SPEC-5.1

INTERPOLATION AND THE CHIRP TRANSFORM: DSP MEETS OPTICS
David C. Munson, Jr. (University of Illinois at Urbana-Champaign), Orhan Arikan (Bilkent University)

This paper considers the problem of interpolating a signal from one uniformly-spaced grid to another, where the grid spacings may be related by an arbitrary, irrational factor. Noting that interpolation is the digital equivalent of magnification, we begin by reviewing optical systems for magnification and "computation" of the chirp Fourier transform. This route suggests several analog schemes for magnification, which can be discretized to produce algorithms for interpolation. We then derive one of these algorithms from first principles, using a digital-signal-processing perspective. The result is an important, but forgotten, algorithm for interpolation first suggested as an application of the chirp-z transform by Rabiner, Schafer, and Rader. Unlike the earlier derivation, our approach is direct -- we do not make use of Bluestein's trick of completing the square. In addition, our approach identifies parameters under user control that can be optimized for best performance.

SPEC-5.2

Estimation of Aircraft Altitude and Altitude Rate with Over-the-Horizon Radar
Michael Papazoglou, Jeffrey L Krolik (Duke University)

In previous work, a matched-field estimate of aircraft altitude which uses multiple over-the-horizon radar dwells was presented. This approach exploited the altitude dependent structure of the micro-multipath rays which result from reflections local to the aircraft. While it was shown that the multi-dwell matched-field estimate is able to accurately estimate altitude while using typical radar parameters, the estimate was derived assuming that the aircraft altitude is constant for the duration of the track. In this paper, a matched-field method for jointly estimating altitude and altitude rate is presented which extends the micro-multipath model to include effects of constant altitude rate on the micro-multipath Doppler frequencies. Simulation results illustrate that altitude and altitude rate can be jointly estimated while achieving an altitude estimation accuracy of +/-2500 feet using 10 radar dwells.

SPEC-5.3

TUMOR TREATMENT BY TIME-REVERSAL ACOUSTICS
M. Porter, P. Roux, H. Song, W. Kuperman (Marine Physical Laboratory)

There has been a great deal of work in ocean science over the last 10 years in using acoustic channel models in the signal processing. The goal has been to compensate for the "barbershop" effect in which a SONAR system confuses the true source with its reflections in the acoustic mirrors formed by the ocean surface and bottom. Separately, in medicine, hyperthermia acoustic beams are trained on tumors with the goal of reversing their growth. Our interest is the question of what (if any) lessons from the SONAR experience can be applied to hyperthermia.

SPEC-5.4

SOURCE AND ENVIRONMENTAL PARAMETER ESTIMATION USING ELECTROMAGNETIC MATCHED FIELD PROCESSING
Peter Gerstoft (University of California), Donald F. Gingras (SPAWAR)

Modern signal and array processing methods now incorporate the physics of wave propagation as an integral part of the processing. Matched field processing (MFP) refers to signal and array processing techniques in which, rather than a plane wave arrival model, complex-valued (amplitude and phase) field predictions for propagating signals are used. Matched field processing has been successfully applied in ocean acoustics and electromagnetics. In this paper, source localization performance via MFP is examined in the electromagnetics domain. Specifically, the impact of uncertainty in the a priori knowledge of the underlying physical parameters, atmospheric refractivity vs height, on source localization performance is examined.

SPEC-5.5

multi-aspect target identification with wave-based matching pursuits and continuous hidden Markov models
Paul Runkle, Lawrence Carin (Department of Electrical and Computer Engineering, Duke University)

A wave-based matching-pursuits algorithm is used to parse multi-aspect time-domain backscattering data into its underlying wavefront-resonance constituents, or features. Consequently, the N multi-aspect waveforms under test are mapped into N feature vectors. Target identification is effected by fusing these N vectors in a maximum-likelihood sense, which we show, under reasonable assumptions, can be implemented via a hidden Markov model (HMM). Algorithm performance is assessed by considering measured acoustic scattering data from five similar submerged elastic targets.

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Last Update: February 4, 1999 Ingo Höntsch