CN110196409A - A kind of robust asynchronous track association method based on regional ensemble relative distance - Google Patents
A kind of robust asynchronous track association method based on regional ensemble relative distance Download PDFInfo
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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
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Abstract
The invention discloses a kind of Data Associations based on regional ensemble relative distance, are a kind of robust Multisensor Asynchronous Track correlating methods.The present invention analyzes influence of the time-varying system error to track, it is described with gray area.It by the ensemble of Asynchronous Track part, defines the center of fan-shaped region and estimates, and then acquire region relative distance and regional ensemble relative distance, the similarity between track is sought by comparing regional ensemble relative distance, track association is realized with this.
Description
Technical field
The present invention relates to a kind of asynchronous track association methods of multisensor.
Background technique
In Distributed Multi-sensor System information fusion process, track association is a crucial ring, it is intended to which judgement is not from
Which track with sensor comes from same target.Since there are the radar asynchronous, communication delay of booting and radar sampling periods
Situations such as not equal, the track from different sensors be often it is asynchronous, this considerably increases the difficulty of track association.Furthermore thunder
It is influenced up to when detecting target by systematic error, measures track value and deviated compared with target actual position larger, cause to navigate
Mark association is more difficult.
The problem asynchronous for track, current main solution be first pass through time domain registration track is registered to it is same
Moment is associated using classical Data Association.But during time domain registration, such method can introduce new
Evaluated error, with the accumulation of time, evaluated error constantly increases, thus will lead to correct association rate occur it is a degree of under
Drop.Meanwhile systematic error also will lead to method performance and sharply decline.Therefore, there is an urgent need to a kind of more preferable method come solve it is asynchronous,
Track association problem in the case of big systematic error.
Summary of the invention
In order to overcome the drawbacks of the prior art, the invention discloses a kind of robust based on regional ensemble relative distance is asynchronous
Data Association.The present invention analyzes influence of the time-varying system error to track, it is described with gray area.Define area
The concepts such as absolute distance, measure of domain and region relative distance between domain, by by track regional ensemble, obtaining with for the moment
Between track set in section, and then acquire the relative distance between regional ensemble, track association realized with this.
In order to achieve the object of the present invention, the present invention provides a kind of asynchronous boats of the robust based on regional ensemble relative distance
Mark correlating method.The Data Association, comprising the following steps:
Step 1 is respectively combined the track value of every radar in a fusion cycle to form track set；
Step 2 calculates to obtain fan-shaped target area according to track value and radar system error range；
Step 3 seeks the relative distance between the track set from different radarsWherein, dihFor
Radar m the target area that the i moment detects it is opposite with region of the radar n between the target area that the h moment detects away from
From s is track value number in radar m track set in fusion cycle；
Step 4 calculates the gray relation grades between track, and then realizes track association.
Further, the number of track value is not identical in the track set of every radar in step 1.When number is not identical
When, the case where this method can be applied directly to asynchronous track association.
Further, region relative distance described in step 3 are as follows:Wherein, d is in radar m, n gray area
Euclidean distance between the heart, σm、σnGray area respectively from radar m, n gray area is estimated；The gray area center is sector
The midpoint of target area angular bisector；The gray area is estimated to put the maximum value of distance in gray area center to gray area.It is logical
Good performance can be kept in the biggish situation of radar-probing system error by crossing the above method.
Compared with prior art, the invention has the following beneficial effects:
1. anti-systematic error performance is good.Correlating method in the present invention retouches influence area of the systematic error to track
It states, and the similarity between track is indicated according to interregional relative distance relationship, there is stronger anti-systematic error performance.
2. strong applicability.In the present invention, suitable for the association under any asynchronous condition, and method accuracy is high, and time-consuming is few,
Without predicting asynchronous speed ratio.
Detailed description of the invention
Fig. 1 is the asynchronous schematic diagram of track.
Fig. 2 is that there are gray area schematic diagrames for target actual position.
Fig. 3 is target area relative positional relationship schematic diagram.
Specific embodiment
Present invention is further described in detail with reference to the accompanying drawings and examples.
Assuming that there is M (M >=2) portion radar in Distributed Multi-sensor System, there is N in target areasCriticize target.Due to thunder
The booting reached is asynchronous and the sampling period is different, cause the track from different radars be often it is asynchronous, as shown in Figure 1.
The track set Γ that radar detection target obtains are as follows:
Γm={ Γm(1),Γm(2),...,Γm(i),...,Γm(k) }, i=1,2 ..., k. (1)
M-th of radar, Γ in m expression system in formulam(i) indicate that radar m detects the track collection that target obtains at the i moment
It closes.
