a cylindrical coordinate system of a target, converting the cylindrical coordinate system into a Cartesian coordinate system and then respectively combining each component in the Cartesian coordinate system with time t to form three groups of two-dimensional data; and respectively filtering each group of the two-dimensional data to obtain filtered values in three directions and finally, synthesizing the filtered values in the three directions to obtain the filtered flight path. By the method, applicability of the filtering method is improved; and the method has very good tracking accuracy.

Description

A kind of method of flight path filtering
Technical field
The present invention relates to secondary radar surveillance technology field, relate in particular to the method for the flight path that is monitored object being carried out filtering.
Background technology
Secondary surveillance radar (SSR) is mainly used in the flight posture of civil aviaton of the army aircraft in the spatial domain of living in is carried out real time monitoring, the controller carries out air traffic control according to radar data to civil aviaton of army aircraft, therefore, carry out accurately, trace and monitor rapidly and adaptively extremely important to the flight path of civil aviaton of army aircraft.
The secondary surveillance radar (SSR) measuring system is subjected to equipment itself (as temperature drift), aircraft accelerates (as the engine shake) and external environment condition (as multipath effect, electromagnetic interference (EMI)) influence at random, occurs gaussian random white noise (stochastic error), sudden random noise (measuring bad point), the in-and-out phenomenon of measurement state in the measuring process easily.In addition, civil aviaton of army aircraft all maneuvering flight may occur in flight course, fighter plane especially, and the maneuvering flight complexity, not only speed is fast, and radius of turn is little.
Therefore, tracing and monitoring of secondary surveillance radar (SSR) need be accomplished filtering gaussian random white noise and sudden random noise, follows the tracks of complicated maneuvering target again fast in real time, adaptively.Should guarantee that flight path is accurate, guarantee that again the flight path maneuver tracking is quick; Should prevent from motor-driven turning is carried out filtering as measuring error, preventing from again measuring error is treated as is that motor-driven turning is handled.
In engineering was used, the method for flight path filtering had a lot, and the most frequently used in the prior art is linear Kalman filtering.Such as alpha-beta-γ filtering, alpha-beta-γ filtering is the most frequently used linear Kalman filtering, and its model is single, and computing is simple.Linear Kalman filtering is applicable to that measuring equipment model, moving target model are all more clear, and equipment state matrix and goal displacement matrix be the measuring system of modeling precisely.And for measuring equipment and the moving target of precisely modeling, there is tangible limitation in existing linear Kalman Filtering.
Summary of the invention
The objective of the invention is the technical matters that to carry out accurate filtering to measuring equipment and the moving target of precisely modeling at the method for the flight path filtering of prior art, disclose a kind of method of flight path filtering.
Purpose of the present invention realizes by following technical proposals:
A kind of method of flight path filtering, it specifically comprises following steps: the cylindrical coordinate of detecting and tracking target , and cylindrical coordinate is converted to cartesian coordinate system , then each component in the cartesian coordinate system is made up with time t respectively, form 3 groups of 2-D datas; Each group 2-D data is carried out filtering respectively, obtain the value on filtered three directions
Figure 2013101439492100002DEST_PATH_IMAGE003
, at last filtered value on three directions is synthesized, obtain filtered flight path.
Further, above-mentioned to each the group 2-D data carry out filtering respectively, obtain the value on filtered three directions
Figure 510551DEST_PATH_IMAGE003
Specifically comprise: obtain the predicted value of detecting and tracking target according to historical data, judge that difference between measured value and the predicted value whether greater than preset threshold, is then measured value to be compressed, otherwise skip this step; Measured value after will compressing at last adopts least square method to calculate with historical data, obtains the matrix of coefficients of matched curve, and carries out filtering.
Further, the above-mentioned method that obtains predicted value is specially: adopt least square method to calculate respectively the historical measurement data on three directions and time, obtain the polynomial expression of three curves of historical measurement data correspondence respectively, obtain predicted value according to historical measurement data
Figure 290288DEST_PATH_IMAGE004
, ,
Figure 560864DEST_PATH_IMAGE006
Further, the polynomial expression of above-mentioned three curves only intercepts polynomial 2 rank, 1 rank and constant term coefficient respectively.
