Coprime array adaptive beamforming based on compressive sensing virtual array signal

In this paper, we propose a novel adaptive beamforming algorithm for coprime array by compressive sensing the virtual uniform linear array signal. Based on the idea of coprime sampling, a much longer virtual uniform linear array can be generated from a coprime array. With a compressive sensing matrix, a connection can be built between the coprime array with fewer physical sensors and the virtual uniform linear array with much more virtual sensors. Hence, the proposed adaptive beamforming algorithm takes full advantage of the longer virtual array. The performance increment provided by the virtual array is much larger than the performance loss due to the introduced compressive sensing. Hence, the beam-former using the virtual array is expected to obtain much better performance than those using the coprime array directly. Simulation results demonstrate the effectiveness of the proposed adaptive beamforming algorithm.

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

[2]  Yujie Gu,et al.  Compressed sensing kernel design for radar range profiling , 2013, 2013 IEEE Radar Conference (RadarCon13).

[3]  Wen-Zhan Song,et al.  Fast decentralized gradient descent method and applications to in-situ seismic tomography , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[4]  Lei Shi,et al.  Decentralised seismic tomography computing in cyber-physical sensor systems , 2015 .

[5]  Yu Li,et al.  Robust adaptive beamforming based on interference covariance matrix sparse reconstruction , 2014, Signal Process..

[6]  P. P. Vaidyanathan,et al.  Sparse Sensing With Co-Prime Samplers and Arrays , 2011, IEEE Transactions on Signal Processing.

[7]  Wen-Zhan Song,et al.  Distributed Power-Line Outage Detection Based on Wide Area Measurement System , 2014, Sensors.

[8]  Xuemin Shen,et al.  An Efficient Data-Driven Particle PHD Filter for Multitarget Tracking , 2013, IEEE Transactions on Industrial Informatics.

[9]  Yujie Gu,et al.  Robust Adaptive Beamforming Based on Interference Covariance Matrix Reconstruction and Steering Vector Estimation , 2012, IEEE Transactions on Signal Processing.

[10]  Braham Himed,et al.  Sparsity-based DOA estimation using co-prime arrays , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

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

[12]  Xuemin Shen,et al.  DECOM: DOA estimation with combined MUSIC for coprime array , 2013, 2013 International Conference on Wireless Communications and Signal Processing.

[13]  Arye Nehorai,et al.  Wideband Gaussian Source Processing Using a Linear Nested Array , 2013, IEEE Signal Processing Letters.

[14]  Yonina C. Eldar,et al.  Direction of Arrival Estimation Using Co-Prime Arrays: A Super Resolution Viewpoint , 2013, IEEE Transactions on Signal Processing.

[15]  P. Vaidyanathan,et al.  Coprime sampling and the music algorithm , 2011, 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE).

[16]  Yujie Gu,et al.  Time domain CS kernel design for mitigation of wall reflections in urban radar , 2014, 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM).

[17]  P. P. Vaidyanathan,et al.  System Identification With Sparse Coprime Sensing , 2010, IEEE Signal Processing Letters.

[18]  Geert Leus,et al.  Direction estimation using compressive sampling array processing , 2009, 2009 IEEE/SP 15th Workshop on Statistical Signal Processing.

[19]  Yujie Gu,et al.  Compressive sensing kernel optimization for time delay estimation , 2014, 2014 IEEE Radar Conference.

[20]  P. P. Vaidyanathan,et al.  Theory of Sparse Coprime Sensing in Multiple Dimensions , 2011, IEEE Transactions on Signal Processing.

[21]  Moeness G. Amin,et al.  Multi-Frequency Co-Prime Arrays for High-Resolution Direction-of-Arrival Estimation , 2015, IEEE Transactions on Signal Processing.

[22]  Yujie Gu,et al.  Radar Target Profiling and Recognition Based on TSI-Optimized Compressive Sensing Kernel , 2014, IEEE Transactions on Signal Processing.

[23]  Lei Shi,et al.  Distributed travel-time seismic tomography in large-scale sensor networks , 2016, J. Parallel Distributed Comput..

[24]  Chengwei Zhou,et al.  Doa estimation by covariance matrix sparse reconstruction of coprime array , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).