Signal processing algorithms in identification of subsurface objects

Authors

DOI:

https://doi.org/10.3103/S0735272707100056

Abstract

This study presents algorithms of preliminary signal processing of subsurface probing radars and an identification technique of objects under observation. Preliminary processing consists of filtration, removal of unwanted nonstationary trends, segmentation and search for connected regions. The identification technique is based on the tools of the Hough class transform. Such approach is founded on the possibility of presenting electromagnetic signal reflected from physical object in the form of analytical model in the space of parameter spectra. Preliminary data processing is performed in the interactive mode ensuring the minimum signal-to-noise ratio when presenting objects in the space of characteristic functions. A procedure for analyzing radar images with respect to connectivity of their elements was proposed. Arguments regarding the choice of thresholds in the space of parameter spectra and the space of signals have been considered. An algorithm of image convolution was developed to enhance the contrast-background sensitivity.

References

Kh. F. Kharmut, Nonsinosoidal Waves in Radio Location and Radio Communication (Radio i Svyaz’, Moscow, 1985) [Russian translation].

V. G. Stroitelev, “Signal processing methods during subsurface radar probing,” Zarubezhnaya Radioelektronika, No. 1, 95–105 (1991).

M. I. Finkel’shtein, V. A. Kutev, and V. P. Zolotarev, Application of Radar Subsurface Probing in Engineering Geology (Nedra, Moscow, 1986) [in Russian, ed. by M. I. Finkel’shtein].

I. Ya. Immoreev, “Superwideband location. Main features and distinctions with respect to conventional radiolocation,” Elektromagnitnye Volny i Elektronnye Sistemy 2, No. 1, 81–88 (1987).

O. V. Sytnik, I. A. Vyaz’mitinov, and A. Yu. Grinev, Issues of Subsurface Radio Location (Radiotekhnika, Moscow, 2005) [in Russian, ed. by A. Yu. Grinev].

R. Otnes and L. Enokson, The Applied Analysis of Time Series. Basic Methods (Mir, Moscow, 1982) [in Russian].

M. Kendall and A. Stuart, Multivariate Statistical Analysis and Time Series (Nauka, Moscow, 1976) [Russian translation, ed by A. N. Kolmogorov and Yu. V. Prokhorov].

E. P. Putyatin and S. I. Averin, Image Processing in Robotics (Mashynostroenie, Moscow, 1990) [in Russian].

D. Forsyth and Zh. Pons, Computer Vision. Modern Approach (Publishing House “Vil’yams,” Moscow, 2004) [Russian translation].

L. Shapiro and J. Stokman, Computer Vision (BINOM, Moscow, 2006) [Russian translation].

V. G. Labunets and S. D. Chernina, “Theory and Application of the Hough Transform,” Zarubezhnaya Radioelektronika, No. 10, 43–56 (1987).

S. D. Shapiro, “Use of the Hough Transform for Image Data Compression,” Pattern Recognition 12, 333–337 (1980).

Published

2007-10-05

Issue

Section

Research Articles