A Class of Correlation Based Tracking Algorithms for BOC signals
Research Article Models and Algorithms for Tracking Target ...Research Article Models and Algorithms...
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Research ArticleModels and Algorithms for Tracking Target withCoordinated Turn Motion
Xianghui Yuan, Feng Lian, and Chongzhao Han
Ministry of Education Key Laboratory for Intelligent Networks and Network Security (MOE KLINNS),School of Electronics and Information Engineering, Xiβan Jiaotong University, Xiβan 710049, China
Correspondence should be addressed to Feng Lian; [email protected]
Received 23 July 2013; Accepted 15 December 2013; Published 12 January 2014
Academic Editor: Shuli Sun
Copyright Β© 2014 Xianghui Yuan et al.This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Tracking target with coordinated turn (CT) motion is highly dependent on the models and algorithms. First, the widely usedmodels are compared in this paperβcoordinated turn (CT)modelwith known turn rate, augmented coordinated turn (ACT)modelwith Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circularmotion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared andthe suggestions on the choice of models for different practical target tracking problems are given. Finally, in the multiple models(MM) framework, the algorithm based on expectation maximization (EM) algorithm is derived, including both the batch formand the recursive form. Compared with the widely used interacting multiple model (IMM) algorithm, the EM algorithm shows itseffectiveness.
1. Introduction
Theproblem of tracking a single target with coordinated turn(CT) motion is considered. The motion of a civil aircraft canusually be modeled as moving by constant speed in straightlines and circle segments. The former is known as constantvelocity (CV)model and the latter is coordinated turnmodel.In tracking applications, only the position part of the state canbe measured by the sensor and the turn rate π is often un-known. So the measurement data can be seen as the incom-plete data. This is a resource-constrained problem for track-ing target with coordinated turn motion.
CTmodel is highly dependent on the choice of state com-ponents [1]. The turn rate π can be augmented in the CTmodel, called ACT model. There are two types of ACT mod-els: ACTmodel with Cartesian velocity and ACTmodel withpolar velocity. The state vectors are [π₯, π¦, οΏ½ΜοΏ½, Μπ¦, π] and[π₯, π¦, V, π, π], respectively. The two are both nonlinear mod-els and have been compared in [2, 3] based on EKF. ForunscentedKalman filter (UKF) is a very efficient tool for non-linear estimation [4, 5], here the two models are comparedbased on UKF.
When the target with CT motion has a constant speed, itsatisfies a kinematic constraint:πβ π΄ = 0, whereπ is the target
velocity vector and π΄ is the target acceleration vector. If thedynamic model incorporates the constraint directly, it willbecome a highly nonlinear one. To avoid this nonlinearity, thekinematic constraint was incorporated into a pseudomea-surement model [6β8].
A maneuver-centered model is introduced in [9]. Thestate components are [π, π, π].Themodelβs state equation hasa linear form, but its measurement equation is pseudolinearbecause the noise covariance is actually state dependent [10].The center of the turn should be accurately determined,which is inherently a nonlinear problem.
Target dynamic models and tracking algorithms haveintimate ties [1]. In the single model tracking framework, thetracking algorithms are interpreted and compared.
The interactingmultiplemodel (IMM) approach has beengenerally considered to be the mainstream approach tomaneuvering target tracking. It utilizes a bank of π Kalmanfilters, each designed tomodel a differentmaneuver [11]. IMMalgorithm is a suboptimal algorithm based on the minimummean square error (MMSE) criterion.Under theMMSE crite-rion, to get the optimal estimation of the target state, the com-putational load grows exponentially when the measurementsare increasing. In recent years, tracking target based onmaxi-mum a posteriori (MAP) criterion has received a lot of
Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2014, Article ID 649276, 10 pageshttp://dx.doi.org/10.1155/2014/649276
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2 Mathematical Problems in Engineering
interest [12β17]. Expectation maximization (EM) algorithmis the state estimation approach based on MAP criterion. Using EMalgorithm, the computational load grows linearly dur-ing per iteration and the optimal estimation based on MAPcriterion can be achieved finally.
