English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we use the optimization formulation of nonlinear Kalmanfiltering and smoothing problems to develop second-order variants of iteratedKalman smoother (IKS) methods. We show that Newton's method corresponds to arecursion over affine smoothing problems on a modified state-space modelaugmented by a pseudo measurement. The first and second derivatives required inthis approach can be efficiently computed with widely available automaticdifferentiation tools. Furthermore, we show how to incorporate line-search andtrust-region strategies into the proposed second-order IKS algorithm in orderto regularize updates between iterations. Finally, we provide numericalexamples to demonstrate the method's efficiency in terms of runtime compared toits batch counterpart.

A Recursive Newton Method for Smoothing in Nonlinear State Space Models