Investigation of the CLEAN deconvolution method for use with Late Time Response analysis of multiple objects

Simon Hutchinson; Christopher T. Taylor; Michael Fernando; David Andrews; Nicholas Bowring

doi:10.1117/12.2067288

7 October 2014 Investigation of the CLEAN deconvolution method for use with Late Time Response analysis of multiple objects

Simon Hutchinson, Christopher T. Taylor, Michael Fernando, David Andrews, Nicholas Bowring

Author Affiliations +

Proceedings Volume 9252, Millimetre Wave and Terahertz Sensors and Technology VII; 92520I (2014) https://doi.org/10.1117/12.2067288
Event: SPIE Security + Defence, 2014, Amsterdam, Netherlands

Abstract

This paper investigates the application of the CLEAN non–linear deconvolution method to Late Time Response (LTR) analysis for detecting multiple objects in Concealed Threat Detection (CTD). When an Ultra-Wide Band (UWB) frequency radar signal is used to illuminate a conductive target, surface currents are induced upon the object which in turn give rise to LTR signals. These signals are re-radiated from the target and the results from a number of targets are presented. The experiment was performed using double ridged horn antenna in a pseudo-monostatic arrangement. A Vector network analyser (VNA) has been used to provide the UWB Frequency Modulated Continuous Wave (FMCW) radar signal. The distance between the transmitting antenna and the target objects has been kept at 1 metre for all the experiments performed and the power level at the VNA was set to 0dBm. The targets in the experimental setup are suspended in air in a laboratory environment. Matlab has been used in post processing to perform linear and non-linear deconvolution of the signal. The Wiener filter, Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) are used to process the return signals and extract the LTR features from the noise clutter. A Generalized Pencil-of-Function (GPOF) method was then used to extract the complex poles of the signal. Artificial Neural Networks (ANN) and Linear Discriminant Analysis (LDA) have been used to classify the data.

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Simon Hutchinson, Christopher T. Taylor, Michael Fernando, David Andrews, and Nicholas Bowring "Investigation of the CLEAN deconvolution method for use with Late Time Response analysis of multiple objects", Proc. SPIE 9252, Millimetre Wave and Terahertz Sensors and Technology VII, 92520I (7 October 2014); https://doi.org/10.1117/12.2067288

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