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Alex Sheng-Yuan Wang (University of British Columbia, Canada):
“Meta level tracking with stochastic grammar,” August 2009.
Advised by Prof. Vikram Krishnamurthy
The ability to learn about a stochastic process from noisy observations is fundamental to many applications. In order to track a dynamic process, typical knowledge representation is the state space model such as a linear Gauss Markov model, where efficient algorithms exist to perform state estimation under many different model assumptions. However, for meta level tracking, we are not only interested in the state estimation, but also temporal and structural classification of the process. Current models that are widely applied in classifying sequential data are mainly Markov models, but they are not only restrictive in the patterns that they can express, they often require state space that grows exponentially in the length of the observation. The solution presented in the thesis is to apply a more expressive and general model than Markov models to characterize the sequential process; the prior knowledge of the sequential process is to be encoded as a declarative language using stochastic context free grammar (SCFG). The objective of the thesis is to formulate a meta level tracking framework, introduce and analyze the use of SCFG as the knowledge representation model, and discuss properties and algorithms involved in two real applications: 1) electronic support measure against a multifunction radar, and 2) ground surveillance with ground moving target indicator radar.
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