Information Aided Navigation: A Review

TL;DR

This survey reviews various information aided navigation methods, categorizing them into direct, indirect, and model aiding, highlighting their use cases, measurement models, and how they improve navigation accuracy.

Contribution

It provides an extensive classification and description of existing information aided navigation techniques, facilitating better scenario-specific application and understanding.

Findings

Different aiding approaches enhance navigation accuracy.

Matching constraints to scenarios compensates for lost information.

Internal states can be uncovered through appropriate information integration.

Abstract

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.