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