The existing EM algorithm to track maneuvering targetcan be classified into two categories: one formulates themaneuver as the unknown input [12β14] and the other for-mulates themaneuver as the systemβs process noise [15]. Aim-ing at the problem to track a target with CTmaneuver, an EMalgorithm is presented. The maneuver is formulated by theturn rate. First, the turn rate sequence is estimated using theEM algorithm. Then, with the estimated turn rate sequence,the target state sequence is estimated accurately.
The rest of this paper is organized as follows. Section 2presents all the CTmodelsβ state equations and measurementequations.The tracking algorithms based on single model areinterpreted in Section 3; the simulations are also presented. InSection 4, the batch and recursive EM algorithms are derivedand compared with the IMM algorithm in simulation.Section 5 provides the paperβs conclusions.
2. Dynamic Models for CT Motion
A maneuvering target can be modeled by
ππ+1
= ππ(ππ) + π€π,
π§π= βπ(ππ) + ππ,
(1)
where ππand π§
πare target state and observation, respec-
tively, at discrete time π‘π; π€πand π
πare process noise and
measurement noise sequences, respectively; ππand β
πare
vector-valued functions.
2.1. CT Model with Known Turn Rate. The coordinated turnmotion can be described by the following equation:
ππ+1
= πΉ (ππ)ππ+ π€π. (2)
The measurement equation is:
π§π= π»CTππ + ππ. (3)
The components of state areπ = [π₯ οΏ½ΜοΏ½ π¦ Μπ¦]. ππstands
for the turn rate in time π.Where
πΉ (ππ) =
[[[[[[[[[[[[
[
1
sin (πππ)
ππ
0 β
1 β cos (πππ)
ππ
0 cos (πππ) 0 β sin (π
ππ)
0
1 β cos (πππ)
ππ
1
sin (πππ)
ππ
0 sin (πππ) 0 cos (π
ππ)
]]]]]]]]]]]]
]
π€ = [π€π₯π€π¦]
πΈ [π€π] = 0, πΈ [π€
ππ€
π] = πCTπΏππ.
(4)
Assume only position could be measured, where
π»CT = [1 0 0 0
0 0 1 0]
πΈ [ππ] = 0, πΈ [π
ππ
π] = π πΏ
ππ.
(5)
This model assumes that the turn rate is known or couldbe estimated. When the range rate measurements are avail-able, the turn rate could be estimated by using range ratemea-surements [18, 19]. The tracking performance will be deteri-orated when the assumed turn rate is far away from the trueone.Thismodel is usually used as one of themodels in amul-tiple models framework.
2.2. ACT Model with Cartesian Velocity. In this model, thestate vector is chosen to be π = [π₯, π¦, οΏ½ΜοΏ½, Μπ¦, π]; the state spaceequation can be written as
ππ+1
= πACT1 (ππ) + πΊACT1π€π, (6)
where
πACT1 (π) =
[[[[[[[[[[[[[[
[
π₯ +
οΏ½ΜοΏ½
π
sin (ππ) βΜπ¦
π
(1 β cos (ππ))
π¦ +
οΏ½ΜοΏ½
π
(1 β cos (ππ)) +Μπ¦
π
(sin (ππ))
οΏ½ΜοΏ½ cos (ππ) β Μπ¦ sin (ππ)
οΏ½ΜοΏ½ sin (ππ) + Μπ¦ cos (ππ)
π
]]]]]]]]]]]]]]
]
, (7)
πΊACT1 =[[[[
[
π2
2
0 π 0 0
0
π2
2
0 π 0
0 0 0 0 1
]]]]
]
, (8)
π€ = [π€π₯π€π¦π€π]
, (9)
πΈ [π€π] = 0, πΈ [π€
ππ€
π] = πACT1πΏππ. (10)
Assume only position could be measured, the measure-ment equation can be written as
π§π= π»ACT1ππ + ππ, (11)
where
π»ACT1 = [1 0 0 0 0
0 1 0 0 0] , (12)
πΈ [ππ] = 0, πΈ [π
ππ
π] = π πΏ
ππ. (13)
2.3. ACT Model with Polar Velocity. This modelβs state vectorisπ = [π₯, π¦, V, π, π], and the dynamic state equation is givenby
ππ+1
= πACT2 (ππ) + πΊACT2π€π, (14)
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Mathematical Problems in Engineering 3
where
πACT2 (π) =
[[[[[[[[[[[
[
π₯ + (
2Vπ
) sin(ππ2
) cos(π + ππ2
)
π¦ + (
2Vπ
) sin(ππ2
) sin(π + ππ2
)
V
π + ππ
π
]]]]]]]]]]]
]
πΊACT2 =[
[
0 0 π2
0 0
0 0 0
π2
2
π2
]
]
π€ = [π€V π€π]
πΈ [π€π] = 0, πΈ [π€
ππ€
π] = πACT2πΏππ.
