- Email: [email protected]
A Multi-Sensor-System for Advanced Surface Movement Guidance and Control Concept and First Results
Copyright C IFAC Control in Transportation Systems, Braunschweig, Gennany, 2000
A MULTI-SENSOR-SYSTEM FOR ADVANCED SURFACE MOVEMENT GUIDANCE AND CONTROL CONCEPT AND FIRST RESULTS
Christopb Meier
DLR, Institute of Flight Guidance, Lilienthalplatz 7, D-38108 Braunschweig, Germany [email protected]
Abstract: Advanced Surface Movement Guidance and Control (A-SMGCS) is an increasingly important element in a seamless, overall Gate-to-Gate Air Traffic Management (ATM) system to ensure safe, efficient air traffic operations. The basis of an A-SMGCS is a surveillance system that automatically determines the traffic situation on the surface and in the neighbourhood of an airport. Only multi sensor systems are capable to meet the surveillance requirements of an A-SMGCS. DLR is conducting a major R&D effort on A-SMGCS and has developed solutions and prototype systems for the surveillance part. The technical evaluation is carried out on the Research Airport Braunschweig. The paper covers the concept, the solutions and sample results from field tests. Copyright @ 2000 IFA C Keywords: Multi Sensor System, Data Fusion, Air Traffic Control, Airport, A-SMGCS
routing' and guidance. Automatic surveillance is the basis for automation of further functions and therefore the first automation step. Automatic surveillance includes tracking and identification of all relevant traffic objects, situation assessment and conflict detection. Distributing this information to controllers as well as via data link to the cockpits would significantly enhance the situation awareness in this environment. This could help to avoid such catastrophes as 1977 in Tenerife, where two B747 collided on the runway - the worst accident of aviation history that occurred at the ground.
1. INTRODUCTION
Increasing air traffic in the last decades has led to important enhancements in air traffic control. It is e.g. possible to land aircraft safely in nearly all weather conditions. But a flight does not end with the touch down of the aircraft on the runway but at the gate. This last phase of a flight has been neglected too long and might become the bottleneck of the air traffic management of the future. Therefore new concepts are evolving under the title "Gate-to-Gate". Systems to support controllers as well as pilots in the management of aircraft on the surface of an airport and in the neighbouring airspace are meanwhile internationally termed "Advanced Surface Movement Guidance and Control System (A-SMGCS)".
DLR is conducting a major R&D effort on ASMGCS. Besides participation in main European projects like DEFAMM (Demonstration Facilities for Airport Movement Management) and aeronautical bodies like EUROCAE, DLR founded in 1997 its internal A-SMGCS project TARMAC (Taxi and Ramp Management and Control) to
In (ICAO 1997) it was defmed, that the main functions of an A-SMGCS are surveillance, control,
I Routing should better be tenned "planning" because in addition to the route also actions and sequences have to be generated.
329
hannonise the fonner independent internal R&D tasks in one integrated R&D platfonn. Starting probably with the year 2000 DLR will lead a new European project named BETA (operational Benefit Evaluation by Testing an A-SMGCS) with the objective to evaluate the operational benefit of ASMGCS implementations at two European airports.
but also the accuracy and correctness of detennined data can be enhanced. This multi sensor system philosophy changes the question "What is generally the best sensor" - that has been discussed for years - to "What is the optimal sensor set for the considered airport". The perfonnance of an individual sensor has not that impact anymore in a multi sensor system. Sensors have to fulfil the requirements as a team. Therefore sensors are already interesting to be integrated in the sensor set if they have one outstanding capability. A good example is the DLR development ARMI (see 3.1), that is doing nothing but identifying aircraft passing a certain point.
2. A-SMGCS SURVEILLANCE
2.1 Fundamental Requirements The main functional requirement for a Gate-to-Gate CNS-System is seamless tracking and identification of all relevant traffic objects. Relevant traffic objects are of course aircraft but also vehicles and obstacles that might endanger the safe or efficient traffic flow. In the DLR concept aircraft become interesting at the time they enter the TMA to approach the considered airport until they arrive at the stand and switch of their engines. This time advance is probably helpful for all kind of planning tasks. Departures are interesting when they request the first clearance until they leave the TMA.
Using such a multi sensor system has a further positive effect. It is not necessary to tune a single sensor system until it fulfils certain requirements. It opens the opportunity to spread the functionality on various sensors. This could lead to cost savings. A set of low cost sensors each with poor perfonnance might beat in the combination one expensive high end sensor. One important issue of DLR's multi sensor concept is its flexibility concerning the sensor selection. The developed data fusion software kernel is highly generic and can be used with a huge variety of sensor systems and therefore copes with a nearly infmite number of different sensor sets. Consulting concerning the optimal sensor set for a given airport with already given sensor and infrastructure can also be provided by DLR. Our principle is to first make use of existing equipment and infonnation and then to install further aiding sensors.
Vehicles and obstacles have to be tracked and identified / classified - as far as possible - when they are in the movement area. Certain vehicles like fire brigade, follow me's, towing tractors are possibly also of interest e.g. for fleet management purposes, when they operate on the aprons. All other traffic objects in the apron area like baggage cars are of nearly no interest and should not be tracked. Therefore automatic conflict detection is possible everywhere except for the aprons. But nonnally aircraft are moving slowly in these areas and airport stuff is monitoring e.g. push backs.
3. R&D ENVIRONMENT AT BRAUNSCHWEIG RESEARCH AIRPORT
2.2 Concept ofa Multi Sensor Solution Some years ago DLR decided to conduct a major R&D effort on A-SMGCS and to set up the necessary development platfonn at the DLR site in Braunschweig. This includes a A-SMGCS CNSSystem, a certain infrastructure on the research airport Braunschweig, specially equipped test traffic objects as well as simulation and evaluation tools.
