Efficient target estimation in distributed MIMO radar via the ADMM

We consider the problem of target estimation in distributed MIMO radars that employ compressive sensing. The problem is formulated as a sparse signal recovery problem with magnitude constraints on the target reflection coefficients, where the signal to be recovered consist of equal size blocks that have the same sparsity profile. A solution is proposed based on the alternating direction method of multipliers (ADMM), which significantly lowers the computational complexity of sparse recovery and improves the estimation accuracy. Due to the block diagonal structure of the sensing matrix, the iterations of all ADMM subproblems are amenable to parallel implementation, which can reduce the running time. A semi-distributed implementation, which relaxes the need of a powerful fusion center is also discussed.

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

[2]  Richard G. Baraniuk,et al.  Distributed Compressive Sensing , 2009, ArXiv.

[3]  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).

[4]  Athina P. Petropulu,et al.  Structured sampling of structured signals , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[5]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[6]  Junfeng Yang,et al.  A New Alternating Minimization Algorithm for Total Variation Image Reconstruction , 2008, SIAM J. Imaging Sci..

[7]  Dmitry M. Malioutov,et al.  A sparse signal reconstruction perspective for source localization with sensor arrays , 2005, IEEE Transactions on Signal Processing.

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

[9]  Jian Li,et al.  On Parameter Identifiability of MIMO Radar , 2007, IEEE Signal Processing Letters.

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

[11]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[12]  Athina Petropulu,et al.  Performance guarantees for distributed MIMO radar based on sparse sensing , 2014, 2014 IEEE Radar Conference.

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