Mr. Vincent Sabio Profile

Vincent Sabio

at Strategic Capabilities Office

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Radar , Signal Processing , Hypersonics , Secure Comms

Publications (3)

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Proceedings Article | 10 June 1996 Paper

Spectral correlation of wideband target resonances

Vincent Sabio

Proceedings Volume 2757, (1996) https://doi.org/10.1117/12.242031

KEYWORDS: Target recognition, Synthetic aperture radar, Transform theory, Radar, Image processing, Target detection, Wavelets, Analytical research, Optical filters, Signal to noise ratio

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The US Army Research Laboratory (ARL), working with the University of Maryland Department of Electrical Engineering, recently developed a novel method for efficient recognition of resonances in imagery from ARL's ultra-wideband (UWB) SAR instrumentation system, currently being used in foliage- and ground-penetration studies. The recognition technique uses linear transforms (Fourier, wavelets, etc.) to provide a basis for the design of spectrally matched filters. Implementation of the technique is very straightforward: an expectation of the target ringdown is projected onto a transform basis set, yielding a set of spectral coefficients (the 'spectral template'). UWB SAR image data are projected onto the same basis set, yielding a second vector of coefficients (the 'spectral image'). A simple correlation coefficient is generated from the two vectors, providing a measure of co-linearity of the spectral template and the spectral image: higher correlation values indicate greater co-linearity. Exceeding a correlation threshold results in a target implemented--a single 32-megabyte bipolar SAR image can be processed in less than five minutes. Initial spectral-correlation efforts focused on canonical targets and the results have been widely reported. Current studies are focusing on tactical targets, such as CUCVs. Early results on CUCVs have shown that sa single resonance-based template can be sued effectively in the recognition of tactical targets. Ongoing studies have demonstrated a substantial reduction in the false-alarm rate over results reported previously. These results, as well as improvements in the recognitions-processing stage, are reported in this paper.

Proceedings Article | 5 July 1995 Paper

Spectral correlation of wideband target resonances

Vincent Sabio

Proceedings Volume 2484, (1995) https://doi.org/10.1117/12.213050

KEYWORDS: Target recognition, Synthetic aperture radar, Transform theory, Automatic target recognition, Radar, Wavelets, Signal to noise ratio, Analytical research, Image processing, Scanning electron microscopy

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The potential for automatic target recognition (ATR) processing of foliage-penetrating (FOPEN) synthetic-aperture radar (SAR) imagery requires very high bandwidth occupancies to achieve sufficient range resolution for the ATR task. The U.S. Army Research Laboratory (ARL) ultra-wideband (UWB) FOPEN SAR -- with greater than 95 percent bandwidth occupancy -- provides a suitable testbed for evaluation of resonance-based ATR approaches. Current resonance-extraction techniques (e.g., SEM) typically have poor performance in the presence of noise, and are often computationally intensive. Recently developed at ARL, the `spectral correlation method' uses linear transforms -- such as Fourier and wavelets -- to resolve resonant components; these transforms are generally quite fast, and have straightforward implementations. Creating a synthetic version of the ringdown and projecting onto the desired transform basis provides a set of expected spectral coefficients (the `spectral template'). The spectral template is correlated with the spectral coefficients acquired from the projection of the focused image data onto the same basis function set; the correlation coefficient is then passed through a simple threshold detector. This yields a fast, efficient scheme for recognition of target resonance effects in UWB imagery. Recent advances in this area include a reduction in false-alarm rate by two orders of magnitude, a reduction in processing time by three orders of magnitude, and recognition of a tactical target.

Proceedings Article | 9 June 1994 Paper

Efficient method of target recognition based on spectral correlation of wideband resonance effects

Vincent Sabio, Rama Chellappa

Proceedings Volume 2230, (1994) https://doi.org/10.1117/12.177182

KEYWORDS: Target recognition, Synthetic aperture radar, Wavelets, Automatic target recognition, Analytical research, Particle filters, Image processing, Radar, Polarization, Optical filters

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The potential for automatic target recognition (ATR) processing of foliage-penetrating (FOPEN) SAR imagery requires very high bandwidth occupancies for sufficient range resolution to be achieved for the ATR task. The Army Research Laboratory (ARL) ultra-wideband (UWB) FOPEN SAR--with greater than 95% bandwidth occupancy--provides a suitable testbed for evaluation of UWB ATR techniques. Target signatures in these data are characterized by two temporally distinct responses: an early-time (driven) response, and a late-time (resonant) response. We propose an ATR technique for UWB data based on recognition of targets by their resonant signatures. Current resonance-extraction techniques, such as the singularity-expansion method, hinge on contemporary adaptations of Prony's algorithm; this method, however, generally performs poorly in the presence of noise and is computationally intensive. We propose a form of resonance analysis by application of linear-transform methods, using both classical Fourier techniques and contemporary multiresolution approaches. Target- declaration confidences are established by the correlation of two sets of spectral coefficients--one set from the transformed image data, and the other from a synthetic target template. This permits a fast, efficient scheme for recognition of target resonance effects in UWB imagery. UWB images from the ARL UWB FOPEN SAR instrumentation system were analyzed with canonical targets (dipoles) of differing dimensions and orientation. Results are presented and summarized for each of the targets and transform methods employed in the analysis.

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