Random Matrix Recursions in Estimation, Control, and Adaptive Filtering

Citation

Vakili, Ali (2011) Random Matrix Recursions in Estimation, Control, and Adaptive Filtering. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/HCKN-7W53. https://resolver.caltech.edu/CaltechTHESIS:06022011-214438378

Abstract

This dissertation is devoted to the study of estimation and control over systems that can be described by linear time-varying state-space models. Examples of such systems are encountered frequently in systems theory, e.g., wireless sensor networks, adaptive filtering, distributed control, etc. Recent developments in distributed catastrophe surveillance, smart transportation, and power grid control systems further motivate such a study.

While linear time-invariant systems are well-understood, there is no general theory that captures various aspects of time-varying counterparts. With little exception, tackling these problems normally boils down to studying time-varying linear or non-linear recursive matrix equations, known as Lyapunov and Riccati recursions that are notoriously hard to analyze. We employ the theory of random matrices to elucidate different facets of these recursions and answer several important questions about the performance, stability, and convergence of estimation and control over such systems.

We make two general assumptions. First, we assume that the coefficient matrices are drawn from jointly stationary matrix-valued random processes. The stationarity assumption hardly restricts the analysis since almost all cases of practical interest fall into this category. We further assume that the state vector size, n, is relatively large. The law of large numbers however guarantees fast convergence to the asymptotic results for n being as small as 10. Under these assumptions, we develop a framework capable of characterizing steady-state and transient behavior of adaptive filters and control and estimation over communication networks. This framework proves promising by successfully tackling several problems for the first time in the literature.

We first study random Lyapunov recursions and characterize their transient and steady-state behavior. Lyapunov recursions appear in several classes of adaptive filters and also as lower bounds of random Riccati recursions in distributed Kalman filtering. We then look at random Riccati recursions whose nonlinearity makes them much more complicated to study. We investigate standard recursive-least-squares (RLS) filtering and extend our analysis beyond the standard case to filtering with multiple measurements, as well as the case of intermittent measurements. Finally, we study Kalman filtering with intermittent observations, which is frequently used to model wireless sensor networks. In all of these cases we obtain interesting universal laws that depend on the structure of the problem, rather than specific model parameters. We verify the accuracy of our results through various simulations for systems with as few as 10 states.

Thesis Files