Research Grant Scheme Braunschweig

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1 Research Grant Scheme Braunschweig Creating wae vortex awareness for pilots and controllers 1 st year progress report INTRODUCTION Most major airports are operating near their capacity limits today and consequently often face significant delays. The forecasted traffic growth of 5% annually [Vision 00] cannot be accommodated without airport extensions and/or significant changes in procedures. The limiting factor usually is the airside capacity, more specifically the separations imposed to avoid flying into wae vortices from preceding aircraft, for both tae-off and landing. The present rules, under national responsibility but generally following the International Civil Aviation Organisation (ICAO) Procedures for Air Navigation Services Air Traffic Management (PANS-ATM), were set up in the 1970s based on the contemporary traffic mix and with simplified, but conservative assumptions lie extremely calm air (i.e. virtually no turbulence) and absence of lateral winds. Set up for a worst-case scenario, it is obvious that during favourable, even average conditions the separations are overly conservative and unnecessarily limit airport capacities. Regarding flight safety, experience of the past three decades proves the separation standards to be sufficiently safe. A capacity increase brought about by any change in separation rules needs at least to preserve (or, given the expected traffic growth, even improve) the current safety level. This is the aim of Wae Vortex Avoidance Systems lie NASA s AVOSS or DLR s WSVBS projected for Franfurt Rhein-Main Airport. All these systems include the following elements: o measurement of relevant meteorological parameters (wind vector over height, temperature stratification, eddy dissipation rate, ) o short-term prediction (nowcasting) of the meteorological conditions along the approach path o simplified prediction of parameters of generated waes (circulation, vortex spacing) o prediction of wae vortex transport (and to a lesser extent decay) to determine when the approach path will be free of hazardous levels of vorticity Most system designs also include wae vortex monitoring, usually based on LIDAR, to verify the wae vortex transport predictions as an important element of the safety net. LIDAR is also propagated as a tool for on-board wae vortex detection. Due to the limitations of LIDAR use in fog and heavy rain also X-band RADAR is coming into focus as an alternative, with the basic operational principle being the same. Both detection tools, however, have the following major drawbacs: their measurement accuracy is quite limited partly due to the limited quality of the models for the velocity distribution to be expected, and their field of view is narrow, so that it is essential to now relatively precisely where to search for a wae vortex. These disadvantages amplify each other: from an inaccurate measurement, no accurate prediction can be obtained so that the search volume cannot be significantly reduced for the next pass of the sensor beam. As a consequence, large safety margins are necessary with regard to the vortex location. A further disadvantage especially for an airborne warning system is that the identification of the flow field is difficult. So we have both the possibility of underestimating circulation and even the complete lac of identification of mature vortices when their flow field differs much from two distinct co-rotating vortices. These effects need to be compensated with appropriate safety margins which in turn decrease capacity and increase false alarm rate.

2 Creating wae vortex awareness for pilots and controllers INTRODUCTION OF CONCEPT The present research activity focuses on creating wae vortex awareness for pilots and controllers by using the positive properties of both wae vortex prediction models and wae vortex detection sensors. The research is rather concentrating on the collaboration of model and sensor characteristics than on the global system design, i.e. the system under research can be used either as an on-board system directly delivering wae vortex awareness to the pilots or as a ground system directly delivering wae vortex awareness to air traffic controllers and secondarily to pilots e.g. via broadcast. In more detail the system will deliver the same ind of information as sole model prediction or sole sensor measurement to any superior awareness system but with an increased level of accuracy, continuity, integrity and availability. The first research year focused on the general concept and first proof of concept evaluation. Since the proposed collaboration of model and sensor measurement within the scope of wae votices has not been researched before at least there are no publications available general definitions of such a collaboration have been defined during this first research year. Additionally three simple collaboration layouts have been tested as proof-of-concept, basically to test the general interfaces between wae vortex prediction and measurement. The approach investigated in the scope of this research is coupling of prediction and detection of wae vortices using an observation or estimation filter, the best nown example of which is certainly the KALMAN filter. It has the tas to combine noisy sensor outputs in order to estimate the state of a system with uncertain dynamics. Building on BAYES s rule, the observation filter will find the mathematical optimal estimation for the system states based on the history of the erroneous and incomplete measurements and a mathematical model for the system dynamics. It generally operates in two not necessarily alternating steps: a time update step, where the system state is predicted based on the current state, and a measurement update which is obviously performed when new sensor data are available. This principle is depicted in Figure 1 by means of a simple example: we want to trac an object from a reference point, i.e. to determine its range r and bearing θ. The measurement may be noisy and infrequent, but we may mae use of the nown dynamics of the object by predicting its behaviour between measurements. Model Prediction NED COS r Θ Attitude Body COS NED COS Figure 1: Tracing problem example: tracing an object based on observations from a reference point The present approach uses such integration filters to enhance the observation and prediction capabilities for wae vortices. In contrast to some existing simple tracing algorithms for the internal processing of the detection sensor where internally the wae vortex movement or even the velocity profile is propagated with certain motion models, the proposed system uses established wae vortex prediction models for the collaboration. This is a novel approach so as to introduce the concept let us first discuss an established implementation. In the field of aviation, the classical application for such filters is integrated navigation for aircraft: In order page of 17

