31 May 2013 Array geometries, signal type, and sampling conditions for the application of compressed sensing in MIMO radar
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Abstract
MIMO radar utilizes the transmission and reflection of multiple independent waveforms to construct an image approximating a target scene. Compressed sensing (CS) techniques such as total variation (TV) minimization and greedy algorithms can permit accurate reconstructions of the target scenes from undersampled data. The success of these CS techniques is largely dependent on the structure of the measurement matrix. A discretized inverse scattering model is used to examine the imaging problem, and in this context the measurement matrix consists of array parameters regarding the geometry of the transmitting and receiving arrays, signal type, and sampling rate. We derive some conditions on these parameters that guarantee the success of these CS reconstruction algorithms. The effect of scene sparsity on reconstruction accuracy is also addressed. Numerical simulations illustrate the success of reconstruction when the array and sampling conditions are satisfied, and we also illustrate erroneous reconstructions when the conditions are not satisfied.
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Juan Lopez, Juan Lopez, Zhijun Qiao, Zhijun Qiao, "Array geometries, signal type, and sampling conditions for the application of compressed sensing in MIMO radar", Proc. SPIE 8717, Compressive Sensing II, 871702 (31 May 2013); doi: 10.1117/12.2016296; https://doi.org/10.1117/12.2016296
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