CN113569430B - Method for identifying reentry flight turning direction under external measurement-only observation - Google Patents

Method for identifying reentry flight turning direction under external measurement-only observation Download PDF

Info

Publication number
CN113569430B
CN113569430B CN202111013991.3A CN202111013991A CN113569430B CN 113569430 B CN113569430 B CN 113569430B CN 202111013991 A CN202111013991 A CN 202111013991A CN 113569430 B CN113569430 B CN 113569430B
Authority
CN
China
Prior art keywords
turning
value
observation
speed
vector
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202111013991.3A
Other languages
Chinese (zh)
Other versions
CN113569430A (en
Inventor
淡鹏
李恒年
崔卫华
谭炜
黄普
李军锋
王丹
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Xian Satellite Control Center
Original Assignee
China Xian Satellite Control Center
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China Xian Satellite Control Center filed Critical China Xian Satellite Control Center
Priority to CN202111013991.3A priority Critical patent/CN113569430B/en
Publication of CN113569430A publication Critical patent/CN113569430A/en
Application granted granted Critical
Publication of CN113569430B publication Critical patent/CN113569430B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00Computer-aided design [CAD]
    • G06F30/20Design optimisation, verification or simulation
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/16Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/18Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00Computer-aided design [CAD]
    • G06F30/10Geometric CAD
    • G06F30/15Vehicle, aircraft or watercraft design
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00Details relating to CAD techniques
    • G06F2111/10Numerical modelling
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14Force analysis or force optimisation, e.g. static or dynamic forces
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • Algebra (AREA)
  • Evolutionary Computation (AREA)
  • Software Systems (AREA)
  • Databases & Information Systems (AREA)
  • Computer Hardware Design (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Operations Research (AREA)
  • Probability & Statistics with Applications (AREA)
  • Automation & Control Theory (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computing Systems (AREA)
  • Radar Systems Or Details Thereof (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The invention discloses a method for identifying a reentry flight turning direction under external measurement and observation only, which comprises the following steps of: step 1, only using external observation data to carry out non-dynamic modeling, and calculating the speed vector of each point through filtering trajectory; step 2, calculating the increment of the current point speed vector; step 3, calculating a projection value of the current point speed vector increment in the normal direction of the track surface at the upper point according to the step 2; step 4, judging the turning direction of the single point according to the projection value; and 5, carrying out sliding window statistics on the turning directions calculated by the multiple points to obtain the turning directions judged by statistics. The method solves the problems of low accuracy and high misjudgment rate in the prior art for identifying the turning direction.

