ITT-7.1

Audio Data Hiding by use of band-limited Random Sequences
Mikio IKEDA, Kazuya TAKEDA (Graduate School of Engineering, Nagoya University), Fumitada ITAKURA (Center for Information Media Studies, Nagoya University)

This paper proposes the use of band-limited random sequences to introduce further flexibility in the spread spectrum based audio data hiding. To realize the sub-band data hiding, a systematic method is developed in order to generate band-limited and orthonormal random sequences of any length. In experiments, we evaluated the selective use of frequency channels to be used for information embedding, and the robustness against the MPEG1 layer 3 encoding and decoding. From the results, it is clarified that the proposed method is robust against more than 160 kbps MPEG1 coding and decoding when the center frequency of the sub-band is lower than 11 kHz.

ITT-7.2

Image Domain Feature Extraction from Synthetic Aperture Imagery
Michael A Koets, Randolph L Moses (The Ohio State University)

We consider the problem of estimating a parametric model that describes radar backscattering from synthetic aperture radar imagery. We adopt a scattering center model that incorporates both frequency and aspect dependence of scattering. We develop an approximate maximum likelihood algorithm for parameter estimation directly on regions of the SAR image. The algorithm autonomously selects model order and structure. Results are presented for both synthetic and measured SAR imagery, and algorithm accuracy is compared with the Cramer-Rao bound.

ITT-7.3

Clutter Mitigation Techniques for Space-Based Radar
Stephen M Kogon, Daniel J Rabideau, Richard M Barnes (MIT Lincoln Laboratory)

The mission of a ground moving target indication (GMTI) radar, as its name implies, is to detect and classify ground-based vehicles, even ones with very low velocities. This type of radar can provide a wide area of coverage and frequent updates of a specific area of interest if the radar is placed in a low earth orbit. However, because of the large footprint of the radar on the ground and the high satellite velocity, target signals must compete with very strong, nearby clutter. This paper describes how space-time adaptive processing (STAP) can be used for the purposes of clutter rejection in order to perform the GMTI function. In addition, we confront several important issues for a space-based radar such as pulse repetition frequency (PRF) selection, the choice of a STAP algorithm, and the number of spatial channels. These results are quantified in terms of clutter cancellation and angle accuracy.

ITT-7.4

Classification of landmine-like metal targets using wideband electromagnetic induction
Ping Gao (Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708-0291), Leslie M Collins (Department of Electrical and Computer Engineering, Duke University, Durham NC 27708-0291), Norbert Geng, Lawrence Carin (Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708-0291), Dean A Keiswetter, I. J Won (Geophex Ltd., 605 Mercury Street, Raleigh, NC 27603-2343)

Our previous work has indicated that the careful application of signal detection theory can dramatically improve detectability of landmines using time-domain electromagnetic induction (EMI) data [L. Collins, P. Gao, and L. Carin, IEEE Trans. Geosc. Remote Sens., in press]. In this paper, classification of various metal targets via signal detection theory is investigated using a prototype wideband frequency-domain EMI sensor [I.J. Won, D.A. Keiswetter, and D.R. Hansen, J. Envir. Engin. Geophysics, 2:53-64 (1997)]. An algorithm that incorporates both the uncertainties regarding the target-sensor orientation and a theoretical model of the response of such a sensor is developed. The performance of this approach is evaluated using both simulated and experimental data. The results show that this approach affords substantial classification performance gains over the traditional matched filter approach, on the average by 60%.

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Last Update: February 4, 1999 Ingo Höntsch