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Application of clutter reduction techniques for detection of metallic and low dielectric target behind the brick wall by stepped frequency continuous wave radar in ultra-wideband range

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TLDR
In this paper, a study of clutter reduction techniques for detection of metallic and non-metallic (low dielectric constant) targets behind a brick wall with the help of ultra wideband (UWB) through wall imaging system is presented.
Abstract
A study of clutter reduction techniques for detection of metallic and non-metallic (low dielectric constant) targets behind a brick wall with the help of ultra-wideband (UWB) through wall imaging system is presented. It is known that sometimes the clutter level is comparable to the level of target reflection that makes it difficult to detect the target correctly. Detection of low dielectric constant materials becomes more difficult due to low reflection from such targets. Therefore there is a need to analyse various clutter removal techniques and check the performance of these techniques for enhancement of target signal-to-clutter ratio. For this purpose, an UWB stepped frequency wave radar is indigenously assembled with the use of vector network analyser, which works in the frequency range of 3.95–5.85 GHz. An experiment is carried out for detection of metal as well as Teflon (low dielectric constant) targets with the application of clutter reduction techniques. The authors have considered statistical-based techniques like singular value decomposition, principle component analysis, factor analysis and independent component analysis (ICA) for clutter removal. It is observed that the signal-to-clutter ratio for metal target detection is quite enhanced by all the four techniques, whereas only ICA is able to enhance the signal-to-clutter ratio for a low dielectric constant target like Teflon.

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Journal ArticleDOI

On-Site Validation of a Microwave Breast Imaging System, before First Patient Study

TL;DR: The Wavelia microwave breast imaging system that has been recently installed at the Galway University Hospital, Ireland, for a first-in-human pilot clinical test is presented, as are the radar signal processing algorithms used in forming the microwave images in which small tumors could be detectable for disease diagnosis.
Journal ArticleDOI

Phase- and Self-Injection-Locked Radar for Detecting Vital Signs with Efficient Elimination of DC Offsets and Null Points

TL;DR: In this paper, a phase-and self-injection-locked radar was proposed for robust vital-sign detection using a dual-tuning voltage-controlled oscillator without complex clutter cancellation techniques.
Journal ArticleDOI

FMCW SAR System for Near-Distance Imaging Applications—Practical Considerations and Calibrations

TL;DR: A signal processing procedure with system calibration methods to mitigate the effect of deramp, phase noise, and nonlinearity of the VCO on the beat spectrum and the reconstructed images show the improvement of image quality and accuracy in target position.
Journal ArticleDOI

Multipath ghost elimination for through-wall radar imaging

TL;DR: A new ghost elimination method that removes multipath echoes from the raw radar data is proposed that can preserve the true targets while eliminating the ghosts even if the targets overlap with those ghosts.
Journal ArticleDOI

Convergence Analysis for Initial Condition Estimation in Coupled Map Lattice Systems

TL;DR: An inverse largest Lyapunov exponent (ILLE) is proposed to investigate the strength of convergence and divergence in the inverse CML systems, and it can determine if the CML initial condition estimation method is effective.
References
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Journal ArticleDOI

Independent component analysis, a new concept?

Pierre Comon
- 01 Apr 1994 - 
TL;DR: An efficient algorithm is proposed, which allows the computation of the ICA of a data matrix within a polynomial time and may actually be seen as an extension of the principal component analysis (PCA).
Book

Independent Component Analysis

TL;DR: Independent component analysis as mentioned in this paper is a statistical generative model based on sparse coding, which is basically a proper probabilistic formulation of the ideas underpinning sparse coding and can be interpreted as providing a Bayesian prior.
Journal ArticleDOI

Fast and robust fixed-point algorithms for independent component analysis

TL;DR: Using maximum entropy approximations of differential entropy, a family of new contrast (objective) functions for ICA enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions.
Reference EntryDOI

Independent Component Analysis

TL;DR: A statistical generative model called independent component analysis is discussed, which shows how sparse coding can be interpreted as providing a Bayesian prior, and answers some questions which were not properly answered in the sparse coding framework.
Journal ArticleDOI

Blind signal separation: statistical principles

TL;DR: The objectives of this paper are to review some of the approaches that have been developed to address blind signal separation and independent component analysis, to illustrate how they stem from basic principles, and to show how they relate to each other.
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