Flight Dataset Research Articles

Measuring extraordinary rendition and international cooperation

Following the launch of the War on Terror, the United States of America established a global rendition network that saw the transfer of US Central Intelligence Agency terrorist suspects to secret detention sites across the world. There has been considerable debate over how many countries participated in rendition, secret detention and interrogation during the post-9/11 period, and conventional accounts of foreign complicity suggest that diverse countries were involved, including many established democracies. However, research on rendition has continually suffered from uncertainty, a lack of data, and systematic empirical evidence due to the secret nature of counterterrorism cooperation. In this article, I argue that it is possible to study the practice of rendition, unlike many other forms of clandestine security cooperation, as it is partially observable. Specifically, suspected extraordinary rendition flight paths can be tracked using publicly available flight data. This article uses the world’s largest set of public flight data relating to rendition to estimate cross-country collaboration in rendition, secret detention and interrogation. The result suggests 307 likely rendition flights and 15 new participating countries beyond the 54 known cases, with cross validation tests demonstrating high levels of model accuracy.

Non-linear aerodynamic modelling of unmanned cropped delta configuration from experimental data

ABSTRACTThe paper presents the aerodynamic characterization of a low-speed unmanned aerial vehicle, with cropped delta planform and rectangular cross section, at and around high angles-of-attack using flight test methods. Since the linear models used for identification from flight data at low and moderate angles of attack become unsuitable for accurate parameter estimation at high angles of attack, a non-linear aerodynamic model has to be considered. Therefore, the Kirchhoff's flow separation model was used to incorporate the non-linearity in the aerodynamic model in terms of flow separation point and stall characteristic parameters. The Maximum Likelihood (ML) and Neural Gauss-Newton (NGN) methods were used to perform the parameter estimation on one set of low angle-of-attack and one set of near-stall flight data. It is evident from the estimates that the NGN method, which does not involve solving equations of motion, performs on a par with the classical ML method. This may be attributed to the reason that NGN method uses a neural network which has been trained by performing point to point mapping of the measured flight data. This feature of NGN method enhances its application over a wider envelope of high angles of attack flight data.

Anomaly Detection for Aviation Safety Based on an Improved KPCA Algorithm

Thousands of flights datasets should be analyzed per day for a moderate sized fleet; therefore, flight datasets are very large. In this paper, an improved kernel principal component analysis (KPCA) method is proposed to search for signatures of anomalies in flight datasets through the squared prediction error statistics, in which the number of principal components and the confidence for the confidence limit are automatically determined by OpenMP-based K-fold cross-validation algorithm and the parameter in the radial basis function (RBF) is optimized by GPU-based kernel learning method. Performed on Nvidia GeForce GTX 660, the computation of the proposed GPU-based RBF parameter is 112.9 times (average 82.6 times) faster than that of sequential CPU task execution. The OpenMP-based K-fold cross-validation process for training KPCA anomaly detection model becomes 2.4 times (average 1.5 times) faster than that of sequential CPU task execution. Experiments show that the proposed approach can effectively detect the anomalies with the accuracy of 93.57% and false positive alarm rate of 1.11%.

Beyond Linear Delay Multipliers in Air Transport

Delays are considered one of the most important burdens of air transport, both for their social and environmental consequences and for the cost they cause for airlines and passengers. It is therefore not surprising that a large effort has been devoted to study how they propagate through the system. One of the most important indicators to assess such propagation is the delay multiplier, a ratio between outbound and inbound average delays; in spite of its widespread utilisation, its simplicity precludes capturing all details about the dynamics behind the diffusion process. Here we present a methodology that extracts a more complete relationship between the in- and outbound delays, distinguishing a linear and a nonlinear phase and thus yielding a richer description of the system’s response as a function of the delay magnitude. We validate the methodology through the study of a historical data set of flights crossing the European airspace and show how its most important airports have heterogeneous ways of reacting to extreme delays and that this reaction strongly depends on some of their global properties.

A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors.

