An adaptive distributed asynchronous algorithm with application to target localization

This paper introduces a constant step size adaptive algorithm for distributed optimization on a graph. The algorithm is of diffusion-adaptation type and is asynchronous: at every iteration, some randomly selected nodes compute some local variable by means of a proximity operator involving a locally observed random variable, and share these variable with neighbors. The algorithm is built upon a stochastic version of the DouglasRachford algorithm. A practical application to target localization using measurements from multistatic continuous active sonar systems is investigated at length.

[1]  Martina Daun,et al.  System design and fusion techniques for multistatic active sonar , 2009, OCEANS 2009-EUROPE.

[2]  Jian Li,et al.  On Designing the Transmission and Reception of Multistatic Continuous Active Sonar Systems , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Jian Li,et al.  Exact and Approximate Solutions of Source Localization Problems , 2008, IEEE Transactions on Signal Processing.

[4]  Ali H. Sayed,et al.  Diffusion LMS Strategies for Distributed Estimation , 2010, IEEE Transactions on Signal Processing.

[5]  P. Lions,et al.  Splitting Algorithms for the Sum of Two Nonlinear Operators , 1979 .

[6]  Pascal Bianchi,et al.  Ergodic Convergence of a Stochastic Proximal Point Algorithm , 2015, SIAM J. Optim..

[7]  Ali H. Sayed,et al.  Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior , 2013, IEEE Signal Processing Magazine.

[8]  J. J. Moré.  Generalizations of the trust region problem , 1993 .

[9]  Pascal Bianchi,et al.  A Constant Step Stochastic Douglas-Rachford Algorithm with Application to non Separable Regularizations , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]  S. Coraluppi,et al.  Multistatic Sonar Localization , 2006, IEEE Journal of Oceanic Engineering.