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Permanent link (DOI): https://doi.org/10.7939/R3884M

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Handling Target Obscuration through Markov Chain Observations. Open Access

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Author or creator
Kouritzin, Michael
Wu, Biao
Additional contributors
Subject/Keyword
particle filter
classical filter equation
target obscuration
tracking problem
target identification
random blockage
Type of item
Conference/workshop Presentation
Language
English
Place
Time
Description
Target Obscuration, including foliage or building obscuration of ground targets and landscape or horizon obscuration of airborne targets, plagues many real world filtering problems. In particular, ground moving target identification Doppler radar, mounted on a surveillance aircraft or unattended airborne vehicle, is used to detect motion consistent with targets of interest. However, these targets try to obscure themselves (at least partially) by, for example, traveling along the edge of a forest or around buildings. This has the effect of creating random blockages in the Doppler radar image that move dynamically and somewhat randomly through this image. Herein, we address tracking problems with target obscuration by building memory into the observations, eschewing the usual corrupted, distorted partial measurement assumptions of filtering in favor of dynamic Markov chain assumptions. In particular, we assume the observations are a Markov chain whose transition probabilities depend upon the signal. The state of the observation Markov chain attempts to depict the current obscuration and the Markov chain dynamics are used to handle the evolution of the partially obscured radar image. Modifications of the classical filtering equations that allow observation memory (in the form of a Markov chain) are given. We use particle filters to estimate the position of the moving targets. Moreover, positive proof-of-concept simulations are included.
Date created
2008
DOI
doi:10.7939/R3884M
License information
Rights
Copyright 2008 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Citation for previous publication
M.A. Kouritzin and B. Wu (2008), "Handling Target Obscuration through Markov Chain Observations" in Signal Processing, Sensor Fusion, and Target Recognition Proceedings of SPIE. 6968, 69680S. doi:10.1117/12.779837
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