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

Abstract

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

Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode

“…Lower limit value (3 NM): in en-route phase when the preceding aircraft belongs to an equal or lower category than the succeeding one, the MWS separation may be smaller than 5 NM [28]. There are several projects which have studied or are studying the reduction of MWS and proposing new en-route MWS values: [28,30,31]. Therefore, it was decided to take the value of 3 NM as the lower limit of the range of variable separations.…”