State estimation is the most critical capability for MAV (Micro-Aerial Vehicle) localization, autonomous obstacle avoidance, robust flight control and 3D environmental mapping. There are three main challenges for MAV state estimation: (1) it can deal with aggressive 6 DOF (Degree Of Freedom) motion; (2) it should be robust to intermittent GPS (Global Positioning System) (even GPS-denied) situations; (3) it should work well both for low- and high-altitude flight. In this paper, we present a state estimation technique by fusing long-range stereo visual odometry, GPS, barometric and IMU (Inertial Measurement Unit) measurements. The new estimation system has two main parts, a stochastic cloning EKF (Extended Kalman Filter) estimator that loosely fuses both absolute state measurements (GPS, barometer) and the relative state measurements (IMU, visual odometry), and is derived and discussed in detail. A long-range stereo visual odometry is proposed for high-altitude MAV odometry calculation by using both multi-view stereo triangulation and a multi-view stereo inverse depth filter. The odometry takes the EKF information (IMU integral) for robust camera pose tracking and image feature matching, and the stereo odometry output serves as the relative measurements for the update of the state estimation. Experimental results on a benchmark dataset and our real flight dataset show the effectiveness of the proposed state estimation system, especially for the aggressive, intermittent GPS and high-altitude MAV flight.

Flight-Test Evaluation of Navigation Information in Wide-Field Optical Flow

Wide-field optical flow information can benefit the navigation of small or micro unmanned aerial vehicles in GPS-degraded/denied environments, inspired by the study of insect/bird flights. This paper focuses on a flight-test evaluation of navigation information in wide-field optical flow, using flight data collected by a wide-angle camera installed on a fixed-wing unmanned aerial vehicle at altitudes ranging from 30 to 183 m above the ground. A comprehensive evaluation methodology is proposed for the comparison between optical flows computed from videos and the reference motion fields determined from conventional navigation sensors including GPS, a laser range finder, and inertial sensors. Seven sets of unmanned aerial vehicle flight data including a total of approximately 72,000 image frames are used for both full-flight temporal evaluation and representative interframe spatial evaluation of wide-field optical flow information for unmanned aerial vehicle navigation purposes. The evaluation results showed a good match between vision-derived optical flows and reference motion fields from conventional navigation sensors. Finally, one benchmark data set is shared at the authors’ website for open access from the research community.

Parameter Estimation of Unmanned Flight Vehicle Using Wind Tunnel Testing and Real Flight Data

AbstractThe work presented in this paper addresses the longitudinal parameter estimation of an unmanned configuration using wind tunnel testing and flight data. For this purpose, an unmanned aerial vehicle has been designed with a cropped delta planform and rectangular cross-section. Exhaustive full-scale wind tunnel tests were carried out on the designed unmanned flight vehicle to capture the linear and nonlinear variation of aerodynamic force and moment coefficients. The measured wind tunnel test data, in the form of signals, have been processed to forces and moments about the desired reference point of the designed unmanned platform. A comprehensive discussion on the obtained results from wind tunnel testing has been presented. In order to enhance the confidence in the generated aerodynamic database from wind tunnel testing and also to estimate the dynamic derivatives, two sets of real flight data pertaining to longitudinal dynamics (acquired during the flight tests) have been used. The parameter estim...

Toward Improved Predictions of Slender Airframe Aerodynamics Using the F-16XL Aircraft

A coordinated project has been underway to improve computational fluid dynamics predictions of slender airframe aerodynamics. The work is focused on two flow conditions and leverages a unique flight data set obtained with an F-16XL aircraft. These conditions, a low-speed high angle-of-attack case and a transonic low angle-of-attack case, were selected from a prior prediction campaign wherein the computational fluid dynamics failed to provide acceptable results. In this paper, the background, objectives, and approach to the current project are presented. The work embodies predictions from multiple numerical formulations that are contributed from multiple organizations, and the context of this campaign to other multicode, multi-organizational efforts is included. The relevance of this body of work toward future supersonic commercial transport concepts is also briefly addressed.