(15)
However themeasurement equation is the same as (11) to (13).
2.4. Kinematic Constraint Model. For a constant speed target,the acceleration vector is orthogonal to the velocity vector:
πΆ (π) = π β π΄ = 0, (16)where π is the target velocity vector andπ΄ is the target accel-eration vector.
This kinematic constraint can be used as a pseudom-easurement. The state vector is chosen to be π =[π₯ οΏ½ΜοΏ½ οΏ½ΜοΏ½ π¦ Μπ¦ Μπ¦]
. So the dynamic model is the constantacceleration (CA) model, given by
ππ+1
= πΉππ+ πΊπ€π, (17)
where
πΉ =
[[[[[[[[[[[
[
1 π
π2
2
0 0 0
0 1 π 0 0 0
0 0 1 0 0 0
0 0 0 1 π
π2
2
0 0 0 0 1 π
0 0 0 0 0 1
]]]]]]]]]]]
]
πΊ =
[[[
[
π2
2
π 1 0 0 0
0 0 0
π2
2
π 1
]]]
]
π€ = [π€π₯π€π¦]
πΈ [π€π] = 0, πΈ [π€
ππ€
π] = πCAπΏππ.
(18)
The measurement equation is given byπ§π= π»π₯π+ ππ, (19)
where
π» = [
1 0 0 0 0 0
0 0 0 1 0 0]
πΈ [ππ] = 0, πΈ [π
ππ
π] = π πΏ
ππ.
(20)
The pseudomeasurement is
ππ|π
ππ|π
β π΄π+ ππ= 0, (21)
where ππ|π= [οΏ½ΜοΏ½π|π
Μπ¦π|π]
and π΄π= [οΏ½ΜοΏ½π
Μπ¦π].
ππ|π
is the filtered speed at time π :
ππ|π= βοΏ½ΜοΏ½2
π|π+ Μπ¦2
π|π, (22)
ππβΌ π (0, π
π
π) , (23)
π π
π= π1(πΏ)π+ π0, 0 β€ πΏ < 1, (24)
where π1is chosen to be large for initialization and π
0is chosen
for steady-state conditions.
2.5. Maneuver-Centered CT Model. This modelβs state vectoris given byπ = [π π π]. The process state space equation is
ππ+1
= Ξ¦ππ+ Ξπ€π, (25)
where
Ξ¦ =[
[
1 0 0
0 1 π
0 0 1
]
]
Ξ =[[
[
1 0
0
π
2
0 1
]]
]
π€ = [π€ππ€π]
πΈ [π€π] = 0, πΈ [π€
ππ€
π] = π
ππΏππ.
(26)
Assume the center of the CTmotion is (π₯π, π¦π).The trans-
formation between Cartesian coordinates and maneuver-centered coordinates is given by
π = β(π₯ β π₯π)2
+ (π¦ β π¦π)2
π = tanβ1 (π¦ β π¦π
π₯ β π₯π
) .
(27)
So the measurement equation is given by
π§π= π»πππ+ ππ, (28)
where
π»π= [
1 0 0
0 1 0]
πΈ [πππ
π] = π π= π½πππ π½
ππ.
(29)
π½ππis the Jacobian matrix based on (27), which leads to
π½ππ=[
[
cos π sin π
β sin ππ
cos ππ
]
]
. (30)
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3. Tracking Algorithms ina Single Model Framework
3.1. UKF Filter with ACT Models. If the turn rate is aug-mented to the state vector, it will become a nonlinear pro-blem. The extended Kalman filter (EKF) has been used totrack this kind of motion. Since unscented Kalman filter(UKF) is very suitable for nonlinear estimation [4, 5], here theUKF algorithm is introduced.
