Post-doctoral Researcher

Distributed learning algorithms based on methods such as least-mean square (LMS) or recursive least squares (RLS) were proposed not long ago, and allow sensor networks to collaborate to solve estimation and/or detection problems efficiently.  In addition, distributed versions of the Kalman filter were also proposed that allow optimal (or quasi-optimal) tracking of a parameter vector, under the constraints of linearity and Gaussianity.

Despite their superior performance, Kalman filters may not be adequate for use in sensor networks, due to their high computational cost  (the number of operations per sample needed grows with the cube of the number of parameters to be estimated), and also due to the need of a-priori knowledge of an accurate model for the parameter evolution.  We recently proved that it is possible to use combinations of adaptive filters to approximate the performance of Kalman filters with low computational cost (linear in the number of unknown parameters).  We also developed a new class of adaptive filters that allows near-optimal tracking with low computational cost for a larger class of models than possible with classical adaptive filters.

The goal of this work is to extend these results and to develop low-cost approximations to the Kalman filter that are robust against uncertainties in the model for the variation of the parameter vector to be estimated.  Due to the low complexity, this kind of algorithm would allow the implementation of algorithms with better tracking properties in sensor networks, and the robustness would allow the application to a class of problems in which performance guarantees are important, such as distributed control (cooperative autonomous systems, for example).

Candidates with a strong background in Signal Processing and Automatic Control or Telecommunications are encouraged to apply.  The position will be at the Electronic Systems Engineering Department at the University of São Paulo, São Paulo, Brazil, and may extend to up to four years.  The salary will be R$ 9,047.40 (tax-free), plus 10% for expenses.  Funding for relocation to São Paulo is also available, including for spouses.  

            Applications should be sent till Jan. 15, 2024 by e-mail to vitnasci@usp.br, with a short CV following the guidelines found here: 

https://fapesp.br/6351/instructions-for-the-elaboration-of-a-curricular-...