School of Physics, Engineering and Technology

Multi-Candidate Adaptive Re-Weighting Homotopy for Sparse Beamforming

Sparsity-aware algorithms have been drawing attention lately, since they use information about sparsity on the data to reduce the computational cost in problems where large arrays are difficult to implement with traditional techniques. Sparse beamforming algorithms benefit from these techniques, and recent works report performance gains in radar applications.

The purpose of this project is to devise sparsity-aware algorithms for adaptive beamforming, based on the homotopy algorithm, which is an L1-norm regularised algorithm. The algorithms will be used to minimize the degradation caused by sparsity in arrays with faulty sensors, or when the required degrees of freedom to suppress interference is significantly less than the number of sensors.

Key objectives

To develop sparsity-aware beamforming algorithms based on the adaptive re-weighting homotopy, incorporating a multi-candidate scheme to choose the regularisation factor to improve the signal-to-interference plus noise ratio (SINR) performance.
To devise a recursive implementation of the algorithm, and a low-cost variation, which is suitable for hardware implementation.
To compare the proposed techniques with traditional algorithms from the literature.
The activities of this project will involve the development and the analysis of the algorithms, and the performance will be evaluated by simulations. The contributions will be published in conference and journal papers.

Members

Fernando Goncalves de Almeida Neto

Rodrigo C de Lamare

Funding

Science without Borders

Dates

May 2013 to
April 2014

Research