On the Connection Between Different Noise Structures for LPV-SS Models

TL;DR

This paper explores the relationship between different noise structures in LPV-SS models, analyzing how dynamic dependencies can be approximated by static ones and the implications for Kalman filtering.

Contribution

It establishes the connection between general noise structures and innovation structures in LPV-SS models, and investigates static approximations of the Kalman gain.

Findings

The Kalman gain can be approximated by a static, affine function using the fading memory effect.

Additional states can transform rational dependency into static, affine dependency.

Error bounds decrease asymptotically with partial trajectory dependence.

Abstract

Different representations to describe noise processes and finding connections or equivalence between them have been part of active research for decades, in particular for linear time-invariant case. In this paper the linear parameter-varying (LPV) setting is addressed; starting with the connection between an LPV state-space (SS) representation with a general noise structure and the LPV-SS model in an innovation structure, i.e., the Kalman filter. More specifically, the considered LPV-SS representation with general noise structure has static, affine dependence on the scheduling signal; however, we show that its companion innovation structure has a dynamic, rational dependency structure. Following, we would like to highlight the consequences of approximating this Kalman gain by a static, affine dependency structure. To this end, firstly, we use the "fading memory" effect of the Kalman filter to reason how the Kalman gain can be approximated to depend only on a partial trajectory of the scheduling signal. This effect is shown by proving an asymptotically decreasing error upper bound on the covariance matrix associated to the innovation structure in case the covariance matrix is subjected to an incorrect initialization or disturbance. Secondly, we show by an example that an LPV-SS representation that has dynamical, rational dependency on the scheduling signal can be transformed into static, affinely dependent representation by introducing additional states. Therefore, an approximated Kalman gain can, in some cases, be represented by a static, affine Kalman gain at the cost of additional states.