School of Aeronautics and Astronautics

Data-Driven Precursor Detection Algorithm for Terminal Airspace Operations

Raj Deshmukh, Dawei Sun and Inseok Hwang

School of Aeronautics and AstronauticsPurdue University

Thirteenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2019)

Presentation Outline

• Research background and motivation

• Algorithm development: Precursor detection in terminal airspace

– Preprocessing

– Anomaly detection and precursor detection model generated in the

form of signal temporal logic (STL)

– Test and analysis

• Conclusion and future work

Data-driven Learning for ATM• Modern ATM technologies record detailed information regarding states of aircraft,

and status of airport as time-series datasets

• Detecting outliers (called anomalies) in such time-series datasets allows understanding air transportation system complexity and behaviour, while getting insight into emergent behaviour Anomalies have correlation to operational or safety issues Anomaly detection by manually hunting through datasets is not feasible, requiring the

use of machine learning (data-driven) algorithms

ASPM

ASRS

TAIS

ASDE-XADS-B

ETMSSWIM

ITWS

ITWS

METAR

TBFM

TFMS

TAIS

ASDE-X

Level-set Definitions

• Anomaly: a rare event with a meaningful impact. Anomaly is different from coincidence, and must have potential for some operational or safety related impact

• Precursor: Events or conditions of the systems that have correlation to occurrence of anomaly and precede it

• Develop machine learning models to identify safety-related and operationally-significant anomalies in time-series observations of surveillance datasets in terminal airspace

• Use results from anomaly detection to predict anomalies before they occur, using precursor detection (prognosis)

• Terminal airspace (or terminal control area) Controlled airspace surrounding major airports, with highest volume of traffic

Problem Setup

Data preprocessingvia clustering

Anomaly detectionfrom time-series data

Model describing temporal, and physical relations

Surveillance data

TempAD

Anomalous

Normal

Precursor detection model

Precursor detectionfrom time-series data

Reactive TempAD

• R. Deshmukh and I. Hwang, “Anomaly Detection Using Temporal Logic Based Learning for Terminal Airspace Operations”, In AIAA Scitech 2019 Forum (p. 0682). (Finalist for ‘Intelligent Systems’ Best Student Paper Competition)

• R. Deshmukh and I. Hwang, “Incremental Learning Based Unsupervised Anomaly Detection Algorithm for Terminal Airspace Operations ”, AIAA Journal of Aerospace Information Systems, 2018 (revised version submitted; under review)

• R. Deshmukh, D. Sun and I. Hwang, “Anomaly Detection Using Surveillance Data and Airport Conditions in the Terminal Airspace”, in preparation

Machine Learning• Machine learning approaches

Supervised Unsupervised

SVM, ANN, decision tree, Naïve-Bayes, random forest, max. entropy classifier

k-means, DBSCAN, OCSVM, hierarchical clustering, autoencoders

6

• Anomaly detection: unsupervised learning

• Precursor detection: supervised learning

Objectives

Proposed anomaly detection algorithm: Temporal logic learning based AnomalyDetection (TempAD)

– Signal temporal logic (STL) is learned from data via learning algorithm to segregatedata as normal and anomalous

– TempAD model is generated such that they bound the states of all normal aircraft• Thus, any violation of any of these bounds makes flight anomalous

Proposed precursor detection algorithm: Reactive Temporal logic learning basedAnomaly Detection (reactive TempAD)

– Generate temporal logic models using supervised learning which can classify systemproperties before the anomaly occurred

– Results from reactive TempAD algorithm should deliver a high correlation betweenthe occurrence of precursor and anomaly – minimum missed detection and falsealarms

– Demonstrate application of the developed algorithm using real air traffic surveillancedata

Signal Temporal Logic

• Proposed algorithms infer a signal temporal logic (STL) in a human-readable format

• STL model can express system properties such as bounds (calledpredicates) on time and physical parameters