Generation and recovery of airborne delays in air transport

The analysis of the causes behind the appearance and propagation of delays is one of the major topics inside Air Transport Management research. Existing research focuses by and large on Air Traffic Flow Management regulations and reactionary delays; less attention has been devoted to the study of the mechanisms governing the generation and absorption of delays while airborne, in spite of their important economical and environmental consequences. Here we present a methodology to detect delay-generating events, based on the comparison of planned and real trajectories; these events are then used to characterise several aspects of the dynamics of the system, e.g. its resilience. We apply this methodology to a historical data set of flights crossing the European airspace during 2011, and observe an overall resilient system, able to absorb as much delays as it generates; yet resilience is not constant, but strongly depends on the phase of the flight, and shows high spatial and temporal heterogeneities. We anticipate the proposed methodology to open new doors for the development of a better systemic performance, by enabling the characterisation and understanding of this fundamental type of delay.

What Was Learned in Predicting Slender Airframe Aerodynamics with the F-16XL Aircraft

The second Cranked-Arrow Wing Aerodynamics Project, International, coordinated project has been underway to improve high-fidelity computational-fluid-dynamics predictions of slender airframe aerodynamics. The work is focused on two flow conditions and leverages a unique flight data set obtained with the F-16XL aircraft for comparison and validation. These conditions, a low-speed high-angle-of-attack case and a transonic low-angle-of-attack case, were selected from a prior prediction campaign wherein the computational fluid dynamics failed to provide acceptable results. In revisiting these two cases, approaches for improved results include better, denser grids using more grid adaptation to local flow features as well as unsteady higher-fidelity physical modeling like hybrid Reynolds-averaged Navier–Stokes/unsteady Reynolds-averaged Navier–Stokes/large-eddy simulation methods. The work embodies predictions from multiple numerical formulations that are contributed from multiple organizations where some authors investigate other possible factors that could explain the discrepancies in agreement (e.g., effects due to deflected control surfaces during the flight tests as well as static aeroelastic deflection of the outer wing). This paper presents the synthesis of all the results and findings and draws some conclusions that lead to an improved understanding of the underlying flow physics, finally making the connections between the physics and aircraft features.

Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems

Detecting anomalies in datasets, where each data object is a multivariate time series, possibly of different length for each data object, is emerging as a key problem in certain domains. The problem is considered in the context of aviation safety, where data objects are flights of various durations and the multivariate time series corresponds to sensor readings. The goal is then to detect anomalous flight segments, due to mechanical, environmental, or human factors. In this paper, a general framework is presented for anomaly detection in such settings by representing each multivariate time series using a vector autoregressive exogenous model, constructing a distance matrix among the objects based on their respective vector autoregressive exogenous models, and finally detecting anomalies based on the object dissimilarities. The framework is scalable, due to the inherent parallel nature of most computations, and can be used to perform online anomaly detection. Experimental results on a real flight dataset illustrate that the framework can detect different types of multivariate anomalies along with the key parameters involved.

Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring

Safety is key to civil aviation. To further improve its already respectable safety records, the airline industry is transitioning towards a proactive approach which anticipates and mitigates risks before incidents occur. This approach requires continuous monitoring and analysis of flight operations; however, modern aircraft systems have become increasingly complex to a degree that traditional analytical methods have reached their limits – the current methods in use can only detect ‘hazardous’ behaviors on a pre-defined list; they will miss important risks that are unlisted or unknown. This paper presents a novel approach to apply data mining in flight data analysis allowing airline safety experts to identify latent risks from daily operations without specifying what to look for in advance. In this approach, we apply a Gaussian Mixture Model (GMM) based clustering to digital flight data in order to detect flights with unusual data patterns. These flights may indicate an increased level of risks under the assumption that normal flights share common patterns, while anomalies do not. Safety experts can then review these flights in detail to identify risks, if any. Compared with other data-driven methods to monitor flight operations, this approach, referred to as ClusterAD-DataSample, can (1) better establish the norm by automatically recognizing multiple typical patterns of flight operations, and (2) pinpoint which part of a detected flight is abnormal. Evaluation of ClusterAD-DataSample was performed on two sets of A320 flight data of real-world airline operations; results showed that ClusterAD-DataSample was able to detect abnormal flights with elevated risks, which make it a promising tool for airline operators to identify early signs of safety degradation even if the criteria are unknown a priori.

