• Designed least-squares algorithm that combines, in an optimal manner, data arising from a finite collection of uncertain models. The algorithm can take into account data uncertainties with different sophistication levels. The algorithm demonstrated improved performance when it was applied to fusion of data arriving from a distributed network of sensors with varying degrees of reliability. The Algorithm was also applied to diversity combining of signals in the presence of microscopic or macroscopic fading.
     
  • Developed adaptive algorithm with optimum error nonlinearity in the adaptation equation. Nonlinearity is a function of the pdf of the additive noise. Algorithm attains a lower steady-state error compared with adaptive algorithms employing other nonlinearities. Research resulted in 4 conference publications.