NDLI: Robust probabilistic conflict prediction for sense and avoid

Content Provider	IEEE Xplore Digital Library
Author	Adaska, J.W. Obermeyer, K. Schmidt, E.
Copyright Year	2014
Description	Author affiliation: Numerica Corp., Loveland, CO, USA (Adaska, J.W.; Obermeyer, K.; Schmidt, E.)
Abstract	Safe integration of Unmanned Aircraft Systems (UAS) into the National Airspace (NAS) will require the development and fielding of a sense-and avoid (SAA) capability to augment the traditional “see-and-avoid” regulations of manned aircraft. In this paper, we focus on the problem of correctly predicting an intruder trajectory. Approaches to intruder prediction are typically grouped into three categories: (i) nominal (deterministic), (ii) worst-case, and (iii) probabilistic. Most prediction algorithms envisioned for SAA fall within the probabilistic category. The benefit of a probabilistic approach is that it provides a mechanism to represent unknown variations in the intruder state at future times while also avoiding the overly conservative assumptions inherent in worst-case prediction. The downside of a probabilistic prediction is that it necessitates the construction of a stochastic model that is both useful for computation and accurately represents the “true” uncertainty in intruder predictions. Markovian structure and time-discretization are very common simplifying assumptions made to satisfy the first goal. Data-driven model tuning is typically used for the latter. However, a model is never exact, and a large quantity of data may be needed to guarantee the approximation accuracy is sufficient. The primary contribution of this paper is to present a fourth option for intruder prediction that we refer to as “robust probabilistic prediction.” It is meant to address the risk of model mismatch associated with traditional probabilistic predictions. Conceptually, the idea is to specify only those features of the stochastic model that can be justified by data or expert judgment, leaving a full stochastic model underspecified. Typically, one needs a full stochastic model to “turn the crank” on risk calculations (e.g., probability of Near Mid-Air Collision). However, in robust probabilistic predictions, risk is defined as the worst-case risk over a space of stochastic models. This relaxes the need for ensuring that all elements of the model are correct. We show that a computationally efficient semi-definite program (SDP) can be used for performing the optimization over the space of stochastic models. Such an approach greatly reduces the risk of model mismatch as well as reducing the data burden required for model validation.
Starting Page	1198
Ending Page	1203
File Size	496458
Page Count	6
File Format	PDF
ISBN	9781479932726
ISSN	07431619
e-ISBN	9781479932740
DOI	10.1109/ACC.2014.6859435
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Stochastic processes Trajectory Probabilistic logic Computational modeling Atmospheric modeling Data models Uncertainty Uncertain systems Stochastic systems Air traffic management
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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

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