• For example,

𝜑𝜑𝜃𝜃 = 𝐹𝐹 0,320 𝑥𝑥𝑠𝑠 > 21.73 ∧ 𝐺𝐺 308,313 𝑦𝑦𝑠𝑠 < 34.51

can be interpreted as “for a system behaving normally, its 𝑥𝑥-coordinate should beeventually greater than 21.73 between 0 and 320 time steps and its 𝑦𝑦-coordinateshould be always less than 34.51 between 308 and 313 time steps”

Predicate

Time stepsGlobalFinal

Z. Kong, A. Jones, A., A.M. Ayala, E.A. Gol, and C. Belta, “Temporal logic inference for classification and prediction from data,” Proceedings of the 17th international conference on Hybrid systems: computation and control, pp. 273-282, April 2014.

Background: Anomaly Detection

• Input: altitude data during final 20 nautical miles before touchdown, for arrivals to a runway at LGA during a specific day

• Output:

Vertical anomaly detection model

𝐺𝐺 7.5,0 −284𝑥𝑥 + 𝑦𝑦 − 500 > 0 ∧ 𝐺𝐺 20,7.5 −164𝑥𝑥 + 𝑦𝑦 − 1880 > 0∧ 𝐺𝐺 8,0 −420𝑥𝑥 + 𝑦𝑦 − 500 < 0 ∧ 𝐺𝐺 20,8 −306.7𝑥𝑥 + 𝑦𝑦 − 1066.7 < 0

where 𝑥𝑥: distance to touchdown [nm]; 𝑦𝑦: altitude [ft]

• Natural language interpretation:Any approaching aircraft should have a glideslope angle between 1.5o and 2.8o from 20nm to 7.5 nm away from touchdown pointand then between 2.6o and 3.9o in the last 7.5nm of final approach.

Input: Surveillance Data• Both ASDE-X data and TAIS data are surveillance data

– Recorded variables: time, flight ID, longitude, latitude, altitude, and groundspeed

• Differences between ASDE-X data and TAIS data

ASDE-X Airport Surface Detection Equipment - Model XTAIS Terminal Automation Information Service

ASDE-X data TAIS data

Period 19 days(Apr 6 – 24, 2016)3 months

(Sep – Nov, 2016)

Detection range ~20 nm from airport ~141 nm from airport

Sampling rate 1 sec 5 sec

No. of arrivalflights

LGA 9,748 36,243

JFK 11,909 46,852

EWR 10,841 45,471

0 5 10 15 20 25 30 35 40-100

-50

0

50

Algorithm Development for Precursor Detection

TempAD

Signal 𝑠𝑠𝑖𝑖 (𝑎𝑎 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓)

𝑇𝑇𝑇𝑇 − �̃�𝑓0

𝒔𝒔𝒊𝒊,𝒄𝒄 𝒔𝒔𝒊𝒊,𝒆𝒆

Effect model violationCause model violation

• Model describing normal behaviors:EFFECT formula (𝜑𝜑𝑒𝑒)

• Labels (normal 𝒑𝒑𝒊𝒊 = 𝟏𝟏/abnormal 𝒑𝒑𝒊𝒊 = 𝟎𝟎)

Unlabeled time-series data𝑠𝑠𝑖𝑖,𝑒𝑒

Anomaly Detection

Reactive TempAD

• Model separating normal/abnormal behaviors: CAUSE formula (𝜑𝜑𝑐𝑐)

Labeled time-series data𝑠𝑠𝑖𝑖,𝑐𝑐 , 𝑝𝑝𝑖𝑖

Precursor Detection

Unsupervised Learning

Supervised Learning

“If and only if 𝒔𝒔𝒊𝒊,𝒄𝒄violates 𝝋𝝋𝒄𝒄 in [𝟎𝟎,𝑻𝑻 −

Assumption: normal or abnormal behaviors occur in the last �̃�𝑓 seconds of the observed signals, i.e., the effect formula 𝝋𝝋𝒆𝒆 can adequately classify 𝒔𝒔𝒊𝒊 based on the signal 𝒔𝒔𝒊𝒊,𝒆𝒆over time interval 𝑻𝑻 − �𝒕𝒕,𝑻𝑻

