電子航法研究所研究発表会（第14回平成 26年6月）

㸱㸬DAPs ࢆ⏝࠸ࡓ㧗⢭ᗘ㏣ᑿᢏ⾡࡟㛵ࡍࡿ◊✲
監視通信領域※呂暁東，古賀禎

is often delayed by the response of Kalman filters. In
1. INTRODUCTION

this paper, to improve the accuracy, the DAPs based

In response to the rapid increase in air traffic

IMM tracking system is proposed. The system

demands, the more accurate and reliable tracking

consists of an IMM filter with 3 different models andsystems for aircraft surveillance are required toa maneuver detector which is able to dynamically

improve the capacity, safety and efficiency of airrevise the mode probabilities in real time according totraffic control (ATC) services. With the advancement

the different motions of aircrafts by using DAPs

of the sensor and communication technologies, theinformation. Moreover, as the availability andextensive deployment of air-to-ground data links

certification of DAPs cannot be guaranteed, theleads to the emergence of complementary means forsystem with two tracking functions, the EHS based

the aircraft tracking system.

one and the usual one, is also implemented.

As a considered part of the basis of the surveillance

The paper is structured as follows. In the next

infrastructure, Secondary Surveillance Radar (SSR)

section, the Mode S and DAPs concept are described.

Mode S has been widely utilized. There are two

In section 3, the DAPs based IMM tracking system ispossible configurations for the Mode S radar, the firstpresented. The results of computer simulations andone is called the Mode S Elementary Surveillance

practical experiments are shown and discussed in(ELS) and the other one is the Mode S Enhanced

section 4, and the paper is concluded in section 5.

Surveillance (EHS) [1]. Each aircraft is assigned one
unique ICAO 24-bit address by the State Registration

2. MODE S EHS

Authority [2]. This address is the one used for

The SSR Mode S includes two elements: anselective interrogation, which permits the obtaining

interrogative ground station (GS) and a transponderof the flight ID and the altitude in 25 feet steps. The

on board of the aircraft. Each GS has its own

Mode S EHS consists of ELS supplemented by theinterrogation code which permits the configuration ofextraction of downlink aircraft parameters (DAPs)

the target answers, as they know who is interrogating

which can be used to improve the performance ofthem. Currently, most of Mode S radars are using thecurrent tracking systems. In [3], Roll-Angle is used tomultisite surveillance protocol [7].

assisttrackers

duringmaneuvers

toimprove

The Mode S radar has the elementary surveillance

accuracies during straight-line flight. And in [4], theand the enhanced surveillance modes. The differences

DAPs information is applied to calculate the control

between them fall on uplink and also downlink datanoise of the Kalman filter.

transmissions. The data items sent by a Mode S ELSThe Interacting Multiple Model (IMM) estimator is

are the following ones:a suboptimal hybrid filter that was shown to achieve-ICAO 24-bit address

an excellent compromise between performance and-Data-link capability reporting

complexity [5, 6]. However, it is difficult to estimate-

Common usage airborne parameter register

precisely the aircraft state when the target has themaneuvering motion since the detection of maneuvers－ 17 －

capability reporting-Aircraft identification reporting電子航法研究所研究発表会（第14回平成 26年6月）

Table 1. Characteristics of DAPsDAPs
Roll Angle
True Track Angle

Units
degreesdegrees

Update Rate
1s90/512

Precision
45/256
1sRange[-90, +90][-180, +180]

Ground Speed

knots

1s

2

[0, 2046]

Track Angle Rate

degrees1s

8/256[-16, +16]

True Air Speed
Barometric Altitude Rateknots
feet/min

1s
1s

32
32

[0, 2046][-16384, +16352]-

Mode 3/A code reportingin a ground-fixed coordinate system, the most-Mode S pressure altitude reporting

suitable DAPs are the ones referred to the ground-Special position identification reporting/Flightsystem (BDS 5.0). Four parameters are directly

status reporting

related to the ground based tracking system: Track

From a tracking point of view, the most important
aspectofELS

istheheightmeasurement

Angle Rate, True Track Angle, Ground Speed andRoll Angle
The characteristics of the DAPs considered for theimprovement (measured in 25 feet steps), whichallows a better vertical tracking.

tracking function are given in Table 1. The update

Mode S EHS delivers further information. Thus, anrate in Table 1 is the refresh time of DAPs data in theaircraft can send to the ground station several aircraft

aircraft registers. Regarding error analysis, it has beenregisters or BDS with flight information. The

found two main error sources: one related to thedecision of what BDSs are sent depends on the local

quantification performed before sending the chosenconfiguration and also on the ATC Authority. TheDAPs through the Mode S downlink and the other toones that seem to be more useful from an Air Traffic

a delay due to update rates [8].

