The spectrum parted linked image test (SPLIT) algorithm for estimating the frequency dependence of scattering center amplitudes

Fuller, Dane F.; Saville, Michael A.

doi:10.1117/12.819329

Cited by 4 publications

(3 citation statements)

References 18 publications

(15 reference statements)

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“…The following section introduces our proposed technique for extracting frequency invariant scattering centers. The SPLIT algorithm, introduced in [7] and reviewed here, is a simple, low-order approach for scattering center extraction. However, for complex targets, such as automobiles, its application is limited because SPLIT uses a reduced basis set comprising only ideal canonical scatters.…”

Section: Freqency Invariance Of Doubly-curved Scattering Centersmentioning

confidence: 99%

“…As a result, the presence of doubly-curved scattering centers can then be used as a discriminator between automobiles and nonautomobiles. We use the spectrum parted linked image test (SPLIT) algorithm [7] as our low-order feature extraction method. Low-order methods are attractive because under the right conditions, they can rapidly generate scattering models for complex objects.…”

Section: Introductionmentioning

confidence: 99%

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Rapid extraction of doubly-curved scattering centers for automobile detection in the presence of clutter

Fuller

Saville

2009

2009 IEEE Radar Conference

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This paper presents an algorithm for low-order automobile detection based on doubly-curved surface scattering phenomenon. As reported in recent works, detection based on doubly-curved scattering features is possible because automobiles consist of scattering centers that are characteristic of canonical shapes. Using a basis consisting of frequency dependent functions from well-known shapes, the proposed algorithm exploits frequency invariance of doubly-curved scattering centers to locate possible automobile scatterers in wideband radar data. The algorithm compares the magnitudes of two coarse images constructed from a single set of wideband data. Details of the basis, size of the partitions, and a demonstration with measured radar data are presented. While more work remains, the results begin to establish a performance baseline for rapidly detecting automobiles in time-sensitive applications.

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Section: Freqency Invariance Of Doubly-curved Scattering Centersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid extraction of doubly-curved scattering centers for automobile detection in the presence of clutter

Fuller

Saville

2009

2009 IEEE Radar Conference

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“…A richer feature set using a attributed scattering centers could be leveraged using multipolarization data via the a CLEAN approach 21, 22 or a direct approach. 23 A combination of dynamic and pre-learned templates could be applied in order to provide separability of a wider range of azimuth differences. These templates could be stored at the class level, i.e.…”

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confidence: 99%

SAR based classification of ground moving targets to assist vehicle tracking

2013

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Feature-aided tracking of targets in synthetic aperture radar is a topic of increasing interest. The aperture synthesized through the combination of target and platform motion facilitates the application of two-dimensional target recognition algorithms through noncooperative imaging of the target in question. Many non-parametric inverse synthetic aperture radar imaging techniques maximize image sharpness by estimating the phase error imposed by the unknown target motion. The resultant images can suffer from small unresolved phase errors and ambiguous cross range resolution. Downstream image exploitation algorithms must be robust to these effects. A set of civilian vehicles is investigated, which exacerbates image quality based ISAR algorithms due to their comparatively small radar cross section. This paper addresses the feasibility of peak-based classification of civilian targets moving through challenging tracking scenarios using ISAR images. Classifier performance is evaluated over a set of sensor, target, and environmental operating conditions through use of synthetically generated data.

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