CN109001724B - Target automatic starting track and tracking positioning method - Google Patents

Target automatic starting track and tracking positioning method Download PDF

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CN109001724B
CN109001724B CN201810578521.3A CN201810578521A CN109001724B CN 109001724 B CN109001724 B CN 109001724B CN 201810578521 A CN201810578521 A CN 201810578521A CN 109001724 B CN109001724 B CN 109001724B
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state
track
data
observation
flight path
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CN109001724A (en
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陈金鑫
董蛟
张志祥
黄海
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Naval University of Engineering PLA
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66—Radar-tracking systems; Analogous systems
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00

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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 method for automatically starting a track and tracking and positioning a target, which comprises the following steps: acquiring observation data, associating each observation data with track data, and determining whether the observation data and the track data come from the same target; carrying out state transition according to the correlation result of the flight path and the observation data; and for the observation data which is not associated with any track in an observation period, establishing a new track according to the observation data which is not associated with any track. The state transition method selected by the invention reduces the probability of radar false target navigation and improves the success rate of automatic navigation and stable tracking of the real target.

Description

Target automatic starting track and tracking positioning method
Technical Field
The invention relates to target tracking processing, in particular to a target automatic starting track and tracking positioning method.
Background
The method can be used as one of important means of environmental perception, and plays an important role in autonomous navigation of the unmanned surface vessel. How to process radar echoes, how to accurately detect targets, how to establish target tracks and the like become popular research subjects.
Image data acquired by a radar is subjected to preliminary processing to obtain rough target data, the target data consists of a real target and a false target, and the position information of the real target also comprises errors such as observation errors and system processing errors. On one hand, the targets are positioned only by utilizing the relatively rough target data, and the requirements of users on radar target information are difficult to meet; on the other hand, unmanned ship systems loaded with radar operate in an unmanned environment. And obtaining a relatively accurate target position, and performing automatic track initiation and filtering tracking on the target by using the primarily processed target data, so that radar target data can be automatically processed in an unmanned state, the position accuracy of a real target can be improved, and the number of false targets can be reduced.
The invention content is as follows:
in order to overcome the defects of the background technology, the invention provides a method for automatically starting a track and tracking and positioning a target, which improves the success rate of automatically establishing a true target and stably tracking the true target.
In order to solve the technical problems, the invention adopts the technical scheme that:
an automatic target track starting and tracking positioning method is characterized by comprising the following steps:
acquiring observation data, associating each observation data with track data, and determining whether the observation data and the track data come from the same target; carrying out state transition according to the correlation result of the flight path and the observation data; and for the observation data which is not associated with any track in an observation period, establishing a new track according to the observation data which is not associated with any track.
Preferably, the data associating each observation data with each track data, and determining whether the observation data and the track data come from the same target includes:
four track states are determined:
in the state 1, the length of track data is equal to 1;
state 2, track data length in interval [2, N0]Internal;
state 3, track data Length>N0And the number of the lost observation data is equal to 0;
state 4, track Length>N0And the number of the lost observation data is within the interval (0, n)0]And (4) the following steps.
Wherein N is0As track length threshold, n0A length threshold for missing observation data.
Preferably, the data association is performed on each observation data and each track data, whether the observation data and the track data come from the same target is determined, and the data association is performed on the track in the state 3 and the observation data, and the specific method is as follows:
projecting all new observations in the period to a plane where the state three-correlation circle is located, comparing all the observations in the state three-correlation circle, and associating the observation closest to the circle center with a target point at the circle center;
the circle center of the state three-association circle is P point and extends V along the course CfilterT, radius of the associated circle R ═ I1·1+I2·2+R0，
Wherein P is the latest point position of the current track, VfilterIs the navigational speed, t is the radar scanning period,1and2respectively a distance observation error and an azimuth observation error I1And I2Respectively a distance error coefficient and an orientation error coefficient, R0Is the smallest associated circle radius.
