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Adaptive Algorithms for Sensor Array Beamforming and Wireless Communications

This project will investigate innovative methods of adaptive multidimensional signal processing algorithms for wireless communications applications. One important application is interference mitigation using adaptive signal processing and co-located and distributed antenna arrays. The activities will deal with the use of cutting-edge signal processing algorithms, low-rank decompositions, optimization tools and matrix computations. The work will involve the development of system models using linear algebra, simulation tools with MATLAB, FPGA and analytical approaches.

In the work, we propose a low-complexity robust adaptive beamforming (RAB) technique which estimates the steering vector using a Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) algorithm. The proposed LOCSME algorithm estimates the covariance matrix of the input data and the interference-plus-noise covariance (INC) matrix by using the Oracle Approximating Shrinkage (OAS) method. LOCSME only requires prior knowledge of the angular sector in which the actual steering vector is located and the antenna array geometry. LOCSME does not require a costly optimization algorithm and does not need to know extra information from the interferers, which avoids direction finding for all interferers. Simulations show that LOCSME outperforms previously reported RAB algorithms and has a performance very close to the optimum.

Members

  • Hang Ruan
  • Rodrigo C de Lamare

Dates

  • Start: October 2012

Research