(i) Calculate the Weights of Sigma Points
ππ
0=
π
(π + π)
ππ
0
=
π
(π + π)
+ (1 β πΌ2+ π½)π
π
π
= ππ
π=
0.5
(π + π)
, π = 1, 2, . . . 2π,
(31)
where π is the dimension of the state vector. π =πΌ2(π + π ) β π is a scaling parameter. πΌ determines
the sigma points around π₯ and is usually set to a smallpositive value (e.g., 1π β 3). π is a secondary scalingparameter which is usually set to 0, and π½ = 2is optimal forGauss distributions.Where the (β(π + π)π
π₯)πis
the πth row of the matrix square root.
(ii) Calculate the Sigma Points
π0
πβ1|πβ1= ππβ1|πβ1
π(π)
πβ1|πβ1= ππβ1|πβ1
+ (β(π + π) ππ₯)
π
π = 1, 2, . . . , π
π(π)
πβ1|πβ1= ππβ1|πβ1
β (β(π + π) ππ₯)
π
π = π + 1, π + 2, . . . , 2π.
(32)
(iii) Time Update
π(π)
π= ππ(π(π)
πβ1|πβ1) , π = 0, 1, . . . 2ππ
π|πβ1
=
2π
β
π=0
ππ
ππ(π)
πππ|πβ1
=
2π
β
π=0
ππ
π(π(π)
πβ ππ|πβ1
) (π(π)
πβ ππ|πβ1
)
+ πΊππβ1πΊ.
(33)
(iv) Measurement Update. Because we assume the measure-ment equation is linear, the following is just the same as thetraditional Kalman filter:
οΏ½ΜοΏ½π|πβ1
= π»ππ|πβ1
ππ
= π»ππ|πβ1
π»+ π ππΎπ
= ππ|πβ1
π»πβ1
πππ|π
= ππ|πβ1
+ πΎπ(π§πβ π§π|πβ1
) ππ|π
= ππ|πβ1
β πΎππππΎ
π.
(34)
For the cases where the measurement equation is also non-linear, the measurement update can be referred to [10] fordetails.
3.2. Kinematic Constraint Tracking Filter. The Kalman filter-ing equations for processing this kinematic constraint as apseudomeasurement are given below, where the filtered stateestimate and error covariance after the constraint have beenapplied are denoted byππΆ
π|πand ππΆ
π|π, respectively [8].
(i) Time Update
ππ|πβ1
= πΉππΆ
πβ1|πβ1
ππ|πβ1
= πΉππΆ
πβ1|πβ1πΉ+ πΊππβ1πΊ.
(35)
(ii) Measurement Update. The measurement update is thesame as (34).
(iii) Constraint Update
πΎπΆ
π= ππ|ππΆπ
π[πΆπππ|ππΆ
π+ π π
π]
β1
ππΆ
π|π
= [πΌ β πΎπΆ
ππΆπ]ππ|πππΆ
π|π
= [πΌ β πΎπΆ
ππΆπ] ππ|π,
(36)
where
πΆπ=
1
ππ|π
[0 0 ΜΜπ₯π|π
0 0 ΜΜπ¦π|π] . (37)
3.3. Maneuver-Centered Tracking Filter
(i) EstimatingCenter ofManeuver.The center of themaneuvershould be estimated from the measurements. It can be esti-mated through least square method which requires an iter-ative search procedure. The following simple geometricallyoriented procedure of estimating the center was proposed in[9]. The main idea is as follows: if two points are on a circlethen the perpendicular bisector of the chord between thosepoints will pass through the center of the circle.The slope (π)and π¦ intercept (π) of the perpendicular bisector is given by
π =
(π₯1β π₯2)
(π¦2β π¦1)
π =
(π¦1+ π¦2)
2
β π
(π₯1+ π₯2)
2
,
(38)
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Mathematical Problems in Engineering 5
where (π₯1, π¦1) and (π₯
2, π¦2) are the coordinates of the two
points. The center can be given by
π₯π=
(π1β π2)
(π2β π1)
π¦π=
(π1π2β π2π1)
(π1β π2)
.
(39)
(ii) Maneuver Detection. In the absence of a maneuver, thetarget is assumed to be traveling in a straight line andmodeledby a constant velocity (CV)motion. (CVmodel is very simpleand commonly used, which will not be listed here.)When themaneuver is detected, the filter switches to the maneuver-centerCTmodel.While the endof amaneuver is detected, thefilter will then switch back to CV model.