3 Creating wae vortex awareness for pilots and controllers to provide the necessary parameters for flight control, all states of the aircraft need to be observed (for a rigid aeroplane this includes at least position and velocity vector, attitude and heading, and body rates). Not all of these quantities are easily or sufficiently accurately measurable: The usual approach is to employ Inertial Reference Systems (IRSs), which in turn include an Inertial Measurement Unit (IMU) providing accelerations and body rates and process the input from the GPS sensor, which may be position and velocity or raw measurements (pseudo ranges). The sensors are imperfect in the sense that, besides the omnipresent sensor noise, the inertial sensors have biases which lead to drifting integrated quantities and the GPS sensor has a good absolute accuracy but non-zero mean error and noise levels which preclude the determination of derivatives, in addition to a low measurement frequency. Moreover, GPS is susceptible to large errors to an extent which is not compatible with the integrity requirements. The integration filter combines these sensor inputs based on a mathematical model of the aircraft inematics, the so-called navigation equation. This navigation equation cannot tae into account disturbances from the atmosphere and does not regard pilot control input such that its predictions are necessarily inaccurate even if the current state were perfectly nown. Nonetheless, it can be shown that with these inputs the coupling filter determines the optimum estimation of system parameters including those not observed but included in the system dynamics. Several types of filters are in use, besides the well nown Kalman Filter (KF) which requires linear (or linearised) system dynamics also the Extended Kalman Filter (EKF) that is nonlinear in the propagation of the system dynamics but not for the covariances propagation. The Unscented Kalman Filter (UKF) that is fully non-linear is investigated in research applications but not yet used in operational systems. All filters have the underlying assumption that the measurement and system modelling errors are Gaussian, i.e. normally distributed with zero mean. This is obviously not true for some types of errors, so the modelling of errors as additional states is an important part of the successful implementation. For applications such as terrain following, so-called Particle Filters (PF) have been successfully used. These filters employ Sequential Monte Carlo Methods (SMC) and hence are non-deterministic (meaning that different output is generated if the filter is run again with exactly the same input) but they allow to model arbitrary probability density functions for measurement and process noise. The IFF has decades of experience in fusion filters with all types of sensors common in aviation (IMUs, GPS, radio navigation aids, LIDAR and RADAR, optical systems, ) and in the modelling of wae vortex behaviour, as evidenced in the literature ([3][4]). With this bacground, this approach proposes to apply the integrated filter concept to the detection and monitoring of wae vortices. The wae detection by a sensor such as LIDAR or RADAR irrespective of its installation on ground or on-board will be coupled with wae prediction by modelling. This sensor/model fusion has the potential to significantly improve the precision and reliability of wae alerting. Individual advantages lie the high trustworthiness of physical wae detection and the forecasting ability of wae prediction would remain. Additionally, fusion would allow improvement of wae characterisation, decrease uncertainties in prediction, and limit the number of unnecessary alerts, which is important for acceptance and thus operational viability of any wae vortex warning system. Additionally the system could deliver more reliable a priori information about the position page 3 of 17