Capturing recurring concepts using discrete Fourier transform

SummaryIn this research, we address the problem of capturing recurring concepts in a data stream environment. Recurrence capture enables the reuse of previously learned classifiers without the need for relearning while providing for better accuracy during the concept recurrence interval. We capture concepts by applying the discrete Fourier transform to decision tree classifiers to obtain highly compressed versions of the trees at concept drift points in the stream and store such trees in a repository for future use. In addition, the impact of drift detector in enabling stable performance is also studied with the two drift detectors: ADWIN and SeqDrift2 in recurring concept capturing context. Our empirical results on real world and synthetic data exhibiting varying degrees of recurrence show that the Fourier compressed trees are more robust to noise and are able to capture recurring concepts with higher precision than a meta‐learning approach that chooses to reuse classifiers in their originally occurring form. A case study on a flight dataset that closely matches the target data stream environment where concepts recur in similar form in a time critical system is also conducted and the benefits of discrete Fourier transform application is evaluated. Copyright © 2016 John Wiley & Sons, Ltd.

Identification of flight state under different simulator modes using improved diffusion maps

To identify the difference between dynamic and static simulator modes, a novel data analyzing method was presented in this paper using flight data sampled from manual flight task. The proposed method combined diffusion maps and kernel fuzzy c-means algorithm (KFCM) to identify types of flight data. Hybrid bacterial foraging (BF) and particle swarm optimization (PSO) algorithm (BF-PSO) was also introduced to optimize unknown parameters of the KFCM. This algorithm increased the possibility to find the optimal values avoided being trapped in local minima. The clustering accuracy of the proposed method applied in flight dataset demonstrated this method had the ability to recognize the types of flight state. The results of the paper indicated that the pilots movement sensing influenced pilot performance under the manual departure task.

Sky Region Obstacle Detection and Tracking for Vision-Based UAS Sense and Avoid

A customized detection and tracking algorithm for vision-based non cooperative UAS sense and avoid aimed at obstacles approaching from above the horizon is presented in this paper. The proposed approach comprises two main steps. Specifically, the first processing step is relevant to obstacle detection and tentative tracking for track confirmation and is based on top-hat and bottom-hat morphological filtering, local image analysis for a limited set of regions of interest, and multi-frame processing in stabilized coordinates. Once firm tracking is achieved, template matching and state estimation based on Kalman filtering are used to track the intruder aircraft and estimate its angular position and velocity. An extensive experimental analysis is presented which is based on a large set of flight data gathered in realistic near collision scenarios, in different operating conditions in terms of weather and illumination, and adopting different navigation units onboard the ownship. In particular, the focus is set on flight segments at a range between 3 km and 1.3 km, since the major interest is in understanding algorithm potential for relatively large time to collision. System performance is evaluated in terms of declaration range, probability of correct declaration, average number of false positives, tracking accuracy (angles and angular rates in a stabilized North-East-Down reference frame) and robustness with respect to track loss phenomena. Promising results are achieved regarding the trade-off between declaration range and false alarm probability, while the onboard navigation unit is found to heavily impact tracking accuracy.