Description

Method for identifying reentry flight turning direction under external measurement-only observation
Technical Field
The invention belongs to the technical field of aerospace navigation, and particularly relates to a method for identifying a reentry flight turning direction under external measurement and observation only.
Background
In the lift-type reentry and return of the spacecraft to the earth, in order to land at a specific position or area, the turning direction needs to be continuously adjusted during reentry and flight, so that the spacecraft flies towards a target point or a target area according to a preset guidance control method.
Due to the influence of the black obstacle, telemetry downloading interruption phenomenon can sometimes occur in the reentry flight process of the spacecraft, and effective gesture and pneumatic parameter information cannot be obtained from telemetry data at the moment, so that the turning direction cannot be identified. In this case, the observed data from the ground external measurement device becomes another dependable data for estimating the flight state of the spacecraft in the black obstacle or in the unstable attitude process. In addition, the observation of some non-cooperative reentrant targets is also mostly dependent on external measurement data such as radar. However, due to lack of spacecraft attitude information, identification of the turning direction in the reentry flight process of the spacecraft under pure external measurement observation can only start from the observation quantity such as ranging, azimuth angle, elevation angle, ranging change rate and the like in the external measurement data, which brings certain difficulty to the identification of the turning direction, and causes the problems of low accuracy of the identification of the turning direction and high probability of misjudgment.
Disclosure of Invention
The invention aims to provide a method for identifying a reentry flight turning direction under external measurement and observation only, which solves the problems of low accuracy and high misjudgment rate in the prior art for identifying the turning direction.
The technical scheme adopted by the invention is that the method for identifying the reentry flight turning direction under the external measurement and observation only comprises the following steps:
step 1, carrying out non-dynamic modeling by using only external observation data, and calculating a speed vector of each point state vector through a filtering trajectory;
the step 1 is as follows:
step 1.1, adopting a current statistical model and a UKF filtering frame to carry out non-dynamic modeling of a reentry process;
step 1.2, calculating the state vector of each point by using the UKF filtering frame and the current statistical model
Figure SMS_1
Step 2, calculating the increment of the current point speed vector;
step 3, calculating a projection value of the current point speed vector increment in the normal direction of the track surface at the upper point according to the step 2;
step 4, judging the turning direction of the single point according to the projection value;
and 5, carrying out sliding window statistics on the turning directions calculated by the multiple points to obtain the turning directions judged by statistics.
The present invention is also characterized in that,
step 1.1, let UKF filtered system state vector under J2000.0 coordinate system be:
Figure SMS_2
(1)
wherein in each scalar value, x represents the position x coordinate component,
Figure SMS_3
Representing the velocity x-coordinate component,/->
Figure SMS_4
Representing the x-coordinate component of the acceleration, y representing the y-coordinate component of the position,/->
Figure SMS_5
Representing the velocity y-coordinate component,
Figure SMS_6
Representing the y-coordinate component of the acceleration and z representing the z-coordinate component of the position +.>
Figure SMS_7
Representing the velocity z-coordinate component,/->
Figure SMS_8
Representing an acceleration z-coordinate component;
is provided with
Figure SMS_9
Reciprocal of acceleration time constant in current statistical model, +.>
Figure SMS_10
Is->
Figure SMS_11
Time to->
Figure SMS_12
Time increment of time,/->
Figure SMS_13
Is the average value of acceleration, and takes the value of +.>
Figure SMS_14
The method comprises the steps of carrying out a first treatment on the surface of the The state extrapolation model under the current statistical model is:
Figure SMS_15
(2)
wherein,,
Figure SMS_16
is the mean value is zero, the variance is +.>
Figure SMS_17
Discretized process noise of +.>
Figure SMS_18
For acceleration variance +.>
Figure SMS_19
For the maneuver input matrix>
Figure SMS_20
Is a state transition matrix; since the three coordinate directions of x, y and z are orthogonal to each other, there are
Figure SMS_21
Figure SMS_22
Wherein, the state noise covariance matrix is:
Figure SMS_23
(3)
calculating the ith data according to the current three-coordinate directions of x, y and z of the statistical model
Figure SMS_24
Figure SMS_25
Figure SMS_26
Step 1.2 in order to better adapt to different types of external measurement observation quantity such as ranging, angle measurement, speed measurement and the like, an observation model calculated by filtering is directly built under the ground level system of a measuring station, and a position vector is used for measuring the distance between the two measuring stations