4 Creating wae vortex awareness for pilots and controllers and the characteristics (e.g. flow field, velocity distribution) of the wae vortices to the sensor. One example is the lateral and vertical transport of wae vortices IGE, where the sensor has no a priori information on the lateral movement induced by the imaginary vortices or on the vertical rebound induced by secondary and tertiary vortices [3], [5]. WAKE VORTEX PREDICTION MODELS Currently there are three more or less sophisticated models for wae vortex behaviour estimation available, NASA s Aircraft Vortex Spacing System Prediction Algorithm (AVOSS-PA, or APA), UCL s DVM (Deterministic Wae Vortex Model) and DLR s DP (Deterministic Two-Phase Wae Vortex Decay and Transport Model). The latter two come together with a probabilistic version of prediction (PVM and PP). All models propagate the lateral and vertical wae vortex transport and the wae vortex decay. The input required by the models comprises aircraft configuration parameters (lie weight, wing span and speed) as well as weather conditions data (wind profile over height, temperature stratification, turbulence parameters). It is also important to have information about the position of the wae generating aircraft relatively to the ground, as it influences the vortex behaviour. The accuracy and actuality of this input is crucial for correct prediction results and is also the main problem. Although the deterministic prediction of the models is quite good in mean, they do not contain any a posteriori information of incoming LIDAR or RADAR measurements. An additional drawbac of the models can be seen in the increasing uncertainty bounds over time. For some prediction models, also the deterministic prediction can vary to larger extents from the measurements. page 4 of 17

5 Creating wae vortex awareness for pilots and controllers WAKE VORTEX MEASUREMENT TECHNOLOGIES For the remote detection of wae vortices two main technologies exist. The LIDAR system is measuring the line-of-sight velocity of the aerosols in a certain scanning pattern. From the velocity distribution the position of the wae vortex is determined e.g. via the slopes of the tangential velocity distribution. For the LIDAR sensor, the circulation strength can be determined by fitting theoretical velocity distributions and using certain areas of the velocity distribution (Γ 5 15 ). Although position and circulation determination of wae vortices with a LIDAR is quite mature in post-process, the real-time application comes at high computational costs and has to be conducted fully automatically and is thus for example susceptible to secondary vortices as they occur e.g. IGE. In the consequence, the quality of measurement results can suffer from over- or underestimation of the vortex strength as well as from noise. The narrow field of view of the LIDAR sensor in combination with the relatively low update rate maes it also prone to loss of trac. This problem might not be very pressing for a ground installation that usually scans in a limited plane at or before the runway threshold where vortices of passing aircrafts most probably occur. But it becomes very urgent if sensor is to be used on board an aircraft with a large search space that can not be scanned by the LIDAR in appropriate time. Figure : Tangential velocity profile of counter-rotating wae vortex pair [6] Contrary to LIDAR, whose operability is limited in moisture conditions, RADAR is providing wae vortex detection in all weather conditions. The basic measurement principle of the RADAR for wae vortex detection is utilising the radial pressure gradient in the wae vortex for localising the wae vortex. Circulation determination is also possible with a RADAR. As for this research, the data base with exclusively LIDAR measurements is available it has been decided to concentrate on this sensor first. Nevertheless, after the basic fusion process has been developed it shall be possible to use also alternative or additional sensors lie RADAR. page 5 of 17

6 Creating wae vortex awareness for pilots and controllers THE FUSION CONCEPT Since the investigated fusion approach is rather new for wae vortex tracing applications, some fundamental definitions shall be introduced here. Those definitions mainly stem from the field of integrated navigation systems. Several methods of coupling models with measurements exist. Some of them will be introduced here. They mainly differ in the way, how the models are integrated into the fused system and how the measurements are fed to the system. First one has to differ between error state fusion and full state (or total state) fusion. In an error state approach, the fusion filter is estimating the errors of the model prediction, e.g. in our case the lateral and vertical wae vortex position error and the error in wae vortex strength prediction. In an open-loop configuration as presented in Figure 3, the wae vortex prediction models would be corrected by the estimated errors. This approach has the advantage, that the measurement and also the wae vortex prediction model remain untouched by the fusion filter. Only the outputs are corrected. Such open loop system are usually implemented, if due to several reasons the sensing system and the prediction system shall be independent from any other system. The fusion system would be superimposed, ensuring self-contained measurement and model prediction beside the fused output. The main drawbac of the open-loop structure is the decreased benefit compared to other fusion concepts. Figure 3: Loose-coupled open-loop error state system If the estimated errors are fed bac to the models, this can be interpreted as a closed loop system. Thereby the models correct themselves with the estimated errors. Figure 4: Loose-coupled closed-loop error state system page 6 of 17