Control point of view are:

3. DAPS BASED IMM TRACKING SYSTEMBDS 4.0:-Selected Altitude

BDS 5.0:-Roll Angle-Track Angle Rate (or Trueaccurate and timely airspace situational awarenessAirspeed)

supported by surveillance systems. However, there-True Track Angle

are at least two problems that should be considered-Ground Speedfor designing the DAPs based tracking system. One is-

Magnetic Headingthat DAPs are not always available, even if the-Indicated Airspeed or Machaircraft has the ability to send them. This depends on

Numberthe channel congestion and on the ATC Authority.

-

Vertical RateThus, it is difficult to guarantee that the DAPs data-

True Airspeed (if Track Angle

can be continuously processed. The other problem isRate is not available)

the reception of corrupted or erroneous DAPs, which

BDS 6.0:

Safe and effective operation of the ATC relies on

For the tracking function, the useful parameters areis even more dangerous than the former. Since, it can

the vector state DAPs, which tell the ground station

easily lead to wrong predictions and consequently towhat movement the aircraft is performing in thatwrong estimates. Therefore, one appropriate approachmoment. Even though, not all of them are useful foris to construct a tracking system taking ELS data as

the tracking function. As our task is to track aircraftprimary, and EHS data as complementary.

－ 18 －電子航法研究所研究発表会（第14回平成 26年6月）

3.1 Motion Models

3.1.3

Coordinated Turn Model

The models in the horizontal and vertical are

A coordinated turn is a turn with a constant turn

treated separately, due to the fact that the target

rate (rate of angle change) and a constant speed.

motions in horizontal plane and vertical plane are

Although the actual turning of a civilian aircraft is notcomparatively independent. The motion models usedexactly coordinated since the ground speed is thein the IMM filter are defined in this section. The

airspeed plus the wind speed, the kinematic behavior

uniform motion can be described by a second-orderof the aircraft during the turn is suitably described bykinematic (constant velocity) model. The maneuver

the coordinated turn model plus a fairly small noise

motion can be described by a third-order kinematic

representing the modeling error. This will be referred(constant acceleration) model and a coordinated turnto as the nearly coordinated turn model in the sequel.

model.

Such a model is necessarily a nonlinear one if the turn

3.1.1

rate is not a known constant. The state vector

Constant Velocity Model

The state vector corresponding to the constantvelocity model is defined as:with x and y denoting the orthogonal coordinates of
the horizontal plane. And the discrete-time state
equation is given by:
T1000
010x = [ x x y y ω ]T
where ω is the tum rate. The coordinated tumx = [ x x y y ]T1
0x(k ) = 00corresponding to this model isT0 T20 x(k − 1) + T
 0

1
 02


0 0  v(k − 1)T2
2 T 

model is then given by:sin(ω ( k )T )

1
ω(k )

0cos(ω ( k )T )x ( k ) = 0 1 − cos(ω ( k )T )ω(k )0sin(ω ( k )T )
0
0
T 2
 2
T

+ 0

 0
 0


where T is the sampling interval, and ν is azero-mean Gaussian white noise used to modelaccelerations with an appropriate covariance Q,
which is a design parameter.
3.1.2

acceleration model is defined as:

1 0 00 1 T

00 0 100 0 00 0 0100 0errors.

Figure 1 represents the adaptive tracking systemarchitecture which consists of Mode S Radar, Tracker,
T

 T4

 2

G= 0
 0
 1
 0


2T


0
0

0  v ( k − 1)0
T (1) Data Field

The state equation is given by:x(k ) = Fx(k − 1) + Gv(k − 1)
0 0 T2
2 T 0 1 00

3.2 System Architecture

x = [ x x x y y y ]TT22T1000 x ( k − 1)
0

1

different noise statistics to reflect different modeling

The state vector corresponding to the constant


1
0


F = 0
0

0
00
T2
2
T0

0

1 − cos(ω ( k )T )ω(k )− sin(ω ( k )T )sin(ω ( k )T )ω(k )cos(ω ( k )T )0Note that the process noise ν has in general