Figure SMS_27
Speed vector->
Figure SMS_28
The method for calculating the observed quantity of the station horizon comprises the following steps:
step 1.2.1, the time is set
Figure SMS_30
Figure SMS_36
Conversion to ground fixation position->
Figure SMS_39
Speed->
Figure SMS_29
Converting the formula into
Figure SMS_33
,
Figure SMS_37
Figure SMS_40
Figure SMS_32
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure SMS_35
For the time matrix>
Figure SMS_38
For nutation matrix>
Figure SMS_41
Is the earth rotation matrix>
Figure SMS_31
Is polar motion matrix>
Figure SMS_34
A derivative matrix that is an earth rotation matrix;
step 1.2.2, transferring the position and the speed of the ground fixed system to the northeast-north day coordinate system of the measuring station, and calculating a conversion matrix from the ground coordinate of the measuring station to the northeast-north day coordinate system of the measuring station
Figure SMS_50
Position vector of station under ground fixation system +.>
Figure SMS_43
The position and the speed vector of the spacecraft under the northeast and north coordinate system of the measuring station are as follows:
Figure SMS_46
Figure SMS_48
The method comprises the steps of carrying out a first treatment on the surface of the Is provided with->
Figure SMS_53
The three-direction component in the northeast coordinate system is +.>
Figure SMS_54
Figure SMS_57
Figure SMS_51
Figure SMS_55
Is +.>
Figure SMS_45
Figure SMS_47
Figure SMS_44
Calculating 4 observables of external measurement data and measuring distance
Figure SMS_49
The method comprises the steps of carrying out a first treatment on the surface of the Azimuth angle->
Figure SMS_52
The method comprises the steps of carrying out a first treatment on the surface of the Elevation angle->
Figure SMS_56
The method comprises the steps of carrying out a first treatment on the surface of the Speed measurement->
Figure SMS_42
The method comprises the steps of carrying out a first treatment on the surface of the Obtaining a measurement equation of UKF;
step 1.2.3 filtering the state vector at a certain time
Figure SMS_58
Obtaining the spacecraft position vector of +.>
Figure SMS_59
The velocity vector is +.>
Figure SMS_60
Step 2, setting a certain moment obtained by UKF filtering calculation in step 1 as
Figure SMS_61
State vector of time->
Figure SMS_62
The position and velocity vectors at this time are +.>
Figure SMS_63
Figure SMS_64
Is provided withOne time->
Figure SMS_65
The position and speed of the position are +.>
Figure SMS_66
Figure SMS_67
The unit vector of the normal direction of the track surface at the last moment is
Figure SMS_68
(4);
The speed difference between the current moment and the last moment is the speed increment:
Figure SMS_69
(5)。
step 3, according to the speed increment obtained in step 2, namely, the components of the speed difference in the normal direction of the track surface at the last moment are as follows:
Figure SMS_70
(6)。
step 4 giving a defined value
Figure SMS_71
Figure SMS_72
And if the turning direction is 0, defining a turning direction discrimination function as follows:
Figure SMS_73
(7)
obtaining a turning direction judgment value at the current moment according to the turning direction judgment function, when
Figure SMS_74
When the value is 1, the left turn is represented, and the view is toward the flying speed directionPerforming inspection; when the value is-1, the right turn is indicated; when the value is 0, the curve is not turned or the value is too small to be judged.
Step 5, specifically, counting the turning directions judged by single points of m continuous points by adopting a time sequence sliding method, and setting the total reserved point at a certain moment as
Figure SMS_75
Figure SMS_76
If the left turn value is the majority, the current left turn can be judged; if the right turn value is the majority, judging that the current moment is right turn; otherwise, the switching direction at the current time cannot be judged.
In step 5, m is a predetermined value greater than 3, i.e., the turning direction value calculated for m points
Figure SMS_77
Figure SMS_78
……
Figure SMS_79
Figure SMS_80
Counting m points, and counting when the number of the values representing left turn is greater than or equal to a given number n, < >>
Figure SMS_81
Time indicates left turn; when the number of values representing right turn is greater than or equal to a given number n +.>
Figure SMS_82
Right turn is indicated; otherwise, the current point judges the turning direction and does not output the judging result.
The method has the beneficial effects that the method is suitable for calculating the flight state in the lift type reentry and return flight process of the spacecraft, and the turning direction is identified in real time by the non-dynamic modeling filtering and sliding window statistical algorithm under the condition that only the external measurement observation data is used in the lift type reentry and return earth process of the spacecraft, so that the turning direction identification problem in the lift type reentry and flight process of the spacecraft without telemetry data downloading is solved; meanwhile, as the non-dynamic modeling filtering algorithm and the sliding window multipoint statistical algorithm are adopted, the accuracy of identifying the turning direction is improved, and the probability of misjudgment is reduced.