7 Creating wae vortex awareness for pilots and controllers The closed-loop approach would need an input interface of the prediction models. Since the models capture the estimated errors in this case, the error covariance prediction are consistent with the wae vortex model prediction such that the covariance can be used for calculating the uncertainty (sigma-bounds). Not only the observable state quantities lie wae vortex position and strength could be observed in the system. It should be envisaged to also incorporate error estimations of meteorological information lie for example head- and crosswind errors: x = [ Δy Δz ΔΓ Δ Δ K] u w v w (1) In a tight coupled error state approach (see Figure 5) the system would not use the position output from the LIDAR or RADAR sensor, but would use more detailed sensor characteristics such as range and bearing. This would bring advantages especially for on-board systems, where the bearing of the sensor is also predicted between the sensor measurements (taing into account the movement, attitude and heading of the aircraft), so that loss of trac would be minimised. Figure 5: Tight-coupled closed-loop error state system In contrast to the error state approach, Figure 6 shows a loose coupled total state system. This system would not use separate prediction models, but would incorporate the prediction models within the fusion filter propagation step. This approach is the most favourable, since by its use the state quantities could be observed best (e.g. crosswind or crosswind error could be observed by the lateral movement of the wae vortices taing into account the vortex height and circulation strength). page 7 of 17

8 Creating wae vortex awareness for pilots and controllers Figure 6: Loose-coupled total state system The total state approach can be also realised as a tight coupled system utilising more detailed sensor quantities. The presented fusion approaches so far mainly enhance the abilities of the prediction models. If the next step to enhance the sensor is to be taen, a deep coupled approach is most favourable. In this approach (see Figure 7) the processing unit of the sensor is incorporated into the fusion filter. Thereby the fusion filter also contains a model for example of the Doppler spectra for a LIDAR sensor, so that the processing of the sensor raw data has a priori information for position and strength determination and also ambient information (e.g. about the position of secondary or tertiary vortices IGE which are out of scope for the determination of the primary vortex). Such deep-coupled approach is out of scope of this research activity. Nevertheless, the definition of such system is given here for sae of completeness. Figure 7: Deep-coupled total state system page 8 of 17

9 Creating wae vortex awareness for pilots and controllers CONCEPT IMPLEMENTATION As has been shown before, there are several ways for implementation of the fusion concept that differ in terms of complexity, coupling level and effect on the accuracy of prediction and detection. Also different filter types may be used (KF, EKF, UKF, PF, Filter Ban, etc.), with increasing complexity. Thus, in the course of this wor the following steps have been done, beginning with a simple Kalman Filter using a second order linear model of vortex behaviour, proceeding to the implementation of a more sophisticated wae vortex transport and decay model and finally resulting in a loose-coupled error state system with error feedbac to the model. These steps will be described in this chapter. The development progress is divided into following sections: bridging the time between sensor measurement by means of simple dynamic models error estimator for sophisticated wae vortex prediction model with simple dynamic model error estimator for sophisticated wae vortex prediction model with propagation model of estimation errors total estimator for wae vortex prediction This first year report is describing the first two bullet points. To show the general functions of the fused prediction and detection algorithm, a simple Kalman Filter has been implemented in the first step with a simple second order model to predict wae vortex dynamics. With the help of this simple algorithm, the basic equations and relationships of the integration filters will be introduced. Simple wae vortex tracing algorithm The basic differential equation of the tracing algorithm is x& = F () x The system state contains the lateral and vertical position, velocity and acceleration of the wae vortices and the wae vortex strength and decay rate: [ y y& && y z z& && z Γ Γ ] x = & (3) The system dynamic matrix assumes a constant acceleration for the transport and a constant decay for the wae vortices: = F (4) page 9 of 17