Further, above-mentioned predicted value ,
Figure 73065DEST_PATH_IMAGE005
,
Figure 340098DEST_PATH_IMAGE006
Concrete formula be:
Figure 2013101439492100002DEST_PATH_IMAGE007
Wherein,
Figure 414364DEST_PATH_IMAGE008
For the historical data time and 2-D data constitutes the corresponding single order fitting coefficient of curve matrix,
Figure 896292DEST_PATH_IMAGE010
For historical data time t and
Figure 951973DEST_PATH_IMAGE009
2-D data constitutes the corresponding second order fitting coefficient matrix of curve,
Figure 2013101439492100002DEST_PATH_IMAGE011
Be historical data
Figure 581669DEST_PATH_IMAGE012
Transposed matrix, Be historical data
Figure 131730DEST_PATH_IMAGE014
Transposed matrix;
Figure 2013101439492100002DEST_PATH_IMAGE015
For historical data time t and The single order fitting coefficient matrix of these two curves that 2-D data constitutes,
Figure 2013101439492100002DEST_PATH_IMAGE017
For historical data time t and
Figure 22642DEST_PATH_IMAGE016
2-D data constitutes the corresponding second order fitting coefficient matrix of curve,
Figure 264268DEST_PATH_IMAGE018
Be historical data
Figure 2013101439492100002DEST_PATH_IMAGE019
Transposed matrix,
Figure 414758DEST_PATH_IMAGE020
Be historical data
Figure 2013101439492100002DEST_PATH_IMAGE021
Transposed matrix;
Figure 871278DEST_PATH_IMAGE022
For historical data time t and
Figure 2013101439492100002DEST_PATH_IMAGE023
2-D data constitutes the corresponding single order fitting coefficient of curve matrix, For historical data time t and
Figure 810732DEST_PATH_IMAGE023
2-D data constitutes the corresponding second order fitting coefficient matrix of curve,
Figure 2013101439492100002DEST_PATH_IMAGE025
Be historical data
Figure 951864DEST_PATH_IMAGE026
Transposed matrix,
Figure 262890DEST_PATH_IMAGE025
Be historical data Transposed matrix;
Figure 2013101439492100002DEST_PATH_IMAGE027
Further, above-mentioned threshold value is 2 times of absolute value of historical measurement data first difference, the absolute value of its historical measurement data first difference
Figure 860542DEST_PATH_IMAGE028
,
Figure 2013101439492100002DEST_PATH_IMAGE029
,
Figure 805364DEST_PATH_IMAGE030
Concrete computing formula be:
Figure 2013101439492100002DEST_PATH_IMAGE031
;
Figure 44933DEST_PATH_IMAGE032
;
Figure 2013101439492100002DEST_PATH_IMAGE033
;
Wherein m is the number of historical measurement data.
Further, the formula that measured value is compressed described above is specially:
Figure 800531DEST_PATH_IMAGE034
Wherein:
Figure 2013101439492100002DEST_PATH_IMAGE035
Be that independent variable is the function of W, expression is carried out log-compressed to residual error, and wherein residual error refers to the difference between measured value and the predicted value.
By adopting above technical scheme, the present invention has following beneficial effect: adopt flight path filtering method of the present invention, make that the accuracy of maneuvering target tracking is higher than additive method among a small circle, obviously be better than additive method in the motor-driven turning target following accuracy of approximate 90 degree, also apparently higher than additive method, can also realize the continuous motor-driven turning target following of approximate 90 degree in the motor-driven turning target following of circumference class accuracy simultaneously.Tracking effect of the present invention is better than additive method of the prior art, and does not need in advance the accurate modeling of measuring equipment has been improved the applicability of filtering method, has the accuracy of extraordinary tracking.
Description of drawings
Fig. 1 is the general structure synoptic diagram of the method for flight path filtering of the present invention.
Fig. 2 is the particular flow sheet of the method for the flight path filtering on directions X.
Embodiment
Below in conjunction with Figure of description, describe the specific embodiment of the present invention in detail.