Drawings
FIG. 1 is a flow chart of a method for identifying reentry flight turning direction under external measurement only observation according to the present invention.
Detailed Description
The invention will be described in detail below with reference to the drawings and the detailed description.
According to the method for identifying the reentry flight turning direction under the external observation only, the flow chart is shown in figure 1, the speed data of the flight trajectory is fitted by using the external observation data only, then the projection of the increment of each point speed in the normal direction of the track surface of the last point is calculated, the single-point turning estimation is calculated by the projection, and then the judgment of the current turning direction is given by counting sliding windows of a multi-point turning estimation sequence.
The specific implementation process comprises the following steps:
step 1, carrying out non-dynamic modeling by using only external observation data, and calculating a speed vector of each point state vector through a filtering trajectory;
step 1.1, let UKF filtered system state vector under J2000.0 coordinate system be:
Figure SMS_83
(1)
wherein in each scalar value, x represents the position x coordinate component,
Figure SMS_84
Representing the velocity x-coordinate component,/->
Figure SMS_85
Representing an acceleration x coordinate component, a y representing a position y coordinate component,
Figure SMS_86
Representing the velocity y-coordinate component,
Figure SMS_87
Representing the y-coordinate component of the acceleration and z representing the z-coordinate component of the position +.>
Figure SMS_88
Representing the velocity z-coordinate component,/->
Figure SMS_89
Representing an acceleration z-coordinate component;
is provided with
Figure SMS_90
Reciprocal of acceleration time constant in current statistical model, +.>
Figure SMS_91
Is->
Figure SMS_92
Time to->
Figure SMS_93
Time increment of time,/->
Figure SMS_94
Is the average value of acceleration, and takes the value of +.>
Figure SMS_95
The method comprises the steps of carrying out a first treatment on the surface of the The state extrapolation model under the current statistical model is:
Figure SMS_96
(2)
wherein,,
Figure SMS_97
is the mean value is zero, the variance is +.>
Figure SMS_98
Is a discrete of (a)Process noise of the chemistry->
Figure SMS_99
For acceleration variance +.>
Figure SMS_100
For the maneuver input matrix>
Figure SMS_101
Is a state transition matrix; since the three coordinate directions of x, y and z are orthogonal to each other, there are
Figure SMS_102
Figure SMS_103
Wherein, the state noise covariance matrix is:
Figure SMS_104
(3)
calculating the ith data according to the current three-coordinate directions of x, y and z of the statistical model
Figure SMS_105
Figure SMS_106
Figure SMS_107
Step 1.2, calculating the state vector of each point by using the UKF filtering frame and the current statistical model
Figure SMS_108
In order to better adapt to different types of external measurement and observation quantity such as ranging, angle measurement, speed measurement and the like, an observation model calculated by filtering is directly built under the ground level system of a measuring station and is formed by a position vector
Figure SMS_109
Speed vector->
Figure SMS_110
The method for calculating the observed quantity of the station horizon comprises the following steps:
step 1.2.1, the time is set
Figure SMS_111
Figure SMS_118
Conversion to ground fixation position->
Figure SMS_121
Speed->
Figure SMS_114
Converting the formula into
Figure SMS_117
,
Figure SMS_120
Figure SMS_123
Figure SMS_112
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure SMS_116
For the time matrix>
Figure SMS_119
For nutation matrix>
Figure SMS_122
Is the earth rotation matrix>
Figure SMS_113
Is polar motion matrix>
Figure SMS_115
A derivative matrix that is an earth rotation matrix;
step 1.2.2, the earth is retainedThe speed is transferred to the northeast-north day coordinate system of the measuring station, and the conversion matrix from the earth coordinate of the measuring station to the northeast-north day coordinate system of the measuring station is calculated
Figure SMS_133
Position vector of station under ground fixation system +.>
Figure SMS_124
The position and the speed vector of the spacecraft under the northeast and north coordinate system of the measuring station are as follows:
Figure SMS_129
Figure SMS_136
The method comprises the steps of carrying out a first treatment on the surface of the Is provided with->
Figure SMS_138
The components in the directions of x, y and z are +.>
Figure SMS_137
Figure SMS_139
Figure SMS_132
Figure SMS_135
Is +.>
Figure SMS_126
Figure SMS_128
Figure SMS_127
Calculating 4 observables of external measurement data, and measuring distance +.>
Figure SMS_131
The method comprises the steps of carrying out a first treatment on the surface of the Azimuth angle->
Figure SMS_130
