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Compressive imaging (CI) is a subset of computational photography where a scene is captured via a series of optical, transform-based modulations before being recorded at the detector. However, unlike previous transform imagers, compressive sensors take advantage of the inherent sparsity in the image and use specialized algorithms to reconstruct a high-resolution image with far lower than 100% of the total measurements. Initial CI systems exploited the properties of random matrices used in other areas of compressive sensing (CS); however, in the case of imaging, there are immense benefits to be derived by designing measurement matrices that optimize specific objectives and enable novel capabilities. In this article, we survey recent results on measurement matrix designs that provide the ability of real-time previews, signature-selective imaging, and reconstruction-free inference. [ABSTRACT FROM PUBLISHER]

In this paper we present a simple closed-form solution to the Wiener-Hammerstein (W-H) identification problem. The identification process occurs in the log-frequency domain where magnitudes and phases are separable. We show that the theoretically optimal W-H identification is unique up to an amplitude, phase and delay ambiguity, and that the nonlinearity enables the separate identification of the individual linear time invariant (LTI) components in a W-H architecture. [ABSTRACT FROM AUTHOR]

A stylized compressed sensing radar is proposed in which the time-frequency plane is discretized into an N × N grid. Assuming the number of targets K is small (i.e., K ⪡ N2), then we can transmit a sufficiently "incoherent" pulse and employ the techniques of compressed sensing to reconstruct the target scene. A theoretical upper bound on the sparsity K is presented. Numerical simulations verify that even better performance can be achieved in practice. This novel-compressed sensing approach offers great potential for better resolution over classical radar. [ABSTRACT FROM AUTHOR]