
Proceedings Paper
Target recognition using HRR profile-based incoherent SAR (InSAR) image formationFormat | Member Price | Non-Member Price |
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Paper Abstract
Feature-aided target verification is a challenging field of research, with the potential to yield significant increases in the
confidence of re-established target tracks after kinematic confusion events. Using appropriate control algorithms
airborne multi-mode radars can acquire a library of HRR (High Range Resolution) profiles for targets as they are
tracked. When a kinematic confusion event occurs, such as a vehicle dropping below MDV (Minimum Detectable
Velocity) for some period of time, or two target tracks crossing, it is necessary to utilize feature-aided tracking methods
to correctly associate post-confusion tracks with pre-confusion tracks. Many current HRR profile target recognition
methods focus on statistical characteristics of either individual profiles or sets of profiles taken over limited viewing
angles. These methods have not proven to be very effective when the pre- and post- confusion libraries do not overlap in
azimuth angle.
To address this issue we propose a new approach to target recognition from HRR profiles. We present an algorithm that
generates 2-D imagery of targets from the pre- and post-confusion libraries. These images are subsequently used as the
input to a target recognition/classifier process. Since, center-aligned HRR Profiles, while ideal for processing, are not
easily computed in field systems, as they require the airborne platform's center of rotation to line up with the geometric
center of the moving target (this is impossible when multiple targets are being tracked), our algorithm is designed to
work with HRR profiles that are aligned to the leading edge (the first detection above a threshold, commonly referred to
as Edge-Aligned HRR profiles).
Our simulated results demonstrate the effectiveness of this method for classifying target vehicles based on simulations
using both overlapping and non-overlapping HRR profile sets. The algorithm was tested on several test cases using an
input set of .28 m resolution XPATCH generated HRR profiles of 20 test vehicles (civilian and military) at various
elevation angles.
Paper Details
Date Published: 14 April 2008
PDF: 13 pages
Proc. SPIE 6967, Automatic Target Recognition XVIII, 69670F (14 April 2008); doi: 10.1117/12.776977
Published in SPIE Proceedings Vol. 6967:
Automatic Target Recognition XVIII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)
PDF: 13 pages
Proc. SPIE 6967, Automatic Target Recognition XVIII, 69670F (14 April 2008); doi: 10.1117/12.776977
Show Author Affiliations
Nicholas A. O'Donoughue, The MITRE Corp. (United States)
Carnegie Mellon Univ. (United States)
Walter S. Kuklinski, The MITRE Corp. (United States)
Carnegie Mellon Univ. (United States)
Walter S. Kuklinski, The MITRE Corp. (United States)
Constantine Arabadjis, The MITRE Corp. (United States)
Published in SPIE Proceedings Vol. 6967:
Automatic Target Recognition XVIII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)
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