No single sensor system is capable to fulfil the requirement of tracking and identifying all types of traffic objects in the mentioned area of interest. Only a multi sensor system is capable to do that, combining the single sensor strengths in a data fusion process. The multi sensor set can be composed by complementary and / or redundant sensor systems. The complementary sensor selection allows the required spatial and functional coverage, e.g. combining ASR and SMR to track objects in the TMA and on the airport surface or to combine SMR and SSR Mode S Multilateration to track non-cooperative as well as co-operative objects and to identify the latter ones. Redundant sensors enhance of course fault tolerance and continuity of service,
3.1 Experimental CNS-System The experimental CNS System is a multi sensor system composed of four types of sensors a data base to store a priori knowledge and the data fusion. The basic sensor set contains representatives of the main sensor categories:
330
• • •
detected reading out the change of distance between the end of the fibre and the mirror with an interferometer. This sensor principle is immune against electromagnetic impacts as e.g. lighting. Further it is sensible enough to be attached to the side of the taxiway instead of inserting it into the taxiways. The envisaged application is a local passage control sensor similar to today's induction loop. It could be instaIled at sensitive areas that need special protection against intruders e.g. at taxiway runways junctions
Area coverage versus local coverage Non-co-operative versus co-operative Active versus passive
Near range Radar Network: The Near range Radar Network (NRN) is developed by DLR's Institute of Communication and Navigation. It is an active, nonco-operative sensor system based on primary radar technology. Instead of observing the airport with one mechanical rotating antenna from the roof of the tower as today's Surface Movement Radar (SMR) the NRN uses modules of four smaIl low power radar stations observing the surrounded area. No mechanical rotating parts are needed, the NRN antennas are fixed sector antennas. Further today's SMR emits several Kilowatts of RF-Power in a narrow antenna pattern of approx. 0,25°. In contrast the NRN emits just 6W over a typical antenna sector of 90° in the azimuth. More information can be found in (Bethke et. al., 1999).
SSR Multilateration System: An SSR Mode A/C/S Multilateration system will be instaIled at Braunschweig airport within the year 2000. This cooperative passive sensor system detects all traffic objects equipped with a SSR transponder taxiing on the airport or flying in the neighbourhood of the airport. DBMS: The data base stores information on traffic objects and the airport. It contains e.g. possible identities, performance parameters of aircraft, the topography and topology of the airport network of taxiways and runways.
Global Positioning and Communication: The Global Positioning and Communication system (GP&C) is a local area DGPS system with integrated communication capability via a VHF data link. The system is developed by several Scandinavian companies and ATC authorities. It is a co-operative system, it needs a special onboard equipment - the GP&C transponder. Communication between the GPS reference station and the equipped traffic objects as weIl as between the traffic objects themselves is realised by a STDMA data link. AIl transponders receive the GPS correction data from the reference station via this channel. Further the position reports are exchanged between all transponders. In the Braunschweig experimental CNS system the transponder of the reference station is used to feed all the position reports to the data fusion.
Data Fusion: The data fusion system (Meier Ch., 1998) is developed by DLR's Institute of Flight Guidance. It fuses the partly situation descriptions of the sensor systems to a global situation representation using the complementary data parts to enhance the functional coverage of the system and the redundant data parts to enhance accuracy and correctness of data. This is possible due the independence of data sources, it is unlikely that sensors with different physical principles are effected by errors in the same manner at a considered time. To enhance the data fusion process a priori data from the data base are massively used - e.g. the fact that aircraft are using the taxiways and runways most of the time is used to enhance the situation tracking and prediction capabilities. The motion of the traffic objects is mapped to the network of possible routes, also taking into account that traffic object might enter or leave that network at any point. An added higher level of situation description is the result. The data fusion "knows" that an aircraft is not solely at position x=572,63 and y=930,83 but also that it is "Taxiing on Taxiway A".
Aircraft Registration Mark Identification: Two Aircraft Registration Mark Identification (ARMI) sensors (Dtlhler, et. aI., 1996) are presently instaIled in Braunschweig . The system is a in-house development of the image processing branch of DLR's Institute of Flight Guidance. The fact that all aircraft carry their registration on the tail of the fuselage is used to identify passing aircraft by OCR. A TV camera is starring at a taxiway section. Each video image is scanned for letters. Found letters are composed to words using plausibility rules concerning. Finally potential registrations are compared with a dictionary of expected aircraft.
Identification of traffic objects is a further important function of the data fusion. Nearly each new sensor system introduces a new addressing scheme to the A-SMGCS. At least the controIler must not be confronted by that technical addresses. He is used to work with caIl-signs. So the data fusion has to correlate between the different addresses of the
Fiber Optic Sensors: Sensors fully made of glass are a new sensor principle developed by DLR's Institute of Flight Guidance (FUrstenau, et. aI., 1996). In the Fiber Optic Sensor (FOS) a fibre is fixed oscillatory in front of a mirror. Vibrations of the fibre are
331
traffic objects in order to maintain a translation dictionary between all addressing schemes.
functionality is operational validated. Also the acceptance of the humans is tested. Further scenarios with or without a new system or new operational procedures can be compared to get estimates of the benefit of such modifications. Finally training of controller on new systems or procedures can be carried out.
3.2 Airport Infrastructure In order to technically evaluate the new CNS components a test implementation within a suitable infrastructure is absolutely necessary. Testing in simulation environment is the first step but at least simulation model parameters of sensors, traffic objects etc. have to be validated. Finally the new systems have to be tested themselves in a real environment because reality is much more cruel to technical systems than simulated scenarios.