10 Creating wae vortex awareness for pilots and controllers Within the time update step of the Kalman filter, the system is propagated in time with a frequency of 50 Hz t 0 : xˆ 1 + Γu (5) = Φxˆ + The state transition matrix is derived with a first order truncated Taylor series expansion lie it is done in common Kalman filtering techniques: 1 [( si F ) ] ( Ft) 1 Φ( t) = L = I + Ft + +. (6)! The steering vector u in Equ. (5) is not used in this simple approach, but it is planned to be used for meteorological input for the wae vortex prediction models in later implementations. The state covariances (which represent the 1-sigma uncertainty bounds) are propagated in time as stated in Equ. (7): T = ΦP Φ Q +1 (7) P + The additive process noise covariance matrix Q is used to fine-tune the system, i.e. adjusting the trust in the model prediction. Q is applied in Kalman filtering to cover the uncertainties generated by imperfect dynamic modelling. Within the measurement update step of the Kalman filter, which is performed when new sensor (LIDAR or RADAR) measurements are available, first the Kalman gain is calculated: T T ( H P H + R ) 1 K = P H (8) For the measurement uncertainty (measurement noise matrix R ) dedicated quantities of the sensor can be used, e.g. covariances from the sensor processing algorithms or adapted signalto-noise ratio. Since those values are not available on all databases, a fixed uncertainty value is used at the moment. The system state itself is corrected by the incoming measurements (for a loose-coupled approach position and circulation strength): ( z H xˆ ) xˆ ˆ (9) + = x + K The magnitude of the correction is influenced by the proportion of system and measurement uncertainty in the Kalman gain (see Equ. (8)). Additionally the state covariances are corrected by the incoming measurements: ( I K H ) P + = P (10) page 10 of 17

11 Creating wae vortex awareness for pilots and controllers Figure 8 shows an example of the lateral and vertical transport of wae vortices IGE estimated by the fusion filter (blue line) vs. LIDAR measurements (red circles and a DP reference (green line). At this implementation stage the wae vortex behaviour had not been modelled yet, however the results can be regarded as very positive. The circulation strength shows a two phase decay, a rebound of the wae vortices and the lateral movement due to crosswind are also visible, although the model delivers no information on these quantities. Figure 8: Circulation and wae vortex transport of the simple tracing algorithm But it is obvious that with realistic modelling of wae vortex behaviour the system will generate a better performance, especially for cases with poorer measurement quality than in the case shown above. Therefore the next implementation step was to adapt one of the currently available models for filter use. Of the previously mentioned models, DP was chosen and the published model equations and algorithms (e.g. in [5]) were implemented and shall be described here in brief. DP model implementation For utilizing a sophisticated wae vortex prediction model we choose the published algorithms of DLRs Deterministic Phase Wae Vortex Decay and Transport Model (DP) ([5], [9], [10], [11]). DP provides predictions about wae vortex decay in form of circulation strength calculation, which are done in two phases. The first phase taes place out of ground effect and is caused by turbulent diffusion which is described by the first part of Equ. (11), followed by a rapid decay phase parameterised by the full equation R R ) = A exp( ) exp( ν 1 ( t T1 ) ν ( t T ) (11) Γ5 15 ( t ) The first decay phase does not depend on atmospheric influences whereas during the second phase effective viscosity ν and start time T are functions of standardised eddy dissipation rateε and atmospheric temperature stratification N. For wae vortex trajectory calculation several mechanisms are taen into account, lie inground effect (IGE), influence of secondary and tertiary vortices as well as lateral and vertical wind. page 11 of 17