The method comprises the steps of carrying out a first treatment on the surface of the Elevation angle->
Figure SMS_134
The method comprises the steps of carrying out a first treatment on the surface of the Speed measurement->
Figure SMS_125
The method comprises the steps of carrying out a first treatment on the surface of the Obtaining a measurement equation of UKF;
step 1.2.3 filtering the state quantity at a certain moment
Figure SMS_140
The spacecraft position at this time is obtained as +.>
Figure SMS_141
Speed is +.>
Figure SMS_142
Step 2, calculating the increment of the current point speed vector;
according to a certain moment obtained by UKF filtering calculation in the step 1, the method is set as
Figure SMS_143
State vector of time->
Figure SMS_144
The position and velocity vectors at this time are +.>
Figure SMS_145
Figure SMS_146
Let the last moment->
Figure SMS_147
The position and speed of the position are +.>
Figure SMS_148
Figure SMS_149
The unit vector of the normal direction of the track surface at the last moment is
Figure SMS_150
(4);
The speed difference between the current moment and the last moment is the speed increment:
Figure SMS_151
(5);
step 3, calculating a projection value of the current point speed vector increment in the normal direction of the track surface at the upper point according to the step 2; the velocity increment obtained according to the step 2, namely, the component of the velocity difference in the normal direction of the track surface at the last moment is as follows:
Figure SMS_152
(6);
step 4, judging the turning direction of the single point according to the projection value:
given a defined value
Figure SMS_153
Figure SMS_154
And if the turning direction is 0, defining a turning direction discrimination function as follows:
Figure SMS_155
(7)
obtaining a turning direction judgment value at the current moment according to the turning direction judgment function, when
Figure SMS_156
When the value is 1, the left turn is indicated, and the observation is carried out towards the flying speed direction; when the value is-1, the right turn is indicated; when the value is 0, the curve is not turned or the value is too small to be judged;
and 5, carrying out sliding window statistics on the turning directions calculated by the multiple points to obtain the turning directions judged by statistics.
Step 5 adopts time sequenceSliding method, counting turning directions judged by single points of m continuous points, and setting total reserved points at a certain moment as
Figure SMS_157
Figure SMS_158
If the left turn value is the majority, the current left turn can be judged; if the right turn value is the majority, judging that the current moment is right turn; otherwise, the switching direction at the current time cannot be judged.
m is a value greater than 3, i.e. the turning direction value calculated for m points
Figure SMS_159
Figure SMS_160
……
Figure SMS_161
Figure SMS_162
(m points in total) and when the number of values representing left turn is greater than or equal to a given number n (/ -)>
Figure SMS_163
) Time indicates left turn; when the number of values representing right turn is greater than or equal to a given number n (/ -)>
Figure SMS_164
) Right turn is indicated; otherwise, the current point judges the turning direction and does not output the judging result. The turning direction is judged by utilizing a multi-point statistics dominant method, so that the possibility of misjudgment possibly caused by single-point judgment is reduced, and the reentry flight process turning direction identification method only using external observation data is realized.
In this embodiment, let m take 10 and n take 7, which means that 7 out of 10 determinations have consistent results. Counting the number of single-point judgment results representing left turn among the 10 points, and setting the number as
Figure SMS_165
The number of single-point judgment results of right turn is represented; set to->
Figure SMS_166
When (when)
Figure SMS_167
At least 7 points out of the 10 points represent left turn, and the current turning direction can be given as left turn; if->
Figure SMS_168
At least 7 out of 10 points are indicated to indicate a right turn, the current turning direction may be given as a right turn. If the left turn and the right turn are not judged, the turning direction cannot be judged currently.
Aiming at the problem of identifying turning directions without telemetry information in the reentry and return process of a spacecraft under the condition of only external measurement, the invention adopts non-dynamic modeling and UKF filtering algorithm to realize a real-time flight speed estimation method suitable for pure external measurement and observation, then calculates the normal direction component of the track surface of each point relative to the last point to estimate the turning direction of single point judgment at each time point, and further gives out the final current turning direction judgment result by using a method for carrying out sliding window statistics on the single point turning judgment results at a plurality of time points, thereby forming the reentry flight process turning direction judgment method without knowing actual flight guidance model and attitude data and only using external measurement and observation quantity.

