State Space Model-Based Parameter Estimation Methods And Some Applicatons

This paper reviews state space model-based methods for signal processing applications. A state space frame-work is shown to provide a convenient tool for exposing and exploiting structure inherent in many model based methods. It is also shown that there exist state space methods which are robust to noise in data, and to numerical errors. From a computational point of view, the methods are often less complex than existing competing methods. Futhermore they only involve matrix operations which are suitable for systolic/wavefront implementation.