３.SWIM のコンセプトによる監視情報ドメイン構築に関する検討
監視通信領域※呂 暁東，古賀 禎，住谷 泰人，塩見 格一

as a fundamental element for future ATM systems.
1. INTRODUCTION

Moreover,theInternational

CivilAviation

With the rapid increase in local and global air

Organization (ICAO) has made efforts to promote thetraffic, the system-wide operational information

SWIM concept and establish the communication andexchange and life-cycle management technologies areinformation standards.

required to improve the capacity, safety and

The main objective of SWIM is not only to achieve

efficiency of Air Traffic Management (ATM).

seamless

However, today's ATM system comprises a wide

distributed and heterogeneous systems in the air

variety of applications with their own communicationtransportation field but also to enable seamlessprotocols, interfaces and self-contained information

information sharing among the multiple stakeholders

systems.

point-to-pointin the ATM domain. Therefore, how to use thecommunication is difficult to upgrade with new

information from different domains systematicallytechnologies, information exchange is not globallyand efficiently to improve the performance of current

harmonized, and information security is poor.

systems should be considered.

TheTherefore,System Wide

thisInformationintegration

amonggeographicallyManagement

The most traditional aircraft surveillance systems(SWIM) concept is to change the conventional ATM

are operated independently and it is difficult to get

information architecture from point-to-point data

the flight and flow information previously. Therefore,

exchanges to system-wide interoperability, and to

the tracking model does not perform well during theachieve life-cycle management of data, information,

flight mode changes dynamically. Recently, theand service. The SWIM concept provide not only

Required Navigation Performance (RNP) has beensystem architecture for the delivery of information

implemented in many airports in Japan to optimize

services to support meeting the expectations of thethe air route configuration and to enable a smaller

ATM community in different key performance areas,

midair separation standard to safely accommodate

but also a common understanding of different

high-density air traffic. Moreover, the Flight Plan

information domains. Therefore, implementation ofwith 4 Dimension Trajectory (4DT) information will

the SWIM concept must address the challenge ofbe applied from 2020 according to the operational

creating an interoperability environment which

concepts outlined in Flight and Flow Information forallows the SWIM IT systems to cope with the fulla Collaborative Environment (FF-ICE) [7]. In thiscomplexity of operational information exchanges

paper, to improve aircraft tracking accuracy, the[1][2].

SWIM concept based surveillance information

The research and development of SWIM concepts

domain construction is discussed. By incorporating

and solutions has been undertaken in different

thecountries. ATM modernization programs such as

information into the aircraft model, the method toNextSystemdescribe the aircraft with changing flight modes and(NextGen) [3][4] in the United States and Single

model the flight mode switches accurately is

European Sky ATM Research (SESAR) [5][6] inproposed. And the performance of the proposed

Europe both consider the implementation of SWIM

method is evaluated with real air traffic data.

GenerationAir

Transportation

airroutes,

waypoints

and

other

relatedThis paper is structured as follows. The next
sectiondescribes

therelationshipbetweeninformation exchange services, information exchange
models and information domains in SWIM. In section
3, the SWIM concept based surveillance information

Organization for Civil Aviation Equipment) andARINC (Aeronautical Radio, Incorporated).
2.2 Information DomainsAn information domain is focused on identifying,domain construction to improve aircraft tracking
accuracy is presented. The results of computer
simulations are shown and discussed in section 4, andthe paper is concluded in section 5.
2. SWIM AND INFORMATION DOMAINS
SWIM consists of standards, infrastructure andgovernance enabling the management of ATM related
information and its exchange between qualified
parties via interoperable services. The scope ofSWIM includes information exchange services,
information exchange models and the infrastructure
requiredtoexchange

information

betweenFigure 1: ICAO identified Information Domains

SWIM-enabled applications [2].
2.1 Information Exchange Models

defining, and satisfying the information needs of theInformation exchange models are desirable when
information is provided by a large number ofdifferent participants and made available to a wide
range of ATM information consumers. To permit
interoperability, the information needs to be clearly
and unambiguously defined and well understood. In
otherwords,

there

isaneedforsemanticinteroperability. This requires a detailed definition ofthe information both at the conceptual level and at thelevel of the data that is exchanged between systems.
Several exchange modes have been defined for thefollowing

domainsofATM

operations:meteorological information (WXXM), aeronauticalinformation (AIXM), flight and flow information(FIXM), and surveillance information (ASTERIX). In
addition, it is possible for other communities ofinterest to develop exchange models such as is thecase for the industry requirements for aerodrome
mapping, terrain and obstacle information involvingorganizations such as RTCA (Radio TechnicalCommission for Aeronautics), EUROCAE (European

