A rank-deficient and sparse penalized optimization model for compressive indoor radar target localization

This paper aims to tackle these difficulties by formulating the task of wall clutter suppression and target image formation as a penalized minimization problem with low-rank and sparse regularizers. The former penalty is used to model the low-dimensional attribute of the wall reflections and the later regularizer is used to represent the image of the behind-the-wall targets. We develop an iterative algorithm based on the forward-backward proximal gradient technique to solve the regularized minimization problem, which removes wall interferences and forms an indoor target image simultaneously. The effectiveness of the proposed approach is validated using extensive experiments on both simulated and real radar data. |

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