Lagrangian Grid-Based Filters With Application to Terrain-Aided Navigation

Abstract

The column focuses on the state estimation of discrete-time stochastic dynamic systems from noisy or incomplete measurements. State estimation has been a subject of considerable research interest for the last decades. It plays an important role in e.g. navigation, tracking, speech and image processing, fault detection, and optimal control. In this column, we introduce and explain the recent state-of-the-art efficient grid-based filtering techniques that were proven to rival the ubiquitous particle filters based on the Monte Carlo integration in terms of performance and computational complexity. Compared to the particle filters, the grid-based filters provide deterministic results with improved resilience against initialisation error and measurement outliers. The readers are guided through the design of the grid-based filters within the scope of terrain-aided navigation, which is a topical navigation solution due to the latest jamming and spoofing attacks on global navigation satellite systems. The presented algorithms and related codes in MATLAB and Python are made publicly available together with the real-world measured dataset.