User Guide Overview

Welcome to the Strapdown-rs User Guide! This section provides comprehensive information on using the library and simulation tools.

What You'll Learn

This guide covers:

Core Concepts: Understanding strapdown INS, coordinate frames, and state representation
Running Simulations: How to use the strapdown-sim binary
Data Formats: Preparing and formatting your input data
Configuration: Setting up simulations with TOML config files
Logging and Debugging: Monitoring simulation progress

For Beginners

If you're new to strapdown INS or this library:

Start with Core Concepts to understand the fundamentals
Learn about Coordinate Frames used in the library
Review State Representation to understand the 9-state and 15-state models
Try the Quick Start tutorial

For Experienced Users

If you're familiar with INS and want to jump in:

Check Input Data Format to prepare your data
Review Configuration Files for advanced options
Explore Running Simulations for different modes
See Logging for debugging and monitoring

Simulation Modes

Strapdown-rs supports three main simulation modes:

Dead Reckoning (`dr`)

Pure inertial navigation without corrections. Useful for:

Understanding INS error growth
Baseline comparisons
Testing IMU data quality

Note that dead reckoning is dr, not ol. The ol subcommand is a separate feed-forward mode -- its own help describes a Kalman filter estimating and applying corrections, so it consumes GNSS and is not "without corrections" -- and it is not implemented: it validates its paths, writes no output and prints "Open-loop mode is not yet fully implemented".

See: Open-Loop Mode

Closed-Loop (Kalman Filtering)

INS with GNSS corrections using the ESKF (the default), UKF or EKF, selected with --filter. Best for:

Realistic navigation scenarios
GNSS degradation studies
Production-like simulations

See: Closed-Loop Mode

Particle Filter

Non-parametric Bayesian filtering for non-Gaussian distributions. Useful for:

Multimodal uncertainty
Highly nonlinear scenarios
Research applications

See: Particle Filter Mode

The library provides multiple filter implementations:

Error-State Kalman Filter (ESKF): multiplicative attitude error. The default for cl.
Extended Kalman Filter (EKF): Fast, efficient, works well for mildly nonlinear systems
Unscented Kalman Filter (UKF): Better accuracy for nonlinear systems, 2-3x slower
Rao-Blackwellized Particle Filter (RBPF): Canciani & Raquet's marginalized particle filter -- horizontal position as particles; altitude, velocity, tilt, the barometer loop's two states and the map biases as a Kalman filter shared by every particle. See the RBPF page. The only particle filter implementation -- ParticleFilterType has this one variant, and particle.rs is a module of building blocks rather than a filter that can be selected on its own