set of business activities associated with a specificarea. The concept of information domains can beused to manage the definition of exchange models as
smaller and more manageable units. The initial
information domains were identified informally basedon the major service exchanges that were already inuse (pre-SWIM). In Figure 1, several ICAO identified
information domains are shown. Individual States ororganizations may choose to define other domains fortheir own region.
Current information domains are related to thecurrent subdivision of identified activities. Users
however see the information in a more interrelated
way than it is in its current information domain state.
Hence, within each solution space there is a tendency
for users to expect seamless and interoperable
information which can be fused. The concept ofprogressively achieving a seamless information space,supported by exchange models related to information
domains within each user context, is realized by
SWIM-enabledapplications.

Thismeansthatapplications which consume and provide information

with a “reference point” describing the point

services may relate to one or more domains, to one or

traditionally associated with the route. If the flight ismore stakeholders, and to one or more points on theto follow a sequence of airborne elements that arestrategic-tactical time axis.

part of a defined route, the “constituent route” will
also be included. This can be useful if the route has

3. INFORMATION SHARING FOR AIRCRAFTSURVEILLANCE

performance requirements associated with it.

In a traditional aircraft surveillance system, thetracking model does not perform well when theaircraft changes its mode unexpectedly. Thisdifficulty arises because the model on which the filter
is based does not accurately represent the behavior of
the aircraft over all of its flight regime. The flightmode changes in an aircraft depend on the determined
air route in the flight plan that is usually unknown to
the surveillance system. The lack of knowledge of theflight information makes the flight-mode changes ofan aircraft nondeterministic because they cannot be

Figure 2: Airborne Segment of 4DT

determined a priori.
The FF-ICE concept has been developed by ICAO
to illustrate information for flow management, flightplanning, and trajectory management associated with
ATM operational components [7]. The flight planwith gate-to-gate 4DT information will be appliedand shared among related systems. A gate-to-gate
trajectory includes the departure aerodrome, adeparture surface segment, an airborne segment, andan arrival surface segment at a destination aerodrome.

Figure 3: 4D Trajectory

As shown in Figure 2, the airborne segment isdescribed in terms of airborne elements. Each

Currently, aircrafts usually follow air routes andairborne element describes a path from the lastchange their flight modes to maneuver at waypoints

airborne element to the “to-point 4D”. This point isor 4D points whose locations can be extracted fromexpressed using 3D location and time. The airborne

the flight plan and incorporated into the tracking

element includes airspeed at a change point and thesystem. According to the information sharing

type of change point described by the “to-point”. The

between surveillance information domain and flighttype of change point (e.g. top-of-climb, speed

and flow information domain, the air routes andchange) is also described. Constraints can bewaypoints can be extracted from the flight plan andspecified for the airborne element and are applicable

4D trajectory can be generated by data fusion (Figureat the “to-point”. These can be altitude, time, speed,3). In this section, the aircraft models and a method toor lateral constraints. An airborne element may

describe an aircraft’s dynamics with changing flightrequire a turn descriptor; in this case, additionalmodes are presented.

information is included to describe the turn, together

(3.1 Flight Models
The aircraft’s state is defined as:
̇̇)

((̇

(At each time k the aircraft’s continuous-state
dynamics are described by a difference equation)
()

()

( )
( )))

(( ))

[]

defined in the discrete time:
()

( )

( )

( )

where v is a zero-mean Gaussian white noise used tomodel accelerations with an appropriate covariance Q,
F and G are system metrics with appropriate[]

where ω is the tum rate.

dimensions, corresponding to each flight mode.
We consider an aircraft whose dynamics canswitch between three flight modes: constant velocity

3.2 State Transition Model
Since an aircraft is usually required to follow air(CV), constant descent (CD), and coordinated turn(CT).
(1) CV mode
The discrete-time state equation of constantvelocity model is given by:()( )[( )][Figure 4: Transition Modes
routes and take maneuvers at given waypoints or 4D]

points to enter a new segment of the air routes. If an

where T is the sampling interval.
(2) CD mode

aircraft’s flight mode changes to mode j from mode i

The discrete-time state equation of constantwhen it crosses a certain waypoint (denoted by WPn,n =1,…,N), the flight mode transitions can bedescent model is given by:

described as: (WPn, Mij). In this paper, we consider
two kinds of waypoints, fly-over waypoints and()

