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Proceedings Paper

High-resolution radar models for joint tracking and recognition
Author(s): Steven P. Jacobs; Joseph A. O'Sullivan
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Paper Abstract

Identification of airborne or ground targets using high resolution radar (HRR) range-profiles is a notoriously difficult problem, due in large part to the extreme variability of the range-profile for small changes in target aspect angle. In this paper, we address the problem of joint tracking and recognition of a target using a sequence of HRR range-profiles within a likelihood-based framework. The likelihood function for the scene configuration combines a dynamics-based prior on the sequence of target orientations with a likelihood for range-profiles given the target orientation. The recognition system performs joint inference on the target type parameter and the sequence of target orientations at the observation times. The primary issue with respect to successful recognition is modeling of the HRR data. The use of either deterministic or stochastic models for the range-profiles is possible within our framework. A deterministic model and a conditionally Gaussian model for the range-profile are introduced, and the likelihood functions under each model for varying orientations and target types are compared. Fundamental limits on the performance of orientation estimators using HRR data are described in terms of a Hilbert- Schmidt bound on the estimation error. These bounds are computed to provide a comparison of the performance achievable using each model for the HRR data and for HRR sensors operating in different frequency bands.

Paper Details

Date Published: 23 June 1997
PDF: 12 pages
Proc. SPIE 3069, Automatic Target Recognition VII, (23 June 1997); doi: 10.1117/12.277146
Show Author Affiliations
Steven P. Jacobs, Univ. of Pittsburgh (United States)
Joseph A. O'Sullivan, Washington Univ. (United States)


Published in SPIE Proceedings Vol. 3069:
Automatic Target Recognition VII
Firooz A. Sadjadi, Editor(s)

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