12 Creating wae vortex awareness for pilots and controllers Vertical vortex transport down to the IGE onset level z b0 is derived from the velocity profile of two superimposed Lamb-Oseen vortices and vortex spacing r = b0 Δ z r = 1 exp 1,57 Δt r c (1) z b0 After the vortex has reached, secondary and later tertiary vortices are modelled that influence the primary vortex and cause its reascension. Lateral transport is primarily modelled by the initialisation of imaginary vortices according to the same relation as expressed by Equ. (1) with r = z. The additional lateral wind in fluence is described by Δy = v Δt. The model needs information about the aircraft type (wing span, aircraft speed and mass) and the initial vortex state (circulation strength and position, e.g. from a first measurement and aircraft position) as well as meteorological data (wind vector and temperature distribution over height and turbulence information, e.g. the eddy dissipation rate). The more accurate and current this input is, the more probable the prediction can be. Figure 9: Results of the implemented DP prediction algorithms (Matlab) and simulated LIDAR measurements Adoption of the DP model within error state system At the current implementation stage the DP model has been implemented in a loose-coupled closed-loop error state system, as presented in Figure 4. Figure 10 demonstrates the procedure of prediction and measurement integration. The prediction is the time update of the system and delivers the system state containing the wae vortex strength and position that are being calculated according to the algorithm described above. If a new measurement is available, the filter compares the predicted state to information it has from the measurement. It determines the difference between prediction and measurement and on this basis calculates the error which is fed bac to the model and used for its correction. Again, the magnitude of correction page 1 of 17

13 Creating wae vortex awareness for pilots and controllers is influenced by the Kalman gain to account for the uncertainty of both the model and the measurements. The filter state contains the errors in circulation, decay rate, lateral and vertical position and its change: [ ΔΓ ΔΓ& Δy Δy& Δz Δz& ] T x = (13) Equ. (13) and subsequent are separately calculated for both the port and starboard vortex. During the program flow first the normalised time is projecte d: t +1 = t1 + dt (14) If no LIDAR measurements are available, the error state of the filter is propagated in time with the dynamic matrix: F = (15) The state itself is propagated according to Equ. (5), (6) and (7). Since the error state filter is utilised as a zero-feedbac filter, the propagated state is set to zero. Only the error covariance is propagated in time between the available LIDAR measurements. If LIDAR measurements are available, the filter correction step is conducted. The measurement vector is filled with the errors between LIDAR measurement and estimated model prediction: [( Γ Γ ) 0 ( y y ) 0 ( z z ) ] T zˆ = 0 (16) LIDAR, DP, LIDAR, DP, LIDAR, DP, The KALMAN gain is calculated according to Equ. (8), the error covariance is updated as indicated in Equ. (10). The state itself is updated as described in Equ. ( 9). After the measurement update, the current DP estimations are corrected by the filter output: Γ y z + + DP, DP, + DP, + DP, = Γ = y = z DP, DP, ΔΓ Δy Δz + + (17) To ensure the further recognition of the estimated errors in DP between the incoming LIDAR measurements, the modelling of circulation decay is discretised: Γ + 1 = Γ + Γ& dt (18) page 13 of 17

14 Creating wae vortex awareness for pilots and controllers During the first decay phase the rate of decay reads: Γ& = ν 1 C ( t T ) 1 e ν C 1 1 ( t T ) (19) For the rapid decay phase the decay rate is complemented by: Γ& = ν C ( t T ) e ν C ( t T ) (0) Figure 10: Integrated wae vortex prediction and detection algorithm FIRST RESULTS The previously described error state corrector with the simple error propagation model was used to assess the proof-of-concept of the collaboration. Figure 11 and Figure 1 show two examples of this system applied to simulated LIDAR measurements to demonstrate the benefits of the collaborative approach compared to sole prediction. For this purpose, crosswind input to the prediction model was superimposed with artificial errors. page 14 of 17

15 Creating wae vortex awareness for pilots and controllers Figure 11: Example 1 for closed loop error state corrector Example 1 shows one test case with constant offset in crosswind determination and example introduces crosswind errors at certain altitudes. In both cases, the fused system showed better performance in terms of accuracy than the sole prediction. It should be mentioned that the system was provided with accuracy information for the LIDAR measurements that are required for the measurement covariance matrix in Equ. (8) as these values were delivered by the simulation. This information affects the performance to a great extent and it will be beneficial if similar data is available for real measurements (e.g. adapted signal-to-noise ratio). Figure 1: Example for closed loop error state corrector page 15 of 17