( )
[( )]

fly-by waypoints (Figure 4).
(1) Fly-over waypoints
Consider a landing aircraft in the local navigation[]

frame. The aircraft flies through a series of fly-over(3) CT mode

waypoints, and its flight mode switches between theThe discrete-time state equation of constant turnCV and CD modes when it crosses the fly-overmodel is given by:

waypoints. Let mode 1 be the CV mode and mode 2
be the CD mode. As shown in Figure 4 (a), thetransition from mode 1 to mode 2 at WP1 can bewritten as: (WP1, M12).
(2) Fly-by waypoints
Consider a fly-by waypoint connecting twosegments of an air route. The aircraft usually enterCT flight mode before they reach the fly-by waypoint,

4. EVALUATION

then enter the CV flight mode when their headings

In this section, the proposed method is tested

are aligned with the direction of the next segment ofthrough a 3-dimensional landing aircraft tracking

the route. Let mode 1 be the CV mode and mode 3 bescenario. The aircraft has three flight modes: CV,the CT mode. As shown in Figure 4 (b), the transitionmode 1; CD, mode 2; and CT, mode 3. The aircraft isfrom mode 1 to mode 3 at WP3 can be written as:

initially in the CV mode and crosses one waypoint(WP3, M13).

and switches its flight mode to CT. Then it crosses

3.3. Hybrid Estimation

another waypoint and switches its flight mode to CD

The proposed method is inspired by the Interacting

successively. The aircraft dynamics corresponding toMultiple Model (IMM) estimator, which is a

the three flight modes. With the information about thesuboptimal hybrid filter [8, 9]. However, unlike thewaypoints, the proposed method is used to describe

IMM algorithm which assumes that the discrete

the flight mode transitions of the aircraft.

transitions are governed by a time-homogeneous

Figure 5 shows simulated aircraft trajectories. The

Markov chain, the proposed method uses the state

simulated trajectories are generated based on the realtransition

flight trajectories and measurement data.

modeltocompute

thetransition

probabilities, which integrate the prior information
about the state transition model to hybrid estimation.
If at time k, an aircraft’s flight mode changes frommode i to mode j (i, j=1, 2, 3) at WPn, the modetransition probabilities can be computed as:|(| ( ))
()

()

()

where x(k) is measurement position at time k, en is
uncertainty error at waypoint n, μ1(k+1), μ2(k+1) andμ3(k+1) are mode probabilities for CV model, CD

Figure 5: Simulated Aircraft Trajectorymodel and CT model at time k+1 respectively. P is
the mode transition probability matrix, and P1, P2 andP3 are the transition matrixes for CV model, CD

Table 1. Parameters
Model

Process

Measurement

Noise

Noise (m)Constant velocity

0.01g30

Due to various uncertainties, the flight mode

Constant descentg30

changes usually happen around but not exactly at theCoordinated turn

0.1g

30

model and CT model, given by:waypoints. The uncertainty error at a given waypoint
can be parameterized based on two kinds of air traffic
data: past trajectory records around a given point or

4.1 Parameters

the required navigation performance (RNP) of a

The performance of proposed method is compared

given aircraft. Which kind of data should be usedwith conventional IMM estimator on the real ADS-B

depends on the natures of available data.

(Automatic Dependent Surveillance - Broadcast)measurement data. To obtain the best possible results,

the system has to be properly designed to meet theinformation

special requirements of the particular sensor. Baseddomains are described. Moreover, the SWIM concept

on the preliminary study, the design parameters are

based surveillance information domain construction

given in Table 1.

to improve aircraft tracking accuracy is discussed. By

The model transition probabilities for IMM filter isdesigned as follows [10]: 0.95 0.025 0.025
P  0.025 0.95 0.025
0.025 0.025 0.95 
4.2 Results
The performance of the proposed algorithm iscompared with the standard IMM estimator through aMonte Carlo simulation. Since the proposed method
can use flight information to accurately estimate thecurrent flight mode, it provides better state estimatesexchangemodelsandinformation

incorporating the flight information into the aircraftmodel, the method to describe the flight modetransitions of the aircraft is proposed. From theobtained results, the performance of proposed method
is confirmed and the effectiveness for tracking system
is shown. Compared with the standard IMM estimator,
the proposed method significantly reduces theprediction error through maneuvers. For satisfying
the desired level of accuracy, the further analysis of
uncertainty error under the dynamic changing
situation is required.
ACKNOWLEDGEMENT
This development would not have been implemented
if we did not have a support of JCAB and otherorganizations. Therefore, we would like to extend oursincere gratitude to all of them.
REFERENCES

Figure 6. RMS Prediction Errors
than the IMM algorithm which cannot incorporate theprior information of flight mode transitions. Theresults shown in Figure 6 reveal that the proposedsystem permits to significantly leverage prediction
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average improvement of the prediction error is about
40% compared with the standard IMM estimator.
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