Here a fading memory average of the innovations is usedto detect if a maneuver occurs. The equation is given by
π’π= ππ’πβ1
+ ππ
(40)
with
ππ= ]ππβ1
π]π, (41)
where 0 < π < 1, ]πis the innovation vector, and π
πis its cov-
ariance matrix.π’πwill have a chi-squared distribution with degrees
ππ’= ππ§
1 + π
1 β π
, (42)
where ππ§is the dimension of the measurement vector. When
π’πexceeds a threshold (e.g., 95% or 99% confidence interval),
then a maneuver onset is declared. The end time of a maneu-verwill be determined in a similar fashion.Theprocedure canbe referred to [9] for details.
3.4. Simulation Results
(i)The Scenario.The scenario simulated here is very similar tothat described in [20]. It includes few rectilinear stages andfew CT maneuvers. Four consecutive 180β turns with ratesπ = 1.87,β2.8, 5.6,β4.68 are simulated, respectively, for scans[56, 150], [182, 245], [285, 314], and [343, 379]. The targettrajectory can be seen in Figure 1.
The initial target position and velocities are π0= 60 km,
π0= 40 km, οΏ½ΜοΏ½
0= β172 km, and οΏ½ΜοΏ½
0= 246 km. It is assumed
that the sensor measures Cartesian coordinates π and π di-rectly. It is also assumed thatπ
π= ππ= 100mand the sample
rate π = 1.
(ii) Algorithmsβ Parameters. UKF controlled ACT modelβsparameter:
πΌ = 10β3, π½ = 2, π = 0
πACT1 = diag {1 1 10β4}
πACT2 = diag {1 10β4} .
(43)
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 62
2.5
3
3.5
4
4.5
5
5.5
6
k = 0
Γ104
Γ104
Y(m
)
X (m)
Figure 1: The test trajectory.
Kinematic constraint modelβs parameter:
πCA = diag {1 1}
πΏ = 0.92, π0= 1, π
1= 200.
(44)
Maneuver centered modelβs parameter:
ππ= diag {106 10β4}
π = 0.8.
(45)
(iii) Results. The four models are listed as follows.
Method 1: ACT model with Cartesian velocity.Method 2: ACT model with polar velocity.Method 3: kinematic constraint model.Method 4: maneuver-centered CT model.
Rootmean squared errors (RMSE) are used here for com-parison. The RME position errors are defined as follows:
RMS.P.E (π) = β 1π
π
β
π=1
[(π₯π
πβ π₯π
π)2
+ (π¦π
πβ π¦π
π)2
] , (46)
whereπ = 200 are the Monte-Carlo simulation runs. π₯ππand
π¦π
πstand for the true position, while π₯π
π|πand π¦π
πare the posi-
tion estimates.The RMS position errors of all but the first ten are shown
in Figure 2.Table 1 summarizes the average RMS of the position
errors.Table 2 summarizes the relative computational complex-
ity, normalized to method 4.It can be seen from the figure and tables thatmethod 2 has
the best performance and its computational load is roughly
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0 50 100 150 200 250 300 350 40050
100
150
200
250
300
350
Time (s)
RMS
posit
ion
erro
rs (m
)
Method 4
Method 3
Method 2
Method 1
Figure 2: RMS position errors of the four methods.
Table 1: Average RMS of position errors.
Method Average RMS of position errors (m)1 94.262 81.573 109.514 183.46
Table 2: Relative computational load.
Method Relative computational load1 7.262 7.073 1.424 1
the same as method 1. So we can conclude that ACT modelwith polar velocity is better than ACT model with Cartesianvelocity. Method 4 has the least computational load but itsperformance is poor. Method 3 is slightly more complexthan method 4 but can decrease the error greatly. So if thecomputational load is of great concern, kinematic constraintmodel is a good choice.
4. The Expectation Maximization (EM)Algorithm for Tracking CT Motion Target
In this part, the model in Section 2.1 is used.The turn rate π
πcan be described by a Markov chain [21,
22] and has π possible values:
ππβ ππ= {π (1) , π (2) , . . . , π (π)} . (47)
Assume the initial probability ππand the one-step transi-
tion matrix are known, as follows:
ππ= π (π
0= π (π)) , π = 1, 2, . . . , π
ππ,π= π (π
π+1= π (π) | π
π= π (π)) , π, π = 1, 2, . . . , π.
