Compressive Sensing and Sparse Reconstruction in Modern Radar Signal Processing

Compressive Sensing (CS) and Sparse Reconstruction (SR) techniques constitute a powerful signal processing framework which has rapidly evolved in the last years. CS theory states that, if a signal can be sparsely represented, then it can be exactly recovered from a small set of linear, non-adaptive measurements. In radar applications, this implies that it is possible to reduce the sampling in time (i.e. reducing Nyquist bandwidth restrictions) or in space (i.e. less TX/RX elements) and still capture the essential information of the signal. In this talk, we introduce the basic principles of CS along with typically used SR algorithms and most recent adaptive techniques. Then, a series of practical radar examples are illustrated using synthetic and real measurements. Finally, we focus our attention in some cases of interest in the automotive field such as high-resolution angular estimation, design of sparse arrays, data fusion of distributed radar systems, interference mitigation, imaging and target classification.