# Kalman Filter Learning Path

A complete journey from elementary concepts to expert-level implementation.

## Learning Path

### Level 1: Elementary (Ages 8-12)
- [01 - What is Estimation?](01-what-is-estimation.md) - Understanding guessing and improving guesses
- [02 - Noisy Measurements](02-noisy-measurements.md) - Why sensors aren't perfect
- [03 - Combining Information](03-combining-information.md) - Using multiple clues

### Level 2: Middle School (Ages 13-15)
- [04 - Averages and Uncertainty](04-averages-and-uncertainty.md) - Mean and variance
- [05 - Weighted Averages](05-weighted-averages.md) - Trusting some measurements more
- [06 - Prediction](06-prediction.md) - Guessing the future from the past

### Level 3: High School (Ages 16-18)
- [07 - The Kalman Filter Idea](07-kalman-filter-idea.md) - Putting it all together
- [08 - One Dimensional Kalman Filter](08-1d-kalman-filter.md) - First real implementation
- [09 - Tracking a Moving Object](09-tracking-moving-object.md) - Position and velocity

### Level 4: Undergraduate (College)
- [10 - Matrix Form](10-matrix-form.md) - Mathematical notation
- [11 - Multidimensional Kalman Filter](11-multidimensional-kf.md) - Multiple states
- 12 - Process and Measurement Noise — Covariance matrices *(coming soon)*
- 13 - Kalman Filter Algorithm — Complete equations *(coming soon)*

### Level 5: Graduate (Advanced)
- 14 - Extended Kalman Filter — Handling non-linearity *(coming soon)*
- 15 - Unscented Kalman Filter — Better non-linear handling *(coming soon)*
- 16 - Error State Kalman Filter — For orientation estimation *(coming soon)*
- 17 - Multi-State Constraint KF — For visual odometry *(coming soon)*

### Level 6: Expert (Professional)
- 18 - Kalman Smoothing — RTS smoother *(coming soon)*
- 19 - Outlier Rejection — Robust estimation *(coming soon)*
- 20 - Implementation Tricks — Numerical stability *(coming soon)*
- 21 - Real World Applications — GPS, IMU, cameras *(coming soon)*

## How to Use This Guide

1. **Start at your level** - Don't skip ahead too fast
2. **Do the exercises** - Each chapter has hands-on problems
3. **Code along** - Implementation examples in Python
4. **Build intuition first** - Understand "why" before "how"

## Prerequisites by Level

- **Level 1-2**: Basic arithmetic
- **Level 3**: Algebra, basic probability
- **Level 4**: Linear algebra, probability theory
- **Level 5**: Calculus, statistics
- **Level 6**: Advanced mathematics, programming

## Quick Reference

Once you've learned the basics, use these for quick lookup:
- [Kalman Filter Equations Cheat Sheet](cheat-sheet.md)
- Common Mistakes and Solutions (coming soon)
- Python Implementation Templates (coming soon)

Let's begin the journey!