The general structure synoptic diagram of the method for flight path filtering of the present invention as shown in Figure 1.The invention discloses a kind of method of flight path filtering, it specifically comprises following steps: the cylindrical coordinate of detecting and tracking target
Figure 113831DEST_PATH_IMAGE001
, and cylindrical coordinate is converted to cartesian coordinate system
Figure 534448DEST_PATH_IMAGE002
, then each component in the cartesian coordinate system is made up with time t respectively, form 3 groups of 2-D datas; Each group 2-D data is carried out filtering respectively, obtain the value on filtered three directions
Figure 69335DEST_PATH_IMAGE003
, at last filtered value on three directions is synthesized, obtain filtered flight path.The present invention at first is converted to cartesian coordinate system with cylindrical coordinate, then each component is made up with time t respectively, and 3 groups of 2-D datas that will form respectively carry out filtering, obtain synthesizing again after the value on filtered three directions, adopt the method that spatial movement is decomposed X, Y, three directions of Z respectively, carry out filtering on three directions respectively, this filtering method does not need in advance measuring equipment to be carried out accurate modeling, and possesses extraordinary effect when measuring moving target.
Further, above-mentioned to each the group 2-D data carry out filtering respectively, obtain the value on filtered three directions
Figure 995834DEST_PATH_IMAGE003
Specifically comprise according to historical data obtaining predicted value, judge that difference between measured value and the predicted value whether greater than preset threshold, is then measured value to be compressed, otherwise skip this step; Then with measured value with historical data, adopt least square method to calculate, obtain the matrix of coefficients of matched curve, and carry out filtering.
Wherein, obtain predicted value according to historical data and specifically may further comprise the steps: step 1, with historical measurement data (t,
Figure 921064DEST_PATH_IMAGE009
), (t,
Figure 207689DEST_PATH_IMAGE016
), (t,
Figure 82236DEST_PATH_IMAGE023
) adopt least square method to calculate respectively, obtain the polynomial expression of three curves of historical measurement data correspondence respectively.According to mathematical theory, any continuous curve all can carry out the Taylor series polynomial expansion by certain point on curve, and therefore, arbitrary continuous curve all can be represented with its corresponding polynomial expression.Suppose that historical measurement data is 9 groups.
Below with (t,
Figure 366586DEST_PATH_IMAGE009
) be example, specify the using method of least square method.Time t is replaced with in the least square method formula
Figure 841430DEST_PATH_IMAGE009
, will (t,
Figure 682478DEST_PATH_IMAGE009
) in
Figure 864061DEST_PATH_IMAGE009
Replace with in the minimum least square method formula
Figure 116051DEST_PATH_IMAGE016
The formula of least square method is
Figure 828923DEST_PATH_IMAGE036
M is the number of historical measurement data.When n=1, obtain the matrix of coefficients of single order match, when n=2, obtain the matrix of coefficients of second order match.Above-mentionedly obtaining m(m=9) calculate the matrix of coefficients of single order match after the historical data in the group matched curve, the matrix of coefficients of second order match also can be realized by Matlab.
Further, consider the requirement of necessity, computational complexity and real-time, the polynomial high-order of Taylor series is blocked, can only keep limited low order item.The secondary surveillance radar (SSR) target trajectory is a continuous curve, spatial movement decomposes after the motion on X, Y, three directions of Z, owing to can think that at short notice the motion of target is the synthetic of linear uniform motion and uniformly accelrated rectilinear motion, therefore, the above component proportion in 3 rank (containing) can be ignored, and only needs polynomial 2 rank of intercepting, 1 rank and constant term just can characterize the rectilinear motion that target makes progress at folk prescription.