Preferably, the data association is performed on each observation data and each track data, whether the observation data and the track data come from the same target is determined, and the data association is performed on the track in the state 4 and the observation data, and the specific method is as follows:
projecting all new observations in the period to a plane where the state four-association circle is located, comparing all the observations in the state four-association circle, and associating the observation closest to the circle center with a target point at the circle center;
the center of the state four-association circle is P point and extends V along the course Cfilter(number of lost cycles +1) · radar scan cycle, radius of associated circle R ═ I1·1+I2·2+R0，
Wherein P is the latest point position of the current track, VfilterThe speed of the aircraft is the size of the navigational speed,1and2respectively a distance observation error and an azimuth observation error I1And I2Respectively a distance error coefficient and an orientation error coefficient, R0The loss period is an artificial empirical parameter for the smallest associated circle radius.
Preferably, the data association is performed on each observation data and each track data, whether the observation data and the track data come from the same target is determined, and the data association is performed on the track in the state 1 and the observation data, and the specific method is as follows:
projecting the periodic observation data to a plane where the state-associated circle is located, comparing all observations in the state-associated circle, selecting an observation closest to the circle center, and associating the observation with a target point at the circle center;
the circle center of the first state association circle is the current position of a track, and the radius is the product of the cruising speed of the unmanned ship and the radar scanning period;
the unmanned boat cruising speed included 20 knots.
Preferably, the data association is performed on each observation data and each track data, whether the observation data and the track data come from the same target is determined, and the data association is performed on the track in the state 2 and the observation data, and the specific method is as follows:
projecting the periodic observation data to a plane where the state two-association sector is located, comparing all observations in the state two-association sector, and selecting an observation closest to the circle center to be associated with a target point at the circle center;
the circle center of the two-state associated sector is the current position of the track, the associated radius is the sector radius, the sector radius is the product of the speed of the target corresponding to the track and the radar scanning period, the sector direction is the course direction of the track, and the sector angle delta is-N.V + delta0Wherein N is a positive coefficient, V is the speed, and Delta0Is the minimum fan angle.
Preferably, the state transition specifically includes the following processes according to the correlation result between the flight path and the observation data:
process 1: if the track is in the state 1, if a new observation is associated, the length of the track is changed into 2, the track meets the characteristics of the track in the state 2, and the track state is changed into 2;
and (2) a process: if the flight path is in the state 1, if the new observation is not associated, destroying the flight path;
and 3, process: if the flight path is in the state 2, if the new observation is not associated, destroying the flight path;
and 4, process: if the flight path is in the state 2, if the new observation is associated, the length of the flight path is added with 1; and further judging whether the track length is still in the interval [2, N ] after being increased0]If yes, the state number is not changed;
and (5) a process: if the flight path is in the state 2, if the new observation is associated, the length of the flight path is added with 1; judging whether the flight path length is greater than N0If yes, the characteristic of the state 3 is met, and the state number of the flight path is changed into 3;
and 6, a process: if the flight path is in the state 3, if a new observation is associated, the number of lost cycles is 0, the flight path accords with the characteristics of the flight path in the state 3, and the state number is kept unchanged;
and (7) a process: if the track is in the state 3, if the new observation is not associated, adding 1 to the number of lost cycles; and further determines that the number of lost cycles is still within the interval (0, n)0) If yes, and the characteristic of the state 4 is met, the state number is changed into 4;
and (8) a process: if the track is in the state 4, and the number of lost cycles returns to zero if the track is associated with a new observation, the track conforms to the characteristics of the state 3, and the state number is changed into 3;
and a process 9: if the track is in the state 4, if the new observation is not associated, adding 1 to the number of lost cycles; and further determines whether the number of lost cycles is still within the interval (0, n)0]If yes, the characteristic of the state 4 is met, and the state number is unchanged;
the process 10: if the track is in the state 4, if the new observation is not associated, adding 1 to the number of lost cycles; and further determining whether the number of lost cycles is greater than n0If yes, and the characteristics of the state 4 are not met, the flight path is destroyed.