(48)
The measurement sequence is defined by π1:π
=
{π§1, π§2, . . . , π§
π}, state sequence is π
1:π= {π1, π2, . . . , π
π},
and maneuver sequence isΞ©1:π
= {π1, π2, . . . , π
π}.
4.1. Batch EMAlgorithm. Assume themeasurement sequenceis known, this algorithm focuses on finding the best maneu-ver sequence based onMAP criterion.There is one best man-euver sequenceΞ©(B)
1:πin ππ possible sequences that makes the
conditional probability density function be the maximum.WhenΞ©(B)
1:πis achieved, the state sequenceπ
1:πcan be estim-
ated accurately.According to EM algorithm, π
1:πis considered to be the
incomplete data,π1:π
to be the βlostβ data, andΞ©1:π
to be thedata that needs to be estimated. EM algorithm carries out thefollowing two steps iteratively.
(1) Expectation Step (E step)
π½ (Ξ©1:π, Ξ©(π)
1:π)
= πΈX1:π
{lnπ (π1:π, π1:π, Ξ©1:π) | π1:π, Ξ©(π)
1:π} ,
(49)
where π½(Ξ©1:π, Ξ©(π)
1:π) is defined as the cost function,Ξ©(π)
1:πis the
maneuver sequence estimation after π times iteration.
(2) Maximization step (M step)
Ξ©(π+1)
1:π= argmax
Ξ©1:π
π½ (Ξ©1:π, Ξ©(π)
1:π) . (50)
If the initial value is given, the above E step and M step arecarried out repeatedly, until convergence.
(i) E step. The union probability density function can be de-composed as follows:
π (π1:π, π1:π, Ξ©1:π)
=
π
β
π=1
π (π§π| ππ) Γ
π
β
π=1
π (ππ| ππβ1, ππβ1) Γ π (π
0)
Γ
π
β
π=1
π (ππππβ1) Γ π (π
0) .
(51)
π(ππ| ππβ1, ππβ1) and π(π
π| ππβ1) rely on the maneuver
sequenceΞ©1:π
. The state equation is Gaussian distribution:
π (ππ| ππβ1, ππβ1) = π {π
πβ πΉ (π
πβ1)ππβ1, ππ} , (52)
where π{π; Ξ£} is the Gaussian probability density functionwith mean π and covariance Ξ£.
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Mathematical Problems in Engineering 7
From the above analysis,
π½ (Ξ©1:π, Ξ©(π)
1:π)
= πΈπ1:π
{lnπ (π1:π, π1:π, Ξ©1:π) | π1:π, Ξ©(π)
1:π}
=
π
β
π=1
{lnπ (ππ| ππβ1) β
1
2
(ππ|π
β πΉ(ππβ1)ππβ1|π
)
Γ πβ1
π(ππ|π
β πΉ (ππβ1)ππβ1|π
) } ,
(53)
where
ππ|π
= πΈ [ππ| π1:π, Ξ©(π)
1:π] . (54)
Those terms which are independent of Ξ©1:π
are omittedhere.
In the E step, if Ξ©(π)1:π
is given, the cost function can beachieved using Kalman smoothing algorithm.
(ii) M step. In themaximization step, a newΞ©1:π
is chosen fora higher conditional probability.Then a better parameter esti-mation is achieved compared to the former iteration.The fol-lowing Viterbi algorithm can solve this problem perfectly.
Viterbi algorithm is a recursive algorithm looking for thebest path. As shown in Figure 3, the path connects the adjac-ent points with theweights to be the logarithm function of thelikelihood, named cost. The pathβs total cost is the sum of itseach pointβs cost. The best path has the maximum cost. Thedetailed method to find the best path can be found in [12].
(iii) Calculating Algorithm
(1) Initialization: the initial maneuver sequenceΞ©(1)1:π
andthreshold π should be given.
(2) Iteration: for each circle (π = 1, 2, . . .), carry outthe following steps: (1) E step, according to (53),calculate the cost between the adjacent point. (2) Mstep, according to Viterbi algorithm, find a bettermaneuver sequence.