Constant Acceleration Model

where00 −0 0 
T24 T 2 0 
1 

DAPs Monitor and Position Monitor. The nodes inthe system are connected through Data Field (DF)thatserves

asa communication

medium ofcoordination between the nodes. It can be a Local
Area Network (LAN) or LANs connected by WideArea Network (WAN). All necessary data is
broadcast into the DF, where the data logically－ 19 －電子航法研究所研究発表会（第14回平成 26年6月）

motion model changes of aircraft. The equations ofcirculates in the DF.
(2) Radar and Monitors
Mode S Radar gets targets’ information from its
own surveillance area. Then it sends the target report

detection are as follow:
C v = Vgs (k ) − Vgs (k − 1)message which contains the position, identification

C a = R(k − 1) ⋅ W (k − 1)
where Vgs(k) is ground speed at time k, R(k-1) is rollnumber, time and DAPs information to the DF.

angle at time k-1, and W(k-1) is track angle rate atDAPs Monitor stores the DAPs information oftime k-1. If the Track Angle Rate is not available, weeach aircraft, and shows the values of eachcan calculate the angular velocity which is related toparameters of the selected aircraft in the figures.

the Roll Angle and True Air Speed by following

Position

expression:

Monitordisplaysandupdates

themeasurement, smoothing and predicted positions ofeach aircraft.

true airspeed at time k-1.

IMM Filter

One computational cycle of IMM filter consists ofMixing

four major steps: interaction (mixing), filtering,

Mode probability
updateprobability update, and combination. At each time,the initial condition for the filter matched to a certain

Combination
Position
Processortan( R( k − 1))
Vtas ( k − 1)

where g is the acceleration due to gravity and Vtas(k-1) isTracker

Estimation, Smoothing,
Likelihood calculation

W ( k − 1) = g ⋅mode is obtained by mixing the state estimates of all
filters at the previous time under the assumption that

ManeuverDetector

this particular mode is in effect at the current time.
This is followed by a regular filtering (prediction andData Fieldupdate) step, performed in parallel for each mode.
Then, the mixing and model probabilities are updatedbyMode S Radar

DAPs Monitor

Position Monitor

usingthelikelihoodfunction.

Finallyacombination (weighted sum) of the updated state
estimates of all filters yields the state estimate. TheFigure 1: System Architecture

probability of a mode being in effect plays a key rolein the weighting of the mixing and the combination of(3) Tracker
Tracker is composed of Position Processor,
Maneuver Detector and IMM Filter. Position
Processor is in charge of storing and updating theposition information of aircrafts. After receiving atarget report message, the processor extracts theaircraft position information, and converts it fromlocal radar coordinates (Rang, Azimuth and FlightLevel) to geodetic coordinates (Latitude, Longitudeand Altitude).
Maneuver Detector is responsible for extracting
and storing DAPs information from the target reportmessage. And based on this information, it detects thestates and covariances.
In the standard IMM filter, the default mode
probabilities are used for initialization since it isdifficult to predict the motion model of aircraft at thebeginning.

Therefore,

ifthedefaultmodeprobabilities do not match the motion model or themotion model changes during the initial tracking
period, the prediction accuracy is degraded. Moreover,during the later tracking period, each mode
probability becomes uniform since the difference ofresidual vectors between modes decreased throughmixing process. As a result, if motion model changes
during the later tracking period, the prediction error－ 20 －

電子航法研究所研究発表会（第14回平成 26年6月）

Table 3. RMS Prediction Errors in Horizontal
Sampling time(sec)

Measurement
pointsRMS error (nmi)
Proposal
IMM%Reduction[0, 100)62

0.04420.100656.06[100, 200)51

0.06570.102235.69[200, 300)

49

0.04330.102257.63[300, 400)

370.0673

0.133149.41[400, 500)440.04320.111161.09[500, 600)

480.04270.080747.16[600, 700)64

0.0311

0.05

37.73

[700, 800]57

0.03170.0601

47.33

increases because the response of filters is often
delayed. To solve this problem, based on the DAPs

model, which are defined as:
ε i ( k − 1) = | x m ( k ) − x p ( k − 1) | +information, the predictions of each mode probability| y m ( k ) − y p ( k − 1) |during the initial tracking period are presented below:if (C a ≠ 0), thenei ( k − 1) =ε i ( k − 1)
∑ ε i (k − 1)i =1

μ 1 (k) = min{μ 1 (k - 1), μ 2 (k - 1), μ 3 (k - 1)}
μ 2 (k) = mid{μ 1 (k - 1), μ 2 (k - 1), μ 3 (k - 1)}

where xm(k) and ym(k) are measurement position inμ 3 (k) = max{μ 1 (k - 1), μ 2 (k - 1), μ 3 (k - 1)}

latitude and longitude at time k, and xp(k-1) andif (C a = 0 & C v ≠ 0), thenyp(k-1) are prediction position in latitude andμ 1 (k) = mid{μ 1 (k - 1), μ 2 (k - 1), μ 3 (k - 1)}

longitude at time k-1.