3.5 Evaluation Tools In order to optimally use the data of the (expensive) real world trials recording and replaying tools are used. To develop the data fusion software trials with the test traffic objects are carried out for several days and data from all sensors are recorded. These multi sensor multi object files are replayed later multiple times to e.g. tune data fusion parameters or to test new algorithms.
Therefore DLR equipped the research airport Braunschweig with a glass fibre network and operates a high speed airport LAN based on FDDI. This LAN connects shelters distributed around the airport and DLR buildings. Sensor systems are mounted close to the shelters, their pre-processing equipment is installed within the shelters and central components as data fusion and data base are installed in the buildings of DLR. A bridge to the internet has proven its usefulness during first integration pre-tests of NRN (at that stage in the laboratory in Munich) and data fusion (in Braunschweig) across the internet.
Further these data are evaluated in a post processing to improve the sensor models. The main difficulty of this evaluation task is to split the files into multi sensor single object files or to associate the sensor measurements with the traffic objects. The normal airport traffic continues during the trials and additional traffic objects are also sensed by the nonco-operative sensor systems. Sensor specific heuristic processing supervised and plausibility checked by a human operator is used to obtain a correct reference solution.
3.3 Test Traffic Objects 4. SAMPLES OF FIELD TEST RESULTS
DLR uses the following test aircraft and vehicles for A-SMGCS trials: • VFW 614 (ATTAS) as sample of a cooperatively equipped aircraft • D0228 as sample of a co-operatively equipped aircraft • Mercedes Test Van as sample of a cooperatively equipped vehicle • Ford Test Van as sample of a non-cooperatively vehicle The test equipment contains e.g. a high precision DGPS system and an INS platform for reference trajectory purposes.
The multi sensor system concept is a quite general solution for surveillance of taxiing aircraft with a lot of degrees of freedom for implementation (e.g. sensor set composition, data fusion methods, system architecture). Since multi sensor systems are used in different applications (e.g. military) for years there is no doubt in the general feasibility of such a solution. The scientific and engineering tasks are e.g. to adapt existing and develop enhanced methods for data fusion or to investigate special topics to decide between design alternatives. In the following one example of data fusion technique developments and one example of special investigations are presented.
3.4 Simulation Facilities For operational testing with pilots and controllers in the loop DLR is operating an airport simulator with tower controllers, apron controllers and pilots working environment. The traffic objects appear using predefined scenarios. The further situation depends on controller and pseudo-pilots actions.
4. I IMM Filtering ofposition plots using the airport layout as a priori knowledge Position measurements - or plots - associated with a single traffic object coming from sensors are normally noisy due to various reasons. A smoothing process using some kind of Kalman filtering seems
With this environment the interaction between humans and systems can be analysed. The system
332
to be appropriate. Kalman filtering uses stochastically sensor and system models. A widely used system model is that of a point mass that moves with constant speed through the mathematical 2D space and is effected by random accelerations. Implementing this model to the A-SMGCS data fusion led to predictable track losses in curves. The accelerations during the curve are of course not random as the model assumes. The Kalman filter predicts the movement straight ahead because it is not aware of the airport layout. One solution is to increase the order of expected random accelerations. But this decreases the smoothing capabilities of the Kalman filter. The following figure presents simulated data filtered with a standard Kalman filter (state vector x, y, v x, vy).
(Meier, Ch., 1998) on the basis of IMM (Interacting Multiple Model) filtering. IMM filtering was developed strictly speaking for systems that stay for a certain time in a certain mode. After a time they switch over to a further mode. The different modes of behaviour are represented by one system model each. Mode switching is modeled by a first order Markov chain. So we have a bank of Kalman filters, one for each possible system mode and one extra filter for keeping track of the active mode. The evaluation of the individual Kalman filters is done using the innovation when processing a measurement. If the prediction of a Kalman filters fits well with a measurement, this mode is given a high probability. An important feature is the soft decision between the different modes. Single outliers are not leading to detection of mode switches.
·100 . . - - - - - - - - - - - - - - - - - - ,
y
A drawback of the standard IMM is that the number of system modes has to be fixed. The models have to be known a priori. Applying the IMM algorithm to the A-SMGCS data fusion task needs a dynamic mode management. The IMM was use in that way that one filter was tracking the movement in world co-ordinates, representing the hypothesis that the traffic object is really using no route, that it is e.g. travelling just by chance close to a taxiway. A further filter tracking in route co-ordinates is than created for each possible route and is dropped when this route becomes implausible. The search for new possible routes was aided by some rules of thumb. The dynamic mode managment was solved by renormalisation of the IMM after mode insertion or deletion - mathematical doubtful but working stable. The result is presented in figure 3.
