A Matrix Formulation of Discrete Chirp Fourier Transform Algorithms

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This work presents a computational matrix framework in terms of tensor signal algebra for the formulation of discrete chirp Fourier transform algorithms.These algorithms are used in this work to estimate the point target functions(impulse response functions)of multiple-input multiple-output(MIMO)synthetic aperture radar(SAR)systems.This estimation technique is being studied as an alternative to the estimation of point target functions using the discrete cross-ambiguity function for certain types of environmental surveillance applications.The tensor signal algebra is presented as a mathematics environment composed of signal spaces,finite dimensional linear operators,and special matrices where algebraic methods are used to generate these signal transforms as computational estimators.Also,the tensor signal algebra contributes to analysis,design,and implementation of parallel algorithms.An instantiation of the framework was performed by using the MATLAB Parallel Computing Toolbox,where all the algorithms presented in this paper were implemented. This work presents a computational matrix framework in terms of tensor signal algebra for the formulation of discrete chirp Fourier transform algorithms. These algorithms are used in this work to estimate the point target functions (impulse response functions) of multiple-input multiple-output (MIMO synthetic aperture radar (SAR) systems. This estimation technique is being studied as an alternative to the estimation of point target functions using the discrete cross-ambiguity function for certain types of environmental surveillance applications. The tensor signal algebra is presented as a mathematics environment composed of signal spaces, finite dimensional linear operators, and special matrices where algebraic methods are used to generate these signal transforms as computational estimators. Als, the tensor signal algebra contributes to analysis, design, and implementation of parallel algorithms. Ann instantiation of the framework was performed by using the MATLAB Parallel Computing Toolbox, where all the algorithms presented in this paper were implemented.
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