16 Creating wae vortex awareness for pilots and controllers OUTLOOK For creating wae vortex awareness for pilots and controllers increased accuracy, continuity, integrity and availability of information about the current position and strength of wae vortices is demanded. To reach this goal the collaboration of wae vortex prediction and wae vortex measurement is favoured within the present research activity. During the first year of this research general definitions were introduced for the different modes of collaboration between wae vortex prediction and measurement. Based on these definitions, three different approaches were developed and tested as proof-of-concept. The system described still uses the DP predictions in a separate module and the possible corrections are limited to the feedbac of error state. As the next development step the derivation of model ordinary differential equations (ODE) is planned in order to incorporate them into the fusion filter algorithm and to use them directly within the propagation step. This approach will allow observation of relevant parameters lie e.g. wind and will lead to a more accurate vortex state and reduce the uncertainty. Additionally the error state system will be further developed to incorporate a model which propagates the errors in wae vortex prediction. Such error state modelling comes along with the advantage of eeping the prediction and detection parts rather untouched, as it is not the case for the total state system. The results of a full state system of this ind shall then be compared to the performance of prediction-only or measurement-only. The basis for this shall be the data basis of measurement campaigns provided by EUROCONTROL (e.g. data from Franfurt, Charlesde-Gaulle or London Heathrow). This process will request an intermediate step to investigate how the influence of turbulence can be considered in the system, as the parameter EDR (eddy dissipation rate) required by the prediction algorithms has not been measured during the campaigns. Benchmaring of the results will also help to optimise filter algorithms and to adjust the model/measurements uncertainties. As the chosen model has already been assessed in various experiments, these experiences can be used. The sensor and its measurement qualities shall be object to a closer investigation, which can partly be done on the basis of the data amount. If in the course of further investigation the DP model capabilities should not meet the needs of the prospected system, the implementation of alternative models (e.g. APA or DVM) can be envisaged. As the investigated fusion approach can be adapted to any prediction algorithm, the implementation is mainly a question of the availability of information about the models. REFERENCES [1] Steen, M., Sasse A., Bestmann, U., Becer, M., Hecer, P.; Performance Evaluation of Extended and Unscented Kalman Filter in a high dynamic Environment on Flight Trials; ION GNSS 0 th International Technical Meeting of the Satellite Division, 5-8, September 007, Fort Worth, TX [] Bestmann, U., Steen M., Becer, M., Sasse A., Hecer, P.; Comparison of state and error state INS coupling filter based on real flight test data; ION GNSS 0 th International Technical Meeting of the Satellite Division, 5-8, September 007, Fort Worth, TX page 16 of 17

17 Creating wae vortex awareness for pilots and controllers [3] Steen, M.; Analyse und Parametrisierung des Wirbelschleppenverhaltens in Bodennähe; Diplomarbeit Institut für Flugführung/Institut für Physi der Atmosphäre; OA 005/0; Braunschweig/Oberpfaffenhofen 005 [4] Heintsch, T,; Beiträge zur Modellierung von Wirbelschleppen zur Untersuchung des Flugzeugverhaltens beim Landeanflug; Dissertation, Institut für Flugführung; Braunschweig 1994 [5] Holzäpfel, F., Steen, M.; Aircraft Wae-Vortex Evolution in Ground Proximity: Analysis and Parameterization; AIAA Journal, Vol. 45, No. 1, January 007 [6] Holzäpfel et al.; Strategies for Circulation Evaluation of Aircraft Wae Vortices Measured by Lidar; Journal of Atmospheric and Oceanic Technology, Volume 0, ; August 003 [7] Schön, Th., Gustafsson, F., Nordlund, P.-J.; Marginalized Particle Filters for Mixed Linear/Nonlinear State-Space Models; IEEE Transactions on Signal Processing, Vol. 53, No. 7, July 005 [8] Merwe, R. van der, Wan, E. A.; The Square-Root Unscented Kalman Filter for State and Parameter-Estimation; Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Salt Lae City, UT, 001 [9] Holzäpfel, F., Robins, R.E.; Probabilistic Two-Phase Aircraft Wae-Vortex Model : Application and Assesment; Journal of Aircraft, Volume 41, Number 5, ; September-Otober 004 [10] Holzäpfel, F.; Probabilistic Two-Phase Wae Vortex Decay and Transport Model; Journal of Aircraft, Volume 40, Number, ; März-April 003 [11] Holzäpfel, F.; Probabilistic Two-Phase Aircraft Wae-Vortex Model: Further Development and Assessment; subm. to Journal of Aircraft page 17 of 17