A Propagation Environment Modeling in Foliage
<p/> <p>Foliage clutter, which can be very large and mask targets in backscattered signals, is a crucial factor that degrades the performance of target detection, tracking, and recognition. Previous literature has intensively investigated land clutter and sea clutter, whereas foliage clu...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
SpringerOpen
2010-01-01
|
Series: | EURASIP Journal on Wireless Communications and Networking |
Online Access: | http://jwcn.eurasipjournals.com/content/2010/873070 |
id |
doaj-1fc4f54b11684e68a6987b24825fdd70 |
---|---|
record_format |
Article |
spelling |
doaj-1fc4f54b11684e68a6987b24825fdd702020-11-25T00:37:53ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14721687-14992010-01-0120101873070A Propagation Environment Modeling in FoliageSamn SherwoodWLiang JingLiang Qilian<p/> <p>Foliage clutter, which can be very large and mask targets in backscattered signals, is a crucial factor that degrades the performance of target detection, tracking, and recognition. Previous literature has intensively investigated land clutter and sea clutter, whereas foliage clutter is still an open-research area. In this paper, we propose that foliage clutter should be more accurately described by a log-logistic model. On a basis of pragmatic data collected by ultra-wideband (UWB) radars, we analyze two different datasets by means of maximum likelihood (ML) parameter estimation as well as the root mean square error (RMSE) performance. We not only investigate log-logistic model, but also compare it with other popular clutter models, namely, log-normal, Weibull, and Nakagami. It shows that the log-logistic model achieves the smallest standard deviation (STD) error in parameter estimation, as well as the best goodness-of-fit and smallest RMSE for both poor and good foliage clutter signals.</p>http://jwcn.eurasipjournals.com/content/2010/873070 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Samn SherwoodW Liang Jing Liang Qilian |
spellingShingle |
Samn SherwoodW Liang Jing Liang Qilian A Propagation Environment Modeling in Foliage EURASIP Journal on Wireless Communications and Networking |
author_facet |
Samn SherwoodW Liang Jing Liang Qilian |
author_sort |
Samn SherwoodW |
title |
A Propagation Environment Modeling in Foliage |
title_short |
A Propagation Environment Modeling in Foliage |
title_full |
A Propagation Environment Modeling in Foliage |
title_fullStr |
A Propagation Environment Modeling in Foliage |
title_full_unstemmed |
A Propagation Environment Modeling in Foliage |
title_sort |
propagation environment modeling in foliage |
publisher |
SpringerOpen |
series |
EURASIP Journal on Wireless Communications and Networking |
issn |
1687-1472 1687-1499 |
publishDate |
2010-01-01 |
description |
<p/> <p>Foliage clutter, which can be very large and mask targets in backscattered signals, is a crucial factor that degrades the performance of target detection, tracking, and recognition. Previous literature has intensively investigated land clutter and sea clutter, whereas foliage clutter is still an open-research area. In this paper, we propose that foliage clutter should be more accurately described by a log-logistic model. On a basis of pragmatic data collected by ultra-wideband (UWB) radars, we analyze two different datasets by means of maximum likelihood (ML) parameter estimation as well as the root mean square error (RMSE) performance. We not only investigate log-logistic model, but also compare it with other popular clutter models, namely, log-normal, Weibull, and Nakagami. It shows that the log-logistic model achieves the smallest standard deviation (STD) error in parameter estimation, as well as the best goodness-of-fit and smallest RMSE for both poor and good foliage clutter signals.</p> |
url |
http://jwcn.eurasipjournals.com/content/2010/873070 |
work_keys_str_mv |
AT samnsherwoodw apropagationenvironmentmodelinginfoliage AT liangjing apropagationenvironmentmodelinginfoliage AT liangqilian apropagationenvironmentmodelinginfoliage AT samnsherwoodw propagationenvironmentmodelinginfoliage AT liangjing propagationenvironmentmodelinginfoliage AT liangqilian propagationenvironmentmodelinginfoliage |
_version_ |
1725299217151295488 |