J
Jason L. Speyer
Researcher at University of California, Los Angeles
Publications - 424
Citations - 10139
Jason L. Speyer is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Fault detection and isolation & Linear system. The author has an hindex of 53, co-authored 420 publications receiving 9687 citations. Previous affiliations of Jason L. Speyer include Massachusetts Institute of Technology & University of California.
Papers
More filters
Proceedings ArticleDOI
Computation and transmission requirements for a decentralized linear-quadratic-Gaussian control problem
TL;DR: A decentralized control problem involving K nodes is formulated, and it is shown that if the dimension of the controls at each node l is less than both the dimensions of the data at node m and thedimension of the state, then a data vector can be transmitted from m to l.
Journal ArticleDOI
Detection filter design: Spectral theory and algorithms
John E. White,Jason L. Speyer +1 more
TL;DR: In this paper, a new formulation of the detection filter problem is generated by assignment of the closed-loop eigenstructure under certain constraints, and necessary and sufficient conditions for the solution of these algebraic equations are determined which produce a complete theory for detection filters.
Journal ArticleDOI
New necessary conditions of optimality for control problems with state-variable inequality constraints
TL;DR: In this article, the necessary conditions of optimality for control problems with state variable inequality constraints, using separating hyperplane theorem, were defined for the case of control problems under state variable inequalities.
Journal ArticleDOI
A stochastic analysis of a modified gain extended Kalman filter with applications to estimation with bearings only measurements
Taek Lyul Song,Jason L. Speyer +1 more
TL;DR: In this article, a modified gain extended Kalman observer (MGEKO) was developed for a special class of systems and a sufficient condition for the estimation errors of the MGEKF to be exponentially bounded in the mean square was obtained.
Journal ArticleDOI
Optimization and Control of Nonlinear Systems Using the Second Variation
TL;DR: In this article, a feedback control scheme is described that maximizes a terminal quantity while satisfying specified terminal conditions, in the presence of small disturbances, and the scheme can also be used in a rapidly converging computation technique to find exact solutions to the nonlinear two point boundary value problems occurring in the calculus of variations.