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Sparse Recovery Methods for MIMO Radar Systems

Abtahi Fahliani, Azra | 2018

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 51131 (05)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Electrical Engineering
  6. Advisor(s): Marvasti, Farokh
  7. Abstract:
  8. Due to its higher degrees of freedom in comparison with a Single-Input Single-Output (SISO) radar , a Multiple-Input Multiple-Output (MIMO) radar has superior resolution , higher accuracy in detection and estimation , and more flexibility in beamforming . As there are multiple receivers in a MIMO radar system , if we can reduce the sampling rate and send fewer samples to the common processing center , the cost can significantly be reduced . Sometimes , the problem is not even the cost . It is the technology issues of high sampling rates . The reduction in sampling rate can be achieved using Compressive Sensing (CS) or in a much simpler form Random Sampling (RS) . In CS , we take a number of linear combinations of sparse signal samples which is smaller than what is necessary according to Shannon-Nyquist sampling theory . The sparse signal can be recovered from these linear combinations exploiting sparse recovery methods . By using sparse recovery methods , not only can the sampling rate be reduced , but also the performance of the radars in the detection and the estimation procedures can be improved . In this thesis , we have proposed three sparse recovery methods for a sparsity-based MIMO radar
  9. Keywords:
  10. Compressive Sensing ; Random Sampling ; Multi-Input Multi-Output (MIMO)Radar ; Sparse Recovery ; Parameter Estimation ; Multiple Targets Parameter Estimation

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