My research interest is in hybrid estimation theories and applications. Hybrid estimation (or multiple-model) algorithms have been used in applications, such as target tracking and signal processing, that involve systems with both continuous and discrete states. In most hybrid estimation algorithms, the discrete state evolution (or mode transition) is modeled as a Markov process with a constant mode transition probability independent of the continuous state evolution. However, in many hybrid systems, the mode transitions may be induced by the evolution of the continuous state variables. For example, in an electronic circuit, the discrete state (or mode) of an ideal diode (‘on’ or ‘off’) depends on the continuous state (the voltage across the diode). My current work involves modeling of the mode transition probability as a function of the continuous state variables. An efficient algorithm to solve the estimation problem has been proposed based on Gaussian mixture approximations and Monte Carlo integration. One application of the proposed algorithm is in aircraft tracking in Air Traffic Control. I am now working on improving the efficiency of the algorithm for some special class of systems and on analyzing the convergence property of the Monte Carlo integration.

 

Conference Proceedings:

 

C.E. Seah and I. Hwang, “Hybrid Estimation Algorithm using State-Dependent

Mode Transition Matrix for Aircraft Tracking”, submitted to AIAA Guidance, Navigation, and Control Conference, August 2006, accepted.

 

Contacts:

Room 337B

Potter building, Purdue University

West Lafayette, 47906

 

Email: seah@purdue.edu

Office phone: +1 765-496-6633

Flight Dynamics & Control/Hybrid System Laboratory