CN110320511A - A kind of Data Association based on all-azimuth search - Google Patents

A kind of Data Association based on all-azimuth search Download PDF

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CN110320511A
CN110320511A CN201910623834.0A CN201910623834A CN110320511A CN 110320511 A CN110320511 A CN 110320511A CN 201910623834 A CN201910623834 A CN 201910623834A CN 110320511 A CN110320511 A CN 110320511A
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coordinate
period
predicted
characteristic point
matrix
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CN110320511B (en
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赵艳杰
张伟峰
王志明
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Beijing Institute of Remote Sensing Equipment
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Beijing Institute of Remote Sensing Equipment
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • 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
    • G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention discloses a kind of Data Associations based on all-azimuth search, comprising: each process cycle inputs a measured result and generates actual measurement matrix；Filtering matrix can be calculated by actual measurement matrix and prediction matrix in each process cycle, each process cycle calculates prediction matrix by the filtering matrix of a upper process cycle, and initial filter matrix has the measured result of the first two process cycle to be calculated；Each process cycle judges to survey matrix whether within the scope of prediction window, to form a completely stable track.The problem of caused association quality decline chaotic the present invention overcomes system track, according to track information prediction subsequent time target position, effectively improve all-azimuth search effect.

Description

Track association method based on omnidirectional search
Technical Field
The invention relates to the field of active radar omnidirectional search, in particular to a track association method based on omnidirectional search.
Background
The target tracking algorithm based on track association is a research hotspot of scholars at home and abroad, and the main principle is to establish association among a plurality of short tracking segments of a tracked target so as to obtain a continuous and smooth tracking track of the tracked target. In complex scenarios, when there is an interaction between objects with similar behavior, it is easy to cause erroneous track association. This requires that the target trajectory correlation algorithm not only accurately track the newly-appearing target, but also maintain continuity with the original tracked target.
Due to the randomness of the tracked target, the motion trail is nonlinear.
Disclosure of Invention
The invention aims to provide a track correlation method based on omnidirectional search, which solves the problem of reduced correlation quality caused by disordered system tracks, predicts a tracking track obtained by a target position at the next moment according to track information and effectively improves the omnidirectional search effect.
In view of this, the technical solution provided by the present invention is: a track association method based on omnidirectional search comprises the following steps: acquiring feature points, recording the feature points once after the full-airspace 360-degree rotation search is completed as a period, and recording the searched feature points in each period, wherein the information of the feature points comprises: obtaining the measured spherical coordinates of the n-th period characteristic point by distance, azimuth angle and pitch angle and recording as Qt(n)(rn,fyn,fzn)，rn、fyn、fznRespectively representing the distance, azimuth angle and pitch angle of the characteristic point of the nth period, n>0；
The measured spherical coordinates are coordinates under a spherical coordinate system, the spherical coordinate system is converted into a rectangular coordinate system through coordinate system conversion, the coordinates under the rectangular coordinate system comprise position xyz coordinates and speed xyz coordinates, and the speed xyz coordinates are obtained through relative movement of the position xyz coordinates in a time interval; the actually measured spherical coordinates Q of the characteristic pointst(n)(rn,fyn,fzn) Obtaining the position xyz coordinate of the feature point through coordinate conversion, establishing an actual measurement matrix by the position xyz coordinate, and recording the actual measurement matrix