Preferably, after the process 6 and the process 8 change the track status number, the course and the speed of the track are obtained and updated by using a filtering algorithm according to the new observed coordinates.
Preferably, when a track is newly created according to the observation data which is not associated with any track, a track with a state number of 1 is created.
The invention has the beneficial effects that: the invention relates the observation of the same target by using the position incidence relation between the observation point traces of the same target in the adjacent scanning periods of the radar. Setting 4 states for describing the change of the target track, limiting the false target navigation by limiting the length of the track in the state 2, and realizing the automatic navigation and the filtering tracking of the target by constructing a state transition relation. The state transition method selected by the invention reduces the probability of radar false target navigation and improves the success rate of automatic navigation and stable tracking of the real target.
Drawings
FIG. 1 is a schematic diagram of data association between a flight path and observed data at state 3 according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of data association between a flight path and observation data in state 1 according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of data association between a flight path and observed data in state 2 according to an embodiment of the present invention;
fig. 4 is a state transition diagram and a corresponding state transition process diagram according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings and examples.
Several objects referred to in this embodiment include:
the target is as follows: refers to the object for which the method is directed, in particular an object having a direction and a speed of movement.
And (3) observation: reference is made to the target location data obtained by the sensor. Assuming that m targets exist on a two-dimensional plane, n observations are obtained through sensor detection. Due to sensor accuracy and environmental interference, there are three relationships between m and n:
the number of m-n targets is equal to the number of observations
The number of m > n targets is greater than the number of observations
The number of m < n targets is less than the number of observations
And (3) observation period: the period of receiving sensor data is generally equal to the scan period of the sensor.
Track: the moving route of the target is the track due to the existence of the moving direction and the moving speed. The track at least has three attributes of course, speed and track length. The track length here, i.e. the number of observations which, by judgment, already belong to the track, is unambiguous.
And (3) track state: the term is self-defined in the invention, that is, a flight path is in a development stage, and each stage has different characteristics, so that different flight path states are defined and distinguished. For convenience of use, the track state numbers represented by Arabic numerals are used for representing the states, and a total of 4 states are states 1, 2, 3 and 4.
Track data sheet: the term is self-defined by the invention, and a plurality of tracks are stored together to form a track list. Each piece of data in the table corresponds to the attributes of the course, the speed and the like of one track.
Observation data table: the term is self-defined by the invention, and all observations in a certain period are stored together to form an observation data table. Each piece of data in the table corresponds to the attributes of the position, the size and the like of one observation. The data in the table is updated according to the observation period.
And (3) state transition: the attribute of the flight path changes from time to time, and because each state has its own characteristic, when the flight path attribute meets the characteristic of another flight path state, the state of the flight path will be transferred, and the state number of the flight path changes.
Data association: that is, the process of checking whether an observation belongs to a track or not, that is, whether the observation and the track are the same target, is performed by using certain rules.
Associating the circles: one concept in data association is a circle centered at a point and having an association radius as a radius.
Associating sectors: one concept in data association is a sector centered at a point and having an association radius as a radius.
The scheme of the embodiment is as follows: updating an observation database with the sensor data; inputting a new observation data in the observation database for data association, wherein the observation needs to be judged by data association with all tracks in the track data sheet; according to the result of the state transition, each flight path carries out the state transition; and processing the observation which is not associated with any flight path in the observation period. And adding a new item, namely the flight path with the state number of 1, into the flight path data table, and filling the flight path item with the unassociated observed information. And if the number of the unassociated observations is larger than 1, establishing a track project with the state number of 1 for each observation. The ultimate goal is to maintain a track data table.
The embodiment specifically comprises the following steps:
an automatic target track starting and tracking positioning method comprises the following steps:
step 1, acquiring observation data, associating each observation data with track data, and determining whether the observation data and the track data are from the same target;
step 1.1, four track states are determined:
in the state 1, the length of track data is equal to 1;
state 2, track data length in interval [2, N0]Internal;
state 3, track data Length>N0And the number of the lost observation data is equal to 0;
state 4, track Length>N0And the number of the lost observation data is within the interval (0, n)0]And (4) the following steps. (Bu take 0)
Wherein N is0As track length threshold, n0A length threshold for missing observation data.
