3 research outputs found
Kronecker STAP and SAR GMTI
As a high resolution radar imaging modality, SAR detects and localizes
non-moving targets accurately, giving it an advantage over lower resolution
GMTI radars. Moving target detection is more challenging due to target smearing
and masking by clutter. Space-time adaptive processing (STAP) is often used on
multiantenna SAR to remove the stationary clutter and enhance the moving
targets. In (Greenewald et al., 2016) it was shown that the performance of STAP
can be improved by modeling the clutter covariance as a space vs. time
Kronecker product with low rank factors, providing robustness and reducing the
number of training samples required. In this work, we present a massively
parallel algorithm for implementing Kronecker product STAP, enabling
application to very large SAR datasets (such as the 2006 Gotcha data
collection) using GPUs. Finally, we develop an extension of Kronecker STAP that
uses information from multiple passes to improve moving target detection.Comment: 12 pgs, to appear at SPIE 2016. arXiv admin note: text overlap with
arXiv:1501.0748
Kronecker PCA Based Robust SAR STAP
In this work the detection of moving targets in multiantenna SAR is
considered. As a high resolution radar imaging modality, SAR detects and
identifies stationary targets very well, giving it an advantage over classical
GMTI radars. Moving target detection is more challenging due to the "burying"
of moving targets in the clutter and is often achieved using space-time
adaptive processing (STAP) (based on learning filters from the spatio-temporal
clutter covariance) to remove the stationary clutter and enhance the moving
targets. In this work, it is noted that in addition to the oft noted low rank
structure, the clutter covariance is also naturally in the form of a space vs
time Kronecker product with low rank factors. A low-rank KronPCA covariance
estimation algorithm is proposed to exploit this structure, and a separable
clutter cancelation filter based on the Kronecker covariance estimate is
proposed. Together, these provide orders of magnitude reduction in the number
of training samples required, as well as improved robustness to corruption of
the training data, e.g. due to outliers and moving targets. Theoretical
properties of the proposed estimation algorithm are derived and the significant
reductions in training complexity are established under the spherically
invariant random vector model (SIRV). Finally, an extension of this approach
incorporating multipass data (change detection) is presented. Simulation
results and experiments using the real Gotcha SAR GMTI challenge dataset are
presented that confirm the advantages of our approach relative to existing
techniques.Comment: Tech report. Shorter version submitted to IEEE AE
Robust SAR STAP via Kronecker Decomposition
This paper proposes a spatio-temporal decomposition for the detection of
moving targets in multiantenna SAR. As a high resolution radar imaging
modality, SAR detects and localizes non-moving targets accurately, giving it an
advantage over lower resolution GMTI radars. Moving target detection is more
challenging due to target smearing and masking by clutter. Space-time adaptive
processing (STAP) is often used to remove the stationary clutter and enhance
the moving targets. In this work, it is shown that the performance of STAP can
be improved by modeling the clutter covariance as a space vs. time Kronecker
product with low rank factors. Based on this model, a low-rank Kronecker
product covariance estimation algorithm is proposed, and a novel separable
clutter cancelation filter based on the Kronecker covariance estimate is
introduced. The proposed method provides orders of magnitude reduction in the
required number of training samples, as well as improved robustness to
corruption of the training data. Simulation results and experiments using the
Gotcha SAR GMTI challenge dataset are presented that confirm the advantages of
our approach relative to existing techniques.Comment: to appear at IEEE AES. arXiv admin note: text overlap with
arXiv:1604.03622, arXiv:1501.0748