Fixed-Gain Augmented-State Tracking-Filters

TL;DR

This paper introduces a fixed-gain tracking filter design using an augmented-state observer that incorporates models for signals and interference, enabling improved transient and steady-state tracking performance.

Contribution

It proposes a novel fixed-gain tracking filter design method utilizing augmented-state observers with specific signal and interference subspace models.

Findings

Incorporates an integrating Newtonian model for signals.

Uses a second-order maneuver model for constant-g turn scenarios.

Creates a Nyquist null to achieve smoother track estimates.

Abstract

A procedure for the design of fixed-gain tracking filters, using an augmented-state observer with signal and interference subspaces, is proposed. The signal subspace incorporates an integrating Newtonian model and a second-order maneuver model that is matched to a sustained constant-g turn; the deterministic interference model creates a Nyquist null for smoother track estimates. The selected models provide a simple means of shaping and analyzing the (transient and steady-state) response of tracking-filters of elevated order.