-200
-300 ~~Lreference track ~
, ~oo
I
sensor pkHs :-s1andard fitter :
>(
l...-_...-
o
100
200
1 I
......._
_ . _ - - - - -.......,---J
300
400
500
600
7
800
Fig I. Filtering with a standard Kalman filter The airport layout should be taken into account. Just switching over from filtering in world co-ordinates as in figure I to filtering in route co-ordinates (x is progress on the route and y is the deviation from the route) enhances the smoothing effect as shown in figure 2. -100
y
r-----------------, ·100
y
r-----------------,
-200 -200
-300 -300 , ~OO
•
I-route fitter
~
o
:_ _elerence treck :
sensor plots
100
200
•
...,J._-------"--_==_....I
300
400
500
600
700
X
800
~
sensor plots
l...-,MMfitter
o
100
200
""__-=--J
300
400
500
600
700
X
800
Fig. 2. Filtering in route co-ordinates Fig. 3. Filtering with the IMM filter with dynamic mode management
But filtering in route co-ordinates requires of course the knowledge of the correct route. It was found that selecting the wrong route could even lead to filter instability. So a permanent search for potentially used routes and checking for leaving found routes is necessary. Junctions are a further problem. There route hypothesises have to be generated, evaluated and dropped if implausible. An algorithm able to cope with these problems was developed at DLR
The track is as smooth as using the route filter in figure 2 and route searching and checking is performed automatically in the background. Traffic objects may enter or leave the airport network of taxiways etc. at any point, the filter is detecting that. Even more the filter "knows" continually the topological position ("Taxiing on taxiway A") of the
333
traffic object. This mapping is a pre-requisite for further automatic assistant systems as e.g. planning tools. Exhaustive tests with that algorithms in a simulation environment were carried out in [6].
x
x
I I I
'I I
'I
• 4.2 Fluctuation ofNRN measurements on the aircraft surface
x
I
L
! ., .
If a sensor delivers a measurement associated with an aircraft the question arises what is the measured point of that aircraft. In case of co-operative sensor systems like e.g. DGPS or SSR Mode S Multilateration the case is quite clear - the antenna is localised. But in cases of non-co-operative sensors e.g. based on primary radar technology this point can be anywhere on the aircraft or even beside it. Even worse that point might fluctuate dynamically on the aircraft, depending on the aspect angle between sensor and aircraft.
!
I
•
.x
.
.:
x
•
•
x
Fig. 5. Along and Cross Track Error ofNRN Measurements The position differences are tagged according to the heading the aircraft had at the moment of measurement. So if a fluctuation on the aircraft surface is present one should recognise distinct clusters of measurements with the same tag. That would correspond with the fact that e.g. during one phase an engine is detected whether during a further phase e.g. the aircraft's tail is detected. Obviously this is not the case. Distinct clusters of tags are not recognisable. The NRN measurement noise is the more relevant effect compared to the fluctuation. An explanation for that behaviour of the NRN is, that it observes the traffic objects with its slave stations from all sides. So the results are probable different with a standard primary radar located on the roof of the tower.
Knowledge on such effects is essential for a data fusion algorithm design. If this fluctuation occurs and is not modelled a poor data fusion performance might be the result. If this effects are modelled carefully in each detail and do occur never, it would be a waste of engineering and real time computing resources. The NRN could be influenced by such effects because it relies on primary radar technique. In order to clarify the behaviour of the NRN, in spring of 1999 some strange trials were carried out with the D0228 on the airport of Braunschweig. The aircraft taxied several times clockwise and counter clockwise around the triangle of the taxiways A and B and the runway 26/08.
s:a
5. SUMMARY
26/08
An overview on DLR's A-SMGCS research with focus on the surveillance part is presented in this paper. It is stated that only a multi sensor system is capable to fulfil the requirements in this domain. The experimental A-SMGCS environment installed at the research airport Braunschweig is described. Two samples of data fusion algorithm developments at DLR and results of field tests that clarified special sensor characteristics were presented.
Fig. 4. Layout of the research airport Braunschweig In the evaluation of the trials the position difference between each NRN measurement and a high precision DGPS reference trajectory (accuracy of some centimetres) was determined. The differences are presented in figure 5.
334
7. REFERENCES
6. ABBREVIAnONS AOPG
Airport Operations Group
ARMI
ASR
Aircraft Registration Mark Identification Advanced Surface Movement Guidance and Control System Approach Surveillance Radar
ATC
Air Traffic Control
ATM
Air Traffic Management
ATTAS
Advanced Technology Testing Aircraft System All Weather Operation Panel
A-SMGCS
AWOP BETA CNS DBMS
Operational Benefit Evaluation by Testing an A-SMGCS Communication Navigation Surveillance Data Base Management System
FDDI
Demostration Facilities for Airport Movement Management Differential Global Positioning System Deutsches Zentrum fUr Luft- und Raumfahrt Fibre Distributed Data Interface
FOS
Fibre Optical Sensor
ICAO IMM
International Civial Aviation Organisation Interacting Multiple Model Filter
INS
Inertial Navigation System
LAN
Local Area Network
NRN
Near range Radar Network
OCR
Object Character Recognition
SDF
Sensor Data Fusion
SSR
Secondary Surveillance Radar
STDMA
TMA
Self Organising Time Division Multiple Access Taxi And Ramp Management And Control Terminal Manoeuvring Area
VHF
Very High Frequency
DEFAMM DGPS DLR
TARMAC
Bethke K.-H, Bilttner F., Lattner K., Meier Ch., ROde B., Schroth A. (1999). Evaluation of the Near-Range Radar Network (NRN) during Field Trials in a Sensor Data Fusion Experiment. In: A-SMGCS symposium proceedings, DGON, Bonn D5hler H.-U., Groll E., Hecker P. (1996). Automatic Recognition of Aircraft Registration Marks. In: DLR-Mitteilung 96-02, DLR, Koln Filrstenau N. et al. (1996). Fiber-Optic Sensors for Smart Taxiways. In: DLR-Mitteilung 96-02, DLR, K51n ICAO-AWOP (1997). Manual of Advanced Surface Movement Guidance and Control Systems (ASMGCS) Regional Provisions (EUR), Special AOPG meeting on A-SMGCS, Paris. Meier, Ch. (1998). Integrating topographical and topological data in the estimation of the actual traffic situation on airports. In: IRS98 conference proceedings, DGON, Bonn. Meier Ch. (1998). Datenfusionsverfahren fUr die automatische Erfassung des Rollverkehrs auf Flughlifen, DLR-FB 98-32, DLR, Koln
335
A MULTI-SENSOR-SYSTEM FOR ADVANCED SURFACE MOVEMENT GUIDANCE AND CONTROL CONCEPT AND FIRST RESULTS
Christopb Meier
DLR, Institute of Flight Guidance, Lilienthalplatz 7, D-38108 Braunschweig, Germany [email protected]
Abstract: Advanced Surface Movement Guidance and Control (A-SMGCS) is an increasingly important element in a seamless, overall Gate-to-Gate Air Traffic Management (ATM) system to ensure safe, efficient air traffic operations. The basis of an A-SMGCS is a surveillance system that automatically determines the traffic situation on the surface and in the neighbourhood of an airport. Only multi sensor systems are capable to meet the surveillance requirements of an A-SMGCS. DLR is conducting a major R&D effort on A-SMGCS and has developed solutions and prototype systems for the surveillance part. The technical evaluation is carried out on the Research Airport Braunschweig. The paper covers the concept, the solutions and sample results from field tests. Copyright @ 2000 IFA C Keywords: Multi Sensor System, Data Fusion, Air Traffic Control, Airport, A-SMGCS
routing' and guidance. Automatic surveillance is the basis for automation of further functions and therefore the first automation step. Automatic surveillance includes tracking and identification of all relevant traffic objects, situation assessment and conflict detection. Distributing this information to controllers as well as via data link to the cockpits would significantly enhance the situation awareness in this environment. This could help to avoid such catastrophes as 1977 in Tenerife, where two B747 collided on the runway - the worst accident of aviation history that occurred at the ground.