Step 1 calculates target actual position domain of the existence:
Cartesian coordinate system is established using radar m as origin, it is assumed that the errors of the distance measurement system of radar m, angle measuring system error are Target actual position is (r, θ), and target measuring value isRandom error isRadar measurement value is
The influence of error of uniting and random error, and there are certain deviations for target actual position, therefore target measuring value may be expressed as:
Radar range finding, errors of the distance measurement system have transformable range, deposit then can extrapolate target actual position accordingly
Region.Assuming that the ranging of radar m, angle measuring system error range are (0, Δ rm)、(0,Δθm), and drawn by random error
The target position variation risen is smaller, ignores here.Therefore target actual position domain of the existence are as follows:
Fig. 2 is to be the target actual position extrapolated according to aim parameter measured value and systematic error range there are gray area,
Hereinafter referred target area.(do not consider hereinafter) in the case where not considering random error, target must be in the region.
Track set Γ can be converted into regional ensemble T through the above way, it may be assumed that
Tm={ Tm(1),Tm(2),...,Tm(i),...,Tm(k) }, i=1,2 ..., k. (4)
T in formulam(i) the target area set of all track values of i moment radar detection is indicated.
Step 2 calculates gray area center between different radars, absolute between gray area according to the gray area thresholding
Distance, gray area estimates and region relative distance:
Take the target area center of radar m are as follows:
Defining the Euclidean distance between radar m and the target area center of radar n is two interregional absolute distances, will
Two radar measurement values are transformed into the same coordinate system, then interregional absolute distance are as follows:
In formula, xom、yomAnd xon、yonThe target area centre coordinate of respectively two radars.
When target area size is fixed, two interregional absolute distances are bigger, and two target areas are also remoter；Conversely, more
Closely.And the positional relationship in two regions is also related with target area size, and when interregional one timing of absolute distance, region is bigger, and two
Region is also closer；Conversely, remoter.
The concept that definition region is estimated, taking the Euclidean distance maximum value of any point to regional center in region is region survey
Degree.It asks to be equivalent to apart from maximum value and seeks one and target area can be covered by the smallest circle in the center of circle of regional center, and target
The boundary in region is using radar fix as the circular arc in the center of circle, and radian is far smaller than the radian for covering circle.Therefore measure of domain is i.e.:
In formula, O is regional center point, and A, B, C, D are four endpoints of target area.
Definition region relative distance are as follows:
In formula, Euclidean distance of the d between radar m, n gray area center, σm、σnRespectively from radar m, n gray area
Gray area is estimated.
Region relative distance can state the relative positional relationship of target area to a certain extent, as shown in Figure 3.Region phase
It adjusts the distance bigger, two regions are remoter；Conversely, closer.
Relative distance is gathered in step 3, zoning:
Assuming that the sampling period of radar m and radar n is different, then the track target area in the same period in track set
Domain number is also just different.As shown in formula (9), formula (10):
Localized target regional ensemble T from radar mmAre as follows:
In formula,It indicates to gather in the target area that j+i moment radar m is detected.
Localized target regional ensemble T from radar nnAre as follows:
In formula,It indicates to gather in the target area that k+h moment radar n is detected.
In the same periodIn each track set target area number for including be respectively s, l, and s <
l.Element number is different in set, can not correspond and compare, therefore one concept of definition region set relative distance.
Definition region set relative distance are as follows:
In formula, dihIndicate the target area from radar mWith the target area from radar nIt
Between relative distance.
Regional ensemble relative distance is able to reflect the relative positional relationship between two regional ensembles.Regional ensemble relative distance is got over
Greatly, two set are remoter；Conversely, closer.For the local tracks set from different sensors, relative distance is got between two set
Small, two set similarities are higher, and the probability from same target is also bigger.
Step 4 calculates to obtain relative distance matrix between regional ensemble:
In g-th of fusion cycle, a track from radar n and the track set Γ from radar m are fetchedm(g) group
At decision matrix Ψ, it may be assumed that
In formula,The target area centre coordinate of the c articles track detected for radar m in the j+i moment, σmc(j+
I) target area for the radar m the c articles track detected in the j+i moment is estimated；It is detected for radar n in the k+h moment
The f articles track target area centre coordinate, σnfIt (k+h) is the target area of the radar n the f articles track detected in the k+h moment
Estimate in domain；And s ≠ l.
N+1 row is compared with preceding N row respectively, relative distance between regional ensemble is calculated in the above method, constitutes
Regional ensemble relative distance matrix, it may be assumed that
In formula, κfcIndicate opposite between the f articles track regional ensemble of radar n and the c articles track regional ensemble of radar m
Distance.
