NDLI: Using Hidden Markov Models to Detect Mode Changes in Aircraft Flight Data for Conflict Resolution

Content Provider	IEEE Xplore Digital Library
Author	Neogi, N.A. Naseri, A.
Copyright Year	2006
Description	Author affiliation: Univ. of Illinois at Urbana-Champaign, Urbana (Neogi, N.A.; Naseri, A.)
Abstract	Verification and validation of conflict detection and resolution aids is critical in the air traffic management domain. With increasing capacity and safety constraints, the automation of critical tasks to reduce controller workload becomes essential. Classic aircraft conflict resolution strategies involve separating projected conflicting aircraft via altitude. However, the ability to sense, process and actuate upon aircraft data is a non-trivial task. Models of aircraft trajectories are non-linear and stochastic in nature; and their internal parameters are often poorly defined. Verifiable techniques for mode detection are critical in order to enable the accurate projection of aircraft conflicts, and for the enactment of altitude separation resolution strategies. In this paper, we use learning techniques from hidden Markov modeling in order to tune the discrete and continuous parameters of a stochastic hybrid model in order to perform mode detection upon actual flight track data. Comparisons of aircraft in ascending, descending and level flight are performed, and unknown flight track data is evaluated probabilistically against the tuned model.
Starting Page	3732
Ending Page	3737
File Size	2661025
Page Count	6
File Format	PDF
ISBN	1424400996
DOI	10.1109/ICSMC.2006.384710
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-08
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Air traffic control Stochastic processes Traffic control Safety Automatic control Aircraft navigation Communication system traffic control Stochastic systems Predictive models
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Conflict Resolution Advisory Service in autonomous aircraft operations

Predictive Control of Complex Stochastic Systems using Markov Chain Monte Carlo with Application to Air Traffic Control

Probability of conflict analysis of 3D aircraft flight based on two-level Markov chain approximation approach

A stochastic conflict detection method integrating planned heading and velocity changes

Probabilistic safety analysis in three dimensional aircraft flight

A Neuro-Dynamic Based Conflict Resolution Scheme for Air Traffic Management

Noncooperative conflict resolution using differential game

A framework for conflict resolution in air traffic management

A simplified approach to determine airspace complexity maps under automated conflict resolution

Using Hidden Markov Models to Detect Mode Changes in Aircraft Flight Data for Conflict Resolution

Similar Documents

Conflict Resolution Advisory Service in autonomous aircraft operations

Predictive Control of Complex Stochastic Systems using Markov Chain Monte Carlo with Application to Air Traffic Control

Probability of conflict analysis of 3D aircraft flight based on two-level Markov chain approximation approach

A stochastic conflict detection method integrating planned heading and velocity changes

Probabilistic safety analysis in three dimensional aircraft flight

A Neuro-Dynamic Based Conflict Resolution Scheme for Air Traffic Management

Noncooperative conflict resolution using differential game

A framework for conflict resolution in air traffic management

A simplified approach to determine airspace complexity maps under automated conflict resolution

Using Hidden Markov Models to Detect Mode Changes in Aircraft Flight Data for Conflict Resolution