Step 2 obtains predicted value according to historical measurement data
Figure 457350DEST_PATH_IMAGE004
,
Figure 759019DEST_PATH_IMAGE005
,
Figure 932642DEST_PATH_IMAGE006
；
Wherein:
Figure 751879DEST_PATH_IMAGE008
For time t and
Figure 455524DEST_PATH_IMAGE009
2-D data constitutes the corresponding single order fitting coefficient of curve matrix,
Figure 314896DEST_PATH_IMAGE010
For time t and
Figure 923732DEST_PATH_IMAGE009
2-D data constitutes the corresponding second order fitting coefficient matrix of curve,
Figure 910274DEST_PATH_IMAGE011
For
Figure 983272DEST_PATH_IMAGE012
Transposed matrix,
Figure 764277DEST_PATH_IMAGE013
For
Figure 860409DEST_PATH_IMAGE014
Transposed matrix;
Figure 634330DEST_PATH_IMAGE015
Be time t and historical data
Figure 312567DEST_PATH_IMAGE016
The single order fitting coefficient matrix of these two curves that 2-D data constitutes,
Figure 451424DEST_PATH_IMAGE017
For time t and
Figure 97169DEST_PATH_IMAGE016
2-D data constitutes the corresponding second order fitting coefficient matrix of curve,
Figure 159935DEST_PATH_IMAGE018
For
Figure 207525DEST_PATH_IMAGE019
Transposed matrix,
Figure 517284DEST_PATH_IMAGE020
For
Figure 401057DEST_PATH_IMAGE021
Transposed matrix; For time t and
Figure 356561DEST_PATH_IMAGE023
2-D data constitutes the corresponding single order fitting coefficient of curve matrix,
Figure 650270DEST_PATH_IMAGE024
For time t and
Figure 5028DEST_PATH_IMAGE023
2-D data constitutes the corresponding second order fitting coefficient matrix of curve,
Figure 862125DEST_PATH_IMAGE025
For Transposed matrix,
Figure 2013101439492100002DEST_PATH_IMAGE037
For
Figure 83339DEST_PATH_IMAGE038
Transposed matrix;
Figure 929986DEST_PATH_IMAGE027
Preset threshold is 2 times of absolute value of historical data first difference among the present invention, the absolute value of the first difference of its historical measurement data
Figure 387512DEST_PATH_IMAGE028
,
Figure 936305DEST_PATH_IMAGE029
,
Figure 837397DEST_PATH_IMAGE030
Concrete computing formula be:
Figure 166747DEST_PATH_IMAGE031
;
Wherein m is the number of historical measurement data, and for example m can be 9,
Figure 178696DEST_PATH_IMAGE008
For time t and
Figure 581996DEST_PATH_IMAGE009
2-D data constitutes the corresponding single order fitting coefficient of curve matrix,
Figure 903256DEST_PATH_IMAGE010
For time t and 2-D data constitutes the corresponding second order fitting coefficient matrix of curve,
Figure 207646DEST_PATH_IMAGE011
For
Figure 793348DEST_PATH_IMAGE012
Transposed matrix,
Figure 36242DEST_PATH_IMAGE013
For
Figure 12288DEST_PATH_IMAGE014
Transposed matrix,
Figure 615308DEST_PATH_IMAGE027
In like manner, can obtain
Figure 71828DEST_PATH_IMAGE029
,
Figure 406994DEST_PATH_IMAGE030
Figure 198233DEST_PATH_IMAGE032
;
Wherein m is the number of historical measurement data,
Figure 90097DEST_PATH_IMAGE015
Be time t and historical data
Figure 650391DEST_PATH_IMAGE016
The single order fitting coefficient matrix of these two curves that 2-D data constitutes,
Figure 969508DEST_PATH_IMAGE017
For time t and
Figure 248043DEST_PATH_IMAGE016
2-D data constitutes the corresponding second order fitting coefficient matrix of curve, For
Figure 358398DEST_PATH_IMAGE019
Transposed matrix,
Figure 113996DEST_PATH_IMAGE020
For
Figure 551930DEST_PATH_IMAGE021
Transposed matrix,
Figure 34864DEST_PATH_IMAGE027
Figure 507434DEST_PATH_IMAGE033
;
Wherein m is the number of historical measurement data,
Figure 168353DEST_PATH_IMAGE022
For time t and
Figure 155901DEST_PATH_IMAGE023
2-D data constitutes the corresponding single order fitting coefficient of curve matrix,
Figure 380209DEST_PATH_IMAGE024
For time t and
Figure 520334DEST_PATH_IMAGE023
2-D data constitutes the corresponding second order fitting coefficient matrix of curve,
Figure 867002DEST_PATH_IMAGE025
For
Figure 92578DEST_PATH_IMAGE026
Transposed matrix,
Figure 917315DEST_PATH_IMAGE037
For Transposed matrix,
Figure 367199DEST_PATH_IMAGE027
The formula that among the present invention measured value is compressed is specially:
Figure 329339DEST_PATH_IMAGE034
Wherein
Figure 2013101439492100002DEST_PATH_IMAGE039
Be current measured value, Be the current predicted value that obtains according to historical data, Be that independent variable is the function of W, expression is carried out log-compressed to residual error, and wherein residual error refers to the difference between measured value and the predicted value.Residual error is carried out log-compressed, measurement data can be compressed to the normalization zone, G(W) compression adopts the characteristic of logarithmic function to compress; The characteristic of logarithmic function causes the bigger compression degree height of residual error, and what residual error was medium compresses comparatively linearly, and what residual error was less can not need compression.