Claims (8)

1. The method for identifying the reentry flight turning direction under the external measurement observation only is characterized in that the specific implementation process comprises the following steps:
step 1, carrying out non-dynamic modeling by using only external observation data, and calculating a speed vector of each point state vector through a filtering trajectory;
the step 1 is as follows:
step 1.1, adopting a current statistical model and a UKF filtering frame to carry out non-dynamic modeling of a reentry process;
step 1.2, calculating the state vector of each point by using the UKF filtering frame and the current statistical model
Figure QLYQS_1
Step 2, calculating the increment of the current point speed vector;
step 3, calculating a projection value of the current point speed vector increment in the normal direction of the track surface at the upper point according to the step 2;
step 4, judging the turning direction of the single point according to the projection value;
and 5, carrying out sliding window statistics on the turning directions calculated by the multiple points to obtain the turning directions judged by statistics.
2. The method for identifying the reentry direction of the turning in the outward measurement only observation according to claim 1, wherein the step 1.1 is to set the system state vector of the UKF filter in the J2000.0 coordinate system as:
Figure QLYQS_2
(1)
wherein in each scalar value, x represents the position x coordinate component,
Figure QLYQS_3
Representing the velocity x-coordinate component,/->
Figure QLYQS_4
Representing the x-coordinate component of the acceleration, y representing the y-coordinate component of the position,/->
Figure QLYQS_5
Representing the velocity y-coordinate component,
Figure QLYQS_6
Representing the y-coordinate component of the acceleration and z representing the z-coordinate component of the position +.>
Figure QLYQS_7
Representing the velocity z-coordinate component,/->
Figure QLYQS_8
Representing an acceleration z-coordinate component;
is provided with
Figure QLYQS_9
Reciprocal of acceleration time constant in current statistical model, +.>
Figure QLYQS_10
Is->
Figure QLYQS_11
Time to->
Figure QLYQS_12
Time increment of time,/->
Figure QLYQS_13
Is the average value of acceleration, and takes the value of +.>
Figure QLYQS_14
The method comprises the steps of carrying out a first treatment on the surface of the The state extrapolation model under the current statistical model is:
Figure QLYQS_15
(2)
wherein,,
Figure QLYQS_16
is the mean value is zero, the variance is +.>
Figure QLYQS_17
Discretized process noise of +.>
Figure QLYQS_18
For acceleration variance +.>
Figure QLYQS_19
For the maneuver input matrix>
Figure QLYQS_20
Is a state transition matrix; since the three coordinate directions of x, y and z are orthogonal to each other, there are
Figure QLYQS_21
Figure QLYQS_22
Wherein, the state noise covariance matrix is:
Figure QLYQS_23
(3)
calculating the ith data according to the current three-coordinate directions of x, y and z of the statistical model
Figure QLYQS_24
Figure QLYQS_25
Figure QLYQS_26
3. The method for identifying the reentry direction of the turning in the future under the observation of only external measurement as set forth in claim 2, wherein in the step 1.2, in order to better adapt to the different types of external measurement such as ranging, angle measurement, speed measurement, etc., the observation model calculated by filtering is directly built under the ground system of the measuring station, and the method is characterized by comprising the following steps of
Figure QLYQS_27
Speed vector->
Figure QLYQS_28
The method for calculating the observed quantity of the station horizon comprises the following steps:
step 1.2.1, the time is set
Figure QLYQS_30
Figure QLYQS_33
Conversion to ground fixation position->
Figure QLYQS_37
Speed->
Figure QLYQS_31
Conversion formula is +.>
Figure QLYQS_35
,
Figure QLYQS_38
Figure QLYQS_41
Figure QLYQS_32
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure QLYQS_34
For the time matrix>
Figure QLYQS_39
For nutation matrix>
Figure QLYQS_40
Is the earth rotation matrix>
Figure QLYQS_29
Is polar motion matrix>
Figure QLYQS_36
A derivative matrix that is an earth rotation matrix;
step 1.2.2, turning the ground-fixed system position speed toUnder the northeast coordinate system of the measuring station, the conversion matrix from the earth coordinate of the measuring station to the northeast coordinate system of the measuring station is calculated
Figure QLYQS_51
Position vector of station under ground fixation system +.>
Figure QLYQS_43
The position and the speed vector of the spacecraft under the northeast and north coordinate system of the measuring station are as follows:
Figure QLYQS_47
Figure QLYQS_53
The method comprises the steps of carrying out a first treatment on the surface of the Is provided with->
Figure QLYQS_56
The three-direction component in the northeast coordinate system is +.>
Figure QLYQS_55
Figure QLYQS_57
Figure QLYQS_50
Figure QLYQS_54
Is +.>
Figure QLYQS_42
Figure QLYQS_49
Figure QLYQS_44
Calculating 4 observables of external measurement data and measuring distance
Figure QLYQS_46
The method comprises the steps of carrying out a first treatment on the surface of the Azimuth angle->
Figure QLYQS_48
The method comprises the steps of carrying out a first treatment on the surface of the Elevation angle->
Figure QLYQS_52
The method comprises the steps of carrying out a first treatment on the surface of the Speed measurement->
Figure QLYQS_45
The method comprises the steps of carrying out a first treatment on the surface of the Obtaining a measurement equation of UKF;
step 1.2.3 filtering the state vector at a certain time
Figure QLYQS_58
Obtaining the spacecraft position vector of +.>
Figure QLYQS_59
The velocity vector is +.>
Figure QLYQS_60
4. The method for identifying a reentry direction of a turning in a vehicle under external observation only as set forth in claim 3, wherein said step 2 is set to
Figure QLYQS_61
State vector of time->
Figure QLYQS_62
The position and velocity vectors at this time are +.>
Figure QLYQS_63
Figure QLYQS_64
Let the last moment->
Figure QLYQS_65
The position and speed of the position are +.>
Figure QLYQS_66
Figure QLYQS_67
The unit vector of the normal direction of the track surface at the last moment is
Figure QLYQS_68
(4);
The speed difference between the current moment and the last moment is the speed increment:
Figure QLYQS_69
(5)。
5. the method for identifying a reentry direction of a turning flight under external observation only according to claim 4, wherein the step 3 is characterized in that the velocity increment obtained in the step 2, that is, the component of the velocity difference in the normal direction of the track surface at the last moment is:
Figure QLYQS_70
(6)。
6. the method for identifying a reentry direction of a turn under external observation only as set forth in claim 5, wherein said step 4 gives a defined value
Figure QLYQS_71
Figure QLYQS_72
And if the turning direction is 0, defining a turning direction discrimination function as follows:
Figure QLYQS_73
(7)
obtaining a turning direction judgment value at the current moment according to the turning direction judgment function, when
Figure QLYQS_74
When the value is 1, the left turn is indicated, and the observation is carried out towards the flying speed direction; when the value is-1, the right turn is indicated; when the value is 0, the curve is not turned or the value is too small to be judged.
7. The method for identifying a reentry flight turning direction under external measurement-only observation according to claim 1, wherein said step 5 specifically adopts a method of sliding in time sequence, counts turning directions determined by single points of m consecutive points, and sets a total reserved point at a certain time as
Figure QLYQS_75
Figure QLYQS_76
If the left turn value is the majority, the current left turn can be judged; if the right turn value is the majority, judging that the current moment is right turn; otherwise, the switching direction at the current time cannot be judged.
8. The method for identifying a reentry direction of a flight turning under external observation only as set forth in claim 7, wherein m in said step 5 is a predetermined value greater than 3, i.e., a turning direction value calculated for m points
Figure QLYQS_77
Figure QLYQS_78
……
Figure QLYQS_79
Figure QLYQS_80
Counting m points, and counting when the number of the values representing left turn is greater than or equal to a given number n, < >>
Figure QLYQS_81
Time indicates left turn; when the number of values representing right turn is greater than or equal to a given number n +.>
Figure QLYQS_82
Right turn is indicated; otherwise, the current point judges the turning direction and does not output the judging result. />
CN202111013991.3A 2021-08-31 2021-08-31 Method for identifying reentry flight turning direction under external measurement-only observation Active CN113569430B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111013991.3A CN113569430B (en) 2021-08-31 2021-08-31 Method for identifying reentry flight turning direction under external measurement-only observation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111013991.3A CN113569430B (en) 2021-08-31 2021-08-31 Method for identifying reentry flight turning direction under external measurement-only observation