The data format definition of a single track is as follows:
status number Course of course Speed of flight Current position Length of flight path Data of Number of lost cycles
The "data" here is the stored observation data that has been associated with the flight path. "current position", the latest position of the target is recorded. The number of lost cycles is recorded, the number of times of occurrence of the event that a certain track is not related to any observation in a certain observation period is recorded, and the event is in the interval [0, n ]0]An internal value.
Step 1.2, performing data association on the flight path in the state 3 and observation data:
as shown in fig. 1, all new observations in the present period are projected onto the plane where the state three-association circle is located, all observations in the state three-association circle are compared, and one observation closest to the center of the circle is associated with the target point at the center of the circle;
the circle center of the state three-association circle is P point and extends V along the course CfilterT, radius of the associated circle R ═ I1·1+I2·2+R0，
Wherein P is the latest point position of the current track, VfilterIs the navigational speed, t is the radar scanning period,1and2respectively a distance observation error and an azimuth observation error I1And I2Respectively a distance error coefficient and an orientation error coefficient, R0Is the smallest associated circle radius.
Step 1.3, performing data association on the flight path in the state 4 and observation data:
projecting all new observations in the period to a plane where the state four-association circle is located, comparing all the observations in the state four-association circle, and associating the observation closest to the circle center with a target point at the circle center;
the center of the state four-association circle is P point and extends V along the course Cfilter(number of lost cycles +1) · radar scan cycle, radius of associated circle R ═ I1·1+I2·2+R0，
Wherein P is the latest point position of the current track, VfilterThe speed of the aircraft is the size of the navigational speed,1and2respectively a distance observation error and an azimuth observation error I1And I2Respectively a distance error coefficient and an orientation error coefficient, R0The loss period is an artificial empirical parameter for the smallest associated circle radius.
Step 1.4, performing data association on the flight path in the state 1 and observation data:
as shown in fig. 2, the observation data in this period are projected to the plane where the state-associated circle is located, all the observations in the state-associated circle are compared, and one observation closest to the center of the circle is selected to be associated with the target point at the center of the circle;
the circle center of the first state association circle is the current position of a track, and the radius is the product of the cruising speed of the unmanned ship and the radar scanning period; the cruising speed of the unmanned boat in the embodiment is 20 knots.
Step 1.5, performing data association on the flight path in the state 2 and observation data:
as shown in fig. 3, the observation data in this period are projected to the plane where the state two associated sector is located, all the observations in the state two associated sector are compared, and one observation closest to the center of the circle is selected to be associated with the target point at the center of the circle;
the circle center of the two-state associated sector is the current position of the track, the associated radius is the sector radius, the sector radius is the product of the speed of the target corresponding to the track and the radar scanning period, the sector direction is the course direction of the track, and the sector angle delta is-N.V + delta0Wherein N is a positive coefficient, V is the speed, and Delta0Is the minimum fan angle.
Step 2, performing state transition according to the correlation result of the flight path and the observation data, as shown in fig. 4, specifically including the following processes:
process 1: if the track is in the state 1, if a new observation is associated, the length of the track is changed into 2, the track meets the characteristics of the track in the state 2, and the track state is changed into 2; and simultaneously updating the parameters of the flight path.
And (2) a process: if the track is in the state 1, deleting the item in the track data sheet if the new observation is not associated, namely destroying the track;
and 3, process: if the track is in the state 2, deleting the item in the track data sheet if the new observation is not associated, namely destroying the track;
and 4, process: if the flight path is in the state 2, if the new observation is associated, the length of the flight path is added with 1; and further judging whether the track length is still in the interval [2, N ] after being increased0]If yes, the state number is not changed; and simultaneously updating the parameters of the flight path.