With historical data, adopt least square method to calculate measured value, obtain the matrix of coefficients of matched curve.Adopt least square method to calculate 9 groups of data and the new measured value of history, obtain the polynomial coefficient of three new curve correspondences respectively.10 groups of data are carried out match, when n=1, obtain the matrix of coefficients of single order match
Figure 2013101439492100002DEST_PATH_IMAGE041
, when n=2, obtain the matrix of coefficients of second order match
Figure 246107DEST_PATH_IMAGE042
Be respectively the matrix of coefficients of curve movement on time and 10 groups of X, Y, the Z-direction.
Among the present invention, the formula that current measured value is carried out filtering is specially:
Figure DEST_PATH_IMAGE043
Wherein
Figure 695543DEST_PATH_IMAGE044
,
Figure DEST_PATH_IMAGE045
Be current measured value
Figure 686850DEST_PATH_IMAGE009
The polynomial single order fitting coefficient matrix of the new matched curve that the 2-D data that constitutes with historical data and time forms;
Figure 843025DEST_PATH_IMAGE046
Be current measured value
Figure 453129DEST_PATH_IMAGE016
The polynomial single order fitting coefficient matrix of the new matched curve that the 2-D data that constitutes with historical data and time forms; Be current measured value The polynomial single order fitting coefficient matrix of the new matched curve that the 2-D data that constitutes with historical data and time forms;
Figure 110824DEST_PATH_IMAGE011
For
Figure 183822DEST_PATH_IMAGE012
Transposed matrix,
Figure 151778DEST_PATH_IMAGE013
For
Figure 60959DEST_PATH_IMAGE014
Transposed matrix;
Figure 569301DEST_PATH_IMAGE018
For
Figure 700068DEST_PATH_IMAGE019
Transposed matrix,
Figure 651975DEST_PATH_IMAGE020
For
Figure 235403DEST_PATH_IMAGE021
Transposed matrix;
Figure 547435DEST_PATH_IMAGE025
For
Figure 345758DEST_PATH_IMAGE026
Transposed matrix, For
Figure 788558DEST_PATH_IMAGE038
Transposed matrix.For example historical data be 9 groups (t,
Figure 655014DEST_PATH_IMAGE009
), add current measured value and time, 10 groups of data just can obtain the polynomial expression of curve, calculate the fitting coefficient matrix on every rank then.X, Y-axis in like manner.Wherein
Figure 494794DEST_PATH_IMAGE048
Can be 0.8.
Given coefficient and parameter in the above embodiments; provide to those skilled in the art and realize or use of the present invention; the present invention does not limit and only gets aforementioned disclosed numerical value; under the situation that does not break away from invention thought of the present invention; those skilled in the art can make various modifications or adjustment to above-described embodiment; thereby protection scope of the present invention do not limit by above-described embodiment, and should be the maximum magnitude that meets the inventive features that claims mention.

Claims (8)

1. the method for a flight path filtering, it specifically comprises following steps: the cylindrical coordinate of detecting and tracking target
Figure 2013101439492100001DEST_PATH_IMAGE001
, and cylindrical coordinate is converted to cartesian coordinate system
Figure 241345DEST_PATH_IMAGE002
, then each component in the cartesian coordinate system is made up with time t respectively, form 3 groups of 2-D datas; Each group 2-D data is carried out filtering respectively, obtain the value on filtered three directions
Figure 2013101439492100001DEST_PATH_IMAGE003
, at last filtered value on three directions is synthesized, obtain filtered flight path.