1. INTRODUCTION
Increasing air traffic in the last decades has led to important enhancements in air traffic control. It is e.g. possible to land aircraft safely in nearly all weather conditions. But a flight does not end with the touch down of the aircraft on the runway but at the gate. This last phase of a flight has been neglected too long and might become the bottleneck of the air traffic management of the future. Therefore new concepts are evolving under the title "Gate-to-Gate". Systems to support controllers as well as pilots in the management of aircraft on the surface of an airport and in the neighbouring airspace are meanwhile internationally termed "Advanced Surface Movement Guidance and Control System (A-SMGCS)".
DLR is conducting a major R&D effort on ASMGCS. Besides participation in main European projects like DEFAMM (Demonstration Facilities for Airport Movement Management) and aeronautical bodies like EUROCAE, DLR founded in 1997 its internal A-SMGCS project TARMAC (Taxi and Ramp Management and Control) to
In (ICAO 1997) it was defmed, that the main functions of an A-SMGCS are surveillance, control,
I Routing should better be tenned "planning" because in addition to the route also actions and sequences have to be generated.
329
hannonise the fonner independent internal R&D tasks in one integrated R&D platfonn. Starting probably with the year 2000 DLR will lead a new European project named BETA (operational Benefit Evaluation by Testing an A-SMGCS) with the objective to evaluate the operational benefit of ASMGCS implementations at two European airports.
but also the accuracy and correctness of detennined data can be enhanced. This multi sensor system philosophy changes the question "What is generally the best sensor" - that has been discussed for years - to "What is the optimal sensor set for the considered airport". The perfonnance of an individual sensor has not that impact anymore in a multi sensor system. Sensors have to fulfil the requirements as a team. Therefore sensors are already interesting to be integrated in the sensor set if they have one outstanding capability. A good example is the DLR development ARMI (see 3.1), that is doing nothing but identifying aircraft passing a certain point.
2. A-SMGCS SURVEILLANCE
2.1 Fundamental Requirements The main functional requirement for a Gate-to-Gate CNS-System is seamless tracking and identification of all relevant traffic objects. Relevant traffic objects are of course aircraft but also vehicles and obstacles that might endanger the safe or efficient traffic flow. In the DLR concept aircraft become interesting at the time they enter the TMA to approach the considered airport until they arrive at the stand and switch of their engines. This time advance is probably helpful for all kind of planning tasks. Departures are interesting when they request the first clearance until they leave the TMA.
Using such a multi sensor system has a further positive effect. It is not necessary to tune a single sensor system until it fulfils certain requirements. It opens the opportunity to spread the functionality on various sensors. This could lead to cost savings. A set of low cost sensors each with poor perfonnance might beat in the combination one expensive high end sensor. One important issue of DLR's multi sensor concept is its flexibility concerning the sensor selection. The developed data fusion software kernel is highly generic and can be used with a huge variety of sensor systems and therefore copes with a nearly infmite number of different sensor sets. Consulting concerning the optimal sensor set for a given airport with already given sensor and infrastructure can also be provided by DLR. Our principle is to first make use of existing equipment and infonnation and then to install further aiding sensors.
Vehicles and obstacles have to be tracked and identified / classified - as far as possible - when they are in the movement area. Certain vehicles like fire brigade, follow me's, towing tractors are possibly also of interest e.g. for fleet management purposes, when they operate on the aprons. All other traffic objects in the apron area like baggage cars are of nearly no interest and should not be tracked. Therefore automatic conflict detection is possible everywhere except for the aprons. But nonnally aircraft are moving slowly in these areas and airport stuff is monitoring e.g. push backs.
3. R&D ENVIRONMENT AT BRAUNSCHWEIG RESEARCH AIRPORT
2.2 Concept ofa Multi Sensor Solution Some years ago DLR decided to conduct a major R&D effort on A-SMGCS and to set up the necessary development platfonn at the DLR site in Braunschweig. This includes a A-SMGCS CNSSystem, a certain infrastructure on the research airport Braunschweig, specially equipped test traffic objects as well as simulation and evaluation tools.
