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16 August 2001 Joint tracking, pose estimation, and target recognition using HRRR and track data: new results
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Abstract
The work presented here is a continuation of research first reported in Mahler, et. Al. Our goal is a generalization of Bayesian filtering and estimation theory to the problem of multisensor, multitarget, multi-evidence unified joint detection, tracking and target identification. Our earlier efforts were focused on integrating the Statistical Features algorithm with a Bayesian nonlinear filter, allowing simultaneous determination of target position, velocity, pose and type via maximum a posteriori estimation. In this paper we continue to address the problem of target classification based on high range resolution radar signatures. While we continue to consider feature based techniques, as in StaF and our earlier work, instead of considering the location and magnitude of peaks in a signature as our features, we consider three alternative features. The features arise from applying either a Wavelet Decomposition, Principal Component Analysis or Linear Discriminant Analysis to the signature. We discuss briefly also, in the wavelet decomposition setting, the challenge of assigning a measure of uncertainty with a classification decision.
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Tim Zajic, Constantino Rago, Ronald P. S. Mahler, Melvyn Huff, and Michael J. Noviskey "Joint tracking, pose estimation, and target recognition using HRRR and track data: new results", Proc. SPIE 4380, Signal Processing, Sensor Fusion, and Target Recognition X, (16 August 2001); https://doi.org/10.1117/12.436948
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