Hybrid of unscented Kalman filter and genetic algorithm for state and parameter estimation in sigma–delta modulators

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.