Alan S. Willsky

TL;DR: This paper surveys a number of methods for the detection of abrupt changes in stochastic dynamical systems, focusing on the class of linear systems, but the basic concepts carry over to other classes of systems.

TL;DR: This work presents a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold that has a number of advantages over other source localization techniques, including increased resolution, improved robustness to noise, limitations in data quantity, and correlation of the sources.

TL;DR: In this article, a robust failure detection and identification (FDI) process is viewed as consisting of two stages: residual generation and decision making, and it is argued that a robust FDI system can be achieved by designing a robust residual generation process.

TL;DR: A number of methods for detecting abrupt changes (such as failures) in stochastic dynamical systems are surveyed in this paper, where tradeoffs in complexity versus performance are discussed, ranging from the design of specific failure-sensitive filters, to the use of statistical tests on filter innovations, and the development of jump process formulations.

TL;DR: This paper provides a general framework to convert notions of simplicity into convex penalty functions, resulting in convex optimization solutions to linear, underdetermined inverse problems.