On-board axial detection of wake vortices using a 2-<font>m</font> m LiDAR

Douxchamps, D.; Lugan, Sébastien; Verschueren, Yannick; Mutuel, Laurence H.; Chihara, K.

doi:10.1109/taes.2008.4667709

Cited by 14 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For shorter ranges, however, the ability to remotely detect spatially small phenomena like wake vortices has been demonstrated in an airborne vertical setup during the European AWIATOR project (Rahm et al 2007) and in an airborne axial setup within the European I-Wake project (Douxchamps et al 2008), both employing DLR's 2 μm wind lidar. This lidar is based on CTI's transceiver (Henderson et al 1993;K€ opp et al 2004) and was operated on the German Aerospace Center's Falcon 20 aircraft and on the Dutch Aerospace Laboratory NLR's Cessna Citation 2 aircraft.…”

Section: Coherent Doppler Lidarmentioning

confidence: 99%

Aviation Turbulence

2016

View full text Add to dashboard Cite

Section: Coherent Doppler Lidarmentioning

confidence: 99%

Aviation Turbulence

2016

View full text Add to dashboard Cite

“…(6). For the position in the local coordinate system of the virtual planes, they are completely dependend such that the dynamic matrix is completely filled.…”

Section: A Ekf Layoutmentioning

confidence: 99%

“…On the other hand, research on wake vortex detection by on-board sensors like LIDAR has been conducted over the last years. While detection and tracking by LIDAR has become a mature technology at least for ground based applications 3 , the usage on-board of aircraft comprises several technological challenges like a large scanning and processing domain -compared to ground based installations -and the movement of the own aircraft that needs to be taken into account during processing of the LIDAR data 6 .…”

Section: Introductionmentioning

confidence: 99%

In-Flight Testing of Airborne LIDAR for Wake Vortex Detection, Characterization and Tracking

Steen

Stanisak

Feuerle

et al. 2014

6th AIAA Atmospheric and Space Environments Conference

View full text Add to dashboard Cite

Today wake vortex dependent spacing between aircraft is achieved by static distances for arrival but also for en-route. With the upcoming operational novelties foreseen in NextGen and SESAR like in-trail procedures and aircraft self-separation, aircraft wake vortex encounter mitigation comes into focus especially for the en-route flight phase. For the localization of aircraft wake vortices generated by surrounding aircraft, two possibilities exist in general: determining the position, movement and strength by means of wake vortex model prediction and the detection by dedicated airborne sensors. While prediction is prone to uncertainties in model input quantities, airborne detection sensors like LIDAR are prone to a large scanning domain, adverse scanning geometry and challenging signal processing. To overcome the drawbacks of both model prediction and sensor detection, the Institute of Flight Guidance of the Technische Universitaet Braunschweig is planning to conduct flight trials with an airborne LIDAR equipment for wake vortex detection and tracking utilizing special prediction-detection fusion approaches. This paper describes the airborne LIDAR installation, the system layout and the connection to the aircraft avionics for the steering of the LIDAR scanning. NomenclatureΓ = Circulation T = Vector containing traffic information received by ADS-B messages ρ = Vector of measured pseudoranges to GNSS satellites E = Received GNSS ephemeris data A = Vector containing measured air data b ib a = Vector containing the measured accelerations b ib ω = Vector containing the measured turn rates L = LIDAR data W = Estimated Wake Vortex information data P L = Vector containing the LIDAR position in WGS-84 coordinates V L = Vector containing the LIDAR velocity in North, East, Down direction P G = Vector containing the position of the wake vortex reference planes χ = Azimuth angle of LIDAR beam ε = Elevation of LIDAR beam R = LIDAR measured range ADR = Air Data Reference IMU = Inertial Measurement Unit x = KALMAN Filter state vector y = KALMAN Filter measurement vector P = KALMAN Filter state covariance matrix K = KALMAN gain matrix 1 Senior Research Engineer, m.steen@tu-braunschweig.de 2 Q = KALMAN Filter process noise matrix R = KALMAN Filter measurement noise covariance matrix Φ = KALMAN Filter state transition matrix F = KALMAN Filter system state dynamic matrix I = Identity matrix H = KALMAN Filter measurement matrix ( ) h = KALMAN Filter non-linear measurement function

show abstract

“…Automation of data processing is prone to errors like over-or underestimation or even failure of recognition of mature vortices from the velocity field and suffers, in addition, from the measurement noise due to the complexity of the flow field in an operational environment. Impact of low signal-to-noise ratio, deformed vortex structures as well as unfavourable viewing angles further complicate wake vortex detection and estimation of vortex strength, as has been found in different studies such as Holzaepfel et al 9 and Douxchamps et al 11 As no information is available between single measurements and the update rate is relatively low, even loss of track may occur (which is an even greater problem for on-board LIDAR systems). But in the case of successful wake detection, the system has updated information on the vortex state that can be used to decrease its uncertainty.…”

Section: Wake Vortex Detection Sensorsmentioning

confidence: 99%

Towards wake vortex safety and capacity increase: the integrated fusion approach and its demands on prediction models and detection sensors

Schönhals

Steen

Hecker

2012

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

Wake vortices and the prevention of wake vortex encounters are both an issue of safety and capacity in today's air transportation system. Current wake vortex separations are safe but also very conservative and thus have an adverse effect on capacity of airports. This article deals with the concept of fused wake vortex prediction and detection with the objective to deliver wake vortex strength and position with a high level of accuracy and reliability. The collaboration approach aims at fusion of models and sensors available for forecast and detection of hazardous wake turbulence in order to improve the overall system performance using the complementary capabilities of the single components. Different methods of coupling models with measurements are introduced and resulting the aspect of requirements for the prediction model and measurement sensor is presented. The implementation of an error-state system is presented and compared to sole prediction and sole sensor results. The results indicate that the fusion approach delivers benefits like reduced uncertainty of prediction and increased availability of detection and thus has the ability to increase airport and air space capacity while maintaining or even improving current wake vortex safety.

show abstract

On-board axial detection of wake vortices using a 2-m m LiDAR

Cited by 14 publications

References 26 publications

Aviation Turbulence

Aviation Turbulence

In-Flight Testing of Airborne LIDAR for Wake Vortex Detection, Characterization and Tracking

Towards wake vortex safety and capacity increase: the integrated fusion approach and its demands on prediction models and detection sensors

Contact Info

Product

Resources

About