No single sensor system is capable to fulfil the requirement of tracking and identifying all types of traffic objects in the mentioned area of interest. Only a multi sensor system is capable to do that, combining the single sensor strengths in a data fusion process. The multi sensor set can be composed by complementary and / or redundant sensor systems. The complementary sensor selection allows the required spatial and functional coverage, e.g. combining ASR and SMR to track objects in the TMA and on the airport surface or to combine SMR and SSR Mode S Multilateration to track non-cooperative as well as co-operative objects and to identify the latter ones. Redundant sensors enhance of course fault tolerance and continuity of service,
3.1 Experimental CNS-System The experimental CNS System is a multi sensor system composed of four types of sensors a data base to store a priori knowledge and the data fusion. The basic sensor set contains representatives of the main sensor categories:
330
• • •
detected reading out the change of distance between the end of the fibre and the mirror with an interferometer. This sensor principle is immune against electromagnetic impacts as e.g. lighting. Further it is sensible enough to be attached to the side of the taxiway instead of inserting it into the taxiways. The envisaged application is a local passage control sensor similar to today's induction loop. It could be instaIled at sensitive areas that need special protection against intruders e.g. at taxiway runways junctions
Area coverage versus local coverage Non-co-operative versus co-operative Active versus passive
Near range Radar Network: The Near range Radar Network (NRN) is developed by DLR's Institute of Communication and Navigation. It is an active, nonco-operative sensor system based on primary radar technology. Instead of observing the airport with one mechanical rotating antenna from the roof of the tower as today's Surface Movement Radar (SMR) the NRN uses modules of four smaIl low power radar stations observing the surrounded area. No mechanical rotating parts are needed, the NRN antennas are fixed sector antennas. Further today's SMR emits several Kilowatts of RF-Power in a narrow antenna pattern of approx. 0,25°. In contrast the NRN emits just 6W over a typical antenna sector of 90° in the azimuth. More information can be found in (Bethke et. al., 1999).
SSR Multilateration System: An SSR Mode A/C/S Multilateration system will be instaIled at Braunschweig airport within the year 2000. This cooperative passive sensor system detects all traffic objects equipped with a SSR transponder taxiing on the airport or flying in the neighbourhood of the airport. DBMS: The data base stores information on traffic objects and the airport. It contains e.g. possible identities, performance parameters of aircraft, the topography and topology of the airport network of taxiways and runways.
Global Positioning and Communication: The Global Positioning and Communication system (GP&C) is a local area DGPS system with integrated communication capability via a VHF data link. The system is developed by several Scandinavian companies and ATC authorities. It is a co-operative system, it needs a special onboard equipment - the GP&C transponder. Communication between the GPS reference station and the equipped traffic objects as weIl as between the traffic objects themselves is realised by a STDMA data link. AIl transponders receive the GPS correction data from the reference station via this channel. Further the position reports are exchanged between all transponders. In the Braunschweig experimental CNS system the transponder of the reference station is used to feed all the position reports to the data fusion.
Data Fusion: The data fusion system (Meier Ch., 1998) is developed by DLR's Institute of Flight Guidance. It fuses the partly situation descriptions of the sensor systems to a global situation representation using the complementary data parts to enhance the functional coverage of the system and the redundant data parts to enhance accuracy and correctness of data. This is possible due the independence of data sources, it is unlikely that sensors with different physical principles are effected by errors in the same manner at a considered time. To enhance the data fusion process a priori data from the data base are massively used - e.g. the fact that aircraft are using the taxiways and runways most of the time is used to enhance the situation tracking and prediction capabilities. The motion of the traffic objects is mapped to the network of possible routes, also taking into account that traffic object might enter or leave that network at any point. An added higher level of situation description is the result. The data fusion "knows" that an aircraft is not solely at position x=572,63 and y=930,83 but also that it is "Taxiing on Taxiway A".
Aircraft Registration Mark Identification: Two Aircraft Registration Mark Identification (ARMI) sensors (Dtlhler, et. aI., 1996) are presently instaIled in Braunschweig . The system is a in-house development of the image processing branch of DLR's Institute of Flight Guidance. The fact that all aircraft carry their registration on the tail of the fuselage is used to identify passing aircraft by OCR. A TV camera is starring at a taxiway section. Each video image is scanned for letters. Found letters are composed to words using plausibility rules concerning. Finally potential registrations are compared with a dictionary of expected aircraft.
Identification of traffic objects is a further important function of the data fusion. Nearly each new sensor system introduces a new addressing scheme to the A-SMGCS. At least the controIler must not be confronted by that technical addresses. He is used to work with caIl-signs. So the data fusion has to correlate between the different addresses of the
Fiber Optic Sensors: Sensors fully made of glass are a new sensor principle developed by DLR's Institute of Flight Guidance (FUrstenau, et. aI., 1996). In the Fiber Optic Sensor (FOS) a fibre is fixed oscillatory in front of a mirror. Vibrations of the fibre are
331
traffic objects in order to maintain a translation dictionary between all addressing schemes.
functionality is operational validated. Also the acceptance of the humans is tested. Further scenarios with or without a new system or new operational procedures can be compared to get estimates of the benefit of such modifications. Finally training of controller on new systems or procedures can be carried out.
3.2 Airport Infrastructure In order to technically evaluate the new CNS components a test implementation within a suitable infrastructure is absolutely necessary. Testing in simulation environment is the first step but at least simulation model parameters of sensors, traffic objects etc. have to be validated. Finally the new systems have to be tested themselves in a real environment because reality is much more cruel to technical systems than simulated scenarios.