EVALUATING THE POTENTIAL OF MULTISPECTRAL AIRBORNE LIDAR FOR TOPOGRAPHIC MAPPING AND LAND COVER CLASSIFICATION

Abstract. Recently multispectral LiDAR became a promising research field for enhanced LiDAR classification workflows and e.g. the assessment of vegetation health. Current analyses on multispectral LiDAR are mainly based on experimental setups, which are often limited transferable to operational tasks. In late 2014 Optech Inc. announced the first commercially available multispectral LiDAR system for airborne topographic mapping. The combined system makes synchronic multispectral LiDAR measurements possible, solving time shift problems of experimental acquisitions. This paper presents an explorative analysis of the first airborne collected data with focus on class specific spectral signatures. Spectral patterns are used for a classification approach, which is evaluated in comparison to a manual reference classification. Typical spectral patterns comparable to optical imagery could be observed for homogeneous and planar surfaces. For rough and volumetric objects such as trees, the spectral signature becomes biased by signal modification due to multi return effects. However, we show that this first flight data set is suitable for conventional geometrical classification and mapping procedures. Additional classes such as sealed and unsealed ground can be separated with high classification accuracies. For vegetation classification the distinction of species and health classes is possible.

Unmanned Aerial Vehicle Navigation Using Wide-Field Optical Flow and Inertial Sensors

This paper offers a set of novel navigation techniques that rely on the use of inertial sensors and wide-field optical flow information. The aircraft ground velocity and attitude states are estimated with an Unscented Information Filter (UIF) and are evaluated with respect to two sets of experimental flight data collected from an Unmanned Aerial Vehicle (UAV). Two different formulations are proposed, a full state formulation including velocity and attitude and a simplified formulation which assumes that the lateral and vertical velocity of the aircraft are negligible. An additional state is also considered within each formulation to recover the image distance which can be measured using a laser rangefinder. The results demonstrate that the full state formulation is able to estimate the aircraft ground velocity to within 1.3 m/s of a GPS receiver solution used as reference “truth” and regulate attitude angles within 1.4 degrees standard deviation of error for both sets of flight data.

Mach number virtual sensor for civil aircraft extended guidance navigation and control

This paper proposes an airspeed virtual sensor to extend the guidance navigation and control functions of an aircraft. The main idea is to exploit the indirect measurement of the aerodynamic forces applying on a control surface which are clearly related to the aircraft airspeed. Focusing on those aircraft equipped with a Fly By Wire system, at least one actuator is installed on each of the control surfaces; the actuators operate the surface by fighting against the aerodynamic forces. Generally speaking, aircraft are designed with a minimum set of control surfaces (one elevator, one rudder and two ailerons) but there are lots of examples of aircraft with redundant control surfaces. To the best of authors’ knowledge, the proposed ”Least Squares – Sliding Mode” estimator is new and it merges two concepts: the least squares, which exploit the available redundant actuator–surfaces systems to minimize the errors, and the sliding mode, that, in finite time, pushes the aircraft airspeed estimation toward the actual value. The algorithm has been applied to real Airbus aircraft flight data set leading to very encouraging preliminary results.

Diagnosis of Airspeed Measurement Faults for Unmanned Aerial Vehicles

Airspeed sensor faults are common causes for incidents with unmanned aerial vehicles (UAV) with pitot tube clogging or icing being the most common causes. Timely diagnosis of such faults or other artifacts in signals from airspeed sensing systems could potentially prevent crashes. This paper employs parameter adaptive estimators to provide analytical redundancies and a dedicated diagnosis scheme is designed. Robustness is investigated on sets of flight data to estimate distributions of test statistics. The result is robust diagnosis with adequate balance between false alarm rate and fault detectability.

System Identification of UAV Alap-Alap Using Back Propagation Neural Network

A good model is necessary in order to design a controller of a system off-line. It is especially beneficial in the implementation of new advanced control schemes in Unmanned Aerial Vehicle (UAV). Considering the safety and benefit of an off-line tuning of the UAV controllers, this paper identifies a dynamic MIMO UAV nonlinear system which is derived based on the collection of input-output data taken from a test flights (36250 samples data). These input-output sample flight data are grouped into two flight data sets. The first flight data set, a chirp signal, is used for training the neural network in order to determine parameters (weights) for the network. Validation of the network is performed using the second data set, which is not used for training, and is a representation of UAV circular flight movement. An artificial neural network is trained using the training data set and thereafter the network is excited by the second set input data set. The predicted outputs based on our proposed Neural Network model is similar to the desired outputs (roll, pitch and yaw) which has been produced by real UAV system.