2. the method for flight path filtering as claimed in claim 1 is characterized in that described each group 2-D data being carried out filtering respectively, obtains the value on filtered three directions
Figure 33851DEST_PATH_IMAGE003
Specifically comprise: obtain the predicted value of detecting and tracking target according to historical data, judge that difference between measured value and the predicted value whether greater than preset threshold, is then measured value to be compressed, otherwise skip this step; Measured value after will compressing at last adopts least square method to calculate with historical data, obtains the matrix of coefficients of matched curve, and carries out filtering.
3. the method for flight path filtering as claimed in claim 2, it is characterized in that the described method that obtains predicted value is specially: adopt least square method to calculate respectively the historical measurement data on three directions and time, obtain the polynomial expression of three curves of historical measurement data correspondence respectively, obtain predicted value according to historical measurement data ,
Figure 2013101439492100001DEST_PATH_IMAGE005
,
Figure 84164DEST_PATH_IMAGE006
4. the method for flight path filtering as claimed in claim 3 is characterized in that the polynomial expression of described three curves only intercepts polynomial 2 rank, 1 rank and constant term coefficient respectively.
5. the method for flight path filtering as claimed in claim 4 is characterized in that obtaining described predicted value
Figure 960853DEST_PATH_IMAGE004
,
Figure 596365DEST_PATH_IMAGE005
,
Figure 925715DEST_PATH_IMAGE006
Concrete formula be:
Figure 2013101439492100001DEST_PATH_IMAGE007
Wherein,
Figure 937665DEST_PATH_IMAGE008
For the historical data time and
Figure 2013101439492100001DEST_PATH_IMAGE009
2-D data constitutes the corresponding single order fitting coefficient of curve matrix,
Figure 481909DEST_PATH_IMAGE010
For historical data time t and
Figure 537590DEST_PATH_IMAGE009
2-D data constitutes the corresponding second order fitting coefficient matrix of curve, Be historical data Transposed matrix,
Figure 2013101439492100001DEST_PATH_IMAGE013
Be historical data Transposed matrix;
Figure 2013101439492100001DEST_PATH_IMAGE015
For historical data time t and
Figure 303049DEST_PATH_IMAGE016
The single order fitting coefficient matrix of these two curves that 2-D data constitutes,
Figure 2013101439492100001DEST_PATH_IMAGE017
For historical data time t and
Figure 545943DEST_PATH_IMAGE016
2-D data constitutes the corresponding second order fitting coefficient matrix of curve,
Figure 849885DEST_PATH_IMAGE018
Be historical data
Figure 2013101439492100001DEST_PATH_IMAGE019
Transposed matrix,
Figure 938058DEST_PATH_IMAGE020
Be historical data
Figure 2013101439492100001DEST_PATH_IMAGE021
Transposed matrix;
Figure 643846DEST_PATH_IMAGE022
For historical data time t and
Figure 2013101439492100001DEST_PATH_IMAGE023
2-D data constitutes the corresponding single order fitting coefficient of curve matrix,
Figure 792061DEST_PATH_IMAGE024
For historical data time t and
Figure 583300DEST_PATH_IMAGE023
2-D data constitutes the corresponding second order fitting coefficient matrix of curve, Be historical data
Figure 475164DEST_PATH_IMAGE026
Transposed matrix,
Figure 35458DEST_PATH_IMAGE025
Be historical data Transposed matrix;
Figure 2013101439492100001DEST_PATH_IMAGE027
6. the method for flight path filtering as claimed in claim 5 is characterized in that described threshold value is 2 times of absolute value of historical measurement data first difference, the absolute value of its historical measurement data first difference
Figure 633110DEST_PATH_IMAGE028
, ,
Figure 328664DEST_PATH_IMAGE030
Concrete computing formula be:
Figure DEST_PATH_IMAGE031
;
Figure 743465DEST_PATH_IMAGE032
;
;
Wherein m is the number of historical measurement data.
