KEYWORDS: General packet radio service, Interference (communication), Signal processing, Signal to noise ratio, Principal component analysis, Time-frequency analysis, Signal analysis, Signal analyzers, Signal detection, Error analysis
In this paper, we apply a time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) in GPR signal processing. It decomposes the GPR signal into a sum of oscillatory components, with guaranteed positive and smoothly varying instantaneous frequencies. The key idea of this method relies on averaging the modes obtained by EMD applied to several realizations of Gaussian white noise added to the original signal. It can solve the mode mixing problem in empirical mode decomposition (EMD) method and improve the resolution of ensemble empirical mode decomposition (EEMD) when the signal has low signal noise ratio (SNR). First, we analyze the difference between the basic theory of EMD, EEMD and CEEMD. Then, we compare the time and frequency analysis results of different methods. The synthetic and real GPR data demonstrate that CEEMD promises higher spectral-spatial resolution than the other two EMDs method. Its decomposition is complete, with a numerically negligible error.
KEYWORDS: Transceivers, Radar imaging, Associative arrays, Compressed sensing, Signal to noise ratio, Data modeling, Data acquisition, Detection and tracking algorithms, Optimization (mathematics), Radar
Compressive sensing techniques have been widely used to decrease the data acquisition time while generating high-resolution images due to the sparsity of the target space in through-the-wall radar imaging application. The CS-based imaging techniques mainly discretize the continuous target space into grid points and generate a dictionary of model data to form an optimization problem. The choice of the grid for generating the sparsity inducing basis or dictionary is a central point of CS and sparse approximation. However, good sparse recovery performance is based on the assumption that the targets are positioned at the pre-discretized grid locations; otherwise, the performance would significantly degrade. In this paper, the first-order approximation to estimate the targets' off-grid shifts and the joint sparse recovery method are used for reducing the effect of the grid to locate the off-grid target. Numerical examples demonstrate the robust results with lower localization errors using the joint sparse recovery method are obtained for off-grid targets compared to standard sparse reconstruction techniques.
Ultra-wideband (UWB) technology has been widely utilized in radar system because of the advantage of the
ability of high spatial resolution and object-distinction capability. A major challenge in UWB signal processing
is the requirement for very high sampling rate under Shannon-Nyquist sampling theorem which exceeds the
current ADC capacity. Recently, new approaches based on the Finite Rate of Innovation (FRI) allow significant
reduction in the sampling rate. A system for sampling UWB radar echo signal at an ultra-low sampling rate
and the estimation of time-delays is presented in the paper. An ultra-low rate sampling scheme based on FRI
is applied, which often results in sparse parameter extraction for UWB radar signal detection. The parameters
such as time-delays are estimated using the framework of compressed sensing based on total-variation norm
minimization. With this system, the UWB radar signal can be accurately reconstructed and detected with
overwhelming probability at the rate much lower than Nyquist rate. The simulation results show that the
proposed method is effective for sampling and detecting UWB radar signal at an ultra-low sampling rate.
The human’s Micro-Doppler signatures resulting from breathing, arm, foot and other periodic motion can provide
valuable information about the structure of the moving parts and may be used for identification and classification
purposes. In this paper, we carry out simulate with FDTD method and through wall experiment with UWB radar for
human being’s periodic motion detection. In addition, Advancements signal processing methods are presented to classify
and to extract the human’s periodic motion characteristic information, such as Micro-Doppler shift and motion
frequency. Firstly, we apply the Principal Component Analysis (PCA) with singular value decomposition (SVD) to denoise
and extract the human motion signal. Then, we present the results base on the Hilbert-Huang transform (HHT) and
the S transform to classify and to identify the human’s micro-Doppler shift characteristics. The results demonstrate that
the combination of UWB radar and various processing methods has potential to detect human’s Doppler signatures
effectively.
