The AWIATOR airborne LIDAR turbulence sensor

Schmitt, Nikolaus P.; Rehm, Wolfgang; Zeller, P.; Pistner, Thomas; Reithmeier, G.; Stilkerich, S.; Diehl, Hermann; Schertler, K.; Zinner, H.

doi:10.1109/cleoe.2005.1568226

Cited by 8 publications

(10 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A sensor system that meets these requirements is a so-called short pulse UV Doppler LIDAR, and was developed in (Schmitt, Pistner, Zeller, Diehl & Navé, 2007). This short pulse UV Doppler LIDAR was successfully applied to an Airbus 340 to measure the vertical gust speed (Schmitt, Rehm, Pistner, Diehl, Jenaro-Rabadan, Mirand & Reymond, 2007).…”

Section: Basic Idea Of the Feedforward Controllermentioning

confidence: 99%

“…As mentioned in (Schmitt, Pistner, Zeller, Diehl & Navé, 2007), such a sensor should meet the following criteria: • A feedforward-looking measuring of 50 to 150 m to ensure that the measured air flow is the one actually affecting the aerodynamics around the aircraft.…”

Section: Basic Idea Of the Feedforward Controllermentioning

confidence: 99%

“…With the novel development of the airborne LIght Detection and Ranging (LIDAR) turbulence sensor available for an accurate vertical gust velocity measurement at a considerable distance ahead of the aircraft (Schmitt, Pistner, Zeller, Diehl & Navé, 2007), it becomes feasible to design an adaptive feedforward control to alleviate the structural loads induced by any turbulence and extend the life of the structure. The adaptive feedforward control algorithm developed in (Wildschek et al, 2006) showed promising results for vibration suppression of the first wing bending mode.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Feedforward Control for Gust Loads Alleviation

Zeng

Moulin

Callafon

et al. 2008

AIAA Atmospheric Flight Mechanics Conference and Exhibit

View full text Add to dashboard Cite

Section: Basic Idea Of the Feedforward Controllermentioning

confidence: 99%

Section: Basic Idea Of the Feedforward Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Feedforward Control for Gust Loads Alleviation

Zeng

Moulin

Callafon

et al. 2008

AIAA Atmospheric Flight Mechanics Conference and Exhibit

View full text Add to dashboard Cite

“…The latter seem effective and reliable means of terrain data collection in relatively large areas with cloudy weather conditions (Baltsavias, 1999;Brian et al, 2007;Schmitt et al, 2007;Liu, 2008). The established triangular irregular network (TIN) was converted using a TOPOGRID algorithm to an ArcGIS grid of 1.6-m pixel resolution.…”

Section: Generation Of Digital Elevation Modelmentioning

confidence: 99%

The application of GIS based decision-tree models for generating the spatial distribution of hydromorphic organic landscapes in relation to digital terrain data

Kheir

Bøcher

Greve

2010

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Accurate information about organic/mineral soil occurrence is a prerequisite for many land resources management applications (including climate change mitigation). This paper aims at investigating the potential of using geomorphometrical analysis and decision tree modeling to predict the geographic distribution of hydromorphic organic landscapes in unsampled area in Denmark. Nine primary (elevation, slope angle, slope aspect, plan curvature, profile curvature, tangent curvature, flow direction, flow accumulation, and specific catchment area) and one secondary (steady-state topographic wetness index) topographic parameters were generated from Digital Elevation Models (DEMs) acquired using airborne LIDAR (Light Detection and Ranging) systems. They were used along with existing digital data collected from other sources (soil type, geological substrate and landscape type) to explain organic/mineral field measurements in hydromorphic landscapes of the Danish area chosen. A large number of tree-based classification models (186) were developed using (1) all of the parameters, (2) the primary DEM-derived topographic (morphological/hydrological) parameters only, (3) selected pairs of parameters and (4) excluding each parameter one at a time from the potential pool of predictor parameters. The best classification tree model (with the lowest misclassification error and the smallest number of terminal nodes and predictor parameters) combined the steady-state topographic wetness index and soil type, and explained 68% of the variability in organic/mineral field measurements. The overall accuracy of the predictive organic/inorganic landscapes' map produced (at 1:50 000 cartographic scale) using the best tree was estiCorrespondence to: R. Bou Kheir (rania.boukheir@agrsci.dk) mated to be ca. 75%. The proposed classification-tree model is relatively simple, quick, realistic and practical, and it can be applied to other areas, thereby providing a tool to facilitate the implementation of pedological/hydrological plans for conservation and sustainable management. It is particularly useful when information about soil properties from conventional field surveys is limited.

show abstract

“…Such systems were previously developed for short-range sounding (Schmitt et al, 2007;Jentink and Bogue, 2005). Medium-range lidars have recently been developed, built, and tested (Huffaker and Hardesty, 1996;Inokuchi et al, 2009a, b;Veerman et al, 2014;Vrancken et al, 2016;Targ et al, 1996;Thales Avionics and ONERA, 2004).…”

Section: Introductionmentioning

confidence: 99%

Impact of pitch angle fluctuations on airborne lidar forward sensing along the flight direction

Gurvich

Куликов

2017

Atmos. Meas. Tech.

View full text Add to dashboard Cite

Abstract. Airborne lidar forward sensing along the flight direction can serve for notification of clear air turbulence (CAT) and help to prevent injuries or fatal air accidents. The validation of this concept was presented in the framework of the DELICAT (DEmonstration of LIdar-based CAT detection) project. However, the strong variations in signal level, which were observed during the DELICAT measurements but not explained, sometimes indicated the need of a better understanding the observational errors due to geometrical factors. In this paper, we discuss possible error sources pertinent to this technique, related to fluctuations of the flight parameters, which may lead to strong signal variations caused by the random deviations of the sensing beam from the forward flight trajectory. We analyze the variations in backscattered lidar signal caused by fluctuations of the most important forward-sensing flight parameter, the pitch angle. The fluctuation values considered in the paper correspond to the error limits of the compensational gyro platform used in civil aviation. The part of the pitch angle fluctuations not compensated for by the beam-steering device in the presence of aerosol concentration variations can lead to noticeable signal variations that can be mistakenly attributed to wind shear, turbulence, or fast evolution of the aerosol layer. We formulate the criteria that allow the recognition of signal variations caused by pitch angle fluctuations. Influence of these fluctuations is shown to be stronger for aerosol variations on smaller vertical scales. An example of DELICAT observations indicating a noticeable pitch angle fluctuation impact is presented.

show abstract

The AWIATOR airborne LIDAR turbulence sensor

Cited by 8 publications

References 0 publications

Adaptive Feedforward Control for Gust Loads Alleviation

Adaptive Feedforward Control for Gust Loads Alleviation

The application of GIS based decision-tree models for generating the spatial distribution of hydromorphic organic landscapes in relation to digital terrain data

Impact of pitch angle fluctuations on airborne lidar forward sensing along the flight direction

Contact Info

Product

Resources

About