Track Detection of Low Observable Targets Using a Motion Model

Track Detection of Low Observable Targets Using a Motion Model

A method for detecting a low observable target track using an acceleration-based overall motion model is proposed. Unlike the existing track-before-detect methods that are based on sequential state updates, this method computes integrated echo energy for the entire hypothesized motion. The detection...

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Bibliographic Details
Main Authors: J. Daniel Park, John F. Doherty
Format: Article
Language:English
Published: IEEE 2015-01-01
Series:IEEE Access
Subjects:
Tracking
Track before detect
Detection
Batch processing
Hidden Markov model
Motion model
Online Access:https://ieeexplore.ieee.org/document/7219367/
id doaj-898ed8e86aea47e9bb2dce2c717d1c29
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spelling doaj-898ed8e86aea47e9bb2dce2c717d1c292021-03-29T19:34:20ZengIEEEIEEE Access2169-35362015-01-0131408141510.1109/ACCESS.2015.24719357219367Track Detection of Low Observable Targets Using a Motion ModelJ. Daniel Park0John F. Doherty1Applied Research Laboratory, The Pennsylvania State University, P.O. Box 30, State College, PA, USADepartment of Electrical Engineering, The Pennsylvania State University, University Park, PA, USAA method for detecting a low observable target track using an acceleration-based overall motion model is proposed. Unlike the existing track-before-detect methods that are based on sequential state updates, this method computes integrated echo energy for the entire hypothesized motion. The detection and the estimation of the track are made simultaneously using the batch processing approach. A comparison of track detection probability shows higher performance against low observable targets. Using a motion similarity metric and motion model homogeneity, a performance prediction model is derived and compared with the simulation results.https://ieeexplore.ieee.org/document/7219367/TrackingTrack before detectDetectionBatch processingHidden Markov modelMotion model
collection DOAJ
language English
format Article
sources DOAJ
author J. Daniel Park
John F. Doherty
spellingShingle J. Daniel Park
John F. Doherty
Track Detection of Low Observable Targets Using a Motion Model
IEEE Access
Tracking
Track before detect
Detection
Batch processing
Hidden Markov model
Motion model
author_facet J. Daniel Park
John F. Doherty
author_sort J. Daniel Park
title Track Detection of Low Observable Targets Using a Motion Model
title_short Track Detection of Low Observable Targets Using a Motion Model
title_full Track Detection of Low Observable Targets Using a Motion Model
title_fullStr Track Detection of Low Observable Targets Using a Motion Model
title_full_unstemmed Track Detection of Low Observable Targets Using a Motion Model
title_sort track detection of low observable targets using a motion model
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2015-01-01
description A method for detecting a low observable target track using an acceleration-based overall motion model is proposed. Unlike the existing track-before-detect methods that are based on sequential state updates, this method computes integrated echo energy for the entire hypothesized motion. The detection and the estimation of the track are made simultaneously using the batch processing approach. A comparison of track detection probability shows higher performance against low observable targets. Using a motion similarity metric and motion model homogeneity, a performance prediction model is derived and compared with the simulation results.
topic Tracking
Track before detect
Detection
Batch processing
Hidden Markov model
Motion model
url https://ieeexplore.ieee.org/document/7219367/
work_keys_str_mv AT jdanielpark trackdetectionoflowobservabletargetsusingamotionmodel
AT johnfdoherty trackdetectionoflowobservabletargetsusingamotionmodel
_version_ 1724195945021702144

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