Adaptive re-weighting homotopy for sparse beamforming

Adaptive re-weighting homotopy for sparse beamforming. Neto, F. G. A., Nascimento, V. H., Zakharov, Y. V., & de Lamare , R. C. In 2014 22nd European Signal Processing Conference (EUSIPCO), pages 1287-1291, Sep., 2014.

Paper abstract bibtex

In this paper, a complex adaptive re-weighting algorithm based on the homotopy technique is developed and used for beamforming. A multi-candidate scheme is also proposed and incorporated into the adaptive re-weighting homotopy algorithm to choose the regularization factor and improve the signal-to-interference plus noise (SINR) performance. The proposed algorithm is 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. Simulations illustrate the algorithm's performance.

@InProceedings{6952457,
  author = {F. G. A. Neto and V. H. Nascimento and Y. V. Zakharov and R. C. {de Lamare}},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Adaptive re-weighting homotopy for sparse beamforming},
  year = {2014},
  pages = {1287-1291},
  abstract = {In this paper, a complex adaptive re-weighting algorithm based on the homotopy technique is developed and used for beamforming. A multi-candidate scheme is also proposed and incorporated into the adaptive re-weighting homotopy algorithm to choose the regularization factor and improve the signal-to-interference plus noise (SINR) performance. The proposed algorithm is 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. Simulations illustrate the algorithm's performance.},
  keywords = {array signal processing;interference suppression;sensors;sparse beamforming;complex adaptive reweighting algorithm;multicandidate scheme;adaptive reweighting homotopy algorithm;regularization factor;signal-to-interference plus noise performance;SINR performance;faulty sensors;degree of freedom;interference suppression;Interference;Signal to noise ratio;Sensor arrays;Array signal processing;Vectors;Signal processing algorithms;Multi-candidate re-weighting homotopy;beamforming;adaptive algorithms},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569924589.pdf},
}

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