Compressive sensing for improved MIMO radar performance — A review

Multiple-input multiple-output (MIMO) radar systems have received much attention in recent times due to their ability to detect and estimate targets better. These radar systems utilize multiple transmit waveforms, either orthogonal or non-orthogonal, providing additional diversity to improve on delay-Doppler as well as angular resolutions. Recent advances in Compressive Sensing (CS) offer an effective means to address for further performance improvements in MIMO radar signal processing. CS based MIMO radar systems emphasize the inherent target sparsity in the target space and aim to achieve better resolution while using considerably reduced number of measurements; or significantly enhance the radar performance for the same number of measurements. The benefits of data volume reduction appears as savings in memory and power, as well as lowering the acquisition time. In this paper, a number of representative and recent articles pertaining to CS techniques in MIMO radar systems are reviewed and categorized. The discussion here particularly emphasizes the various design techniques that improve the MIMO radar performance.

[1]  Benjamin Friedlander,et al.  Analysis of Sparse MIMO Radar , 2012, ArXiv.

[2]  Q. Zhang,et al.  Compressive Sensing in ISAR spectrogram data transmission , 2009, 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar.

[3]  Bo Liu,et al.  Sparse Frequency Diverse MIMO Radar Imaging for Off-Grid Target Based on Adaptive Iterative MAP , 2013, Remote. Sens..

[4]  Alexander M. Haimovich,et al.  Target Localization Accuracy Gain in MIMO Radar-Based Systems , 2008, IEEE Transactions on Information Theory.

[5]  Sandeep Gogineni,et al.  Adaptive design for distributed MIMO radar using sparse modeling , 2010, 2010 International Waveform Diversity and Design Conference.

[6]  L.J. Cimini,et al.  MIMO Radar with Widely Separated Antennas , 2008, IEEE Signal Processing Magazine.

[7]  B. Friedlander,et al.  Waveform Design for MIMO Radars , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[8]  Erik Blasch,et al.  Compressed sensing for MIMO radar: A stochastic perspective , 2012, 2012 IEEE Statistical Signal Processing Workshop (SSP).

[9]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[10]  Qun Zhang,et al.  Reconstruction of Moving Target's HRRP Using Sparse Frequency-Stepped Chirp Signal , 2011, IEEE Sensors Journal.

[11]  Sandeep Gogineni,et al.  Adaptive waveform design for colocated MIMO radar using sparse modeling , 2011, 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[12]  Tao Yu,et al.  Joint sparse modeling for target parameter estimation in distributed MIMO radar , 2013 .

[13]  Kaushik Mahata,et al.  A joint sparse signal representation perspective for target detection using bistatic MIMO radar system , 2011, 2011 17th International Conference on Digital Signal Processing (DSP).

[14]  Athina P. Petropulu,et al.  Power allocation and waveform design for the compressive sensing based MIMO radar , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[15]  Sandeep Gogineni,et al.  Target Estimation Using Sparse Modeling for Distributed MIMO Radar , 2011, IEEE Transactions on Signal Processing.

[16]  H. Vincent Poor,et al.  Power allocation for CS-based colocated MIMO radar systems , 2012, 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM).

[17]  Jian Li,et al.  Transmit codes and receive filters for radar , 2008, IEEE Signal Processing Magazine.

[18]  H. Vincent Poor,et al.  MIMO Radar Using Compressive Sampling , 2009, IEEE Journal of Selected Topics in Signal Processing.

[19]  Kaushik Mahata,et al.  An Improved Smoothed $\ell^0$ Approximation Algorithm for Sparse Representation , 2010, IEEE Transactions on Signal Processing.

[20]  H. Vincent Poor,et al.  CSSF MIMO RADAR: Compressive-Sensing and Step-Frequency Based MIMO Radar , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[21]  P.P. Vaidyanathan,et al.  Compressed sensing in MIMO radar , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.

[22]  Athina P. Petropulu,et al.  On exploring sparsity in widely separated MIMO radar , 2011, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[23]  Michael B. Wakin.  Compressive Sensing Fundamentals , 2014 .

[24]  H. Vincent Poor,et al.  Compressive sensing for MIMO radar , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[25]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[26]  Haichao Wang,et al.  Accurate imaging of moving targets via random sensor arrays and Kerdock codes , 2013, ArXiv.

[27]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[28]  Alexander M. Haimovich,et al.  Target Velocity Estimation and Antenna Placement for MIMO Radar With Widely Separated Antennas , 2010, IEEE Journal of Selected Topics in Signal Processing.

[29]  Jian Li,et al.  MIMO Radar with Colocated Antennas , 2007, IEEE Signal Processing Magazine.