Lie group parameterization for dynamics based prior in ATR

Anuj Srivastava; Michael I. Miller; Ulf Grenander

Lie group parameterization for dynamics based prior in ATR

Anuj Srivastava, Michael I. Miller, Ulf Grenander

Research output: Contribution to conference › Paper › peer-review

Abstract

In recent ATR literature, there is increasing utilization of motion dynamics for tracking and recognizing moving targets. The dynamics along with the sensor models provide a Bayesian framework for conditional mean estimation of scene parameters. Previously, the authors have presented a random sampling algorithm for empirical generation of estimates based on jump-diffusion processes ([1]). Here we describe a different parameterization for simplifying the derivation of a more informative prior, from Newtonian mechanics, on the target configurations.

Original language	English (US)
Pages	97-100
Number of pages	4
State	Published - 1994
Externally published	Yes
Event	Proceedings of the 1994 6th IEEE Digital Signal Processing Workshop - Yosemite, CA, USA Duration: Oct 2 1994 → Oct 5 1994

Conference

Conference	Proceedings of the 1994 6th IEEE Digital Signal Processing Workshop
City	Yosemite, CA, USA
Period	10/2/94 → 10/5/94

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering

Cite this

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title = "Lie group parameterization for dynamics based prior in ATR",

abstract = "In recent ATR literature, there is increasing utilization of motion dynamics for tracking and recognizing moving targets. The dynamics along with the sensor models provide a Bayesian framework for conditional mean estimation of scene parameters. Previously, the authors have presented a random sampling algorithm for empirical generation of estimates based on jump-diffusion processes ([1]). Here we describe a different parameterization for simplifying the derivation of a more informative prior, from Newtonian mechanics, on the target configurations.",

author = "Anuj Srivastava and Miller, {Michael I.} and Ulf Grenander",

year = "1994",

language = "English (US)",

pages = "97--100",

note = "Proceedings of the 1994 6th IEEE Digital Signal Processing Workshop ; Conference date: 02-10-1994 Through 05-10-1994",

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TY - CONF

T1 - Lie group parameterization for dynamics based prior in ATR

AU - Srivastava, Anuj

AU - Miller, Michael I.

AU - Grenander, Ulf

PY - 1994

Y1 - 1994

N2 - In recent ATR literature, there is increasing utilization of motion dynamics for tracking and recognizing moving targets. The dynamics along with the sensor models provide a Bayesian framework for conditional mean estimation of scene parameters. Previously, the authors have presented a random sampling algorithm for empirical generation of estimates based on jump-diffusion processes ([1]). Here we describe a different parameterization for simplifying the derivation of a more informative prior, from Newtonian mechanics, on the target configurations.

AB - In recent ATR literature, there is increasing utilization of motion dynamics for tracking and recognizing moving targets. The dynamics along with the sensor models provide a Bayesian framework for conditional mean estimation of scene parameters. Previously, the authors have presented a random sampling algorithm for empirical generation of estimates based on jump-diffusion processes ([1]). Here we describe a different parameterization for simplifying the derivation of a more informative prior, from Newtonian mechanics, on the target configurations.

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UR - http://www.scopus.com/inward/citedby.url?scp=0028733530&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0028733530

SP - 97

EP - 100

T2 - Proceedings of the 1994 6th IEEE Digital Signal Processing Workshop

Y2 - 2 October 1994 through 5 October 1994

ER -

Lie group parameterization for dynamics based prior in ATR

Abstract

Conference

ASJC Scopus subject areas

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