A novel breast image registration method is proposed to obtain a composite mammogram from several images with
partial breast coverage, for the purpose of accurate breast density estimation. The breast percent density estimated as a
fractional area occupied by fibroglandular tissue has been shown to be correlated with breast cancer risk. Some
mammograms, however, do not cover the whole breast area, which makes the interpretation of breast density estimates
ambiguous. One solution is to register and merge mammograms, yielding complete breast coverage. Due to elastic
properties of breast tissue and differences in breast positioning and deformation during the acquisition of individual
mammograms, the use of linear transformations does not seem appropriate for mammogram registration. Non-linear
transformations are limited by the changes in the mammographic projections pixel intensity with different positions of
the focal spot. We propose a novel method based upon non-linear local affine transformations. Initially, pairs of feature
points are manually selected and used to compute the best fit affine transformation in their small neighborhood. Finally, Shepherd interpolation is employed to compute affine transformations for the rest of the image area. The pixel values in the composite image are assigned using bilinear interpolation. Preliminary results with clinical images show a good match of breast boundaries, providing an increased coverage of breast tissue. The proposed transformation is continued and can be controlled locally. Moreover, the method is converging to the ground truth deformation if the paired feature points are evenly distributed and its number large enough.
A modification to our previous simulation of breast anatomy is proposed, in order to improve the quality of
simulated projections generated using software breast phantoms. Anthropomorphic software breast phantoms have
been used for quantitative validation of breast imaging systems. Previously, we developed a novel algorithm for
breast anatomy simulation, which did not account for the partial volume (PV) of various tissues in a voxel; instead,
each phantom voxel was assumed to contain single tissue type. As a result, phantom projection images displayed
notable artifacts near the borders between regions of different materials, particularly at the skin-air boundary. These
artifacts diminished the realism of phantom images. One solution is to simulate smaller voxels. Reducing voxel
size, however, extends the phantom generation time and increases memory requirements. We achieved an
improvement in image quality without reducing voxel size by the simulation of PV in voxels containing more than
one simulated tissue type. The linear x-ray attenuation coefficient of each voxel is calculated by combining
attenuation coefficients proportional to the voxel subvolumes occupied by the various tissues. A local planar
approximation of the boundary surface is employed, and the skin volume in each voxel is computed by
decomposition into simple geometric shapes. An efficient encoding scheme is proposed for the type and proportion
of simulated tissues in each voxel. We illustrate the proposed methodology on phantom slices and simulated
mammographic projections. Our results show that the PV simulation has improved image quality by reducing
quantization artifacts.
KEYWORDS: Signal attenuation, General packet radio service, Radar, Image processing, Antennas, Radio propagation, Feature extraction, Signal to noise ratio, Ground penetrating radar, Optical spheres
A method for ground-penetrating radar amplitude recovery of targets is presented in this paper. We use
the migration processing for target imaging and the clean method to extract featured target points.
Based on the featured target points, target reflection hyperbolas are calculated. Curve fitting is applied
to get amplitude attenuation parameters. The amplitude is recovered after compensating the parameters
to the raw data. We performed a field experiment with 5 sphere targets under water. Comparing to the
conventional methods, our proposed method is more effective for target classification.
We propose a novel algorithm for automatic aircraft classification. The proposed method makes numerical equivalents to
shape, size and other aircraft features as critical criteria to constitute the algorithm for their correct classification. This
method uses Inverse Synthetic Aperture Radar (ISAR) aircraft images that are making maneuvers that introduce aircraft
rotation relative to the radar station. By means of analyzing the shape of the radar pulse and Doppler shifts that are
caused by rotation of the aircraft, an image of the aircraft shape can be constructed. We computer simulated five
different categories of ISAR images. We tested the proposed classification algorithm on these five categories and on two
more categories taken from the Internet. One aircraft model is simulated and the other one is a real sequence with much
added noise. All seven different aircraft models are flying a holding pattern. We investigated where in the holding
patterns ISAR reflections made it possible to identify each category of aircraft. Our experimental results demonstrate that
in most parts of the holding pattern the category of the aircraft can be successfully identified. The performed tests show
that the proposed algorithm appears to be noise resistant.
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