Hybrid of unscented Kalman filter and genetic algorithm for state and parameter estimation in sigma–delta modulators. Issue 17 (1st August 2015)

• Record Type: Journal Article
• Authors: Lorenz, M.; Becker, J.; Ortmanns, M.
• Is Part Of: Electronics letters. Volume 51:Issue 17(2015)
• Page Start: 1318
• Page End: 1320
• Publication Date: 2015-08-01
• Subjects: sigma‐delta modulation -- genetic algorithms -- Kalman filters -- nonlinear filters -- state estimation -- parameter estimation; low‐cost on‐chip estimation procedures -- state estimator -- optimal parameter set -- nonideality estimation -- sigma‐delta modulators -- parameter estimation -- state estimation -- genetic algorithm -- unscented Kalman filter; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2015.0651
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : A mathematically simple hybrid of the unscented Kalman filter and the genetic algorithm (GA) is presented and applied to the non‐ideality estimation in sigma–delta modulators. Parameter estimation is a complicated task, especially if a system must be observed continuously and its internal states have to be tracked in addition. A GA is a low‐cost method to find an optimal parameter set but if the system is vastly changing, it cannot be applied. In contrast, the basic Kalman filter is an effective state estimator but cannot be used to estimate parameters of a system without complex mathematical extensions. A combination of both techniques can be beneficial to enable fast and especially low‐cost on‐chip estimation procedures.