@InProceedings{6952852,
  author = {S. Xu and R. C. {de Lamare} and H. V. Poor},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Distributed reduced-rank estimation based on joint iterative optimization in sensor networks},
  year = {2014},
  pages = {2360-2364},
  abstract = {This paper proposes a novel distributed reduced-rank scheme and an adaptive algorithm for distributed estimation in wireless sensor networks. The proposed distributed scheme is based on a transformation that performs dimensionality reduction at each agent of the network followed by a reduced-dimension parameter vector. A distributed reduced-rank joint iterative estimation algorithm is developed, which has the ability to achieve significantly reduced communication overhead and improved performance when compared with existing techniques. Simulation results illustrate the advantages of the proposed strategy in terms of convergence rate and mean square error performance.},
  keywords = {adaptive signal processing;iterative methods;wireless sensor networks;distributed reduced rank joint iterative estimation algorithm;reduced dimension parameter vector;dimensionality reduction;wireless sensor network;adaptive algorithm;joint iterative optimization;distributed reduced rank estimation;Estimation;Optimization;Signal processing algorithms;Vectors;Convergence;Joints;Wireless sensor networks;Dimensionality reduction;distributed estimation;reduced-rank methods;wireless sensor networks},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569920951.pdf},
}

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The proposed distributed scheme is based on a transformation that performs dimensionality reduction at each agent of the network followed by a reduced-dimension parameter vector. A distributed reduced-rank joint iterative estimation algorithm is developed, which has the ability to achieve significantly reduced communication overhead and improved performance when compared with existing techniques. Simulation results illustrate the advantages of the proposed strategy in terms of convergence rate and mean square error performance.","keywords":"adaptive signal processing;iterative methods;wireless sensor networks;distributed reduced rank joint iterative estimation algorithm;reduced dimension parameter vector;dimensionality reduction;wireless sensor network;adaptive algorithm;joint iterative optimization;distributed reduced rank estimation;Estimation;Optimization;Signal processing algorithms;Vectors;Convergence;Joints;Wireless sensor networks;Dimensionality reduction;distributed estimation;reduced-rank methods;wireless sensor networks","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569920951.pdf","bibtex":"@InProceedings{6952852,\n author = {S. Xu and R. C. {de Lamare} and H. V. 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