And (5) a process: if the flight path is in the state 2, if the new observation is associated, the length of the flight path is added with 1; judging whether the flight path length is greater than N0If yes, the characteristic of the state 3 is met, and the state number of the flight path is changed into 3; and simultaneously updating the parameters of the flight path.
And 6, a process: if the flight path is in the state 3, if a new observation is associated, the number of lost cycles is 0, the flight path accords with the characteristics of the flight path in the state 3, and the state number is kept unchanged;
and (7) a process: if the track is in the state 3, if the new observation is not associated, adding 1 to the number of lost cycles; and further determines that the number of lost cycles is still within the interval (0, n)0) If yes, and the characteristic of the state 4 is met, the state number is changed into 4;
and (8) a process: if the track is in the state 4, and the number of lost cycles returns to zero if the track is associated with a new observation, the track conforms to the characteristics of the state 3, and the state number is changed into 3;
and a process 9: if the track is in the state 4, if the new observation is not associated, adding 1 to the number of lost cycles; and further determines whether the number of lost cycles is still within the interval (0, n)0]If yes, the characteristic of the state 4 is met, and the state number is unchanged;
the process 10: if the track is in the state 4, if the new observation is not associated, adding 1 to the number of lost cycles; and further determining whether the number of lost cycles is greater than n0If yes, and the item does not accord with the characteristics of the state 4, deleting the item in the track data table, namely destroying the track.
And 6 and 8, after changing the track state number, acquiring and updating the course and the speed of the track by using a filtering algorithm according to the new observed coordinates.
And 3, establishing a new flight path for the observation data which is not associated with any flight path in an observation period according to the observation data which is not associated with any flight path. And when a track is newly established according to the observation data which is not associated with any track, establishing the track with the state number of 1.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.

Claims (7)

1. An automatic target track starting and tracking positioning method is characterized by comprising the following steps:
acquiring observation data, associating each observation data with track data, and determining whether the observation data and the track data are from the same target; carrying out state transition according to the correlation result of the flight path and the observation data; establishing a new track according to observation data which is not associated with any track in an observation period;
the data association of each observation data and each flight path data and the determination of whether the observation data and the flight path data come from the same target comprise:
four track states are determined:
in the state I, the length of track data is equal to 1;
state two, track data length in interval [2, N0]Internal;
state three, navigationTrace data length>N0And the number of the lost observation data is equal to 0;
state four, track data length>N0And the number of the lost observation data is within the interval (0, n)0]Internal;
wherein N is0For track data length threshold, n0A length threshold for missing observation data;
the state transition specifically includes the following processes according to the correlation result of the flight path and the observation data:
process 1: if the flight path in the state I is associated with a new observation, the data length of the flight path is changed into 2, the flight path meets the characteristics of the flight path in the state II, and the flight path state is changed into 2;
and (2) a process: if the flight path is in the state I, if the new observation is not associated, destroying the flight path;
and 3, process: if the flight path is in the state II, the flight path is destroyed if the new observation is not associated;
and 4, process: if the flight path is in the state two, if the new observation is associated, adding 1 to the length of the flight path data; and further judging whether the track data length is still in the interval [2, N ] after being increased0]If yes, the state number is not changed;
and (5) a process: if the flight path is in the state two, if the new observation is associated, adding 1 to the length of the flight path data; judging whether the length of the flight path data is greater than N0If yes, the characteristic of the state three is met, and the state number of the flight path is changed into 3;
and 6, a process: if the flight path in the state III is associated with a new observation, the number of lost cycles is 0, the flight path conforms to the characteristics of the flight path in the state III, and the state number is kept unchanged;
and (7) a process: if the flight path is in the state three, if the flight path is not associated with new observation, adding 1 to the number of lost cycles; and further determines that the number of lost cycles is still within the interval (0, n)0) If yes, and the state number is changed to 4 according to the characteristics of the state four;
and (8) a process: if the track is in the state four, and the number of lost cycles returns to zero if the track is associated with a new observation, the track conforms to the characteristics of the state three, and the state number is changed into 3;
and a process 9: if the track is in the state four, if the new observation is not associated, adding 1 to the number of lost cycles; and further determines whether the number of lost cycles is still within the interval (0, n)0]If yes, the feature of the state four is met, and the state number is unchanged;
the process 10: if the track is in the state four, if the new observation is not associated, adding 1 to the number of lost cycles; and further determining whether the number of lost cycles is greater than n0If yes, the flight path is destroyed if the state is not consistent with the characteristics of the state four.
