Compressive Sensing for Radar Signals – Part III: MIMO Radars


  1. Hedayati, M.
  2. Kim, I.
  3. Chan, F.
Corporate Authors
Defence Research and Development Canada, Ottawa Research Centre, Ottawa ON (CAN)
Compressed sensing (CS) is a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. Based on the CS theory, one can recover certain signals from far fewer samples or measurements than traditional methods (like Shannon theorem) use. To make this possible, CS relies on two principles: sparsity, which pertains to the signals of interest, and incoherence, which pertains to the sensing modality. Sparsity expresses the idea that the information of a signal may be much smaller than suggested by its length. In many application such as digital images and video cameras the Nyquist rate is so high that too many samples result and it is a serious challenge to store or transmit these samples. Also, in applications which deals with high-bandwidth signals, it is necessary to build a high-rate A/D converter which is very expensive if it is not impossible. CS addresses this issue by reducing the number of measurements considerably. Incoherence extends the duality between time and frequency and expresses the idea that signals having a sparse representation must be spread out in the domain in which they are acquired. The important point is that one can design efficient sensing protocols that capture the useful information content embedded in a sparse signal and condense it into a small amount of data. These protocols are nonadaptive and simply require correlating the signal with a small number of fixed waveforms that are incoherent with the sparsifying basis. I
sparse Bayesian learning;waveform detection;blind compressive sensing
Report Number
DRDC-RDDC-2015-C209 — Contract Report
Date of publication
01 Mar 2015
Number of Pages
Electronic Document(PDF)

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