of the feature point as Ht(n)[(axn,ayn,azn)]，axn、ayn、aznRespectively representing the x coordinate, the y coordinate and the z coordinate of the position of the characteristic point of the nth period, n>0; and recording the measured matrixes of the characteristic points of the 1 st cycle and the 2 nd cycle as Ht(1)[(ax1,ay1,az1)]、Ht(2)[(ax2,ay2,az2)](ii) a Calculating to obtain the filter of the 2 nd period characteristic point according to the measured matrixes of the 1 st period characteristic point and the 2 nd period characteristic pointA wave matrix, a filter matrix is established by the position xyz coordinate and the speed xyz coordinate, and the filter matrix of the 2 nd period characteristic point is represented as Hf(2)[(ax2,ay2,az2)；(vx2,vy2,vz2)]，vx2、vy2、vz2Respectively setting a speed x coordinate, a speed y coordinate and a speed z coordinate of the 2 nd period characteristic point; the filter matrix of the n-1 th cycle characteristic point is calculated to obtain a prediction matrix of the n-1 th cycle characteristic point, n>2; calculating a filter matrix of the n-th period characteristic point according to the measured matrix of the n-th period characteristic point and the prediction matrix of the n-th period characteristic point; calculating a predicted spherical coordinate of the characteristic point according to the n-th period characteristic point prediction matrix, and establishing a prediction window of the n-th period; judging whether the actual measurement spherical coordinates of the n-th period feature point are in the prediction window of the n-th period, if so, recording the actual measurement spherical coordinates of the n-th period feature point on the flight path, if not, recording the predicted spherical coordinates of the n-th period feature point on the flight path, and n>2。
Wherein the measured spherical coordinate Q of the n-th period feature pointt(n)(rn,fyn,fzn) The method comprises the following steps: calculating the position x coordinate, the position y coordinate and the position z coordinate of the feature point according to the measured spherical coordinate, and establishing a measured matrix, wherein the measured matrix is expressed as:
Ht(n)[(axn,ayn,azn)]
wherein,
axn＝rn*cos(fyn)*cos(fzn)
ayn＝rn*sin(fzn)
azn＝rn*sin(fyn)*cos(fzn)
rn、fyn、fznrespectively representing the distance, azimuth angle and pitch angle of the characteristic point of the nth period, n>0。
And obtaining a filter matrix of the 2 nd period according to the calculation of the measured matrixes of the characteristic points of the 1 st period and the 2 nd period, wherein the filter matrix of the 2 nd period is represented as:
Hf(2)[(ax2,ay2,az2)；(vx2,vy2,vz2)]
wherein,
vx2＝(ax2-ax1)/Δt
vy2＝(ay2-ay1)/Δt
vz2＝(az2-az1)/Δt
ax2、ay2、az2respectively representing a position x coordinate, a position y coordinate and a position z coordinate of the 2 nd period characteristic point; ax1、ay、az1Respectively representing the x coordinate, the y coordinate and the z coordinate of the position of the 1 st period characteristic point, and vx2、vy2、vz2And respectively setting a speed x coordinate, a speed y coordinate and a speed z coordinate of the 2 nd period characteristic point, wherein delta t is period interval time.
Calculating a prediction matrix of the n-th cycle characteristic point according to the filter matrix of the n-1 th cycle characteristic point to obtain a predicted spherical coordinate of the n-th cycle characteristic point, wherein n is the predicted spherical coordinate of the n-th cycle characteristic point>2, prediction matrix Hp(n)Expressed as:
Hp(n)[(axn,ayn,azn)；(vxn,vyn,vzn)]
wherein,
axn＝Hf(n-1)(axn-1)+Δt*Hf(n-1)(vxn-1)
ayn＝Hf(n-1)(ayn-1)+Δt*Hf(n-1)(vyn-1)
azn＝Hf(n-1)(azn-1)+Δt*Hf(n-1)(vzn-1)
vxn＝Hf(n-1)(vxn-1)
vyn＝Hf(n-1)(vyn-1)
vzn＝Hf(n-1)(vzn-1)
axn、ayn、aznare respectively provided withRepresenting the predicted position x coordinate, position y coordinate and position z coordinate of the n-th period feature point; vxn、vyn、vznRespectively representing a predicted speed x coordinate, a predicted speed y coordinate and a predicted speed z coordinate of the n-th period characteristic point; hf(n-1)(axn-1)、Hf(n-1)(ayn-1)、Hf(n-1)(azn-1) Respectively representing the predicted x coordinate, y coordinate and z coordinate of the position of the characteristic point of the (n-1) th cycle; hf(n-1)(vxn-1)、Hf(n-1)(vyn-1)、Hf(n-1)(vzn-1) Respectively representing a predicted speed x coordinate, a predicted speed y coordinate and a predicted speed z coordinate of the characteristic point of the (n-1) th cycle; Δ t is the cycle interval time.
The measured matrix of the n-th cycle characteristic point and the prediction matrix of the n-th cycle characteristic point are calculated to obtain a filter matrix of the n-th cycle characteristic point, wherein the filter matrix is expressed as:
Hf(n)[(axn,ayn,azn)；(vxn,vyn,vzn)]
wherein,
axn＝α*Ht(n)(axn)+(1-α)*Hp(n)(axn)
ayn＝α*Ht(n)(ayn)+(1-α)*Hp(n)(ayn)
azn＝α*Ht(n)(azn)+(1-α)*Hp(n)(azn)
Ht(n)(axn)、Ht(n)(ayn)、Ht(n)(azn) Respectively representing the x coordinate, the y coordinate and the z coordinate of the measured position of the n-th period feature point; hp(n)(axn)、Hp(n)(ayn)、Hp(n)(azn) Respectively representing a predicted position x coordinate, a position y coordinate and a position z coordinate of the n-th period feature point; axn、ayn、aznRespectively representing a filtering position x coordinate, a position y coordinate and a position z coordinate of the n-th period feature point; vxn、vyn、vznRespectively representing a filtering speed x coordinate, a speed y coordinate and a speed z coordinate of the n-th period characteristic point; α and β denote filter parameters, and Δ t is a period interval time.
Calculating the predicted spherical coordinates of the characteristic points according to the prediction matrix of the characteristic points of the nth period, and establishing a prediction window of the nth period; the predicted spherical coordinates of the n-th cycle feature point are expressed as: qp(n)(rn,fyn,fzn)
Wherein,
Hp(n)(axn)、Hp(n)(ayn)、Hp(n)(azn) Respectively representing a predicted position x coordinate, a position y coordinate and a position z coordinate of the n-th period feature point; r isn、fyn、fznRespectively representing the predicted distance, the predicted azimuth angle and the predicted pitch angle of the n-th period characteristic point;
the prediction window for the nth cycle is expressed as: hPTH(rn,fyn,fzn)
Wherein,
HPTH(rn)＝Qp(n)(rn)+deltaR
HPTH(fyn)＝Qp(n)(fyn)+deltaFY
HPTH(fzn)＝Qp(n)(fzn)+deltaFZ
HPTH(rn)、HPTH(fyn)、HPTH(fzn) Respectively representing a predicted distance window, a predicted azimuth window and a predicted pitch window of the n-th period characteristic point; deltaR, deltaFY, deltaFZ represent distance, azimuth and pitch error values allowed by the method.
Judging whether the actually measured spherical coordinates of the characteristic point of the nth period are in a prediction window of the nth period, if the characteristic point of the nth period simultaneously meets the following three conditions: (1) the measured distance is in the predicted distance window of the nth period; (2) the predicted azimuth is in the predicted azimuth window of the nth period; (3) predicting a pitch angle within a predicted pitch window of the nth period; then the measured spherical coordinate Q of the n-th period feature point is determinedt(n)(rn,fyn,fzn) Recording the predicted spherical coordinate Q of the n-th period characteristic point on the flight path, otherwise, predicting the spherical coordinate Q of the n-th period characteristic pointp(n)(rn,fyn,fzn) Recorded on track, n>2。
Therefore, the track association method based on the omnidirectional search is completed.
Drawings
Fig. 1 is a schematic flow chart of a track association method based on omnidirectional search according to the present invention.
Detailed description of the preferred embodiment
The following description of the present invention will be made in detail with reference to fig. 1.
The invention provides a track association method based on omnidirectional search, which comprises the following steps that as shown in figure 1:
acquiring feature points, recording the feature points once after the full-airspace 360-degree rotation search is completed as a period, and recording the searched feature points in each period, wherein the information of the feature points comprises: distance r, azimuth angle fy, pitch angle fz, and the measured spherical coordinate of the n-th period feature point is recorded as Qt(n)(rn,fyn,fzn)，n>0. The actually measured spherical coordinates are coordinates under a spherical coordinate system, the spherical coordinate system is converted into a rectangular coordinate system through coordinate system conversion, the coordinates under the rectangular coordinate system comprise position xyz coordinates and speed xyz coordinates, and the speed xyz coordinates are obtained through relative movement of the position xyz coordinates in a time interval.
The actually measured spherical coordinates Q of the characteristic pointst(n)(rn,fyn,fzn) Obtaining the xyz coordinates of the feature points through coordinate conversion, establishing an actual measurement matrix at the xyz coordinates of the positions, and recording the actual measurement matrix of the feature points as Ht(n)[(axn,ayn,azn)]，n>0; and recording the measured matrixes of the characteristic points of the 1 st cycle and the 2 nd cycle as Ht(1)[(ax1,ay1,az1)]、Ht(2)[(ax2,ay2,az2)]。
Calculating to obtain a filter matrix of the 2 nd period characteristic point according to the measured matrixes of the 1 st period characteristic point and the 2 nd period characteristic point, establishing a filter matrix according to a position xyz coordinate and a speed xyz coordinate, wherein the filter matrix of the 2 nd period characteristic point is represented as Hf(2)[(ax2,ay2,az2)；(vx2,vy2,vz2)]。
And (3) calculating the filter matrix of the n-1 th cycle characteristic point to obtain a prediction matrix of the n-1 th cycle characteristic point, wherein n is greater than 2.
And calculating to obtain a filter matrix of the n-th cycle characteristic point according to the measured matrix of the n-th cycle characteristic point and the prediction matrix of the n-th cycle characteristic point.
And calculating the predicted spherical coordinates of the characteristic points according to the n-th period characteristic point prediction matrix, and establishing a prediction window of the n-th period.
And judging whether the actually measured spherical coordinates of the characteristic points of the nth period are in the prediction window of the nth period, if so, recording the actually measured spherical coordinates of the characteristic points of the nth period on the flight path, and if not, recording the predicted spherical coordinates of the characteristic points of the nth period on the flight path, wherein n is greater than 2.
The measured spherical coordinate Q of the n-th period feature pointt(n)(rn,fyn,fzn) The method comprises the following steps: distance r, azimuth angle fy, pitch angle fz; calculating the position x coordinate, the position y coordinate and the position z coordinate of the feature point according to the measured spherical coordinates, and establishing a measured matrix, wherein the measured matrix is expressed as:
Ht(n)[(axn,ayn,azn)]
wherein,
axn＝rn*cos(fyn)*cos(fzn)
ayn＝rn*sin(fzn)
azn＝rn*sin(fyn)*cos(fzn)
axn、ayn、aznrespectively representing the x coordinate, the y coordinate and the z coordinate of the position of the characteristic point of the nth period; r isn、fyn、fznRespectively representing the distance, azimuth angle and pitch angle of the characteristic point of the nth period, n>0。
And calculating to obtain a filter matrix of the 2 nd period according to the measured matrixes of the characteristic points of the 1 st period and the 2 nd period, wherein the filter matrix of the 2 nd period is represented as:
Hf(2)[(ax2,ay2,az2)；(vx2,vy2,vz2)]
wherein,
vx2＝(ax2-ax1)/Δt
vy2＝(ay2-ay1)/Δt
vz2＝(az2-az1)/Δt
ax2、ay2、az2respectively representing a position x coordinate, a position y coordinate and a position z coordinate of the 2 nd period characteristic point; ax1、ay、az1Respectively representing the x coordinate, the y coordinate and the z coordinate of the position of the 1 st period characteristic point, and vx2、vy2、vz2And respectively setting a speed x coordinate, a speed y coordinate and a speed z coordinate of the 2 nd period characteristic point, wherein delta t is period interval time.
Calculating a prediction matrix of the n-th cycle characteristic point according to the filter matrix of the n-1 th cycle characteristic point to obtain a prediction sphere coordinate of the n-th cycle characteristic point, wherein n is greater than 2, and the prediction matrix is expressed as:
Hp(n)[(axn,ayn,azn)；(vxn,vyn,vzn)]
wherein,
axn＝Hf(n-1)(axn-1)+Δt*Hf(n-1)(vxn-1)
ayn＝Hf(n-1)(ayn-1)+Δt*Hf(n-1)(vyn-1)
azn＝Hf(n-1)(azn-1)+Δt*Hf(n-1)(vzn-1)
vxn＝Hf(n-1)(vxn-1)
vyn＝Hf(n-1)(vyn-1)
vzn＝Hf(n-1)(vzn-1)
axn、ayn、aznrespectively representing a predicted position x coordinate, a position y coordinate and a position z coordinate of the n-th period feature point; vxn、vyn、vznRespectively representing a predicted speed x coordinate, a predicted speed y coordinate and a predicted speed z coordinate of the n-th period characteristic point; hf(n-1)(axn-1)、Hf(n-1)(ayn-1)、Hf(n-1)(azn-1) Respectively representing the predicted x coordinate, y coordinate and z coordinate of the position of the characteristic point of the (n-1) th cycle; hf(n-1)(vxn-1)、Hf(n-1)(vyn-1)、Hf(n-1)(vzn-1) Respectively representing a predicted speed x coordinate, a predicted speed y coordinate and a predicted speed z coordinate of the characteristic point of the (n-1) th cycle; Δ t is the cycle interval time.
And calculating the measured matrix of the n-th period characteristic point and the prediction matrix of the n-th period characteristic point to obtain a filter matrix of the n-th period characteristic point, wherein the filter matrix is expressed as:
Hf(n)[(axn,ayn,azn)；(vxn,vyn,vzn)]
wherein,
axn＝α*Ht(n)(axn)+(1-α)*Hp(n)(axn)
ayn＝α*Ht(n)(ayn)+(1-α)*Hp(n)(ayn)
azn＝α*Ht(n)(azn)+(1-α)*Hp(n)(azn)
Ht(n)(axn)、Ht(n)(ayn)、Ht(n)(azn) Respectively representing the x coordinate, the y coordinate and the z coordinate of the measured position of the n-th period feature point; hp(n)(axn)、Hp(n)(ayn)、Hp(n)(azn) Respectively representing a predicted position x coordinate, a position y coordinate and a position z coordinate of the n-th period feature point; axn、ayn、aznRespectively representing a filtering position x coordinate, a position y coordinate and a position z coordinate of the n-th period feature point; vxn、vyn、vznRespectively representing a filtering speed x coordinate, a speed y coordinate and a speed z coordinate of the n-th period characteristic point; α and β denote filter parameters, and Δ t is a period interval time.
Calculating the predicted spherical coordinates of the characteristic points according to the prediction matrix of the characteristic points of the nth period, and establishing a prediction window of the nth period; the predicted spherical coordinates of the n-th cycle feature point are expressed as: qp(n)(rn,fyn,fzn)
Wherein,
Hp(n)(axn)、Hp(n)(ayn)、Hp(n)(azn) Respectively representing a predicted position x coordinate, a position y coordinate and a position z coordinate of the n-th period feature point; r isn、fyn、fznAnd respectively representing the predicted distance, the predicted azimuth angle and the predicted pitch angle of the characteristic point of the nth period.
The prediction window for the nth cycle is expressed as: hPTH(rn,fyn,fzn)
Wherein,
HPTH(rn)＝Qp(n)(rn)+deltaR
HPTH(fyn)＝Qp(n)(fyn)+deltaFY
HPTH(fzn)＝Qp(n)(fzn)+deltaFZ
HPTH(rn)、HPTH(fyn)、HPTH(fzn) Respectively representing a predicted distance window, a predicted azimuth window and a predicted pitch window of the n-th period characteristic point; deltaR, deltaFY, deltaFZ represent distance, azimuth and pitch error values allowed by the method.
Judging whether the actually measured spherical coordinates of the characteristic point of the nth period are in a prediction window of the nth period, if the characteristic point of the nth period simultaneously meets the following three conditions: (1) the measured distance is in the predicted distance window of the nth period; (2) the predicted azimuth is in the predicted azimuth window of the nth period; (3) predicted pitch for predicted pitch period nThe window is arranged in the overhead window; then the measured spherical coordinate Q of the n-th period feature point is determinedt(n)(rn,fyn,fzn) Recording the predicted spherical coordinate Q of the n-th period characteristic point on the flight path, otherwise, predicting the spherical coordinate Q of the n-th period characteristic pointp(n)(rn,fyn,fzn) Recorded on track, n>2；
Therefore, the track association method based on the omnidirectional search is completed.

Claims (7)

1. A track association method based on omnidirectional search is characterized by comprising the following steps:
acquiring feature points, recording the feature points once after the full-airspace 360-degree rotation search is completed as a period, and recording the searched feature points in each period, wherein the information of the feature points comprises: obtaining the measured spherical coordinates of the n-th period characteristic point by distance, azimuth angle and pitch angle and recording as Qt(n)(rn,fyn,fzn)，rn、fyn、fznRespectively representing the distance, azimuth angle and pitch angle of the characteristic point of the nth period, n>0；
The actually measured spherical coordinates Q of the characteristic pointst(n)(rn,fyn,fzn) Obtaining the position xyz coordinate of the feature point through coordinate conversion, establishing an actual measurement matrix by the position xyz coordinate, and recording the actual measurement matrix of the feature point as Ht(n)[(axn,ayn,azn)]，axn、ayn、aznRespectively representing the x coordinate, the y coordinate and the z coordinate of the position of the characteristic point of the nth period, n>0; and recording the measured matrixes of the characteristic points of the 1 st cycle and the 2 nd cycle as Ht(1)[(ax1,ay1,az1)]、Ht(2)[(ax2,ay2,az2)]；
Calculating to obtain a filter matrix of the 2 nd period characteristic point according to the measured matrixes of the 1 st period characteristic point and the 2 nd period characteristic point, establishing a filter matrix according to a position xyz coordinate and a speed xyz coordinate, wherein the filter matrix of the 2 nd period characteristic point is represented as Hf(2)[(ax2,ay2,az2)；(vx2,vy2,vz2)]，vx2、vy2、vz2Respectively setting a speed x coordinate, a speed y coordinate and a speed z coordinate of the 2 nd period characteristic point;
the filter matrix of the n-1 th cycle characteristic point is calculated to obtain a prediction matrix of the n-1 th cycle characteristic point, wherein n is greater than 2;
calculating a filter matrix of the n-th period characteristic point according to the measured matrix of the n-th period characteristic point and the prediction matrix of the n-th period characteristic point;
calculating a predicted spherical coordinate of the characteristic point according to the n-th period characteristic point prediction matrix, and establishing a prediction window of the n-th period;
and judging whether the actually measured spherical coordinates of the characteristic points of the nth period are in the prediction window of the nth period, if so, recording the actually measured spherical coordinates of the characteristic points of the nth period on the flight path, and if not, recording the predicted spherical coordinates of the characteristic points of the nth period on the flight path, wherein n is greater than 2.
2. The track correlation method based on omnidirectional search according to claim 1, wherein the measured spherical coordinate Q of the n-th periodic feature pointt(n)(rn,fyn,fzn) Calculating the position x coordinate, the position y coordinate and the position z coordinate of the feature point according to the measured spherical coordinate, and establishing a measured matrix, wherein the measured matrix is expressed as:
Ht(n)[(axn,ayn,azn)]
wherein,
axn＝rn*cos(fyn)*cos(fzn)
ayn＝rn*sin(fzn)
azn＝rn*sin(fyn)*cos(fzn)
rn、fyn、fznrespectively representing the distance, azimuth angle and pitch angle of the characteristic point of the nth period, n>0。
3. The track correlation method based on omnidirectional search according to claim 1, wherein a filter matrix of the 2 nd cycle is obtained by calculation according to the measured matrices of the characteristic points of the 1 st cycle and the 2 nd cycle, and the filter matrix of the 2 nd cycle is represented as:
Hf(2)[(ax2,ay2,az2)；(vx2,vy2,vz2)]
wherein,
vx2＝(ax2-ax1)/Δt
vy2＝(ay2-ay1)/Δt
vz2＝(az2-az1)/Δt
ax2、ay2、az2respectively representing a position x coordinate, a position y coordinate and a position z coordinate of the 2 nd period characteristic point; ax1、ay、az1Respectively representing the x coordinate, the y coordinate and the z coordinate of the position of the 1 st period characteristic point, and vx2、vy2、vz2And respectively setting a speed x coordinate, a speed y coordinate and a speed z coordinate of the 2 nd period characteristic point, wherein delta t is period interval time.
4. The track correlation method based on omnidirectional search according to claim 1, wherein a prediction matrix of the n-th period feature point is obtained by calculating according to the filter matrix of the n-1-th period feature point, a predicted spherical coordinate of the n-th period feature point is obtained, and n is>2, prediction matrix Hp(n)Expressed as:
Hp(n)[(axn,ayn,azn)；(vxn,vyn,vzn)]
wherein,
axn＝Hf(n-1)(axn-1)+Δt*Hf(n-1)(vxn-1)
ayn＝Hf(n-1)(ayn-1)+Δt*Hf(n-1)(vyn-1)
azn＝Hf(n-1)(azn-1)+Δt*Hf(n-1)(vzn-1)
vxn＝Hf(n-1)(vxn-1)
vyn＝Hf(n-1)(vyn-1)
vzn＝Hf(n-1)(vzn-1)
axn、ayn、aznrespectively representing a predicted position x coordinate, a position y coordinate and a position z coordinate of the n-th period feature point; vxn、vyn、vznRespectively representing a predicted speed x coordinate, a predicted speed y coordinate and a predicted speed z coordinate of the n-th period characteristic point; hf(n-1)(axn-1)、Hf(n-1)(ayn-1)、Hf(n-1)(azn-1) Respectively representing the predicted x coordinate, y coordinate and z coordinate of the position of the characteristic point of the (n-1) th cycle; hf(n-1)(vxn-1)、Hf(n-1)(vyn-1)、Hf(n-1)(vzn-1) Respectively representing a predicted speed x coordinate, a predicted speed y coordinate and a predicted speed z coordinate of the characteristic point of the (n-1) th cycle; Δ t is the cycle interval time.
5. The track correlation method based on omnidirectional search according to claim 1, wherein a filter matrix of the n-th period feature point is obtained by calculating a measured matrix of the n-th period feature point and a prediction matrix of the n-th period feature point, and the filter matrix is represented as:
Hf(n)[(axn,ayn,azn)；(vxn,vyn,vzn)]
wherein,
axn＝α*Ht(n)(axn)+(1-α)*Hp(n)(axn)
ayn＝α*Ht(n)(ayn)+(1-α)*Hp(n)(ayn)
azn＝α*Ht(n)(azn)+(1-α)*Hp(n)(azn)
Ht(n)(axn)、Ht(n)(ayn)、Ht(n)(azn) Respectively representing the x coordinate, the y coordinate and the z coordinate of the measured position of the n-th period feature point; hp(n)(axn)、Hp(n)(ayn)、Hp(n)(azn) Respectively representing a predicted position x coordinate, a position y coordinate and a position z coordinate of the n-th period feature point; axn、ayn、aznRespectively representing a filtering position x coordinate, a position y coordinate and a position z coordinate of the n-th period feature point; vxn、vyn、vznRespectively representing a filtering speed x coordinate, a speed y coordinate and a speed z coordinate of the n-th period characteristic point; α and β denote filter parameters, and Δ t is a period interval time.
6. The track correlation method based on omnidirectional search according to claim 1, wherein the predicted spherical coordinates of the characteristic points are calculated according to the prediction matrix of the characteristic points in the nth period, and a prediction window in the nth period is established; the predicted spherical coordinates of the n-th cycle feature point are expressed as: qp(n)(rn,fyn,fzn)
Wherein,
Hp(n)(axn)、Hp(n)(ayn)、Hp(n)(azn) Respectively representing a predicted position x coordinate, a position y coordinate and a position z coordinate of the n-th period feature point; r isn、fyn、fznRespectively representing the predicted distance, the predicted azimuth angle and the predicted pitch angle of the n-th period characteristic point;
the prediction window for the nth cycle is expressed as: hPTH(rn,fyn,fzn)
Wherein,
HPTH(rn)＝Qp(n)(rn)+deltaR
HPTH(fyn)＝Qp(n)(fyn)+deltaFY
HPTH(fzn)＝Qp(n)(fzn)+deltaFZ
HPTH(rn)、HPTH(fyn)、HPTH(fzn) Respectively representing a predicted distance window, a predicted azimuth window and a predicted pitch window of the n-th period characteristic point; deltaR, deltaFY, deltaFZ represent distance, azimuth and pitch error values allowed by the method.
7. The track correlation method based on omnidirectional search according to claim 1, wherein it is determined whether the measured spherical coordinates of the characteristic point of the nth period are within the prediction window of the nth period, and if the characteristic point of the nth period satisfies the following three conditions at the same time: (1) the measured distance is in the predicted distance window of the nth period; (2) the predicted azimuth is in the predicted azimuth window of the nth period; (3) predicting a pitch angle within a predicted pitch window of the nth period; then the measured spherical coordinate Q of the n-th period feature point is determinedt(n)(rn,fyn,fzn) Recording the predicted spherical coordinate Q of the n-th period characteristic point on the flight path, otherwise, predicting the spherical coordinate Q of the n-th period characteristic pointp(n)(rn,fyn,fzn) Recorded on track, n>2。
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