Abstract
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.
Highlights
Introduction and MotivationDetection and identification of military equipment in a strong clutter background, such as foliage, soil cover, or building has been a long-standing subject of intensive study
Our contribution is the new proposal on the foliage clutter model with detailed parameter estimation, and providing the criteria and approaches based on which the statistical analysis is obtained
Many radar clutter models have been proposed in terms of distinct statistical distributions; most of which describe the characteristics of clutter amplitude or power
Summary
Detection and identification of military equipment in a strong clutter background, such as foliage, soil cover, or building has been a long-standing subject of intensive study. S. Army Research Laboratory (ARL) [8, 9] have measured ultra-wideband (UWB) backscatter signals in foliage for different polarizations and frequency ranges. The US Air Force Office of Scientific Research (AFOSR) has conducted field measurement experiment concerning foliage penetration radar since 2004 and noted that metallic targets may be more identified with wideband than with narrowband signals. In this investigation, we will apply ultra-wideband (UWB) radar to model the foliage clutter. We investigate the log-logistic distribution (LLD) to model foliage clutter and illustrate the goodnessof-fit to real UWB clutter data.
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