An algorithm is presented which provides a maximum-likelihood estimate for an unknown parameter contained in a linear dynamic system driven by white, Gaussian noise. Taylor series expansions are used to develop approximations to the estimation equations. These approximations are recursive and can be calculated iteratively. The algorithm can be realized either as an analog or as a digital system and is shown to compare favorably with existing techniques in a simple example.
ASJC Scopus subject areas
- Control and Systems Engineering
- Signal Processing
- Computer Networks and Communications
- Applied Mathematics