2. The method for automatically starting track and tracking and positioning target according to claim 1, wherein the data association of each observation data and each track data is performed to determine whether the observation data and the track data come from the same target, and the data association of the track in the third state and the observation data is further performed, and the specific method is as follows:
projecting all new observations in the period to a plane where a state three-correlation circle is located, comparing all the observations in the state three-correlation circle, and associating an observation closest to the circle center with a target point at the circle center;
the circle center of the state three-association circle is P point and extends V along the course CfilterT, radius R ═ I of the associated circle1·1+I2·2+R0，
Wherein P is the latest point position of the current track, VfilterIs the navigational speed, t is the radar scanning period,1and2respectively a distance observation error and an azimuth observation error I1And I2Respectively a distance error coefficient and an orientation error coefficient, R0Is the smallest associated circle radius.
3. The method for automatically starting track and tracking and positioning target according to claim 1, wherein the data association of each observation data and each track data is performed to determine whether the observation data and the track data come from the same target, and the data association of the track in the state four and the observation data is further performed, and the specific method is as follows:
projecting all new observations in the period to a plane where a state four-association circle is located, comparing all the observations in the state four-association circle, and associating an observation closest to the circle center with a target point at the circle center;
the circle center of the state four-association circle is P point and extends V along the course Cfilter(number of lost cycles +1) · radar scan cycle, radius R ═ I of the associated circle1·1+I2·2+R0，
Wherein P is the latest point position of the current track, VfilterThe speed of the aircraft is the size of the navigational speed,1and2respectively a distance observation error and an azimuth observation error I1And I2Respectively a distance error coefficient and an orientation error coefficient, R0The number of lost cycles is a human empirical parameter for the minimum associated circle radius.
4. The method for automatically starting track and tracking and positioning target according to claim 1, wherein the data association of each observation data and each track data is performed to determine whether the observation data and the track data come from the same target, and the data association of the track in the first state and the observation data is further performed, and the specific method is as follows:
projecting the periodic observation data to a plane where the state-associated circle is located, comparing all observations in the state-associated circle, selecting an observation closest to the circle center, and associating the observation with a target point at the circle center;
the circle center of the state-I association circle is the current position of a track, and the radius is the product of the cruising speed of the unmanned ship and the radar scanning period;
the unmanned boat cruising speed includes 20 knots.
5. The method for automatically starting track and tracking and positioning target according to claim 1, wherein the data association of each observation data and each track data is performed to determine whether the observation data and the track data come from the same target, and the data association of the track in the second state and the observation data is further performed, and the specific method is as follows:
projecting the periodic observation data to a plane where the state two-association sector is located, comparing all observations in the state two-association sector, and selecting an observation closest to the circle center to be associated with a target point at the circle center;
the circle center of the second correlation sector is the current position of the flight path, the correlation radius is the sector radius, the sector radius is the product of the speed of the target corresponding to the flight path and the radar scanning period, the sector direction is the course direction of the flight path, and the sector angle delta is-N.V + delta0Wherein N is a positive coefficient, V is the speed, and Delta0Is the minimum fan angle.
6. The method for automatically starting track and tracking and positioning target according to claim 1, wherein the method comprises the following steps: and after the track state number is changed in the process 6 and the process 8, the course and the speed of the track are obtained and updated by using a filtering algorithm according to the new observed coordinates.
7. The method for automatically starting track and tracking and positioning target according to claim 1, wherein the method comprises the following steps: and when a track is newly established according to the observation data which is not associated with any track, establishing a track with a state number of 1.
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