Publications (2)

Publication Number Publication Date
CN113569430A CN113569430A (en) 2021-10-29
CN113569430B true CN113569430B (en) 2023-07-04

Family

ID=78173301

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111013991.3A Active CN113569430B (en) 2021-08-31 2021-08-31 Method for identifying reentry flight turning direction under external measurement-only observation

Country Status (1)

Country Link
CN (1) CN113569430B (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109682383A (en) * 2018-11-23 2019-04-26 中国西安卫星测控中心 It is a kind of to measure the Real-Time Filtering localization method away from discrete data using deep space three-dimensional
WO2021082790A1 (en) * 2019-10-29 2021-05-06 广东工业大学 Imu-based uwb positioning abnormal value processing method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109682383A (en) * 2018-11-23 2019-04-26 中国西安卫星测控中心 It is a kind of to measure the Real-Time Filtering localization method away from discrete data using deep space three-dimensional
WO2021082790A1 (en) * 2019-10-29 2021-05-06 广东工业大学 Imu-based uwb positioning abnormal value processing method

Also Published As

Publication number Publication date
CN113569430A (en) 2021-10-29

Similar Documents

Publication Publication Date Title
CN110375730B (en) Indoor positioning navigation system based on IMU and UWB fusion
Pham et al. A survey on unmanned aerial vehicle collision avoidance systems
US7765062B2 (en) Method and system for autonomous tracking of a mobile target by an unmanned aerial vehicle
CN105466438A (en) Sensor odometry and application in crash avoidance vehicle
CN111288989B (en) Visual positioning method for small unmanned aerial vehicle
CN108645413A (en) The dynamic correcting method of positioning and map building while a kind of mobile robot
CN113625762B (en) Unmanned aerial vehicle obstacle avoidance method and system, and unmanned aerial vehicle cluster obstacle avoidance method and system
CN109765919B (en) Spacecraft close-range safe operation control method based on equal collision probability line method
CN110764531B (en) Unmanned aerial vehicle formation flying obstacle avoidance method based on laser radar and artificial potential field method
CN111508282B (en) Low-altitude unmanned farmland operation flight obstacle conflict detection method
CN110262555B (en) Real-time obstacle avoidance control method for unmanned aerial vehicle in continuous obstacle environment
CN112180954B (en) Unmanned aerial vehicle obstacle avoidance method based on artificial potential field
CN110929810A (en) Multi-source data fusion method for low-speed small-target detection system
CN116182837A (en) Positioning and mapping method based on visual laser radar inertial tight coupling
WO2022193106A1 (en) Method for fusing gps with laser radar through inertia measurement parameter for positioning
CN105913080B (en) Joint tracking and classification method based on the motor-driven non-elliptical extension target of random matrix
CN115145295A (en) Online autonomous flight path optimization control method for unmanned aerial vehicle in dynamic environment
CN112325885A (en) Factor graph co-location algorithm based on mathematical statistical characteristics
CN104777465B (en) Random extended object shape and state estimation method based on B spline function
CN113763434B (en) Target track prediction method based on Kalman filtering multi-motion model switching
CN112505718B (en) Positioning method, system and computer readable medium for autonomous vehicle
CN113569430B (en) Method for identifying reentry flight turning direction under external measurement-only observation
CN117932894A (en) Multimode seeker information fusion target state estimation method
CN110728026B (en) Terminal trajectory target passive tracking method based on angular velocity measurement
CN113761662B (en) Generation method of trajectory prediction pipeline of gliding target

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant