Postdoctoral Work @ UMES

Sequential Bayesian Estimation

At the University of Maryland Eastern Shore, I participated in a DoD and NSF funded research on the adaptive radar. My responsibilities included development and implementation of adaptive radar signal processing algorithms. In this research, we consider the problem of sequential estimation of properties of an extended radar target with multi-antenna arrays using adaptive waveforms. The estimation problem is studied in a Bayesian framework to formulate sensing and estimation as an adaptive process based on prior knowledge of target models. Using iterative transmission of adaptive radar waveforms, the radar estimates the target parameters and updates its posterior probability (or beliefs) of the target model based on new measurements.

Free Energy Principle

We proposed a unified approach for adaptive radar waveform design and target parameter estimation via the free-energy principle. The free-energy principle stems from the variational Bayesian approximation method in machine learning which aims to find an approximate distribution close to the true target distribution. In this research, this variational approach is applied to the estimation of a radar target’s mean response. The problem of parameter estimation and the waveform design are formulated as optimization problems under a common variational free energy objective function with different forms, which result in respective analytical solutions. We demonstrate that using adaptive waveforms, the convergence of the sequential estimation is accelerated. Furthermore, we show that for a univariate parameter estimation problem, the derived analytical solution is the same as the Bayesian posterior density. Moreover, the optimal waveform derived by the variational free energy approach is similar to the waveform derived by the well known mutual information criterion.