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Performance Improvement of MIMO Radars Based on Compressive Sensing

Tayefi, Javad | 2015

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 47742 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Marvasti, Farrokh
  7. Abstract:
  8. This thesis is dedicated to examine performance of compressive sensing based MIMO radar systems. MIMO radars have the ability to achieve higher target detection and parameter estimation as well as better performance in noisy and clutter environments than SISO or phased array radars. The need for high-speed analog to digital converters is one of the weaknesses in implementation of radar systems. With the advent of compressive sensing providing necessary guarantees for reconstruction of sparse signal using fewer samples than what Nyquist thorem describes, the need for such a high-speed converters that are either not available or too expensive is resolved. What allowes us to use compressive sampling in radar processes is sparsity of target returns in spaces such as range, velocity, delay, doppler, etc. Reduction in number of samples in each of receivers, allowes us to transmit receiver’s samples to central processing center resulting in more and more improvement in performance of MIMO radars. In this thesis first we concentrate on performance improvement of compressive sensing based MIMO radars using random sampling and BIMATCS sparse recovery algorithm and show that simultaneous use of these two methods in lower SNR and sampling percent we can achieve better or at least equal performance compared to random Gaussian measurement matrix and OMP or LASSO recovery algorithms. Using compressive sensing in MIMO radars has some limitations. One of them is to force targets being exactly on pre-defined discrete estimation network. We show this assumption can’t be realized in practice and performance of sparse recovery algorithms is dramatically affected in presense of off-grid error. In the second part of this thesis we examine this problem and propose a method that allowes us to locate targets with high accuracy in presense of off-grid error
  9. Keywords:
  10. Compressive Sensing ; Random Sampling ; Multi-Input Multi-Output (MIMO)Radar ; Block Iterative Method with Adaptive Thresholding Compressive Sensing (BIMATCS) ; Off-Grid Error

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