Compressive sensing for multi-static scattering analysis

Abstract

Compressive sensing (CS) is a framework in which one attempts to measure a signal in a compressive mode, implying that fewer total measurements are required vis-à-vis direct sampling methods.Compressive sensing exploits the fact that the signal of interest is compressible in some basis, and the CS measurements correspond to projections (typically random projections) performed on the basisfunction coefficients. In this paper we demonstrate that when a target is situated in the presence of a complicated background medium, the frequency-dependent multi-static fields scattered from the target may be measured in a CS manner; the CS measurements are performed by exploiting the natural wave propagation of the incident and scattered fields. This phenomenon is related to the field of time reversal, to which relationships are made. We also demonstrate that the CS framework may be employed to reduce the number of computations required in numerical linear scattering analyses. Example scattering results are presented for multi-static electromagnetic scattering, and application areas are also discussed.

A new method based on compressive sensing for monostatic scattering analysis

“…It is a technique for acquiring and reconstructing a signal at sub‐Nyquist sampling rates by exploiting its sparsity or compressibility in a transform domain. In , the CS has been examined as a framework for efficiently performing scattering computations. The CS technique conjugate with MoM has been utilized to solve two‐dimensional wide‐angle monostatic scattering problems in .…”

Sliding Scattering Center Model for Extended Streamlined Targets

“…From the view of radar signal processing, scattering center model is more practical than scattering characteristics as it is provides straight relationship between the signatures in radar images and the physical features of targets, and thus are broadly used in many radar applications, such as shape, velocity and other physical parameters estimation [6,7], automatic target recognition (ATR) [8,9], radar image interpretation [10][11][12], and radar data compression [13][14][15], etc.. The scattering centers due to scattering sources at discontinuities of surface, such as spires, corners and gaps, etc., have been of concern in applications of geometry parameter estimation, radar target tracking and recognition for decades [16][17][18], for their scattering characteristics being stable within a relatively wide radar observation angle.…”

Multi-channel reconstruction from a randomly sampled array

“…We begin with a uniform linear antenna array, and present a technique to use only one "processing chain" to sample from the many antenna elements of the array. Unlike other efforts at multi-sensor or multichannel compressive sampling [10], [11], [12], [13], our goal is to reconstruct the full time series waveform from every antenna, as though all parallel processing chains were present even though only one chain is actually present.…”

“…In [10], the authors used wavelets and other bases to approximate a Green's function for a multi-sensor radar application. Their goal was not to result in full time series, but rather in the scattering matrix around the array elements.…”