Constructing numerically stable Kalman filter-based algorithms for gradient-based adaptive filtering

TL;DR

This paper develops numerically stable square-root algorithms for gradient-based adaptive filtering using Kalman filters, enhancing robustness and accuracy in simultaneous state and parameter estimation.

Contribution

It introduces a simple, elegant square-root approach for gradient-based adaptive filtering that improves numerical stability over conventional methods.

Findings

Enhanced robustness against roundoff errors

Accurate simultaneous state and parameter estimation

Derivation of various square-root AF schemes from two main results

Abstract

This paper addresses the numerical aspects of adaptive filtering (AF) techniques for simultaneous state and parameters estimation arising in the design of dynamic positioning systems in many areas of research. The AF schemes consist of a recursive optimization procedure to identify the uncertain system parameters by minimizing an appropriate defined performance index and the application of the Kalman filter (KF) for dynamic positioning purpose. The use of gradient-based optimization methods in the AF computational schemes yields to a set of the filter sensitivity equations and a set of matrix Riccati-type sensitivity equations. The filter sensitivities evaluation is usually done by the conventional KF, which is known to be numerically unstable, and its derivatives with respect to unknown system parameters. Recently, a novel square-root approach for the gradient-based AF by the method of the maximum likelihood has been proposed. In this paper, we show that various square-root AF schemes can be derived from only two main theoretical results. This elegant and simple computational technique replaces the standard methodology based on direct differentiation of the conventional KF equations (with their inherent numerical instability) by advanced square-root filters (and its derivatives as well). As a result, it improves the robustness of the computations against roundoff errors and leads to accurate variants of the gradient-based AFs. Additionally, such methods are ideal for simultaneous state estimation and parameter identification since all values are computed in parallel. The numerical experiments are given.