Foliage Clutter Research Articles

Comprehensive measurement and modeling in foliage environments using UHF UWB ground-based radar

The precise knowledge of the statistical properties of clutter plays a key role in radar target detection in foliage environments. Owing to a wealth of multiple scattering, the selection of an appropriate model for diverse foliage environments remains a conundrum. Thus the statistical modeling of the foliage clutter in different environments is focused on in this paper. In particular, the primary contribution is to characterize and parameterize the amplitude of the foliage clutter in three typical foliage environments of different densities, including line-of-sight (LOS) forest environment, non line-of-sight (NLOS) forest-penetration environment, and NLOS bush-penetration environment. Based on the real clutter data acquired with an ultra-high frequency (UHF) ultra-wideband (UWB) ground-based radar, the experimental results show that the specific foliage type and configuration has a significant impact on the distribution of foliage clutter. Furthermore, by means of Kullback–Leibler distance (KLD), log-logistic, and distributions with estimated parameters are demonstrated to be the best choice to model the clutter in the LOS forest environment, NLOS forest-penetration environment and NLOS bush-penetration environment, respectively. The proposed models are great advantages on understanding and prediction of the clutter in foliage environments.

Clutter Distribution Analysis of A Tropical Foliage Clutter for Different Profiles Based on FSR Micro-Sensor Network

Comparison of four different clutter profiles (border, seaside, free space and forest) using Forward Scatter Radar (FSR), which operates in Ultra-High and Very High Frequency (UHF and VHF) bands, is analyzed in this paper. Clutter levels ranging from low, medium, strong and very strong on each profile were studied. Based on the standard deviation of each clutter profile, border suits the best profile as the strongest clutter profile amidst seaside and free space, while the forest is determined as the lowest clutter profile. Subsequently, the characteristics of the clutter are investigated and compared based on the five distribution models (Log-Normal, Log-Logistic, Gamma, Weibull and Nakagami). The parameters of the five distributions are evaluated using Root Mean Square Error (RMSE) in order to prove that the distribution model fits best to the clutter data. It can be concluded that Gamma distribution is the best distribution model for all cases of frequency bands and profiles.

Adaptive MIMO Radar Design and Detection in Compound-Gaussian Clutter

Multiple-input multiple-output (MIMO) radars with widely separated transmitters and receivers are useful to discriminate a target from clutter using the spatial diversity of the scatterers in the illuminated scene. We consider the detection of targets in compound-Gaussian clutter, describing heavy-tailed clutter distributions fitting high-resolution and/or low-grazing-angle radars in the presence of sea or foliage clutter. First we introduce a data model using the inverse gamma distribution to represent the clutter texture. Then we apply the parameter-expanded expectation-maximization (PX-EM) algorithm to estimate the clutter texture and speckle as well as the target parameters. We develop a statistical decision test using these estimates and demonstrate its statistical characteristics. Based on the statistical characteristics of this test, we propose an algorithm to adaptively distribute the transmitted energy among the transmitters and maximize the detection performance. We demonstrate the advantages of the MIMO setup and adaptive energy allocation in target detection in the presence of compound-Gaussian clutter using Monte Carlo (MC) simulations.

A Propagation Environment Modeling in Foliage

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.

Sense-Through-Foliage target detection using UWB radar sensor networks

In this paper, we propose two signal processing approaches to detect the target obscured by foliage based on real data collected by an Ultra-wide band (UWB) radar sensor. One is a differential-based four-step signal processing approach that estimates and offsets the impulsive clutter; the other approach employs short-time Fourier transform (STFT) to distinguish the target from foliage clutter. Both of these approaches provide better detection performance compared to the common 2-D image algorithm used for UWB radar. From time to time, due to the significant pulse-to-pulse variability of the foliage clutter, neither differential-based nor STFT approach can detect the target. In this case, we propose radar sensor network (RSN) and a RAKE structure in addition to the previous signal processing approaches for data fusion. The result shows that accurate detection can be achieved. Numerical performances have been analyzed for both cases in terms of probability of detection and probability of false alarm.

Target detection beneath foliage using polarimetric synthetic aperture radar interferometry

In this paper, we demonstrate how the new technology of polarimetric synthetic aperture radar (SAR) interferometry can be used to enhance the detection of targets hidden beneath foliage. The key idea is to note that for random volume scattering, the interferometric coherence is invariant to changes in wave polarization. On the other hand, in the presence of a target the coherence changes with polarization. We show that under general symmetry constraints this change is linear in the complex coherence plane. These observations can be used to devise a filter to suppress the returns from foliage clutter while maintaining the signal from hidden targets. We illustrate the algorithm by applying it to coherent L-band SAR simulations of corner reflectors hidden in a forest. The simulations are performed using a voxel-based vector wave propagation and scattering code coupled to detailed structural models of tree architecture. In this way, the spatial statistics and radar signal fluctuations closely match those observed for natural terrain. We demonstrate significant improvements in the detection of hidden targets, which suggests that this technology has great potential for future foliage penetration (FOPEN) applications.

Target detection trials with a millimeter wave radar system

Unobstructed, large RCS targets, similar radar targets surrounded by moving foliage, and small targets in severe clutter have been used as test cases for two pre-processing algorithms and several threshold levels in an experimental millimeter wave radar system. The rather conventional "six-out-of-eight" pulse radar selection method with binary output has been compared to an algorithm that accepts a target if the pre-defined trigger level is crossed by the average of the eight consecutive pulses. In this case, however, the output is an analog value corresponding to the relative average video amplitude. In terms of plotted video, this process seems to give a slightly better combination of false alarm rate and detection probability. Large targets are easier to detect from foliage clutter with the conventional method.

Statistical-physical model for foliage clutter in ultra-wideband synthetic aperture radar images.

Analyzing foliage-penetrating (FOPEN) ultra-wideband synthetic aperture radar (SAR) images is a challenging problem owing to the noisy and impulsive nature of foliage clutter. Indeed, many target-detection algorithms for FOPEN SAR data are characterized by high false-alarm rates. In this work, a statistical-physical model for foliage clutter is proposed that explains the presence of outliers in the data and suggests the use of symmetric alpha-stable (SalphaS) distributions for accurate clutter modeling. Furthermore, with the use of general assumptions of the noise sources and propagation conditions, the proposed model relates the parameters of the SalphaS model to physical parameters such as the attenuation coefficient and foliage density.