The classical theory of signal processing is based on models which are stationary, linear and in many cases also Gaussian. Recent advances in time series and the theory of signal processing have drawn attention to many new models and methods. Among these are nonlinear autoregressive and nonlinear state-space models, state-space models with time-varying or state-dependent coefficients as models for nonstationary and nonlinear series, linear non-Gaussian processes which pose some specific problems not encountered with Gaussian processes, methods derived from the theory of dynamical systems, and many others. There has been a parallel growth in the applications of signal processing in many areas such as engineering, finance, the environment, etc. The current project aims to develop new research in both the theory and applications of this diverse range of topics.
The project will provide funds for US-based researchers to attend a series of workshops taking place within a six-month program on "Nonlinear and Nonstationary Signal Processing" at the Isaac Newton Institute for Mathematical Sciences, Cambridge, England. The overall theme of the program is to bring together researchers in different areas of statistics and signal processing, with the intention of fostering research links between the two groups. Within this overall structure, a number of workshops on specific themes will be organised. The themes include Bayesian approaches to signal processing, financial applications, environmental applications, dynamical systems and the analysis of large data sets in industry. Participants from the USA will receive travel and subsistence support from the grant; some of the money is reserved for women, minorities and new researchers.
