Hybrid Sparse Array Design for Under-determined Models

Sparse arrays are typically configured considering either the environmental dependent or independent design objectives. In this paper, we investigate hybrid sparse array design satisfying dual design objectives. We consider enhancing the source identifiability and maximizing the Signal-to-Interference-plus-noise-ratio (SINR) as our design criteria. We pose the problem as designing fully augmentable sparse arrays for receive beamforming achieving maximum SINR (MaxSINR) for desired point sources operating in an interference active environment. The problem is formulated as a re-weighted l1-norm squared quadratically constraint quadratic program (QCQP). Simulation results are presented to show the effectiveness of the proposed algorithm for designing fully augmentable arrays in case of under-determined scenarios.

[1]  Elias Aboutanios,et al.  Reconfigurable Adaptive Array Beamforming by Antenna Selection , 2014, IEEE Transactions on Signal Processing.

[2]  Sundeep Prabhakar Chepuri,et al.  Sparsity-Promoting Sensor Selection for Non-Linear Measurement Models , 2013, IEEE Transactions on Signal Processing.

[3]  M. Amin,et al.  Sparse Array Quiescent Beamformer Design Combining Adaptive and Deterministic Constraints , 2017, IEEE Transactions on Antennas and Propagation.

[4]  Visa Koivunen,et al.  Comparison of sparse sensor array configurations with constrained aperture for passive sensing , 2017, 2017 IEEE Radar Conference (RadarConf).

[5]  Z. Yu,et al.  Beampattern Synthesis for Linear and Planar Arrays With Antenna Selection by Convex Optimization , 2010, IEEE Transactions on Antennas and Propagation.

[6]  H. Vincent Poor,et al.  Sensor Selection in Distributed Multiple-Radar Architectures for Localization: A Knapsack Problem Formulation , 2012, IEEE Transactions on Signal Processing.

[7]  Jian Li,et al.  Sparse Antenna Array Design for MIMO Active Sensing Applications , 2011, IEEE Transactions on Antennas and Propagation.

[8]  Zhi-Quan Luo,et al.  Robust adaptive beamforming for general-rank signal models , 2003, IEEE Trans. Signal Process..

[9]  Syed A. Hamza,et al.  Optimum sparse array beamforming for general rank signal models , 2018, 2018 IEEE Radar Conference (RadarConf18).

[10]  B. Fuchs.  Application of Convex Relaxation to Array Synthesis Problems , 2014, IEEE Transactions on Antennas and Propagation.

[11]  Sundeep Prabhakar Chepuri,et al.  Sparsity-enforcing sensor selection for DOA estimation , 2013, 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[12]  Elias Aboutanios,et al.  Thinned Array Beampattern Synthesis by Iterative Soft-Thresholding-Based Optimization Algorithms , 2014, IEEE Transactions on Antennas and Propagation.

[13]  P. P. Vaidyanathan,et al.  Nested Arrays: A Novel Approach to Array Processing With Enhanced Degrees of Freedom , 2010, IEEE Transactions on Signal Processing.

[14]  Yimin Zhang,et al.  Generalized Coprime Array Configurations for Direction-of-Arrival Estimation , 2015, IEEE Transactions on Signal Processing.

[15]  Stephen P. Boyd,et al.  Enhancing Sparsity by Reweighted ℓ1 Minimization , 2007, 0711.1612.

[16]  Stephen P. Boyd,et al.  Sensor Selection via Convex Optimization , 2009, IEEE Transactions on Signal Processing.

[17]  Bjorn Ottersten,et al.  Optimal Downlink Beamforming Using Semidefinite Optimization , 2014 .

[18]  Nikos D. Sidiropoulos,et al.  Joint Multicast Beamforming and Antenna Selection , 2013, IEEE Transactions on Signal Processing.

[19]  Xianbin Cao,et al.  Analysis and Design of Optimum Sparse Array Configurations for Adaptive Beamforming , 2018, IEEE Transactions on Signal Processing.

[20]  A. Moffet.  Minimum-redundancy linear arrays , 1968 .

[21]  Jian Li,et al.  On robust Capon beamforming and diagonal loading , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..