Tracker Component Library

Start Here

  • Getting Started
  • Library Architecture
  • API Navigation Guide
  • Common Use Cases & Recipes

Filtering & Estimation

  • Kalman Filter Tuning Guide
  • Constrained State Estimation
  • Hybrid Linear/Nonlinear Filtering with RBPF
  • Adaptive Filtering
  • When to Use Adaptive Filtering
  • Divergence Detection Techniques
  • Noise Covariance Estimation
  • Adaptive Kalman Filtering
  • Least Mean Squares (LMS) Adaptation
  • Recursive Least Squares (RLS) Adaptation
  • Practical Adaptive Filter Systems
  • Diagnostic Tools
  • Tuning Guidelines
  • Common Pitfalls
  • Information Filters and SRIF
  • When to Use Information Filters
  • Information Filter Fundamentals
  • Standard Information Filter Algorithm
  • Square Root Information Filter (SRIF)
  • Extended Information Filter (EKIF)
  • Decorrelated Measurement Processing
  • Comparison: Information Filter vs Kalman Filter
  • Weak Initial Conditions (High Uncertainty)
  • Practical Diagnostic Tools
  • Batch Information Filter (BIF)
  • Tuning Guidelines
  • Common Pitfalls
  • Advanced Kalman Filter Variants
  • When to Use Advanced KF Variants
  • Cubature Kalman Filter (CKF)
  • Sigma-Point Kalman Filters
  • Centered Difference Kalman Filter
  • Ensemble Kalman Filter (EnKF)
  • Comparison: Advanced KF Variants
  • Hybrid Approaches
  • Practical Diagnostics
  • Tuning Guidelines
  • Common Pitfalls
  • Custom Filter Implementation
  • Why Implement Custom Filters
  • Design Patterns: Base Class Architecture
  • Example 1: Custom Adaptive Constant Velocity Filter
  • Example 2: Wrapping External C++ Filter
  • Integration with TCL Components
  • Testing Custom Filters
  • Performance Optimization
  • Practical Workflow: Algorithm to Integration
  • Documentation and Type Hints
  • Common Pitfalls and Solutions

Tracking & Association

  • Assignment & Data Association
  • Particle Filters & Non-Gaussian Estimation
  • Smoothing Algorithms & Offline Estimation
  • Data Structures & Containers

Domain-Specific

  • Coordinate Systems Deep Dive
  • Astronomical & Celestial Mechanics
  • Atmospheric Modeling with NRLMSISE-00
  • Navigation & Inertial Measurement Systems
  • Signal Processing Fundamentals

Performance & Advanced

  • GPU Acceleration Guide
  • Performance Optimization Guide

Reference & Learning

  • Troubleshooting Guide
  • MATLAB to Python Migration Guide
  • Gap Analysis: Python vs MATLAB TCL
  • Development Roadmap
  • User Guide
    • Filtering and State Estimation
    • Data Association
    • Motion Models
    • Coordinate Systems
    • Astronomical Computations
    • Mathematical Functions
  • Tutorials
  • Interactive Notebooks
  • Examples
  • API Reference
Tracker Component Library
  • User Guide
  • View page source

User Guide

  • Filtering and State Estimation
    • Kalman Filter Family
    • Particle Filters
    • Smoothing
    • Information Filter
    • Square-Root Kalman Filters
    • Interacting Multiple Model (IMM) Estimator
    • See Also
  • Data Association
    • Overview
    • Global Nearest Neighbor (GNN)
    • Joint Probabilistic Data Association (JPDA)
    • Gating
    • Best Practices
  • Motion Models
    • State Transition Matrices
    • Process Noise Covariance
    • Continuous-Time Dynamics
    • Choosing a Motion Model
  • Coordinate Systems
    • Coordinate Conversions
    • Rotation Representations
    • Coordinate Jacobians
    • WGS84 Ellipsoid
  • Astronomical Computations
    • JPL Development Ephemeris
    • Relativistic Corrections
    • Orbital Mechanics
    • Reference Frame Transformations
    • Time Systems
    • Lambert Problem
    • See Also
  • Mathematical Functions
    • Special Functions
    • Statistics
    • Interpolation
    • Numerical Integration
    • Geometry
    • Combinatorics
    • Matrix Operations

Note

Advanced Topics - Detailed tutorials for specialized applications:

  • Constrained State Estimation — State-constrained Kalman filtering with equality/inequality constraints

  • Hybrid Linear/Nonlinear Filtering with RBPF — Hybrid linear/nonlinear filtering with Rao-Blackwellized Particle Filters

  • Atmospheric Modeling with NRLMSISE-00 — NRLMSISE-00 atmospheric model for satellite drag calculations

Previous Next

© Copyright 2024-2026, U.S. Naval Research Laboratory (Python port).

Built with Sphinx using a theme provided by Read the Docs.