Abstract
In this paper, we exploit the notion of partial sparsity for scene reconstruction associated with through-the-wall radar imaging of stationary targets under reduced data volume. Partial sparsity implies that the scene being imaged consists of a sparse part and a dense part, with the support of the latter assumed to be known. For the problem at hand, sparsity is represented by a few stationary indoor targets, whereas the high scene density is defined by exterior and interior walls. Prior knowledge of wall positions and extent may be available either through building blueprints or from prior surveillance operations. The contributions of the exterior and interior walls are removed from the data through the use of projection matrices, which are determined from wall- and corner-specific dictionaries. The projected data, with enhanced sparsity, is then processed using l1 norm reconstruction techniques. Numerical electromagnetic data is used to demonstrate the effectiveness of the proposed approach for imaging stationary indoor scenes using a reduced set of measurements.
Highlights
The ultimate objective of achieving actionable intelligence in an efficient and reliable manner is faced with a host of challenges underlying the urban sensing and throughthe-wall radar imaging (TWRI) applications [1,2,3,4,5,6,7,8,9]
It is noted that as the round-trip signal traveling times from the antennas to each interior wall, which is parallel to the front wall, are constant across the array aperture, both spatial filtering and the subspace decomposition methods will mitigate returns from interior parallel walls as long as they are not shadowed by other contents of the building [27]
We propose an alternate scheme for imaging of stationary indoor scenes which overcomes this limitation of wall clutter mitigation techniques under reduced data volume by exploiting prior knowledge of the room layout
Summary
The ultimate objective of achieving actionable intelligence in an efficient and reliable manner is faced with a host of challenges underlying the urban sensing and throughthe-wall radar imaging (TWRI) applications [1,2,3,4,5,6,7,8,9]. It is noted that as the round-trip signal traveling times from the antennas to each interior wall, which is parallel to the front wall, are constant across the array aperture, both spatial filtering and the subspace decomposition methods will mitigate returns from interior parallel walls as long as they are not shadowed by other contents of the building [27] Both spatial filtering and subspace projection approaches have been shown to be effective for synthetic aperture radar (SAR) imaging under reduced data volume, provided that the same reduced set of frequencies or time samples is used at each available antenna position [28,29]. The wall-free data can be processed using conventional image formation techniques [45]
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