(3) Stop: if βΞ©(π+1)1:π
βΞ©(π)
1:πβ β€ π, then stop the iteration.The
best maneuver sequence is Ξ©(B)1:π
= Ξ©(π+1)
1:π; then the
state estimation sequence is calculated according toΞ©(B)1:π
.
4.2. Recursive EM Algorithm. In target tracking applications,the targetβs state always needs online estimation. So a recur-sive EM algorithm is needed for calculating π
π.
(i) Recursive Equation. Under the MAP criterion,
Ξ©(B)1:π= argmax
Ξ©1:π
{π (Ξ©1:π| π1:π)} , (55)
k = 1 k = 2 k = 3 k = 4
π1
π2
π3
π4 π4 π4 π4
π3 π3 π3
π2 π2 π2
π1π1 π1
Figure 3: Viterbi algorithm for path following.
where π(Ξ©1:π| π1:π) can be calculated online.
π (Ξ©1:π| π1:π) = π (Ξ©
1:π| π§π, π1:πβ1
)
=
π (π§π| Ξ©π, π1:πβ1
) π (Ξ©π| π1:πβ1
)
π (π§π| Ξ©1:πβ1
)
= π (π§π| Ξ©1:π, π1:πβ1
) π (ππ| Ξ©1:πβ1
)
Γ π (Ξ©1:πβ1
| π1:πβ1
) (π (π§π| π1:πβ1
))β1
.
(56)
Because Ξ©1:π
is Markov chain,
π (ππ| Ξ©1:πβ1
) = π (ππ| ππβ1) . (57)
The possible maneuver sequence grows exponentially as thetime grows. For the computation to be feasibility, it is assumedthat
π (π§π| Ξ©1:π, π1:πβ1
) β π (π§π| ππ, π1:πβ1
)
= π (ππ, Sπ) ,
(58)
where ππis the Kalman filterβs innovation and S
πis the covari-
ance of the innovation.The cost function is defined as
π½ (ππ(π)) = lnπ (Ξ©
1:π, ππ(π) π1:π) , π = 1, 2, . . . , π,
(59)
which stands for the cost to model π until time π.From (57) to (59),
π½ (ππ(π)) = π½ (π
πβ1(π)) + lnπ
ππ
β
1
2
π
π(π, π) Sβ1
π(π, π) π
π(π, π) π, π = 1, 2, . . . , π,
(60)
where ππ(π, π) stands for the innovation when model π is
chosen in time πβ1 andmodel π is chosen in time π. Sπ(π, π) is
the corresponding covariance.
-
8 Mathematical Problems in Engineering
Because of using the assumption (58), the iteration algo-rithm is not the optimal algorithm under MAP criterion, buta suboptimal one.
(ii) Calculating Algorithm. Only one-step iteration is listedhere.
(1) E Step Calculation. Using (10), calculate each costfrom time π β 1 to π; π2 costs are needed.
(2) M Step Calculation. According to Viterbi algorithm,find out themaximum cost π½max(ππ(π)) related to eachmodel. π½max(ππ(π)) is the initial value to be the nextiteration.
(3) Filtering. According to the path which reaches eachmodel, calculate each modelβs state estimation π
π(π)
and covariance ππ|π(π), π = 1, 2, . . . , π.
(4) The Final Results. From π½max(ππ(π)), π = 1, 2, . . . , π,choose the maximum one as the final filtering result:
π = argmaxπ
{π½max (ππ(π))}π
π=1. (61)
π(B)π= ππ(π) , π
(B)π|π
= ππ|π(π) . (62)
4.3. Simulation Results
(i) Simulation Scenario. Target initial state is π0
=
[60000m β172m/s 40000m 246m/s] . The sample rateπ = 1 s. The covariance of process noise
π = [
ππ₯
0
0 ππ¦
] , ππ₯= ππ¦=
[[[[[
[
π4
3
π3
2
π3
2
π2
]]]]]
]
. (63)
Assume only position can bemeasured, themeasurementequation is the following:
π§π= [
1 0 0 0
0 0 1 0]ππ+ Vπ. (64)
The covariance of measurement noise π = 2500I, where Iis the 2 Γ 2 unit matrix.
The simulation lasts for 300 s. Targetβs true turn rate is
ππ=
{{
{{
{
0 0 β€ π < 103
0.033 rad/s 104 β€ π < 1980 198 β€ π < 300.