3.5 Evaluation Tools In order to optimally use the data of the (expensive) real world trials recording and replaying tools are used. To develop the data fusion software trials with the test traffic objects are carried out for several days and data from all sensors are recorded. These multi sensor multi object files are replayed later multiple times to e.g. tune data fusion parameters or to test new algorithms.
Therefore DLR equipped the research airport Braunschweig with a glass fibre network and operates a high speed airport LAN based on FDDI. This LAN connects shelters distributed around the airport and DLR buildings. Sensor systems are mounted close to the shelters, their pre-processing equipment is installed within the shelters and central components as data fusion and data base are installed in the buildings of DLR. A bridge to the internet has proven its usefulness during first integration pre-tests of NRN (at that stage in the laboratory in Munich) and data fusion (in Braunschweig) across the internet.
Further these data are evaluated in a post processing to improve the sensor models. The main difficulty of this evaluation task is to split the files into multi sensor single object files or to associate the sensor measurements with the traffic objects. The normal airport traffic continues during the trials and additional traffic objects are also sensed by the nonco-operative sensor systems. Sensor specific heuristic processing supervised and plausibility checked by a human operator is used to obtain a correct reference solution.
3.3 Test Traffic Objects 4. SAMPLES OF FIELD TEST RESULTS
DLR uses the following test aircraft and vehicles for A-SMGCS trials: • VFW 614 (ATTAS) as sample of a cooperatively equipped aircraft • D0228 as sample of a co-operatively equipped aircraft • Mercedes Test Van as sample of a cooperatively equipped vehicle • Ford Test Van as sample of a non-cooperatively vehicle The test equipment contains e.g. a high precision DGPS system and an INS platform for reference trajectory purposes.
The multi sensor system concept is a quite general solution for surveillance of taxiing aircraft with a lot of degrees of freedom for implementation (e.g. sensor set composition, data fusion methods, system architecture). Since multi sensor systems are used in different applications (e.g. military) for years there is no doubt in the general feasibility of such a solution. The scientific and engineering tasks are e.g. to adapt existing and develop enhanced methods for data fusion or to investigate special topics to decide between design alternatives. In the following one example of data fusion technique developments and one example of special investigations are presented.
3.4 Simulation Facilities For operational testing with pilots and controllers in the loop DLR is operating an airport simulator with tower controllers, apron controllers and pilots working environment. The traffic objects appear using predefined scenarios. The further situation depends on controller and pseudo-pilots actions.
4. I IMM Filtering ofposition plots using the airport layout as a priori knowledge Position measurements - or plots - associated with a single traffic object coming from sensors are normally noisy due to various reasons. A smoothing process using some kind of Kalman filtering seems
With this environment the interaction between humans and systems can be analysed. The system
332
to be appropriate. Kalman filtering uses stochastically sensor and system models. A widely used system model is that of a point mass that moves with constant speed through the mathematical 2D space and is effected by random accelerations. Implementing this model to the A-SMGCS data fusion led to predictable track losses in curves. The accelerations during the curve are of course not random as the model assumes. The Kalman filter predicts the movement straight ahead because it is not aware of the airport layout. One solution is to increase the order of expected random accelerations. But this decreases the smoothing capabilities of the Kalman filter. The following figure presents simulated data filtered with a standard Kalman filter (state vector x, y, v x, vy).
(Meier, Ch., 1998) on the basis of IMM (Interacting Multiple Model) filtering. IMM filtering was developed strictly speaking for systems that stay for a certain time in a certain mode. After a time they switch over to a further mode. The different modes of behaviour are represented by one system model each. Mode switching is modeled by a first order Markov chain. So we have a bank of Kalman filters, one for each possible system mode and one extra filter for keeping track of the active mode. The evaluation of the individual Kalman filters is done using the innovation when processing a measurement. If the prediction of a Kalman filters fits well with a measurement, this mode is given a high probability. An important feature is the soft decision between the different modes. Single outliers are not leading to detection of mode switches.
·100 . . - - - - - - - - - - - - - - - - - - ,
y
A drawback of the standard IMM is that the number of system modes has to be fixed. The models have to be known a priori. Applying the IMM algorithm to the A-SMGCS data fusion task needs a dynamic mode management. The IMM was use in that way that one filter was tracking the movement in world co-ordinates, representing the hypothesis that the traffic object is really using no route, that it is e.g. travelling just by chance close to a taxiway. A further filter tracking in route co-ordinates is than created for each possible route and is dropped when this route becomes implausible. The search for new possible routes was aided by some rules of thumb. The dynamic mode managment was solved by renormalisation of the IMM after mode insertion or deletion - mathematical doubtful but working stable. The result is presented in figure 3.
-200
-300 ~~Lreference track ~
, ~oo
I
sensor pkHs :-s1andard fitter :
>(
l...-_...-
o
100
200
1 I
......._
_ . _ - - - - -.......,---J
300
400
500
600
7
800
Fig I. Filtering with a standard Kalman filter The airport layout should be taken into account. Just switching over from filtering in world co-ordinates as in figure I to filtering in route co-ordinates (x is progress on the route and y is the deviation from the route) enhances the smoothing effect as shown in figure 2. -100
y
r-----------------, ·100
y
r-----------------,
-200 -200
-300 -300 , ~OO
•
I-route fitter
~
o
:_ _elerence treck :
sensor plots
100
200
•
...,J._-------"--_==_....I
300
400
500
600
700
X
800
~
sensor plots
l...-,MMfitter
o
100
200
""__-=--J
300
400
500
600
700
X
800
Fig. 2. Filtering in route co-ordinates Fig. 3. Filtering with the IMM filter with dynamic mode management
But filtering in route co-ordinates requires of course the knowledge of the correct route. It was found that selecting the wrong route could even lead to filter instability. So a permanent search for potentially used routes and checking for leaving found routes is necessary. Junctions are a further problem. There route hypothesises have to be generated, evaluated and dropped if implausible. An algorithm able to cope with these problems was developed at DLR
The track is as smooth as using the route filter in figure 2 and route searching and checking is performed automatically in the background. Traffic objects may enter or leave the airport network of taxiways etc. at any point, the filter is detecting that. Even more the filter "knows" continually the topological position ("Taxiing on taxiway A") of the
333
traffic object. This mapping is a pre-requisite for further automatic assistant systems as e.g. planning tools. Exhaustive tests with that algorithms in a simulation environment were carried out in [6].