μ 2 (k) = max{μ 1 (k - 1), μ 2 (k - 1), μ 3 (k - 1)}

4. EVALUATIONμ 3 (k) = min{μ 1 (k - 1), μ 2 (k - 1), μ 3 (k - 1)}
where μ 1(k-1), μ 2(k-1) and μ 3(k-1) are mode

4.1 Parametersprobabilities for constant velocity model, constantacceleration model and coordinated turn model attime k-1 respectively.
In addition, during later tracking period, since eachmode probability becomes uniform they can berevised as follows:

The performance of proposed DAPs based IMM
tracking system is compared with conventional IMM
filter on the real-time radar measurement data. To
obtain the best possible results, the system has to beproperly designed to meet the special requirements ofthe particular sensor. Based on the preliminary study,

if (C a ≠ 0 & μ 3 (k - 1) < 0.5), thenthe design parameters are given in Table 2.

μ 1 (k) = min{e1 (k - 1), e 2 (k - 1), e 3 (k - 1)} - 0.2

Table 2. Parameters

μ 2 (k) = mid{e1 (k - 1), e 2 (k - 1), e 3 (k - 1)} - 0.1

Model

μ 3 (k) = max{e1 (k - 1), e 2 (k - 1), e 3 (k - 1)} + 0.3
if (C a = 0 & C v ≠ 0 & μ 2 (k - 1) < 0.5), thenμ 1 (k) = mid{e1 (k - 1), e 2 (k - 1), e 3 (k - 1)} - 0.1
μ 2 (k) = max{e1 (k - 1), e 2 (k - 1), e 3 (k - 1)} + 0.3

Process

Measurement

Noise

Noise (m)Constant velocity

0.01g60

Constantg600.1g60

acceleration

μ 3 (k) = min{e1 (k - 1), e 2 (k - 1), e 3 (k - 1)} - 0.2

where e1(k-1), e2(k-1), and e3(k-1) are prediction error
rate in latitude and longitude at time k-1 of each－ 21 －

Coordinated turn

電子航法研究所研究発表会（第14回平成 26年6月）

The model transition probabilities for IMM filter is

5. CONCLUSION

designed as follows:

In this paper, the development and practical

 0.95 0.025 0.025
P = 0.025 0.95 0.025
0.025 0.025 0.95 

experiments of DAPs based IMM tracking system foraircraft surveillance are proposed. From the obtainedresults, the performance of proposed system isconfirmed and the effectiveness for real application is4.2 Results
The evaluation results are based on the real-timeMode S radar data that span a time interval about 800
seconds. The aircrafts with DAPs information during
the maneuvering periods are selected for comparison.
The actual prediction errors obtained using these two
methods are tabulated. These results not only
demonstrate the error reduction obtained with theDAPs based estimator, but also indicate themagnitude of the actual errors in a typical ATC
scenario. The RMS prediction errors in the horizontal
aircrafts based on the measurements of maneuveringperiods are given in Table 3. The results indicate
approximately 50% RMS prediction error reductionin the critical maneuvering periods for the proposal
over the standard IMM estimator. During thenon-maneuvering periods, these two methods haveidentical performance. The performance comparisonof RMS on horizontal accuracy is shown in Figure 2.
We see that during the maneuvering periods theproposed system consistently yields lower error andgets much improvement over the standard IMM
estimator. As shown in Figure 2, the average
reduction in RMS prediction error of the proposedsystem is 50.11%.
0.35

IMM
Proposal

RMS Prediction Errors in Horizontal (nmi)

0.3

0.25

0.2

0.15

0.1

0.05

00

100

200

300

400

Sampling time (s)

500

600

700

Figure 2. RMS Prediction Errors

800shown. Compared with the standard IMM filter, theproposed system significantly reduces the prediction
error through maneuvers not only during the initialtracking period but also during later tracking period.
For satisfying the desired level of accuracy, it isrequired to assure the quality of DAPs data andimprove the performance during the dynamicchanging environment.
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