Reactive TempAD for Precursor Detection

• Reactive TempAD: Given a set of labeled signals 𝒔𝒔𝒊𝒊,𝒄𝒄,𝒑𝒑𝒊𝒊 𝒊𝒊=𝟏𝟏𝑵𝑵

generated by

TempAD over [0,𝑇𝑇 − �̃�𝑓), find the cause formula 𝝋𝝋𝒄𝒄 that distinguishes between classes of signals 𝑠𝑠𝑖𝑖,𝑐𝑐 over time interval [0,𝑇𝑇 − �̃�𝑓)

• Supervised learning block which is responsible for separating precursor to normal and anomalous trajectories can comprise of any classification algorithm, such as Support Vector Machine (SVM), Neural Network (NN), random forests, naïve Bayes classifier, etc.

• It is possible that cause model and effect model may be required to be implemented using different features

– Essential for air traffic applications, as cause in one dimension can manifest as an effect in another dimension

– e.g.: go-around/missed approach anomaly detected using TempAD of an effect model 𝜑𝜑𝑒𝑒,𝑎𝑎𝑎𝑎𝑎𝑎 (altitude), but its precursor can be 𝜑𝜑𝑐𝑐,𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 (energy dimension) or a mixture of several dimensions

Reactive TempAD: Support Vector Machine

• SVMs maximize the margin around the separating hyperplane

• The decision function is fully specified by a subset of training samples, the support vectors

• SVM classifier hyperplane is determined using constrained optimization

Support vectors

MaximizesmarginNarrower

margin

Reactive TempAD: Artificial Neural Network• ANN approach is loosely patterned on a neuron in

the human brain, which fires when it encounters sufficient stimuli

• A node combines input from data with a set of coefficients (weights), that either amplify or dampen that input, thereby assigning significance to inputs regarding the task the algorithm is trying to learn

– Basic function of neuron (node) is to sum inputs, and produce output given sum is greater than a threshold (activation)

Input WeightsOutput

• Each layer’s output is simultaneously the subsequent layer’s input, starting from an initial input layer receiving the data

• Pairing adjustable weights with input features is how we assign significance to those features with regard to how the neural network classifies and clusters input

Input Features (Dimensions)• Basic features used for anomaly detection are in horizontal (longitude,

latitude), vertical (altitude), and speed (groundspeed) dimensions

• Derived features: Energy features which are computed from basicfeatures

– Energy management is important for aircraft operation in terminalairspace Better metric for detection of energy excess or energy deficitanomalies Homogenous across a fleet of varying aircraft types

Energy Features FormulaSpecific Total Energy (STE) 𝑓 + 𝑣𝑣2/2𝑓𝑓Specific Kinetic Energy (SKE) 𝑣𝑣2/2𝑓𝑓Specific Potential Energy (SPE) 𝑓Specific Total Energy Rate (STER) �̇� + �̇�𝑣𝑣𝑣/𝑓𝑓Specific Kinetic Energy Rate (SKER) �̇�𝑣𝑣𝑣/𝑓𝑓Specific Potential Energy Rate (SPER) �̇�

Go-around Anomalies• Go-around anomaly: Pilot aborts landing during short final, climbs using

missed approach procedures, circles around and then attempts landing again

Horizontal Vertical

Speed STE

SPER

Go-around at LGAResults in violation of TempAD

models for all dimensions

Go-around at JFK-74.35 -74.3 -74.25 -74.2 -74.15 -74.1 -74.05 -74 -73.95 -73.9

40.65

40.7

40.75

40.8

40.85

40.9

40.95

Go-around at EWR

EWR

LGA

JFK

Go-around anomaly is common across all three airports

Airport No. of arrivals (Sept.-Nov.)No. of go-arounds

LGA 36,243 201

JFK 46,852 348

EWR 45,471 364

Precursor Detection for Go-around Anomalies• We use support vector machine (SVM) algorithm to classify anomaly

precursors and normal flights

• To determine feature which can deliver best go-around precursor detectionperformance, we search across several dimensions and their combinations andchoose the feature that grants the highest F1 classification score:

Precision = ratio of true anomaly detections over total precursor detections

Recall = ratio of anomalies detected by precursors over total true anomalies

F1 score = 2 × 𝑆𝑆𝑃𝑃𝑒𝑒𝑐𝑐𝑖𝑖𝑠𝑠𝑖𝑖𝑃𝑃𝑃𝑃×𝑆𝑆𝑒𝑒𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑆𝑆𝑃𝑃𝑒𝑒𝑐𝑐𝑖𝑖𝑠𝑠𝑖𝑖𝑃𝑃𝑃𝑃+𝑆𝑆𝑒𝑒𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎

• From all tested features, a derived feature (𝑓𝑓) which was a combination ofaircraft energies and energy rate resulted in the highest F1 score:

𝒇𝒇 =𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 × 𝑺𝑺𝑺𝑺𝑺𝑺 =

�̇�𝒉𝒉𝒉 ×

𝒗𝒗𝟐𝟐

𝒈𝒈

Precursor Detection for Go-around Anomalies• Results of precursor detection using feature 𝑓𝑓:

• Thus, if arrival flights violate cause (or precursor) model (𝜑𝜑𝑐𝑐), it is guaranteedwithin a margin of error that effect (or anomaly) model (𝜑𝜑𝑒𝑒) will be violated

Anomaly DetectionPrecursor Detection

𝜑𝜑𝑐𝑐 = 𝐺𝐺[0,55)𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆

𝑆𝑆𝑆𝑆𝑆𝑆 < 37 𝜑𝜑𝑒𝑒 = 𝐺𝐺[55,60) 1650 × 𝑓𝑓 + 75 × h < 123750

Precursor Detection for Go-around Anomalies• Look-ahead time: Time difference between detection of precursor and

occurrence of anomaly– Desirable that this time is as long as possible, to allow more time for ATCs and

pilots to make decision and to accommodate pilot input lag

• Preliminary precursor detection with SVM (reactive TempAD-SVM) andfeature 𝑓𝑓 resulted in average look-ahead time of 7 seconds, which is aborderline feasible reaction time for pilot and ATC to react to theanomaly

• To improve precursor detection performance, we need to allowlearning technique to automatically search the best combination offeatures

– We enhance reactive TempAD to use artificial neural network (ANN), whichexplores and weighs combinations of diverse features to give best test results

– Furthermore, we enhance the algorithm by making feature space richer byintroducing a new feature – distance to preceding flight, which is the horizontaland vertical distance to the nearest preceding aircraft

Demonstration of preliminary application of go-around precursor detection

T. Blajev, and W. Curtis, “Go-Around Decision Making and Execution Project: Final Report to Flight Safety Foundation," Flight Safety Foundation, 2017.

Precursor Detection for Go-around AnomaliesReactive TempAD-SVM

• Average look-ahead time: 7s• F1 score: 79.55%• Confusion matrix:

Reactive TempAD-ANN• Average look-ahead time: 11s (+57%)• F1 score: 87.23%• Confusion matrix:

PredictedTrue

Anomalousflights

Normalflights Total

Anomalousflights

True positive35

False negative13 48

Normalflights

False positive5

True negative9,581 9,596

Total 40 9,594 9,634

PredictedTrue

Anomalousflights

Normalflights Total

Anomalousflights

True positive41

False negative7 48

Normalflights

False positive5

True negative9,581 9,596

Total 46 9,588 9,634

Number of false negatives (FNs) using the reformulated reactive TempAD-ANN has nearly halved. This is important, as minimizing instances of FNs (i.e., missed detections) is vital in safety-critical applications such as air traffic management (ATM)