x
x
I I I
'I I
'I
• 4.2 Fluctuation ofNRN measurements on the aircraft surface
x
I
L
! ., .
If a sensor delivers a measurement associated with an aircraft the question arises what is the measured point of that aircraft. In case of co-operative sensor systems like e.g. DGPS or SSR Mode S Multilateration the case is quite clear - the antenna is localised. But in cases of non-co-operative sensors e.g. based on primary radar technology this point can be anywhere on the aircraft or even beside it. Even worse that point might fluctuate dynamically on the aircraft, depending on the aspect angle between sensor and aircraft.
!
I
•
.x
.
.:
x
•
•
x
Fig. 5. Along and Cross Track Error ofNRN Measurements The position differences are tagged according to the heading the aircraft had at the moment of measurement. So if a fluctuation on the aircraft surface is present one should recognise distinct clusters of measurements with the same tag. That would correspond with the fact that e.g. during one phase an engine is detected whether during a further phase e.g. the aircraft's tail is detected. Obviously this is not the case. Distinct clusters of tags are not recognisable. The NRN measurement noise is the more relevant effect compared to the fluctuation. An explanation for that behaviour of the NRN is, that it observes the traffic objects with its slave stations from all sides. So the results are probable different with a standard primary radar located on the roof of the tower.
Knowledge on such effects is essential for a data fusion algorithm design. If this fluctuation occurs and is not modelled a poor data fusion performance might be the result. If this effects are modelled carefully in each detail and do occur never, it would be a waste of engineering and real time computing resources. The NRN could be influenced by such effects because it relies on primary radar technique. In order to clarify the behaviour of the NRN, in spring of 1999 some strange trials were carried out with the D0228 on the airport of Braunschweig. The aircraft taxied several times clockwise and counter clockwise around the triangle of the taxiways A and B and the runway 26/08.
s:a
5. SUMMARY
26/08
An overview on DLR's A-SMGCS research with focus on the surveillance part is presented in this paper. It is stated that only a multi sensor system is capable to fulfil the requirements in this domain. The experimental A-SMGCS environment installed at the research airport Braunschweig is described. Two samples of data fusion algorithm developments at DLR and results of field tests that clarified special sensor characteristics were presented.
Fig. 4. Layout of the research airport Braunschweig In the evaluation of the trials the position difference between each NRN measurement and a high precision DGPS reference trajectory (accuracy of some centimetres) was determined. The differences are presented in figure 5.
334
7. REFERENCES
6. ABBREVIAnONS AOPG
Airport Operations Group
ARMI
ASR
Aircraft Registration Mark Identification Advanced Surface Movement Guidance and Control System Approach Surveillance Radar
ATC
Air Traffic Control
ATM
Air Traffic Management
ATTAS
Advanced Technology Testing Aircraft System All Weather Operation Panel
A-SMGCS
AWOP BETA CNS DBMS
Operational Benefit Evaluation by Testing an A-SMGCS Communication Navigation Surveillance Data Base Management System
FDDI
Demostration Facilities for Airport Movement Management Differential Global Positioning System Deutsches Zentrum fUr Luft- und Raumfahrt Fibre Distributed Data Interface
FOS
Fibre Optical Sensor
ICAO IMM
International Civial Aviation Organisation Interacting Multiple Model Filter
INS
Inertial Navigation System
LAN
Local Area Network
NRN
Near range Radar Network
OCR
Object Character Recognition
SDF
Sensor Data Fusion
SSR
Secondary Surveillance Radar
STDMA
TMA
Self Organising Time Division Multiple Access Taxi And Ramp Management And Control Terminal Manoeuvring Area
VHF
Very High Frequency
DEFAMM DGPS DLR
TARMAC
Bethke K.-H, Bilttner F., Lattner K., Meier Ch., ROde B., Schroth A. (1999). Evaluation of the Near-Range Radar Network (NRN) during Field Trials in a Sensor Data Fusion Experiment. In: A-SMGCS symposium proceedings, DGON, Bonn D5hler H.-U., Groll E., Hecker P. (1996). Automatic Recognition of Aircraft Registration Marks. In: DLR-Mitteilung 96-02, DLR, Koln Filrstenau N. et al. (1996). Fiber-Optic Sensors for Smart Taxiways. In: DLR-Mitteilung 96-02, DLR, K51n ICAO-AWOP (1997). Manual of Advanced Surface Movement Guidance and Control Systems (ASMGCS) Regional Provisions (EUR), Special AOPG meeting on A-SMGCS, Paris. Meier, Ch. (1998). Integrating topographical and topological data in the estimation of the actual traffic situation on airports. In: IRS98 conference proceedings, DGON, Bonn. Meier Ch. (1998). Datenfusionsverfahren fUr die automatische Erfassung des Rollverkehrs auf Flughlifen, DLR-FB 98-32, DLR, Koln
335