7. the method for flight path filtering as claimed in claim 6 is characterized in that the described formula that measured value is compressed is specially:
Wherein:
Figure DEST_PATH_IMAGE035
Be that independent variable is the function of W, expression is carried out log-compressed to residual error, and wherein residual error refers to the difference between measured value and the predicted value.
8. the method for flight path filtering as claimed in claim 7 is characterized in that the described formula that current measured value is carried out filtering is specially:
Wherein
Figure DEST_PATH_IMAGE037
,
Figure 307016DEST_PATH_IMAGE038
Be current measured value The polynomial single order fitting coefficient matrix of the new matched curve that the 2-D data that constitutes with historical data and time forms;
Figure DEST_PATH_IMAGE039
Be current measured value The polynomial single order fitting coefficient matrix of the new matched curve that the 2-D data that constitutes with historical data and time forms;
Figure 693632DEST_PATH_IMAGE040
Be current measured value The polynomial single order fitting coefficient matrix of the new matched curve that the 2-D data that constitutes with historical data and time forms;
Figure 854803DEST_PATH_IMAGE011
Be current measured value
Figure 139154DEST_PATH_IMAGE012
Transposed matrix,
Figure 364730DEST_PATH_IMAGE013
Be current measured value
Figure 455046DEST_PATH_IMAGE014
Transposed matrix;
Figure 636629DEST_PATH_IMAGE018
Be current measured value
Figure 639351DEST_PATH_IMAGE019
Transposed matrix,
Figure 601491DEST_PATH_IMAGE020
Be current measured value
Figure 167601DEST_PATH_IMAGE021
Transposed matrix;
Figure 282319DEST_PATH_IMAGE025
Be current measured value
Figure 642893DEST_PATH_IMAGE026
Transposed matrix,
Figure 92329DEST_PATH_IMAGE013
Be current measured value
Figure DEST_PATH_IMAGE041
Transposed matrix.
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* Cited by examiner, † Cited by third party
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CN109856622A (en) * 2019-01-03 2019-06-07 中国人民解放军空军研究院战略预警研究所 A kind of single radar rectilinear path line target method for estimating state under constraint condition
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* Cited by examiner, † Cited by third party
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060238411A1 (en) * 2004-05-28 2006-10-26 Time Domain Corporation System and method for spatially diverse radar signal processing
CN102103803A (en) * 2011-01-19 2011-06-22 南京莱斯信息技术股份有限公司 Method for monitoring aircraft in airport terminal area
CN202549080U (en) * 2012-03-16 2012-11-21 中国民用航空总局第二研究所 Fusion system of radar data, flight plan data and ADS-B data
CN102981160A (en) * 2012-11-08 2013-03-20 中国兵器科学研究院 Method and device for ascertaining aerial target track

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060238411A1 (en) * 2004-05-28 2006-10-26 Time Domain Corporation System and method for spatially diverse radar signal processing
CN102103803A (en) * 2011-01-19 2011-06-22 南京莱斯信息技术股份有限公司 Method for monitoring aircraft in airport terminal area
CN202549080U (en) * 2012-03-16 2012-11-21 中国民用航空总局第二研究所 Fusion system of radar data, flight plan data and ADS-B data
CN102981160A (en) * 2012-11-08 2013-03-20 中国兵器科学研究院 Method and device for ascertaining aerial target track

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
姚惠元: "TWS雷达目标航迹跟踪及预测算法研究", 《王方学位论文数据库》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109341699A (en) * 2018-11-30 2019-02-15 四川九洲电器集团有限责任公司 A kind of Intelligent unattended platform path planing method based on avoidance turning quality estimating
CN109856622A (en) * 2019-01-03 2019-06-07 中国人民解放军空军研究院战略预警研究所 A kind of single radar rectilinear path line target method for estimating state under constraint condition
CN109901150A (en) * 2019-03-04 2019-06-18 四川九洲空管科技有限责任公司 A kind of multifunction array radar device and its detection method
CN109901150B (en) * 2019-03-04 2021-01-26 四川九洲空管科技有限责任公司 Multifunctional phased array radar device and detection method thereof
CN113589239A (en) * 2021-06-30 2021-11-02 中国西安卫星测控中心 Radar measurement data precision fault-tolerant estimation method

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