Precursor Detection for S-turn Anomalies• S-turn anomaly: Pilot undertakes an extension of flight path, and a visible path

stretch manifests in horizontal dimension

• Major precursors:– Separation from preceding aircraft– Excess approach speed– Runway occupancy

Horizontal view of S-turn anomaly in arrivals to RWY4 at LGA

Horizontal view of S-turn anomaly in arrivals to RWY22 at LGA

• We generate features and use them as training input for ANN classification. The features correspond to:

• Aircraft separation: vertical and horizontal distance to preceding arriving and departing flight

• Unstable approach: Flight speed and altitude

• Analysis showed that S-turn anomaly was often preceded by sequence of precursors before anomaly occurred

• Look-ahead time can be further increased by analyzing this sequence of precursors• Objective Determine most distant precursor from anomaly (earliest occurring

precursor)

• Average look-ahead time: 8.7 seconds• F1 score: 83.01%

Precursor Detection for S-turn Anomalies

PredictedTrue

Anomalousflights

Normalflights Total

Anomalousflights

True positive66

False negative18 84

NormalFlights

False positive9

True negative3,257 3,266

Total 75 3,275 3,350

56789101112

Distance to touchdown (nm)

1500

2000

2500

3000

3500

4000

4500

5000

Altit

ude

(ft)

• New average look-ahead time: 12.3 seconds

• Objective Determine most distant precursor from anomaly

Aircraft approaches closely to preceding aircraft

Aircraft is below normal glideslope

Aircraft undertakes path stretch maneuver

Vertical precursor precedes S-turn

Loss of separation forces trailing flight to undertake

vertical maneuver

Algorithm determines loss of separation as true

precursor

Distribution of look-ahead times for arrivals to LGA over 11 days

Precursor Detection for S-turn Anomalies

Conclusions• Developed a temporal logic based precursor detection algorithm to

detect precursor to anomalies in unlabeled aviation surveillancedatasets

• Demonstrated the algorithm using two air traffic surveillance datasets ASDE-X and TAIS

• Effectively identified precursors to anomalous flights (go-around and S-turn anomalies) detected by TempAD

• Apply precursor detection algorithm to predict a wider variety of anomalies• Anomalies in vertical dimension (glide slope intercept mismatch), underspeed,

overspeed, energy deficit or excess anomalies

• Acquire a richer and larger dataset to improve precursor detectionperformance

Future WorkLimitations• Precursor detection would benefit from a richer input dataset – adding voice data,

pilot intent data as input could grant a better insight into precursor detection• Precursor detection models are not specific to runway/airports. Using a larger

input dataset could allow us to find precursor to, say, go-around anomalies forarrivals to RWY22 at LGA, making precursor detection very accurate

Acknowledgement

• This research is funded by the National Aeronautics and Space Administration(NASA) through the NASA Big Data Analytics project (NNH15ZEA001N).

• The authors are grateful to Dr. Andrew Churchill at Mosaic ATM, Megan Hawleyat Honeywell, and Dr. Nikunj C. Oza at NASA Ames for their valuable commentsand continued support.

Data-Driven Precursor Detection Algorithm for Terminal Airspace OperationsPresentation OutlineData-driven Learning for ATMLevel-set DefinitionsProblem SetupMachine LearningObjectivesSignal Temporal LogicBackground: Anomaly DetectionInput: Surveillance DataAlgorithm Development for Precursor DetectionReactive TempAD for Precursor DetectionReactive TempAD: Support Vector MachineReactive TempAD: Artificial Neural NetworkInput Features (Dimensions)Go-around AnomaliesPrecursor Detection for Go-around AnomaliesPrecursor Detection for Go-around AnomaliesSlide Number 19Slide Number 20Slide Number 21Slide Number 22Slide Number 23ConclusionsSlide Number 25