Step 5, zoning set between grey relation coefficient:
The grey relation coefficient between the f articles of track of radar n and the c articles track of radar m is calculated, that is:
In formula, ρ ∈ (0,1) is resolution ratio, generally takes 0.5.
ξfcThe similarity of two tracks, ξ are directly reactedfcBigger, probability of two tracks from same target is also bigger.
For track association problem, maximum similarity criterion is taken, ifTrack f and track c is then adjudicated from same
Target.
Embodiment
Assuming that two 2D radars of strange land configuration detect same target area, target number is 5 in region.With thunder
It is that origin establishes cartesian coordinate system up to A, radar A coordinate is (0,0), and radar B coordinate is (100km, 0).The sampling week of radar m
Phase is 1.4s, and the sampling period of radar n is 0.6s.Target initiation region is the rectangular area of 20km × 20km, and target is initially transported
Dynamic speed is to be randomly generated in 200~400m/s, and target inceptive direction is randomly dispersed in 0~2 π rad.Information fusion cycle is
4.2s.The errors of the distance measurement system maximum value of radar A is 0.5km, and errors of the distance measurement system maximum value is 0.5rad, the ranging of radar B
Systematic error maximum value is -0.5km, and angle measuring system max value of error is -0.5rad.Similarity is calculated using this patent method,
It is associated to respectively from the track for carrying out two radars.
Using correlating method proposed by the present invention, above-mentioned design requirement can be implemented by following technical measures.
The data from two radars are read first:
Γm={ Γm(1),Γm(2),...,Γm(i),...,Γm(k)} (17)
Γn={ Γn(1),Γn(2),...,Γn(j),...,Γn(l)} (18)
Wherein, Γm(i) the track set that radar m is detected in moment i, Γ are indicatedn(j) indicate what radar n was detected in moment j
Track set.
Data prediction is carried out to GPR Detection Data, obtains the systematic error gray area center and area of each track value
Estimate in domain, it may be assumed that
Systematic error gray area center:
The coordinate of four terminal As of radar m gray area, B, C, D:
Xm=Rm·Φm1 Ym=Rm·Φm2 (21)
In formula,
The coordinate of radar n gray area four endpoints E, F, G, H:
Xn=Rn·Φn1 Yn=Rn·Φn2 (22)
In formula,
The gray area of radar m, n are estimated are as follows:
Obtain regional center coordinate set and measure of domain set, it may be assumed that
Ox={ Ox(1),Ox(2),...,Ox(i),...,Ox(k)}
Oy={ Oy(1),Oy(2),...,Oy(i),...,Oy(k)} (25)
Ωσ={ Ωσ(1),Ωσ(2),...,Ωσ(i),...,Ωσ(k)}
Fusion cycle is 4.2s, and the track number of radar m is 3 in each period, and the track number of radar n is 7.
According to fusion cycle, track is divided into several track set, it, will be from radar m's in the 1st fusion cycle
Article 5 track and the track set Γ from radar nn(1) decision matrix Ψ is formed, it may be assumed that
In formula, before be regional center coordinate, unit be × 104M, behind be measure of domain, unit m.
It is defined according to regional ensemble relative distance and region relative distance, it may be assumed that
Calculate to obtain regional ensemble relative distance matrix are as follows:
The gray relation grades ζ between track is calculated according to region correlation matrixlj, it may be assumed that
Calculate degree of association matrix is, it may be assumed that
Track association takes maximum region gray relation grades principle, evenTherefore then think to navigate from radar
The Article 5 track of mark n and the Article 5 track association from radar track m.
Claims (3)
1. a kind of robust asynchronous track association method based on regional ensemble relative distance, which comprises the following steps:
Step 1 is respectively combined the track value of every radar in a fusion cycle to form track set；
Step 2 calculates to obtain fan-shaped target area according to track value and radar system error range；
Step 3 seeks the relative distance between the track set from different radarsWherein, dihFor radar m
In the region relative distance of the target area that the i moment detects and radar n between the target area that the h moment detects, s is
Track value number in radar m track set in fusion cycle；
Step 4 calculates the gray relation grades between track, and then realizes track association.
2. the robust asynchronous track association method according to claim 1 based on regional ensemble relative distance, feature exist
In the number of track value is not identical in the track set of every radar in step 1.
3. the robust asynchronous track association method according to claim 1 or 2 based on regional ensemble relative distance, feature
It is, region relative distance described in step 3 are as follows:Wherein, d is European between radar m, n gray area center
Distance, σm、σnGray area respectively from radar m, n gray area is estimated；The gray area center is that fan-shaped target area angle is flat
The midpoint of separated time；The gray area is estimated to put the maximum value of distance in gray area center to gray area.
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