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|Title:||Compressive Sensing and target features: an information preserving approach for MIMO SAR radar|
|Authors:||MARINO GIOVANNI; TARCHI Dario; KYOVTOROV VLADIMIR; VESPE MICHELE|
|Citation:||International Conference Synthetic Aperture Sonar and Synthetic Aperture Radar vol. 36|
|Publisher:||Institute of Acoustics|
|Type:||Articles in periodicals and books|
|Abstract:||A Polar-to-Cartesian interpolation is a nonlinear transform from a uniform grid (i.e. the polar one) to a non-uniform grid, i.e. the Cartesian one. Indeed interpolation operation must be of extremely high quality to prevent aliasing into the final image of reflectors lying outside the terrain patch of interes and avoiding introdcucing false or spurious targets into the region of interest. Possible solutions can be 2D-sinc function operating into the polar domain, i.e. the classical signal recovery method based on the Nyquist-Shannons theorem or a sinc interpolator in range and a Lagrange in azimuth. Compressive Sensing theory indeed defines a set of techniques which allow reconstructing a signal when Nyquist-Shannons theorem assumptions are not satisfied. It asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use. The two most important principles of CS theory are: 1) Sparsity, which expresses the idea that the information rate of a continuous time signal may be much smaller than suggested by its bandwidth, or that a discrete-time signal depends on a number of degrees of freedom which is comparably much smaller than its (finite) length; 2) Incoherence, which extends the duality between time and frequency and expresses the idea that objects having a sparse representation in must be spread out in the domain in which they are acquired, just as a Dirac or a spike in the time domain is spread out in the frequency domain. First experiments have allowed to understand the conditions under which CS is suitable as ground based MIMO SAR image interpolator (i.e. firstly because of the non-uniform cross-range sampling rate and secondly because the sparsity of cross-range sampling increases with the distance from the sensors). The article has been addressed for comparing the performances of the mentioned algorithms with results obtained by adopting basis pursuit algorithm (also known as minimization) as image interpolator. By comparing the scatters covariance matrix of a known target of interest, the assessing of the most important feature (i.e. length and width of the potential target) has been computed and an estimation of the distortion introduced by the interpolation algorithms has been evaluated. First results have stressed the advantages and limits of using Compressive Sensing with respect to the others algorithm as image interpolator. Moreover the investigation allowed understanding possible further developments of the architecture of the processing chain of MELISSA radar.|
|JRC Directorate:||Space, Security and Migration|
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