(65)
Figure 4 gives the targetβs true trajectory.Assume targetβs maximum centripetal acceleration is
30m/s2. Under the speed 300m/s, the corresponding turnrate is 0.1 rad/s. Seven models are used for this simulation.From -0.1 to 0.1, the sevenmodels are distributed evenly.Theirvalues are β0.1, β0.067, β0.033, 0, 0.033, 0.067, and 0.1. Theinitial probability matrix is
π = [
1
7
1
7
1
7
1
7
1
7
1
7
1
7
] . (66)
20 25 30 35 40 45 50 55 6025
30
35
40
45
50
55
60
65
x (km)
y(k
m)
Figure 4: Target trajectory.
The model transition matrix is
ππ,π= {
0.7 π = π
0.05 π ΜΈ= π
π, π = 1, 2, . . . , 7.
(67)
(ii) Simulation Results and Analysis. Batch EM algorithm,recursive algorithm, and IMMalgorithmare compared in thisscenario. Rootmean squared errors (RMSE) are used here forcomparison.The RME position errors are defined as (46) andvelocity error are defined as follows:
RMS.V.E (π) = β 1π
π
β
π=1
[(οΏ½ΜοΏ½π
πβ ΜΜπ₯
π
π)
2
+ ( Μπ¦π
πβ ΜΜπ¦
π
π)
2
], (68)
whereπ = 200 are Monte-Carlo simulation runs and οΏ½ΜοΏ½ππ, Μπ¦ππ
and ΜΜπ₯ππ, ΜΜπ¦ππstand for the true and estimated velocity at time π
in the πth simulation runs, respectively.Figures 5 and 6 show the position and velocity perfor-
mance comparison. It can be concluded that the batch EMalgorithm has much less tracking errors compared to IMMalgorithm. During maneuver onset time and terminationtime, the IMM algorithm is better than recursive EM algo-rithm. But on stable period, the recursive EM algorithmperforms better.
5. Conclusions
Aiming at the CTmotion target tracking, several models andalgorithms are introduced and simulated in this paper.
In single model framework, four CT models have beencompared for tracking applications: ACT model with Carte-sian velocity, ACT model with polar velocity, kinematic con-straint model, and maneuver-centered model. The Monte-Carlo simulations show that the ACT model with polar
-
Mathematical Problems in Engineering 9
0 50 100 150 200 250 30010
20
30
40
50
60
70
Batch EMRecursive EMIMM
t (s)
Ep
(m)
Figure 5: Position performance comparison.
0 50 100 150 200 250 3000
10
20
30
40
50
60
70
t (s)
EοΏ½
(m)
Batch EMRecursive EMIMM
Figure 6: Velocity performance comparison.
velocity has the best tracking performance but the compu-tational load is a bit heavier. The kinematic constraint modelhas a moderate tracking performance, but its computationalload decreases greatly compared with UKF controlled ACTmodel. So if the computational load is of a great concern,the kinematic constraint model is suggested. If the trackingperformance is very important and the computational load isnot a problem, the ACTmodel with polar velocity is suitable.
In multiple models framework, EM algorithm is used fortracking CT motion target. First a batch EM algorithm isderived. The turn rate is acted as the maneuver sequence andestimated based on the MAP criterion. Under the E step, thecost function is calculated using the Kalman smoothing algo-rithm. Under the M step, Viterbi algorithm is used for pathfollowing to find out the pathwithmaximumcost. Simulationresults show that the Batch EM algorithm has better trackingperformance than IMM algorithm. Through modification of
the cost function, a recursive EM algorithm is presented.Thealgorithm can track the target online. Compared with theIMM algorithm, on the stable period, the recursive EM algo-rithm has better tracking performance.
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper.
Acknowledgments
This research work was supported by the National KeyFundamental Research & Development Programs (973) ofChina (2013CB329405), Foundation for Innovative ResearchGroups of the National Natural Science Foundation of China(61221063), Natural Science Foundation of China (61203221,61004087), Ph.D. ProgramsFoundation ofMinistry of Educa-tion of China (20100201120036), China Postdoctoral ScienceFoundation (2011M501442, 20100481338), and FundamentalResearch Funds for the Central University.
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