391 research outputs found
Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery
Classifying buried and obscured targets of interest from other natural and
manmade clutter objects in the scene is an important problem for the U.S. Army.
Targets of interest are often represented by signals captured using
low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar
(SAR) technology. This technology has been used in various applications,
including ground penetration and sensing-through-the-wall. However, the
technology still faces a significant issues regarding low-resolution SAR
imagery in this particular frequency band, low radar cross sections (RCS),
small objects compared to radar signal wavelengths, and heavy interference. The
classification problem has been firstly, and partially, addressed by sparse
representation-based classification (SRC) method which can extract noise from
signals and exploit the cross-channel information. Despite providing potential
results, SRC-related methods have drawbacks in representing nonlinear relations
and dealing with larger training sets. In this paper, we propose a Simultaneous
Decomposition and Classification Network (SDCN) to alleviate noise inferences
and enhance classification accuracy. The network contains two jointly trained
sub-networks: the decomposition sub-network handles denoising, while the
classification sub-network discriminates targets from confusers. Experimental
results show significant improvements over a network without decomposition and
SRC-related methods
Representation of Radar Micro-Dopplers Using Customized Dictionaries
Human motions give rise to frequency modulations, known as micro-Dopplers, to continuous wave radar signals. Micro-Doppler signals have been extensively researched for the classification of different types of human motions as well as to distinguish humans from other moving targets. However, there are two main scenarios where the performance of existing algorithms deteriorates significantly—one, when the channel consists of multiple moving targets resulting in distorted signatures, and two, when the systems conditions during the training stage deviate significantly from the conditions during the test stage. In this chapter, it is demonstrated that both of these limitations can be overcome by representing the radar data through customized dictionaries, fine-tuned to provide sparser representations of the data, than traditional data-independent dictionaries such as Fourier or wavelets. The performances of the algorithms are evaluated with both simulated and measured radar data gathered from moving humans in indoor line-of-sight conditions
Modern GPR Target Recognition Methods
Traditional GPR target recognition methods include pre-processing the data by
removal of noisy signatures, dewowing (high-pass filtering to remove
low-frequency noise), filtering, deconvolution, migration (correction of the
effect of survey geometry), and can rely on the simulation of GPR responses.
The techniques usually suffer from the loss of information, inability to adapt
from prior results, and inefficient performance in the presence of strong
clutter and noise. To address these challenges, several advanced processing
methods have been developed over the past decade to enhance GPR target
recognition. In this chapter, we provide an overview of these modern GPR
processing techniques. In particular, we focus on the following methods:
adaptive receive processing of range profiles depending on the target
environment; adoption of learning-based methods so that the radar utilizes the
results from prior measurements; application of methods that exploit the fact
that the target scene is sparse in some domain or dictionary; application of
advanced classification techniques; and convolutional coding which provides
succinct and representatives features of the targets. We describe each of these
techniques or their combinations through a representative application of
landmine detection.Comment: Book chapter, 56 pages, 17 figures, 12 tables. arXiv admin note:
substantial text overlap with arXiv:1806.0459
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